00 Basics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "import pandas as pd             # for data analysis\n",
    "import numpy as np              # for scientific computing\n",
    "import matplotlib.pyplot as plt # data visualization\n",
    "import plotly.express as px     # data visualization\n",
    "import sklearn                  # for machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Datasets\n",
    "\n",
    "https://scikit-learn.org/stable/datasets.html\n",
    "\n",
    "**The dataset loaders**. They can be used to load small standard datasets, described in the Toy datasets section.\n",
    "\n",
    "**The dataset fetchers**. They can be used to download and load larger datasets, described in the Real world datasets section.\n",
    "\n",
    "Both loaders and fetchers functions return a Bunch object holding at least two items: an array of shape `n_samples * n_features` with key `data` (except for 20newsgroups) and a numpy array of length `n_samples`, containing the target values, with key `target`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Boston house prices dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "\n",
    "dataset = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
       "         4.9800e+00],\n",
       "        [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
       "         9.1400e+00],\n",
       "        [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
       "         4.0300e+00],\n",
       "        ...,\n",
       "        [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "         5.6400e+00],\n",
       "        [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
       "         6.4800e+00],\n",
       "        [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "         7.8800e+00]]),\n",
       " 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "        18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "        15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "        13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "        21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "        35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "        19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "        20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "        23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "        33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "        21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "        20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "        23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "        15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "        17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "        25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "        23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "        32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "        34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "        20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "        26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "        31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "        22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "        42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "        36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "        32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "        20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "        20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "        22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "        21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "        19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "        32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "        18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "        16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "        13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "         7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "        12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "        27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "         8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "         9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "        10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "        15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "        19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "        29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "        20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "        23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),\n",
       " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
       " 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n",
       " 'filename': '/usr/local/lib/python3.8/dist-packages/sklearn/datasets/data/boston_house_prices.csv'}"
      ]
     },
     "execution_count": 9,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Attribute B that is linked to the proportion of black people by town')"
      ]
     },
     "execution_count": 11,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "image/png": {
       "height": 426,
       "width": 710
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(0, 1, 0.01)\n",
    "y = 1000*(x - 0.63)**2\n",
    "plt.plot(x,y)\n",
    "plt.title(\"Attribute B that is linked to the proportion of black people by town\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## The dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "df = pd.DataFrame(dataset.data, columns=dataset.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "df = df.assign(target=dataset.target)  # identical to df[\"target\"] = dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  target  \n",
       "0     15.3  396.90   4.98    24.0  \n",
       "1     17.8  396.90   9.14    21.6  \n",
       "2     17.8  392.83   4.03    34.7  \n",
       "3     18.7  394.63   2.94    33.4  \n",
       "4     18.7  396.90   5.33    36.2  "
      ]
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM   ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "501  0.06263  0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527  0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076  0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959  0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741  0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  target  \n",
       "501     21.0  391.99   9.67    22.4  \n",
       "502     21.0  396.90   9.08    20.6  \n",
       "503     21.0  396.90   5.64    23.9  \n",
       "504     21.0  393.45   6.48    22.0  \n",
       "505     21.0  396.90   7.88    11.9  "
      ]
     },
     "execution_count": 15,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>0.14103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.437</td>\n",
       "      <td>5.79</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>396.9</td>\n",
       "      <td>15.84</td>\n",
       "      <td>20.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM   ZN  INDUS  CHAS    NOX    RM   AGE   DIS  RAD    TAX  PTRATIO  \\\n",
       "297  0.14103  0.0  13.92   0.0  0.437  5.79  58.0  6.32  4.0  289.0     16.0   \n",
       "\n",
       "         B  LSTAT  target  \n",
       "297  396.9  15.84    20.3  "
      ]
     },
     "execution_count": 16,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 19,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506"
      ]
     },
     "execution_count": 20,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    float64\n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  target   506 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Accessing rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      65.2\n",
       "1      78.9\n",
       "2      61.1\n",
       "3      45.8\n",
       "4      54.2\n",
       "       ... \n",
       "501    69.1\n",
       "502    76.7\n",
       "503    91.0\n",
       "504    89.3\n",
       "505    80.8\n",
       "Name: AGE, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"AGE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      65.2\n",
       "1      78.9\n",
       "2      61.1\n",
       "3      45.8\n",
       "4      54.2\n",
       "       ... \n",
       "501    69.1\n",
       "502    76.7\n",
       "503    91.0\n",
       "504    89.3\n",
       "505    80.8\n",
       "Name: AGE, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.2</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.9</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.1</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.8</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.2</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>69.1</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>76.7</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>91.0</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>89.3</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>80.8</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AGE  target\n",
       "0    65.2    24.0\n",
       "1    78.9    21.6\n",
       "2    61.1    34.7\n",
       "3    45.8    33.4\n",
       "4    54.2    36.2\n",
       "..    ...     ...\n",
       "501  69.1    22.4\n",
       "502  76.7    20.6\n",
       "503  91.0    23.9\n",
       "504  89.3    22.0\n",
       "505  80.8    11.9\n",
       "\n",
       "[506 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"AGE\", \"target\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM         0.00632\n",
       "ZN          18.00000\n",
       "INDUS        2.31000\n",
       "CHAS         0.00000\n",
       "NOX          0.53800\n",
       "RM           6.57500\n",
       "AGE         65.20000\n",
       "DIS          4.09000\n",
       "RAD          1.00000\n",
       "TAX        296.00000\n",
       "PTRATIO     15.30000\n",
       "B          396.90000\n",
       "LSTAT        4.98000\n",
       "target      24.00000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  target  \n",
       "0     15.3  396.90   4.98    24.0  \n",
       "1     17.8  396.90   9.14    21.6  \n",
       "2     17.8  392.83   4.03    34.7  \n",
       "3     18.7  394.63   2.94    33.4  \n",
       "4     18.7  396.90   5.33    36.2  "
      ]
     },
     "execution_count": 59,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    65.2\n",
       "1    78.9\n",
       "2    61.1\n",
       "3    45.8\n",
       "4    54.2\n",
       "5    58.7\n",
       "Name: AGE, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:5, \"AGE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.2</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.9</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.1</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.8</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.2</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58.7</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE  target\n",
       "0  65.2    24.0\n",
       "1  78.9    21.6\n",
       "2  61.1    34.7\n",
       "3  45.8    33.4\n",
       "4  54.2    36.2\n",
       "5  58.7    28.7"
      ]
     },
     "execution_count": 67,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:5, [\"AGE\", \"target\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX\n",
       "0  0.00632  18.0   2.31   0.0  0.538\n",
       "1  0.02731   0.0   7.07   0.0  0.469\n",
       "2  0.02729   0.0   7.07   0.0  0.469\n",
       "3  0.03237   0.0   2.18   0.0  0.458\n",
       "4  0.06905   0.0   2.18   0.0  0.458"
      ]
     },
     "execution_count": 68,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:5, :5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Query dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3      False\n",
       "4       True\n",
       "       ...  \n",
       "501     True\n",
       "502     True\n",
       "503     True\n",
       "504     True\n",
       "505     True\n",
       "Name: AGE, Length: 506, dtype: bool"
      ]
     },
     "execution_count": 69,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.AGE > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>359 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "5    0.02985   0.0   2.18   0.0  0.458  6.430  58.7  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  target  \n",
       "0       15.3  396.90   4.98    24.0  \n",
       "1       17.8  396.90   9.14    21.6  \n",
       "2       17.8  392.83   4.03    34.7  \n",
       "4       18.7  396.90   5.33    36.2  \n",
       "5       18.7  394.12   5.21    28.7  \n",
       "..       ...     ...    ...     ...  \n",
       "501     21.0  391.99   9.67    22.4  \n",
       "502     21.0  396.90   9.08    20.6  \n",
       "503     21.0  396.90   5.64    23.9  \n",
       "504     21.0  393.45   6.48    22.0  \n",
       "505     21.0  396.90   7.88    11.9  \n",
       "\n",
       "[359 rows x 14 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.AGE > 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>359 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "5    0.02985   0.0   2.18   0.0  0.458  6.430  58.7  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  target  \n",
       "0       15.3  396.90   4.98    24.0  \n",
       "1       17.8  396.90   9.14    21.6  \n",
       "2       17.8  392.83   4.03    34.7  \n",
       "4       18.7  396.90   5.33    36.2  \n",
       "5       18.7  394.12   5.21    28.7  \n",
       "..       ...     ...    ...     ...  \n",
       "501     21.0  391.99   9.67    22.4  \n",
       "502     21.0  396.90   9.08    20.6  \n",
       "503     21.0  396.90   5.64    23.9  \n",
       "504     21.0  393.45   6.48    22.0  \n",
       "505     21.0  396.90   7.88    11.9  \n",
       "\n",
       "[359 rows x 14 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"AGE > 50\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.12083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.445</td>\n",
       "      <td>8.069</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.4952</td>\n",
       "      <td>2.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.21</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1.51902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.605</td>\n",
       "      <td>8.375</td>\n",
       "      <td>93.9</td>\n",
       "      <td>2.1620</td>\n",
       "      <td>5.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>388.45</td>\n",
       "      <td>3.32</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>0.31533</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.504</td>\n",
       "      <td>8.266</td>\n",
       "      <td>78.3</td>\n",
       "      <td>2.8944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>385.05</td>\n",
       "      <td>4.14</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.52693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.504</td>\n",
       "      <td>8.725</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.8944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>382.00</td>\n",
       "      <td>4.63</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.38214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.504</td>\n",
       "      <td>8.040</td>\n",
       "      <td>86.5</td>\n",
       "      <td>3.2157</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>387.38</td>\n",
       "      <td>3.13</td>\n",
       "      <td>37.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>0.57529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507</td>\n",
       "      <td>8.337</td>\n",
       "      <td>73.3</td>\n",
       "      <td>3.8384</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>385.91</td>\n",
       "      <td>2.47</td>\n",
       "      <td>41.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>0.33147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507</td>\n",
       "      <td>8.247</td>\n",
       "      <td>70.4</td>\n",
       "      <td>3.6519</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>378.95</td>\n",
       "      <td>3.95</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>0.61154</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.647</td>\n",
       "      <td>8.704</td>\n",
       "      <td>86.9</td>\n",
       "      <td>1.8010</td>\n",
       "      <td>5.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>389.70</td>\n",
       "      <td>5.12</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0.52014</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.647</td>\n",
       "      <td>8.398</td>\n",
       "      <td>91.5</td>\n",
       "      <td>2.2885</td>\n",
       "      <td>5.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>386.86</td>\n",
       "      <td>5.91</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>0.57834</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.575</td>\n",
       "      <td>8.297</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.4216</td>\n",
       "      <td>5.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>384.54</td>\n",
       "      <td>7.44</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>3.47428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.718</td>\n",
       "      <td>8.780</td>\n",
       "      <td>82.9</td>\n",
       "      <td>1.9047</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>354.55</td>\n",
       "      <td>5.29</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS   RAD    TAX  \\\n",
       "97   0.12083   0.0   2.89   0.0  0.445  8.069  76.0  3.4952   2.0  276.0   \n",
       "163  1.51902   0.0  19.58   1.0  0.605  8.375  93.9  2.1620   5.0  403.0   \n",
       "224  0.31533   0.0   6.20   0.0  0.504  8.266  78.3  2.8944   8.0  307.0   \n",
       "225  0.52693   0.0   6.20   0.0  0.504  8.725  83.0  2.8944   8.0  307.0   \n",
       "226  0.38214   0.0   6.20   0.0  0.504  8.040  86.5  3.2157   8.0  307.0   \n",
       "232  0.57529   0.0   6.20   0.0  0.507  8.337  73.3  3.8384   8.0  307.0   \n",
       "233  0.33147   0.0   6.20   0.0  0.507  8.247  70.4  3.6519   8.0  307.0   \n",
       "257  0.61154  20.0   3.97   0.0  0.647  8.704  86.9  1.8010   5.0  264.0   \n",
       "262  0.52014  20.0   3.97   0.0  0.647  8.398  91.5  2.2885   5.0  264.0   \n",
       "267  0.57834  20.0   3.97   0.0  0.575  8.297  67.0  2.4216   5.0  264.0   \n",
       "364  3.47428   0.0  18.10   1.0  0.718  8.780  82.9  1.9047  24.0  666.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  target  \n",
       "97      18.0  396.90   4.21    38.7  \n",
       "163     14.7  388.45   3.32    50.0  \n",
       "224     17.4  385.05   4.14    44.8  \n",
       "225     17.4  382.00   4.63    50.0  \n",
       "226     17.4  387.38   3.13    37.6  \n",
       "232     17.4  385.91   2.47    41.7  \n",
       "233     17.4  378.95   3.95    48.3  \n",
       "257     13.0  389.70   5.12    50.0  \n",
       "262     13.0  386.86   5.91    48.8  \n",
       "267     13.0  384.54   7.44    50.0  \n",
       "364     20.2  354.55   5.29    21.9  "
      ]
     },
     "execution_count": 74,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.RM > 8) & (df.AGE > 50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.12083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.445</td>\n",
       "      <td>8.069</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.4952</td>\n",
       "      <td>2.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.21</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1.51902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.605</td>\n",
       "      <td>8.375</td>\n",
       "      <td>93.9</td>\n",
       "      <td>2.1620</td>\n",
       "      <td>5.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>388.45</td>\n",
       "      <td>3.32</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>0.31533</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.504</td>\n",
       "      <td>8.266</td>\n",
       "      <td>78.3</td>\n",
       "      <td>2.8944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>385.05</td>\n",
       "      <td>4.14</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.52693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.504</td>\n",
       "      <td>8.725</td>\n",
       "      <td>83.0</td>\n",
       "      <td>2.8944</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>382.00</td>\n",
       "      <td>4.63</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.38214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.504</td>\n",
       "      <td>8.040</td>\n",
       "      <td>86.5</td>\n",
       "      <td>3.2157</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>387.38</td>\n",
       "      <td>3.13</td>\n",
       "      <td>37.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>0.57529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507</td>\n",
       "      <td>8.337</td>\n",
       "      <td>73.3</td>\n",
       "      <td>3.8384</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>385.91</td>\n",
       "      <td>2.47</td>\n",
       "      <td>41.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>0.33147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507</td>\n",
       "      <td>8.247</td>\n",
       "      <td>70.4</td>\n",
       "      <td>3.6519</td>\n",
       "      <td>8.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>378.95</td>\n",
       "      <td>3.95</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>0.61154</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.647</td>\n",
       "      <td>8.704</td>\n",
       "      <td>86.9</td>\n",
       "      <td>1.8010</td>\n",
       "      <td>5.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>389.70</td>\n",
       "      <td>5.12</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0.52014</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.647</td>\n",
       "      <td>8.398</td>\n",
       "      <td>91.5</td>\n",
       "      <td>2.2885</td>\n",
       "      <td>5.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>386.86</td>\n",
       "      <td>5.91</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>0.57834</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.575</td>\n",
       "      <td>8.297</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.4216</td>\n",
       "      <td>5.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>384.54</td>\n",
       "      <td>7.44</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>3.47428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.718</td>\n",
       "      <td>8.780</td>\n",
       "      <td>82.9</td>\n",
       "      <td>1.9047</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>354.55</td>\n",
       "      <td>5.29</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS   RAD    TAX  \\\n",
       "97   0.12083   0.0   2.89   0.0  0.445  8.069  76.0  3.4952   2.0  276.0   \n",
       "163  1.51902   0.0  19.58   1.0  0.605  8.375  93.9  2.1620   5.0  403.0   \n",
       "224  0.31533   0.0   6.20   0.0  0.504  8.266  78.3  2.8944   8.0  307.0   \n",
       "225  0.52693   0.0   6.20   0.0  0.504  8.725  83.0  2.8944   8.0  307.0   \n",
       "226  0.38214   0.0   6.20   0.0  0.504  8.040  86.5  3.2157   8.0  307.0   \n",
       "232  0.57529   0.0   6.20   0.0  0.507  8.337  73.3  3.8384   8.0  307.0   \n",
       "233  0.33147   0.0   6.20   0.0  0.507  8.247  70.4  3.6519   8.0  307.0   \n",
       "257  0.61154  20.0   3.97   0.0  0.647  8.704  86.9  1.8010   5.0  264.0   \n",
       "262  0.52014  20.0   3.97   0.0  0.647  8.398  91.5  2.2885   5.0  264.0   \n",
       "267  0.57834  20.0   3.97   0.0  0.575  8.297  67.0  2.4216   5.0  264.0   \n",
       "364  3.47428   0.0  18.10   1.0  0.718  8.780  82.9  1.9047  24.0  666.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  target  \n",
       "97      18.0  396.90   4.21    38.7  \n",
       "163     14.7  388.45   3.32    50.0  \n",
       "224     17.4  385.05   4.14    44.8  \n",
       "225     17.4  382.00   4.63    50.0  \n",
       "226     17.4  387.38   3.13    37.6  \n",
       "232     17.4  385.91   2.47    41.7  \n",
       "233     17.4  378.95   3.95    48.3  \n",
       "257     13.0  389.70   5.12    50.0  \n",
       "262     13.0  386.86   5.91    48.8  \n",
       "267     13.0  384.54   7.44    50.0  \n",
       "364     20.2  354.55   5.29    21.9  "
      ]
     },
     "execution_count": 75,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"RM > 8 and AGE > 50\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Descriptive statistics\n",
    "\n",
    "- Descriptive statistics summarizes or describes the characteristics of a data set.\n",
    "- Descriptive statistics consists of two basic categories of measures: measures of central tendency and measures of variability (or spread).\n",
    "- Measures of central tendency describe the center of a data set.\n",
    "- Measures of variability or spread describe the dispersion of data within the set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  5.,  4.,  8.,  6.,  7., 24.])"
      ]
     },
     "execution_count": 78,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.RAD.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0    132\n",
       "5.0     115\n",
       "4.0     110\n",
       "3.0      38\n",
       "6.0      26\n",
       "2.0      24\n",
       "8.0      24\n",
       "1.0      20\n",
       "7.0      17\n",
       "Name: RAD, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.RAD.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "target     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()  # equal to axis=0, axis=1 will sum all rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT      target  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 89,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 89,
     "metadata": {
      "image/png": {
       "height": 443,
       "width": 710
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().loc[\"std\"].plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 710
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 87,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 87,
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 710
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=[\"TAX\", \"B\"]).plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'CRIM'}>,\n",
       "        <AxesSubplot:title={'center':'ZN'}>],\n",
       "       [<AxesSubplot:title={'center':'target'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 4 Axes>"
      ]
     },
     "execution_count": 90,
     "metadata": {
      "image/png": {
       "height": 426,
       "width": 710
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"CRIM\", \"ZN\", \"target\"]].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 104,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "execution_count": 104,
     "metadata": {
      "image/png": {
       "height": 465,
       "width": 891
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.plot(kind=\"hist\", figsize=(15,8), bins=30, alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>                <div id=\"4e80ff3e-87f6-4d91-8b08-4e50ada7c9c3\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"4e80ff3e-87f6-4d91-8b08-4e50ada7c9c3\")) {                    Plotly.newPlot(                        \"4e80ff3e-87f6-4d91-8b08-4e50ada7c9c3\",                        [{\"alignmentgroup\": \"True\", \"box\": {\"visible\": true}, \"hovertemplate\": \"target=%{y}<extra></extra>\", \"legendgroup\": \"\", \"marker\": {\"color\": \"#636efa\"}, \"name\": \"\", \"offsetgroup\": \"\", \"orientation\": \"v\", \"points\": \"outliers\", \"scalegroup\": \"True\", \"showlegend\": false, \"type\": \"violin\", \"x0\": \" \", \"xaxis\": \"x\", \"y\": [24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15.0, 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21.0, 12.7, 14.5, 13.2, 13.1, 13.5, 18.9, 20.0, 21.0, 24.7, 30.8, 34.9, 26.6, 25.3, 24.7, 21.2, 19.3, 20.0, 16.6, 14.4, 19.4, 19.7, 20.5, 25.0, 23.4, 18.9, 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16.0, 22.2, 25.0, 33.0, 23.5, 19.4, 22.0, 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20.0, 20.8, 21.2, 20.3, 28.0, 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2, 23.6, 28.7, 22.6, 22.0, 22.9, 25.0, 20.6, 28.4, 21.4, 38.7, 43.8, 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4, 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22.0, 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18.0, 14.3, 19.2, 19.6, 23.0, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14.0, 14.4, 13.4, 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4, 17.0, 15.6, 13.1, 41.3, 24.3, 23.3, 27.0, 50.0, 50.0, 50.0, 22.7, 25.0, 50.0, 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4, 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50.0, 32.0, 29.8, 34.9, 37.0, 30.5, 36.4, 31.1, 29.1, 50.0, 33.3, 30.3, 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50.0, 22.6, 24.4, 22.5, 24.4, 20.0, 21.7, 19.3, 22.4, 28.1, 23.7, 25.0, 23.3, 28.7, 21.5, 23.0, 26.7, 21.7, 27.5, 30.1, 44.8, 50.0, 37.6, 31.6, 46.7, 31.5, 24.3, 31.7, 41.7, 48.3, 29.0, 24.0, 25.1, 31.5, 23.7, 23.3, 22.0, 20.1, 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6, 42.8, 21.9, 20.9, 44.0, 50.0, 36.0, 30.1, 33.8, 43.1, 48.8, 31.0, 36.5, 22.8, 30.7, 50.0, 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4, 32.0, 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46.0, 50.0, 32.2, 22.0, 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1, 20.3, 22.5, 29.0, 24.8, 22.0, 26.4, 33.1, 36.1, 28.4, 33.4, 28.2, 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1, 21.0, 23.8, 23.1, 20.4, 18.5, 25.0, 24.6, 23.0, 22.2, 19.3, 22.6, 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19.0, 18.7, 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1, 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25.0, 19.9, 20.8, 16.8, 21.9, 27.5, 21.9, 23.1, 50.0, 50.0, 50.0, 50.0, 50.0, 13.8, 13.8, 15.0, 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5.0, 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5.0, 11.9, 27.9, 17.2, 27.5, 15.0, 17.2, 17.9, 16.3, 7.0, 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11.0, 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13.0, 13.4, 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20.0, 16.4, 17.7, 19.5, 20.2, 21.4, 19.9, 19.0, 19.1, 19.1, 20.1, 19.9, 19.6, 23.2, 29.8, 13.8, 13.3, 16.7, 12.0, 14.6, 21.4, 23.0, 23.7, 25.0, 21.8, 20.6, 21.2, 19.1, 20.6, 15.2, 7.0, 8.1, 13.6, 20.1, 21.8, 24.5, 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22.0, 11.9], \"y0\": \" \", \"yaxis\": \"y\"}],                        {\"legend\": {\"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"violinmode\": \"group\", \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"target\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('4e80ff3e-87f6-4d91-8b08-4e50ada7c9c3');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 129,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.violin(df, y=\"target\", box=True, points=\"outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>                <div id=\"6b5f461d-6c8f-4614-b6bd-487972d37f10\" class=\"plotly-graph-div\" style=\"height:1000px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6b5f461d-6c8f-4614-b6bd-487972d37f10\")) {                    Plotly.newPlot(                        \"6b5f461d-6c8f-4614-b6bd-487972d37f10\",                        [{\"dimensions\": [{\"axis\": {\"matches\": true}, \"label\": \"target\", \"values\": [24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15.0, 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21.0, 12.7, 14.5, 13.2, 13.1, 13.5, 18.9, 20.0, 21.0, 24.7, 30.8, 34.9, 26.6, 25.3, 24.7, 21.2, 19.3, 20.0, 16.6, 14.4, 19.4, 19.7, 20.5, 25.0, 23.4, 18.9, 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16.0, 22.2, 25.0, 33.0, 23.5, 19.4, 22.0, 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20.0, 20.8, 21.2, 20.3, 28.0, 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2, 23.6, 28.7, 22.6, 22.0, 22.9, 25.0, 20.6, 28.4, 21.4, 38.7, 43.8, 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4, 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22.0, 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18.0, 14.3, 19.2, 19.6, 23.0, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14.0, 14.4, 13.4, 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4, 17.0, 15.6, 13.1, 41.3, 24.3, 23.3, 27.0, 50.0, 50.0, 50.0, 22.7, 25.0, 50.0, 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4, 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50.0, 32.0, 29.8, 34.9, 37.0, 30.5, 36.4, 31.1, 29.1, 50.0, 33.3, 30.3, 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50.0, 22.6, 24.4, 22.5, 24.4, 20.0, 21.7, 19.3, 22.4, 28.1, 23.7, 25.0, 23.3, 28.7, 21.5, 23.0, 26.7, 21.7, 27.5, 30.1, 44.8, 50.0, 37.6, 31.6, 46.7, 31.5, 24.3, 31.7, 41.7, 48.3, 29.0, 24.0, 25.1, 31.5, 23.7, 23.3, 22.0, 20.1, 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6, 42.8, 21.9, 20.9, 44.0, 50.0, 36.0, 30.1, 33.8, 43.1, 48.8, 31.0, 36.5, 22.8, 30.7, 50.0, 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4, 32.0, 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46.0, 50.0, 32.2, 22.0, 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1, 20.3, 22.5, 29.0, 24.8, 22.0, 26.4, 33.1, 36.1, 28.4, 33.4, 28.2, 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1, 21.0, 23.8, 23.1, 20.4, 18.5, 25.0, 24.6, 23.0, 22.2, 19.3, 22.6, 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19.0, 18.7, 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1, 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25.0, 19.9, 20.8, 16.8, 21.9, 27.5, 21.9, 23.1, 50.0, 50.0, 50.0, 50.0, 50.0, 13.8, 13.8, 15.0, 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5.0, 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5.0, 11.9, 27.9, 17.2, 27.5, 15.0, 17.2, 17.9, 16.3, 7.0, 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11.0, 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13.0, 13.4, 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20.0, 16.4, 17.7, 19.5, 20.2, 21.4, 19.9, 19.0, 19.1, 19.1, 20.1, 19.9, 19.6, 23.2, 29.8, 13.8, 13.3, 16.7, 12.0, 14.6, 21.4, 23.0, 23.7, 25.0, 21.8, 20.6, 21.2, 19.1, 20.6, 15.2, 7.0, 8.1, 13.6, 20.1, 21.8, 24.5, 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22.0, 11.9]}, {\"axis\": {\"matches\": true}, \"label\": \"CRIM\", \"values\": [0.00632, 0.02731, 0.02729, 0.03237, 0.06905, 0.02985, 0.08829, 0.14455, 0.21124, 0.17004, 0.22489, 0.11747, 0.09378, 0.62976, 0.63796, 0.62739, 1.05393, 0.7842, 0.80271, 0.7258, 1.25179, 0.85204, 1.23247, 0.98843, 0.75026, 0.84054, 0.67191, 0.95577, 0.77299, 1.00245, 1.13081, 1.35472, 1.38799, 1.15172, 1.61282, 0.06417, 0.09744, 0.08014, 0.17505, 0.02763, 0.03359, 0.12744, 0.1415, 0.15936, 0.12269, 0.17142, 0.18836, 0.22927, 0.25387, 0.21977, 0.08873, 0.04337, 0.0536, 0.04981, 0.0136, 0.01311, 0.02055, 0.01432, 0.15445, 0.10328, 0.14932, 0.17171, 0.11027, 0.1265, 0.01951, 0.03584, 0.04379, 0.05789, 0.13554, 0.12816, 0.08826, 0.15876, 0.09164, 0.19539, 0.07896, 0.09512, 0.10153, 0.08707, 0.05646, 0.08387, 0.04113, 0.04462, 0.03659, 0.03551, 0.05059, 0.05735, 0.05188, 0.07151, 0.0566, 0.05302, 0.04684, 0.03932, 0.04203, 0.02875, 0.04294, 0.12204, 0.11504, 0.12083, 0.08187, 0.0686, 0.14866, 0.11432, 0.22876, 0.21161, 0.1396, 0.13262, 0.1712, 0.13117, 0.12802, 0.26363, 0.10793, 0.10084, 0.12329, 0.22212, 0.14231, 0.17134, 0.13158, 0.15098, 0.13058, 0.14476, 0.06899, 0.07165, 0.09299, 0.15038, 0.09849, 0.16902, 0.38735, 0.25915, 0.32543, 0.88125, 0.34006, 1.19294, 0.59005, 0.32982, 0.97617, 0.55778, 0.32264, 0.35233, 0.2498, 0.54452, 0.2909, 1.62864, 3.32105, 4.0974, 2.77974, 2.37934, 2.15505, 2.36862, 2.33099, 2.73397, 1.6566, 1.49632, 1.12658, 2.14918, 1.41385, 3.53501, 2.44668, 1.22358, 1.34284, 1.42502, 1.27346, 1.46336, 1.83377, 1.51902, 2.24236, 2.924, 2.01019, 1.80028, 2.3004, 2.44953, 1.20742, 2.3139, 0.13914, 0.09178, 0.08447, 0.06664, 0.07022, 0.05425, 0.06642, 0.0578, 0.06588, 0.06888, 0.09103, 0.10008, 0.08308, 0.06047, 0.05602, 0.07875, 0.12579, 0.0837, 0.09068, 0.06911, 0.08664, 0.02187, 0.01439, 0.01381, 0.04011, 0.04666, 0.03768, 0.0315, 0.01778, 0.03445, 0.02177, 0.0351, 0.02009, 0.13642, 0.22969, 0.25199, 0.13587, 0.43571, 0.17446, 0.37578, 0.21719, 0.14052, 0.28955, 0.19802, 0.0456, 0.07013, 0.11069, 0.11425, 0.35809, 0.40771, 0.62356, 0.6147, 0.31533, 0.52693, 0.38214, 0.41238, 0.29819, 0.44178, 0.537, 0.46296, 0.57529, 0.33147, 0.44791, 0.33045, 0.52058, 0.51183, 0.08244, 0.09252, 0.11329, 0.10612, 0.1029, 0.12757, 0.20608, 0.19133, 0.33983, 0.19657, 0.16439, 0.19073, 0.1403, 0.21409, 0.08221, 0.36894, 0.04819, 0.03548, 0.01538, 0.61154, 0.66351, 0.65665, 0.54011, 0.53412, 0.52014, 0.82526, 0.55007, 0.76162, 0.7857, 0.57834, 0.5405, 0.09065, 0.29916, 0.16211, 0.1146, 0.22188, 0.05644, 0.09604, 0.10469, 0.06127, 0.07978, 0.21038, 0.03578, 0.03705, 0.06129, 0.01501, 0.00906, 0.01096, 0.01965, 0.03871, 0.0459, 0.04297, 0.03502, 0.07886, 0.03615, 0.08265, 0.08199, 0.12932, 0.05372, 0.14103, 0.06466, 0.05561, 0.04417, 0.03537, 0.09266, 0.1, 0.05515, 0.05479, 0.07503, 0.04932, 0.49298, 0.3494, 2.63548, 0.79041, 0.26169, 0.26938, 0.3692, 0.25356, 0.31827, 0.24522, 0.40202, 0.47547, 0.1676, 0.18159, 0.35114, 0.28392, 0.34109, 0.19186, 0.30347, 0.24103, 0.06617, 0.06724, 0.04544, 0.05023, 0.03466, 0.05083, 0.03738, 0.03961, 0.03427, 0.03041, 0.03306, 0.05497, 0.06151, 0.01301, 0.02498, 0.02543, 0.03049, 0.03113, 0.06162, 0.0187, 0.01501, 0.02899, 0.06211, 0.0795, 0.07244, 0.01709, 0.04301, 0.10659, 8.98296, 3.8497, 5.20177, 4.26131, 4.54192, 3.83684, 3.67822, 4.22239, 3.47428, 4.55587, 3.69695, 13.5222, 4.89822, 5.66998, 6.53876, 9.2323, 8.26725, 11.1081, 18.4982, 19.6091, 15.288, 9.82349, 23.6482, 17.8667, 88.9762, 15.8744, 9.18702, 7.99248, 20.0849, 16.8118, 24.3938, 22.5971, 14.3337, 8.15174, 6.96215, 5.29305, 11.5779, 8.64476, 13.3598, 8.71675, 5.87205, 7.67202, 38.3518, 9.91655, 25.0461, 14.2362, 9.59571, 24.8017, 41.5292, 67.9208, 20.7162, 11.9511, 7.40389, 14.4383, 51.1358, 14.0507, 18.811, 28.6558, 45.7461, 18.0846, 10.8342, 25.9406, 73.5341, 11.8123, 11.0874, 7.02259, 12.0482, 7.05042, 8.79212, 15.8603, 12.2472, 37.6619, 7.36711, 9.33889, 8.49213, 10.0623, 6.44405, 5.58107, 13.9134, 11.1604, 14.4208, 15.1772, 13.6781, 9.39063, 22.0511, 9.72418, 5.66637, 9.96654, 12.8023, 10.6718, 6.28807, 9.92485, 9.32909, 7.52601, 6.71772, 5.44114, 5.09017, 8.24809, 9.51363, 4.75237, 4.66883, 8.20058, 7.75223, 6.80117, 4.81213, 3.69311, 6.65492, 5.82115, 7.83932, 3.1636, 3.77498, 4.42228, 15.5757, 13.0751, 4.34879, 4.03841, 3.56868, 4.64689, 8.05579, 6.39312, 4.87141, 15.0234, 10.233, 14.3337, 5.82401, 5.70818, 5.73116, 2.81838, 2.37857, 3.67367, 5.69175, 4.83567, 0.15086, 0.18337, 0.20746, 0.10574, 0.11132, 0.17331, 0.27957, 0.17899, 0.2896, 0.26838, 0.23912, 0.17783, 0.22438, 0.06263, 0.04527, 0.06076, 0.10959, 0.04741]}, {\"axis\": {\"matches\": true}, \"label\": \"ZN\", \"values\": [18.0, 0.0, 0.0, 0.0, 0.0, 0.0, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 12.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 75.0, 75.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 21.0, 21.0, 21.0, 21.0, 75.0, 90.0, 85.0, 100.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 17.5, 80.0, 80.0, 12.5, 12.5, 12.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 25.0, 25.0, 25.0, 25.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 28.0, 28.0, 28.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 45.0, 45.0, 45.0, 45.0, 45.0, 45.0, 60.0, 60.0, 80.0, 80.0, 80.0, 80.0, 95.0, 95.0, 82.5, 82.5, 95.0, 95.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 80.0, 80.0, 90.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 40.0, 40.0, 40.0, 40.0, 40.0, 20.0, 20.0, 20.0, 20.0, 90.0, 90.0, 55.0, 80.0, 52.5, 52.5, 52.5, 80.0, 80.0, 80.0, 0.0, 0.0, 0.0, 0.0, 0.0, 70.0, 70.0, 70.0, 34.0, 34.0, 34.0, 33.0, 33.0, 33.0, 33.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 35.0, 35.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 35.0, 0.0, 55.0, 55.0, 0.0, 0.0, 85.0, 80.0, 40.0, 40.0, 60.0, 60.0, 90.0, 80.0, 80.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, {\"axis\": {\"matches\": true}, \"label\": \"INDUS\", \"values\": [2.31, 7.07, 7.07, 2.18, 2.18, 2.18, 7.87, 7.87, 7.87, 7.87, 7.87, 7.87, 7.87, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 8.14, 5.96, 5.96, 5.96, 5.96, 2.95, 2.95, 6.91, 6.91, 6.91, 6.91, 6.91, 6.91, 6.91, 6.91, 6.91, 5.64, 5.64, 5.64, 5.64, 4.0, 1.22, 0.74, 1.32, 5.13, 5.13, 5.13, 5.13, 5.13, 5.13, 1.38, 3.37, 3.37, 6.07, 6.07, 6.07, 10.81, 10.81, 10.81, 10.81, 12.83, 12.83, 12.83, 12.83, 12.83, 12.83, 4.86, 4.86, 4.86, 4.86, 4.49, 4.49, 4.49, 4.49, 3.41, 3.41, 3.41, 3.41, 15.04, 15.04, 15.04, 2.89, 2.89, 2.89, 2.89, 2.89, 8.56, 8.56, 8.56, 8.56, 8.56, 8.56, 8.56, 8.56, 8.56, 8.56, 8.56, 10.01, 10.01, 10.01, 10.01, 10.01, 10.01, 10.01, 10.01, 10.01, 25.65, 25.65, 25.65, 25.65, 25.65, 25.65, 25.65, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 21.89, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 19.58, 4.05, 4.05, 4.05, 4.05, 4.05, 4.05, 4.05, 2.46, 2.46, 2.46, 2.46, 2.46, 2.46, 2.46, 2.46, 3.44, 3.44, 3.44, 3.44, 3.44, 3.44, 2.93, 2.93, 0.46, 1.52, 1.52, 1.52, 1.47, 1.47, 2.03, 2.03, 2.68, 2.68, 10.59, 10.59, 10.59, 10.59, 10.59, 10.59, 10.59, 10.59, 10.59, 10.59, 10.59, 13.89, 13.89, 13.89, 13.89, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 6.2, 4.93, 4.93, 4.93, 4.93, 4.93, 4.93, 5.86, 5.86, 5.86, 5.86, 5.86, 5.86, 5.86, 5.86, 5.86, 5.86, 3.64, 3.64, 3.75, 3.97, 3.97, 3.97, 3.97, 3.97, 3.97, 3.97, 3.97, 3.97, 3.97, 3.97, 3.97, 6.96, 6.96, 6.96, 6.96, 6.96, 6.41, 6.41, 6.41, 6.41, 6.41, 3.33, 3.33, 3.33, 3.33, 1.21, 2.97, 2.25, 1.76, 5.32, 5.32, 5.32, 4.95, 4.95, 4.95, 13.92, 13.92, 13.92, 13.92, 13.92, 2.24, 2.24, 2.24, 6.09, 6.09, 6.09, 2.18, 2.18, 2.18, 2.18, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 7.38, 7.38, 7.38, 7.38, 7.38, 7.38, 7.38, 7.38, 3.24, 3.24, 3.24, 6.06, 6.06, 5.19, 5.19, 5.19, 5.19, 5.19, 5.19, 5.19, 5.19, 1.52, 1.89, 3.78, 3.78, 4.39, 4.39, 4.15, 2.01, 1.25, 1.25, 1.69, 1.69, 2.02, 1.91, 1.91, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 18.1, 27.74, 27.74, 27.74, 27.74, 27.74, 9.69, 9.69, 9.69, 9.69, 9.69, 9.69, 9.69, 9.69, 11.93, 11.93, 11.93, 11.93, 11.93]}, {\"axis\": {\"matches\": true}, \"label\": \"CHAS\", \"values\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, {\"axis\": {\"matches\": true}, \"label\": \"NOX\", \"values\": [0.538, 0.469, 0.469, 0.458, 0.458, 0.458, 0.524, 0.524, 0.524, 0.524, 0.524, 0.524, 0.524, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.538, 0.499, 0.499, 0.499, 0.499, 0.428, 0.428, 0.448, 0.448, 0.448, 0.448, 0.448, 0.448, 0.448, 0.448, 0.448, 0.439, 0.439, 0.439, 0.439, 0.41, 0.403, 0.41, 0.411, 0.453, 0.453, 0.453, 0.453, 0.453, 0.453, 0.4161, 0.398, 0.398, 0.409, 0.409, 0.409, 0.413, 0.413, 0.413, 0.413, 0.437, 0.437, 0.437, 0.437, 0.437, 0.437, 0.426, 0.426, 0.426, 0.426, 0.449, 0.449, 0.449, 0.449, 0.489, 0.489, 0.489, 0.489, 0.464, 0.464, 0.464, 0.445, 0.445, 0.445, 0.445, 0.445, 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.547, 0.547, 0.547, 0.547, 0.547, 0.547, 0.547, 0.547, 0.547, 0.581, 0.581, 0.581, 0.581, 0.581, 0.581, 0.581, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.624, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.871, 0.605, 0.605, 0.871, 0.605, 0.605, 0.605, 0.605, 0.605, 0.605, 0.605, 0.605, 0.605, 0.605, 0.605, 0.605, 0.51, 0.51, 0.51, 0.51, 0.51, 0.51, 0.51, 0.488, 0.488, 0.488, 0.488, 0.488, 0.488, 0.488, 0.488, 0.437, 0.437, 0.437, 0.437, 0.437, 0.437, 0.401, 0.401, 0.422, 0.404, 0.404, 0.404, 0.403, 0.403, 0.415, 0.415, 0.4161, 0.4161, 0.489, 0.489, 0.489, 0.489, 0.489, 0.489, 0.489, 0.489, 0.489, 0.489, 0.489, 0.55, 0.55, 0.55, 0.55, 0.507, 0.507, 0.507, 0.507, 0.504, 0.504, 0.504, 0.504, 0.504, 0.504, 0.504, 0.504, 0.507, 0.507, 0.507, 0.507, 0.507, 0.507, 0.428, 0.428, 0.428, 0.428, 0.428, 0.428, 0.431, 0.431, 0.431, 0.431, 0.431, 0.431, 0.431, 0.431, 0.431, 0.431, 0.392, 0.392, 0.394, 0.647, 0.647, 0.647, 0.647, 0.647, 0.647, 0.647, 0.647, 0.647, 0.647, 0.575, 0.575, 0.464, 0.464, 0.464, 0.464, 0.464, 0.447, 0.447, 0.447, 0.447, 0.447, 0.4429, 0.4429, 0.4429, 0.4429, 0.401, 0.4, 0.389, 0.385, 0.405, 0.405, 0.405, 0.411, 0.411, 0.411, 0.437, 0.437, 0.437, 0.437, 0.437, 0.4, 0.4, 0.4, 0.433, 0.433, 0.433, 0.472, 0.472, 0.472, 0.472, 0.544, 0.544, 0.544, 0.544, 0.544, 0.544, 0.544, 0.544, 0.544, 0.544, 0.544, 0.544, 0.493, 0.493, 0.493, 0.493, 0.493, 0.493, 0.493, 0.493, 0.46, 0.46, 0.46, 0.4379, 0.4379, 0.515, 0.515, 0.515, 0.515, 0.515, 0.515, 0.515, 0.515, 0.442, 0.518, 0.484, 0.484, 0.442, 0.442, 0.429, 0.435, 0.429, 0.429, 0.411, 0.411, 0.41, 0.413, 0.413, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.77, 0.718, 0.718, 0.718, 0.631, 0.631, 0.631, 0.631, 0.631, 0.668, 0.668, 0.668, 0.671, 0.671, 0.671, 0.671, 0.671, 0.671, 0.671, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.693, 0.693, 0.693, 0.693, 0.693, 0.693, 0.693, 0.693, 0.693, 0.693, 0.693, 0.693, 0.693, 0.659, 0.659, 0.597, 0.597, 0.597, 0.597, 0.597, 0.597, 0.693, 0.679, 0.679, 0.679, 0.679, 0.718, 0.718, 0.718, 0.614, 0.614, 0.584, 0.679, 0.584, 0.679, 0.679, 0.679, 0.584, 0.584, 0.584, 0.713, 0.713, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.74, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.713, 0.655, 0.655, 0.655, 0.584, 0.58, 0.58, 0.58, 0.532, 0.58, 0.614, 0.584, 0.584, 0.614, 0.614, 0.614, 0.614, 0.532, 0.532, 0.532, 0.532, 0.583, 0.583, 0.583, 0.583, 0.609, 0.609, 0.609, 0.609, 0.609, 0.585, 0.585, 0.585, 0.585, 0.585, 0.585, 0.585, 0.585, 0.573, 0.573, 0.573, 0.573, 0.573]}, {\"axis\": {\"matches\": true}, \"label\": \"RM\", \"values\": [6.575, 6.421, 7.185, 6.998, 7.147, 6.43, 6.012, 6.172, 5.631, 6.004, 6.377, 6.009, 5.889, 5.949, 6.096, 5.834, 5.935, 5.99, 5.456, 5.727, 5.57, 5.965, 6.142, 5.813, 5.924, 5.599, 5.813, 6.047, 6.495, 6.674, 5.713, 6.072, 5.95, 5.701, 6.096, 5.933, 5.841, 5.85, 5.966, 6.595, 7.024, 6.77, 6.169, 6.211, 6.069, 5.682, 5.786, 6.03, 5.399, 5.602, 5.963, 6.115, 6.511, 5.998, 5.888, 7.249, 6.383, 6.816, 6.145, 5.927, 5.741, 5.966, 6.456, 6.762, 7.104, 6.29, 5.787, 5.878, 5.594, 5.885, 6.417, 5.961, 6.065, 6.245, 6.273, 6.286, 6.279, 6.14, 6.232, 5.874, 6.727, 6.619, 6.302, 6.167, 6.389, 6.63, 6.015, 6.121, 7.007, 7.079, 6.417, 6.405, 6.442, 6.211, 6.249, 6.625, 6.163, 8.069, 7.82, 7.416, 6.727, 6.781, 6.405, 6.137, 6.167, 5.851, 5.836, 6.127, 6.474, 6.229, 6.195, 6.715, 5.913, 6.092, 6.254, 5.928, 6.176, 6.021, 5.872, 5.731, 5.87, 6.004, 5.961, 5.856, 5.879, 5.986, 5.613, 5.693, 6.431, 5.637, 6.458, 6.326, 6.372, 5.822, 5.757, 6.335, 5.942, 6.454, 5.857, 6.151, 6.174, 5.019, 5.403, 5.468, 4.903, 6.13, 5.628, 4.926, 5.186, 5.597, 6.122, 5.404, 5.012, 5.709, 6.129, 6.152, 5.272, 6.943, 6.066, 6.51, 6.25, 7.489, 7.802, 8.375, 5.854, 6.101, 7.929, 5.877, 6.319, 6.402, 5.875, 5.88, 5.572, 6.416, 5.859, 6.546, 6.02, 6.315, 6.86, 6.98, 7.765, 6.144, 7.155, 6.563, 5.604, 6.153, 7.831, 6.782, 6.556, 7.185, 6.951, 6.739, 7.178, 6.8, 6.604, 7.875, 7.287, 7.107, 7.274, 6.975, 7.135, 6.162, 7.61, 7.853, 8.034, 5.891, 6.326, 5.783, 6.064, 5.344, 5.96, 5.404, 5.807, 6.375, 5.412, 6.182, 5.888, 6.642, 5.951, 6.373, 6.951, 6.164, 6.879, 6.618, 8.266, 8.725, 8.04, 7.163, 7.686, 6.552, 5.981, 7.412, 8.337, 8.247, 6.726, 6.086, 6.631, 7.358, 6.481, 6.606, 6.897, 6.095, 6.358, 6.393, 5.593, 5.605, 6.108, 6.226, 6.433, 6.718, 6.487, 6.438, 6.957, 8.259, 6.108, 5.876, 7.454, 8.704, 7.333, 6.842, 7.203, 7.52, 8.398, 7.327, 7.206, 5.56, 7.014, 8.297, 7.47, 5.92, 5.856, 6.24, 6.538, 7.691, 6.758, 6.854, 7.267, 6.826, 6.482, 6.812, 7.82, 6.968, 7.645, 7.923, 7.088, 6.453, 6.23, 6.209, 6.315, 6.565, 6.861, 7.148, 6.63, 6.127, 6.009, 6.678, 6.549, 5.79, 6.345, 7.041, 6.871, 6.59, 6.495, 6.982, 7.236, 6.616, 7.42, 6.849, 6.635, 5.972, 4.973, 6.122, 6.023, 6.266, 6.567, 5.705, 5.914, 5.782, 6.382, 6.113, 6.426, 6.376, 6.041, 5.708, 6.415, 6.431, 6.312, 6.083, 5.868, 6.333, 6.144, 5.706, 6.031, 6.316, 6.31, 6.037, 5.869, 5.895, 6.059, 5.985, 5.968, 7.241, 6.54, 6.696, 6.874, 6.014, 5.898, 6.516, 6.635, 6.939, 6.49, 6.579, 5.884, 6.728, 5.663, 5.936, 6.212, 6.395, 6.127, 6.112, 6.398, 6.251, 5.362, 5.803, 8.78, 3.561, 4.963, 3.863, 4.97, 6.683, 7.016, 6.216, 5.875, 4.906, 4.138, 7.313, 6.649, 6.794, 6.38, 6.223, 6.968, 6.545, 5.536, 5.52, 4.368, 5.277, 4.652, 5.0, 4.88, 5.39, 5.713, 6.051, 5.036, 6.193, 5.887, 6.471, 6.405, 5.747, 5.453, 5.852, 5.987, 6.343, 6.404, 5.349, 5.531, 5.683, 4.138, 5.608, 5.617, 6.852, 5.757, 6.657, 4.628, 5.155, 4.519, 6.434, 6.782, 5.304, 5.957, 6.824, 6.411, 6.006, 5.648, 6.103, 5.565, 5.896, 5.837, 6.202, 6.193, 6.38, 6.348, 6.833, 6.425, 6.436, 6.208, 6.629, 6.461, 6.152, 5.935, 5.627, 5.818, 6.406, 6.219, 6.485, 5.854, 6.459, 6.341, 6.251, 6.185, 6.417, 6.749, 6.655, 6.297, 7.393, 6.728, 6.525, 5.976, 5.936, 6.301, 6.081, 6.701, 6.376, 6.317, 6.513, 6.209, 5.759, 5.952, 6.003, 5.926, 5.713, 6.167, 6.229, 6.437, 6.98, 5.427, 6.162, 6.484, 5.304, 6.185, 6.229, 6.242, 6.75, 7.061, 5.762, 5.871, 6.312, 6.114, 5.905, 5.454, 5.414, 5.093, 5.983, 5.983, 5.707, 5.926, 5.67, 5.39, 5.794, 6.019, 5.569, 6.027, 6.593, 6.12, 6.976, 6.794, 6.03]}, {\"axis\": {\"matches\": true}, \"label\": \"AGE\", \"values\": [65.2, 78.9, 61.1, 45.8, 54.2, 58.7, 66.6, 96.1, 100.0, 85.9, 94.3, 82.9, 39.0, 61.8, 84.5, 56.5, 29.3, 81.7, 36.6, 69.5, 98.1, 89.2, 91.7, 100.0, 94.1, 85.7, 90.3, 88.8, 94.4, 87.3, 94.1, 100.0, 82.0, 95.0, 96.9, 68.2, 61.4, 41.5, 30.2, 21.8, 15.8, 2.9, 6.6, 6.5, 40.0, 33.8, 33.3, 85.5, 95.3, 62.0, 45.7, 63.0, 21.1, 21.4, 47.6, 21.9, 35.7, 40.5, 29.2, 47.2, 66.2, 93.4, 67.8, 43.4, 59.5, 17.8, 31.1, 21.4, 36.8, 33.0, 6.6, 17.5, 7.8, 6.2, 6.0, 45.0, 74.5, 45.8, 53.7, 36.6, 33.5, 70.4, 32.2, 46.7, 48.0, 56.1, 45.1, 56.8, 86.3, 63.1, 66.1, 73.9, 53.6, 28.9, 77.3, 57.8, 69.6, 76.0, 36.9, 62.5, 79.9, 71.3, 85.4, 87.4, 90.0, 96.7, 91.9, 85.2, 97.1, 91.2, 54.4, 81.6, 92.9, 95.4, 84.2, 88.2, 72.5, 82.6, 73.1, 65.2, 69.7, 84.1, 92.9, 97.0, 95.8, 88.4, 95.6, 96.0, 98.8, 94.7, 98.9, 97.7, 97.9, 95.4, 98.4, 98.2, 93.5, 98.4, 98.2, 97.9, 93.6, 100.0, 100.0, 100.0, 97.8, 100.0, 100.0, 95.7, 93.8, 94.9, 97.3, 100.0, 88.0, 98.5, 96.0, 82.6, 94.0, 97.4, 100.0, 100.0, 92.6, 90.8, 98.2, 93.9, 91.8, 93.0, 96.2, 79.2, 96.1, 95.2, 94.6, 97.3, 88.5, 84.1, 68.7, 33.1, 47.2, 73.4, 74.4, 58.4, 83.3, 62.2, 92.2, 95.6, 89.8, 68.8, 53.6, 41.1, 29.1, 38.9, 21.5, 30.8, 26.3, 9.9, 18.8, 32.0, 34.1, 36.6, 38.3, 15.3, 13.9, 38.4, 15.7, 33.2, 31.9, 22.3, 52.5, 72.7, 59.1, 100.0, 92.1, 88.6, 53.8, 32.3, 9.8, 42.4, 56.0, 85.1, 93.8, 92.4, 88.5, 91.3, 77.7, 80.8, 78.3, 83.0, 86.5, 79.9, 17.0, 21.4, 68.1, 76.9, 73.3, 70.4, 66.5, 61.5, 76.5, 71.6, 18.5, 42.2, 54.3, 65.1, 52.9, 7.8, 76.5, 70.2, 34.9, 79.2, 49.1, 17.5, 13.0, 8.9, 6.8, 8.4, 32.0, 19.1, 34.2, 86.9, 100.0, 100.0, 81.8, 89.4, 91.5, 94.5, 91.6, 62.8, 84.6, 67.0, 52.6, 61.5, 42.1, 16.3, 58.7, 51.8, 32.9, 42.8, 49.0, 27.6, 32.1, 32.2, 64.5, 37.2, 49.7, 24.8, 20.8, 31.9, 31.5, 31.3, 45.6, 22.9, 27.9, 27.7, 23.4, 18.4, 42.3, 31.1, 51.0, 58.0, 20.1, 10.0, 47.4, 40.4, 18.4, 17.7, 41.1, 58.1, 71.9, 70.3, 82.5, 76.7, 37.8, 52.8, 90.4, 82.8, 87.3, 77.7, 83.2, 71.7, 67.2, 58.8, 52.3, 54.3, 49.9, 74.3, 40.1, 14.7, 28.9, 43.7, 25.8, 17.2, 32.2, 28.4, 23.3, 38.1, 38.5, 34.5, 46.3, 59.6, 37.3, 45.4, 58.5, 49.3, 59.7, 56.4, 28.1, 48.5, 52.3, 27.7, 29.7, 34.5, 44.4, 35.9, 18.5, 36.1, 21.9, 19.5, 97.4, 91.0, 83.4, 81.3, 88.0, 91.1, 96.2, 89.0, 82.9, 87.9, 91.4, 100.0, 100.0, 96.8, 97.5, 100.0, 89.6, 100.0, 100.0, 97.9, 93.3, 98.8, 96.2, 100.0, 91.9, 99.1, 100.0, 100.0, 91.2, 98.1, 100.0, 89.5, 100.0, 98.9, 97.0, 82.5, 97.0, 92.6, 94.7, 98.8, 96.0, 98.9, 100.0, 77.8, 100.0, 100.0, 100.0, 96.0, 85.4, 100.0, 100.0, 100.0, 97.9, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 90.8, 89.1, 100.0, 76.5, 100.0, 95.3, 87.6, 85.1, 70.6, 95.4, 59.7, 78.7, 78.1, 95.6, 86.1, 94.3, 74.8, 87.9, 95.0, 94.6, 93.3, 100.0, 87.9, 93.9, 92.4, 97.2, 100.0, 100.0, 96.6, 94.8, 96.4, 96.6, 98.7, 98.3, 92.6, 98.2, 91.8, 99.3, 94.1, 86.5, 87.9, 80.3, 83.7, 84.4, 90.0, 88.4, 83.0, 89.9, 65.4, 48.2, 84.7, 94.5, 71.0, 56.7, 84.0, 90.7, 75.0, 67.6, 95.4, 97.4, 93.6, 97.3, 96.7, 88.0, 64.7, 74.9, 77.0, 40.3, 41.9, 51.9, 79.8, 53.2, 92.7, 98.3, 98.0, 98.8, 83.5, 54.0, 42.6, 28.8, 72.9, 70.6, 65.3, 73.5, 79.7, 69.1, 76.7, 91.0, 89.3, 80.8]}, {\"axis\": {\"matches\": true}, \"label\": \"DIS\", \"values\": [4.09, 4.9671, 4.9671, 6.0622, 6.0622, 6.0622, 5.5605, 5.9505, 6.0821, 6.5921, 6.3467, 6.2267, 5.4509, 4.7075, 4.4619, 4.4986, 4.4986, 4.2579, 3.7965, 3.7965, 3.7979, 4.0123, 3.9769, 4.0952, 4.3996, 4.4546, 4.682, 4.4534, 4.4547, 4.239, 4.233, 4.175, 3.99, 3.7872, 3.7598, 3.3603, 3.3779, 3.9342, 3.8473, 5.4011, 5.4011, 5.7209, 5.7209, 5.7209, 5.7209, 5.1004, 5.1004, 5.6894, 5.87, 6.0877, 6.8147, 6.8147, 6.8147, 6.8147, 7.3197, 8.6966, 9.1876, 8.3248, 7.8148, 6.932, 7.2254, 6.8185, 7.2255, 7.9809, 9.2229, 6.6115, 6.6115, 6.498, 6.498, 6.498, 5.2873, 5.2873, 5.2873, 5.2873, 4.2515, 4.5026, 4.0522, 4.0905, 5.0141, 4.5026, 5.4007, 5.4007, 5.4007, 5.4007, 4.7794, 4.4377, 4.4272, 3.7476, 3.4217, 3.4145, 3.0923, 3.0921, 3.6659, 3.6659, 3.615, 3.4952, 3.4952, 3.4952, 3.4952, 3.4952, 2.7778, 2.8561, 2.7147, 2.7147, 2.421, 2.1069, 2.211, 2.1224, 2.4329, 2.5451, 2.7778, 2.6775, 2.3534, 2.548, 2.2565, 2.4631, 2.7301, 2.7474, 2.4775, 2.7592, 2.2577, 2.1974, 2.0869, 1.9444, 2.0063, 1.9929, 1.7572, 1.7883, 1.8125, 1.9799, 2.1185, 2.271, 2.3274, 2.4699, 2.346, 2.1107, 1.9669, 1.8498, 1.6686, 1.6687, 1.6119, 1.4394, 1.3216, 1.4118, 1.3459, 1.4191, 1.5166, 1.4608, 1.5296, 1.5257, 1.618, 1.5916, 1.6102, 1.6232, 1.7494, 1.7455, 1.7364, 1.8773, 1.7573, 1.7659, 1.7984, 1.9709, 2.0407, 2.162, 2.422, 2.2834, 2.0459, 2.4259, 2.1, 2.2625, 2.4259, 2.3887, 2.5961, 2.6463, 2.7019, 3.1323, 3.5549, 3.3175, 2.9153, 2.829, 2.741, 2.5979, 2.7006, 2.847, 2.9879, 3.2797, 3.1992, 3.7886, 4.5667, 4.5667, 6.4798, 6.4798, 6.4798, 6.2196, 6.2196, 5.6484, 7.309, 7.309, 7.309, 7.6534, 7.6534, 6.27, 6.27, 5.118, 5.118, 3.9454, 4.3549, 4.3549, 4.2392, 3.875, 3.8771, 3.665, 3.6526, 3.9454, 3.5875, 3.9454, 3.1121, 3.4211, 2.8893, 3.3633, 2.8617, 3.048, 3.2721, 3.2721, 2.8944, 2.8944, 3.2157, 3.2157, 3.3751, 3.3751, 3.6715, 3.6715, 3.8384, 3.6519, 3.6519, 3.6519, 4.148, 4.148, 6.1899, 6.1899, 6.3361, 6.3361, 7.0355, 7.0355, 7.9549, 7.9549, 8.0555, 8.0555, 7.8265, 7.8265, 7.3967, 7.3967, 8.9067, 8.9067, 9.2203, 9.2203, 6.3361, 1.801, 1.8946, 2.0107, 2.1121, 2.1398, 2.2885, 2.0788, 1.9301, 1.9865, 2.1329, 2.4216, 2.872, 3.9175, 4.429, 4.429, 3.9175, 4.3665, 4.0776, 4.2673, 4.7872, 4.8628, 4.1403, 4.1007, 4.6947, 5.2447, 5.2119, 5.885, 7.3073, 7.3073, 9.0892, 7.3172, 7.3172, 7.3172, 5.1167, 5.1167, 5.1167, 5.5027, 5.5027, 5.9604, 5.9604, 6.32, 7.8278, 7.8278, 7.8278, 5.4917, 5.4917, 5.4917, 4.022, 3.37, 3.0992, 3.1827, 3.3175, 3.1025, 2.5194, 2.6403, 2.834, 3.2628, 3.6023, 3.945, 3.9986, 4.0317, 3.5325, 4.0019, 4.5404, 4.5404, 4.7211, 4.7211, 4.7211, 5.4159, 5.4159, 5.4159, 5.2146, 5.2146, 5.8736, 6.6407, 6.6407, 6.4584, 6.4584, 5.9853, 5.2311, 5.615, 4.8122, 4.8122, 4.8122, 7.0379, 6.2669, 5.7321, 6.4654, 8.0136, 8.0136, 8.5353, 8.344, 8.7921, 8.7921, 10.7103, 10.7103, 12.1265, 10.5857, 10.5857, 2.1222, 2.5052, 2.7227, 2.5091, 2.5182, 2.2955, 2.1036, 1.9047, 1.9047, 1.6132, 1.7523, 1.5106, 1.3325, 1.3567, 1.2024, 1.1691, 1.1296, 1.1742, 1.137, 1.3163, 1.3449, 1.358, 1.3861, 1.3861, 1.4165, 1.5192, 1.5804, 1.5331, 1.4395, 1.4261, 1.4672, 1.5184, 1.5895, 1.7281, 1.9265, 2.1678, 1.77, 1.7912, 1.7821, 1.7257, 1.6768, 1.6334, 1.4896, 1.5004, 1.5888, 1.5741, 1.639, 1.7028, 1.6074, 1.4254, 1.1781, 1.2852, 1.4547, 1.4655, 1.413, 1.5275, 1.5539, 1.5894, 1.6582, 1.8347, 1.8195, 1.6475, 1.8026, 1.794, 1.8589, 1.8746, 1.9512, 2.0218, 2.0635, 1.9096, 1.9976, 1.8629, 1.9356, 1.9682, 2.0527, 2.0882, 2.2004, 2.3158, 2.2222, 2.1247, 2.0026, 1.9142, 1.8206, 1.8172, 1.8662, 2.0651, 2.0048, 1.9784, 1.8956, 1.9879, 2.072, 2.198, 2.2616, 2.185, 2.3236, 2.3552, 2.3682, 2.4527, 2.4961, 2.4358, 2.5806, 2.7792, 2.7831, 2.7175, 2.5975, 2.5671, 2.7344, 2.8016, 2.9634, 3.0665, 2.8715, 2.5403, 2.9084, 2.8237, 3.0334, 3.0993, 2.8965, 2.5329, 2.4298, 2.206, 2.3053, 2.1007, 2.1705, 1.9512, 3.4242, 3.3317, 3.4106, 4.0983, 3.724, 3.9917, 3.5459, 3.1523, 1.8209, 1.7554, 1.8226, 1.8681, 2.1099, 2.3817, 2.3817, 2.7986, 2.7986, 2.8927, 2.4091, 2.3999, 2.4982, 2.4786, 2.2875, 2.1675, 2.3889, 2.505]}, {\"axis\": {\"matches\": true}, \"label\": \"RAD\", \"values\": [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 3.0, 5.0, 2.0, 5.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 4.0, 4.0, 4.0, 2.0, 2.0, 2.0, 2.0, 2.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 4.0, 2.0, 2.0, 2.0, 3.0, 3.0, 2.0, 2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 1.0, 1.0, 3.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 1.0, 1.0, 6.0, 6.0, 6.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 4.0, 4.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0, 5.0, 5.0, 3.0, 3.0, 4.0, 4.0, 1.0, 1.0, 4.0, 4.0, 5.0, 4.0, 4.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 4.0, 4.0, 4.0, 4.0, 4.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"axis\": {\"matches\": true}, \"label\": \"TAX\", \"values\": [296.0, 242.0, 242.0, 222.0, 222.0, 222.0, 311.0, 311.0, 311.0, 311.0, 311.0, 311.0, 311.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 279.0, 279.0, 279.0, 279.0, 252.0, 252.0, 233.0, 233.0, 233.0, 233.0, 233.0, 233.0, 233.0, 233.0, 233.0, 243.0, 243.0, 243.0, 243.0, 469.0, 226.0, 313.0, 256.0, 284.0, 284.0, 284.0, 284.0, 284.0, 284.0, 216.0, 337.0, 337.0, 345.0, 345.0, 345.0, 305.0, 305.0, 305.0, 305.0, 398.0, 398.0, 398.0, 398.0, 398.0, 398.0, 281.0, 281.0, 281.0, 281.0, 247.0, 247.0, 247.0, 247.0, 270.0, 270.0, 270.0, 270.0, 270.0, 270.0, 270.0, 276.0, 276.0, 276.0, 276.0, 276.0, 384.0, 384.0, 384.0, 384.0, 384.0, 384.0, 384.0, 384.0, 384.0, 384.0, 384.0, 432.0, 432.0, 432.0, 432.0, 432.0, 432.0, 432.0, 432.0, 432.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 188.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 437.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 403.0, 296.0, 296.0, 296.0, 296.0, 296.0, 296.0, 296.0, 193.0, 193.0, 193.0, 193.0, 193.0, 193.0, 193.0, 193.0, 398.0, 398.0, 398.0, 398.0, 398.0, 398.0, 265.0, 265.0, 255.0, 329.0, 329.0, 329.0, 402.0, 402.0, 348.0, 348.0, 224.0, 224.0, 277.0, 277.0, 277.0, 277.0, 277.0, 277.0, 277.0, 277.0, 277.0, 277.0, 277.0, 276.0, 276.0, 276.0, 276.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 307.0, 300.0, 300.0, 300.0, 300.0, 300.0, 300.0, 330.0, 330.0, 330.0, 330.0, 330.0, 330.0, 330.0, 330.0, 330.0, 330.0, 315.0, 315.0, 244.0, 264.0, 264.0, 264.0, 264.0, 264.0, 264.0, 264.0, 264.0, 264.0, 264.0, 264.0, 264.0, 223.0, 223.0, 223.0, 223.0, 223.0, 254.0, 254.0, 254.0, 254.0, 254.0, 216.0, 216.0, 216.0, 216.0, 198.0, 285.0, 300.0, 241.0, 293.0, 293.0, 293.0, 245.0, 245.0, 245.0, 289.0, 289.0, 289.0, 289.0, 289.0, 358.0, 358.0, 358.0, 329.0, 329.0, 329.0, 222.0, 222.0, 222.0, 222.0, 304.0, 304.0, 304.0, 304.0, 304.0, 304.0, 304.0, 304.0, 304.0, 304.0, 304.0, 304.0, 287.0, 287.0, 287.0, 287.0, 287.0, 287.0, 287.0, 287.0, 430.0, 430.0, 430.0, 304.0, 304.0, 224.0, 224.0, 224.0, 224.0, 224.0, 224.0, 224.0, 224.0, 284.0, 422.0, 370.0, 370.0, 352.0, 352.0, 351.0, 280.0, 335.0, 335.0, 411.0, 411.0, 187.0, 334.0, 334.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 666.0, 711.0, 711.0, 711.0, 711.0, 711.0, 391.0, 391.0, 391.0, 391.0, 391.0, 391.0, 391.0, 391.0, 273.0, 273.0, 273.0, 273.0, 273.0]}, {\"axis\": {\"matches\": true}, \"label\": \"PTRATIO\", \"values\": [15.3, 17.8, 17.8, 18.7, 18.7, 18.7, 15.2, 15.2, 15.2, 15.2, 15.2, 15.2, 15.2, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 19.2, 19.2, 19.2, 19.2, 18.3, 18.3, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 17.9, 16.8, 16.8, 16.8, 16.8, 21.1, 17.9, 17.3, 15.1, 19.7, 19.7, 19.7, 19.7, 19.7, 19.7, 18.6, 16.1, 16.1, 18.9, 18.9, 18.9, 19.2, 19.2, 19.2, 19.2, 18.7, 18.7, 18.7, 18.7, 18.7, 18.7, 19.0, 19.0, 19.0, 19.0, 18.5, 18.5, 18.5, 18.5, 17.8, 17.8, 17.8, 17.8, 18.2, 18.2, 18.2, 18.0, 18.0, 18.0, 18.0, 18.0, 20.9, 20.9, 20.9, 20.9, 20.9, 20.9, 20.9, 20.9, 20.9, 20.9, 20.9, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 19.1, 19.1, 19.1, 19.1, 19.1, 19.1, 19.1, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 21.2, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 14.7, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 17.8, 15.2, 15.2, 15.2, 15.2, 15.2, 15.2, 15.6, 15.6, 14.4, 12.6, 12.6, 12.6, 17.0, 17.0, 14.7, 14.7, 14.7, 14.7, 18.6, 18.6, 18.6, 18.6, 18.6, 18.6, 18.6, 18.6, 18.6, 18.6, 18.6, 16.4, 16.4, 16.4, 16.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 17.4, 16.6, 16.6, 16.6, 16.6, 16.6, 16.6, 19.1, 19.1, 19.1, 19.1, 19.1, 19.1, 19.1, 19.1, 19.1, 19.1, 16.4, 16.4, 15.9, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 18.6, 18.6, 18.6, 18.6, 18.6, 17.6, 17.6, 17.6, 17.6, 17.6, 14.9, 14.9, 14.9, 14.9, 13.6, 15.3, 15.3, 18.2, 16.6, 16.6, 16.6, 19.2, 19.2, 19.2, 16.0, 16.0, 16.0, 16.0, 16.0, 14.8, 14.8, 14.8, 16.1, 16.1, 16.1, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 18.4, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 19.6, 16.9, 16.9, 16.9, 16.9, 16.9, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 15.5, 15.9, 17.6, 17.6, 18.8, 18.8, 17.9, 17.0, 19.7, 19.7, 18.3, 18.3, 17.0, 22.0, 22.0, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.2, 20.1, 20.1, 20.1, 20.1, 20.1, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 19.2, 21.0, 21.0, 21.0, 21.0, 21.0]}, {\"axis\": {\"matches\": true}, \"label\": \"B\", \"values\": [396.9, 396.9, 392.83, 394.63, 396.9, 394.12, 395.6, 396.9, 386.63, 386.71, 392.52, 396.9, 390.5, 396.9, 380.02, 395.62, 386.85, 386.75, 288.99, 390.95, 376.57, 392.53, 396.9, 394.54, 394.33, 303.42, 376.88, 306.38, 387.94, 380.23, 360.17, 376.73, 232.6, 358.77, 248.31, 396.9, 377.56, 396.9, 393.43, 395.63, 395.62, 385.41, 383.37, 394.46, 389.39, 396.9, 396.9, 392.74, 396.9, 396.9, 395.56, 393.97, 396.9, 396.9, 396.9, 395.93, 396.9, 392.9, 390.68, 396.9, 395.11, 378.08, 396.9, 395.58, 393.24, 396.9, 396.9, 396.21, 396.9, 396.9, 383.73, 376.94, 390.91, 377.17, 394.92, 383.23, 373.66, 386.96, 386.4, 396.06, 396.9, 395.63, 396.9, 390.64, 396.9, 392.3, 395.99, 395.15, 396.9, 396.06, 392.18, 393.55, 395.01, 396.33, 396.9, 357.98, 391.83, 396.9, 393.53, 396.9, 394.76, 395.58, 70.8, 394.47, 392.69, 394.05, 395.67, 387.69, 395.24, 391.23, 393.49, 395.59, 394.95, 396.9, 388.74, 344.91, 393.3, 394.51, 338.63, 391.5, 389.15, 377.67, 378.09, 370.31, 379.38, 385.02, 359.29, 392.11, 396.9, 396.9, 395.04, 396.9, 385.76, 388.69, 262.76, 394.67, 378.25, 394.08, 392.04, 396.9, 388.08, 396.9, 396.9, 396.9, 396.9, 172.91, 169.27, 391.71, 356.99, 351.85, 372.8, 341.6, 343.28, 261.95, 321.02, 88.01, 88.63, 363.43, 353.89, 364.31, 338.92, 374.43, 389.61, 388.45, 395.11, 240.16, 369.3, 227.61, 297.09, 330.04, 292.29, 348.13, 396.9, 395.5, 393.23, 390.96, 393.23, 395.6, 391.27, 396.9, 395.56, 396.9, 394.12, 396.9, 391.0, 387.11, 392.63, 393.87, 382.84, 396.9, 377.68, 389.71, 390.49, 393.37, 376.7, 394.23, 396.9, 354.31, 392.2, 396.9, 384.3, 393.77, 395.38, 392.78, 390.55, 396.9, 394.87, 389.43, 381.32, 396.9, 393.25, 395.24, 390.94, 385.81, 348.93, 393.63, 392.8, 392.78, 396.9, 393.74, 391.7, 395.24, 390.39, 396.9, 385.05, 382.0, 387.38, 372.08, 377.51, 380.34, 378.35, 376.14, 385.91, 378.95, 360.2, 376.75, 388.45, 390.07, 379.41, 383.78, 391.25, 394.62, 372.75, 374.71, 372.49, 389.13, 390.18, 376.14, 374.71, 393.74, 396.28, 377.07, 386.09, 396.9, 392.89, 395.18, 386.34, 389.7, 383.29, 391.93, 392.8, 388.37, 386.86, 393.42, 387.89, 392.4, 384.07, 384.54, 390.3, 391.34, 388.65, 396.9, 394.96, 390.77, 396.9, 396.9, 389.25, 393.45, 396.9, 396.9, 387.31, 392.23, 377.07, 395.52, 394.72, 394.72, 341.6, 396.9, 396.9, 371.72, 396.9, 396.9, 396.9, 396.9, 396.9, 396.9, 392.85, 396.9, 368.24, 371.58, 390.86, 395.75, 383.61, 390.43, 393.68, 393.36, 396.9, 396.9, 396.9, 396.24, 350.45, 396.9, 396.3, 393.39, 395.69, 396.42, 390.7, 396.9, 395.21, 396.23, 396.9, 396.9, 396.9, 391.13, 396.9, 393.68, 396.9, 396.9, 382.44, 375.21, 368.57, 394.02, 362.25, 389.71, 389.4, 396.9, 396.9, 394.81, 396.14, 396.9, 396.9, 394.74, 389.96, 396.9, 387.97, 385.64, 364.61, 392.43, 390.94, 389.85, 396.9, 370.78, 392.33, 384.46, 382.8, 376.04, 377.73, 391.34, 395.43, 390.74, 374.56, 350.65, 380.79, 353.04, 354.55, 354.7, 316.03, 131.42, 375.52, 375.33, 392.05, 366.15, 347.88, 396.9, 396.9, 396.9, 363.02, 396.9, 396.9, 393.74, 396.9, 396.9, 396.9, 396.9, 285.83, 396.9, 396.9, 396.9, 372.92, 396.9, 394.43, 378.38, 396.9, 396.9, 396.9, 391.98, 396.9, 393.1, 396.9, 338.16, 396.9, 396.9, 376.11, 396.9, 329.46, 384.97, 370.22, 332.09, 314.64, 179.36, 2.6, 35.05, 28.79, 210.97, 88.27, 27.25, 21.57, 127.36, 16.45, 48.45, 318.75, 319.98, 291.55, 2.52, 3.65, 7.68, 24.65, 18.82, 96.73, 60.72, 83.45, 81.33, 97.95, 100.19, 100.63, 109.85, 27.49, 9.32, 68.95, 396.9, 391.45, 385.96, 395.69, 386.73, 240.52, 43.06, 318.01, 388.52, 396.9, 304.21, 0.32, 355.29, 385.09, 375.87, 6.68, 50.92, 10.48, 3.5, 272.21, 396.9, 255.23, 391.43, 396.9, 393.82, 396.9, 334.4, 22.01, 331.29, 368.74, 396.9, 396.9, 395.33, 393.37, 374.68, 352.58, 302.76, 396.21, 349.48, 379.7, 383.32, 396.9, 393.07, 395.28, 392.92, 370.73, 388.62, 392.68, 388.22, 395.09, 344.05, 318.43, 390.11, 396.9, 396.9, 396.9, 393.29, 396.9, 396.9, 396.9, 395.77, 396.9, 391.99, 396.9, 396.9, 393.45, 396.9]}, {\"axis\": {\"matches\": true}, \"label\": \"LSTAT\", \"values\": [4.98, 9.14, 4.03, 2.94, 5.33, 5.21, 12.43, 19.15, 29.93, 17.1, 20.45, 13.27, 15.71, 8.26, 10.26, 8.47, 6.58, 14.67, 11.69, 11.28, 21.02, 13.83, 18.72, 19.88, 16.3, 16.51, 14.81, 17.28, 12.8, 11.98, 22.6, 13.04, 27.71, 18.35, 20.34, 9.68, 11.41, 8.77, 10.13, 4.32, 1.98, 4.84, 5.81, 7.44, 9.55, 10.21, 14.15, 18.8, 30.81, 16.2, 13.45, 9.43, 5.28, 8.43, 14.8, 4.81, 5.77, 3.95, 6.86, 9.22, 13.15, 14.44, 6.73, 9.5, 8.05, 4.67, 10.24, 8.1, 13.09, 8.79, 6.72, 9.88, 5.52, 7.54, 6.78, 8.94, 11.97, 10.27, 12.34, 9.1, 5.29, 7.22, 6.72, 7.51, 9.62, 6.53, 12.86, 8.44, 5.5, 5.7, 8.81, 8.2, 8.16, 6.21, 10.59, 6.65, 11.34, 4.21, 3.57, 6.19, 9.42, 7.67, 10.63, 13.44, 12.33, 16.47, 18.66, 14.09, 12.27, 15.55, 13.0, 10.16, 16.21, 17.09, 10.45, 15.76, 12.04, 10.3, 15.37, 13.61, 14.37, 14.27, 17.93, 25.41, 17.58, 14.81, 27.26, 17.19, 15.39, 18.34, 12.6, 12.26, 11.12, 15.03, 17.31, 16.96, 16.9, 14.59, 21.32, 18.46, 24.16, 34.41, 26.82, 26.42, 29.29, 27.8, 16.65, 29.53, 28.32, 21.45, 14.1, 13.28, 12.12, 15.79, 15.12, 15.02, 16.14, 4.59, 6.43, 7.39, 5.5, 1.73, 1.92, 3.32, 11.64, 9.81, 3.7, 12.14, 11.1, 11.32, 14.43, 12.03, 14.69, 9.04, 9.64, 5.33, 10.11, 6.29, 6.92, 5.04, 7.56, 9.45, 4.82, 5.68, 13.98, 13.15, 4.45, 6.68, 4.56, 5.39, 5.1, 4.69, 2.87, 5.03, 4.38, 2.97, 4.08, 8.61, 6.62, 4.56, 4.45, 7.43, 3.11, 3.81, 2.88, 10.87, 10.97, 18.06, 14.66, 23.09, 17.27, 23.98, 16.03, 9.38, 29.55, 9.47, 13.51, 9.69, 17.92, 10.5, 9.71, 21.46, 9.93, 7.6, 4.14, 4.63, 3.13, 6.36, 3.92, 3.76, 11.65, 5.25, 2.47, 3.95, 8.05, 10.88, 9.54, 4.73, 6.36, 7.37, 11.38, 12.4, 11.22, 5.19, 12.5, 18.46, 9.16, 10.15, 9.52, 6.56, 5.9, 3.59, 3.53, 3.54, 6.57, 9.25, 3.11, 5.12, 7.79, 6.9, 9.59, 7.26, 5.91, 11.25, 8.1, 10.45, 14.79, 7.44, 3.16, 13.65, 13.0, 6.59, 7.73, 6.58, 3.53, 2.98, 6.05, 4.16, 7.19, 4.85, 3.76, 4.59, 3.01, 3.16, 7.85, 8.23, 12.93, 7.14, 7.6, 9.51, 3.33, 3.56, 4.7, 8.58, 10.4, 6.27, 7.39, 15.84, 4.97, 4.74, 6.07, 9.5, 8.67, 4.86, 6.93, 8.93, 6.47, 7.53, 4.54, 9.97, 12.64, 5.98, 11.72, 7.9, 9.28, 11.5, 18.33, 15.94, 10.36, 12.73, 7.2, 6.87, 7.7, 11.74, 6.12, 5.08, 6.15, 12.79, 9.97, 7.34, 9.09, 12.43, 7.83, 5.68, 6.75, 8.01, 9.8, 10.56, 8.51, 9.74, 9.29, 5.49, 8.65, 7.18, 4.61, 10.53, 12.67, 6.36, 5.99, 5.89, 5.98, 5.49, 7.79, 4.5, 8.05, 5.57, 17.6, 13.27, 11.48, 12.67, 7.79, 14.19, 10.19, 14.64, 5.29, 7.12, 14.0, 13.33, 3.26, 3.73, 2.96, 9.53, 8.88, 34.77, 37.97, 13.44, 23.24, 21.24, 23.69, 21.78, 17.21, 21.08, 23.6, 24.56, 30.63, 30.81, 28.28, 31.99, 30.62, 20.85, 17.11, 18.76, 25.68, 15.17, 16.35, 17.12, 19.37, 19.92, 30.59, 29.97, 26.77, 20.32, 20.31, 19.77, 27.38, 22.98, 23.34, 12.13, 26.4, 19.78, 10.11, 21.22, 34.37, 20.08, 36.98, 29.05, 25.79, 26.64, 20.62, 22.74, 15.02, 15.7, 14.1, 23.29, 17.16, 24.39, 15.69, 14.52, 21.52, 24.08, 17.64, 19.69, 12.03, 16.22, 15.17, 23.27, 18.05, 26.45, 34.02, 22.88, 22.11, 19.52, 16.59, 18.85, 23.79, 23.98, 17.79, 16.44, 18.13, 19.31, 17.44, 17.73, 17.27, 16.74, 18.71, 18.13, 19.01, 16.94, 16.23, 14.7, 16.42, 14.65, 13.99, 10.29, 13.22, 14.13, 17.15, 21.32, 18.13, 14.76, 16.29, 12.87, 14.36, 11.66, 18.14, 24.1, 18.68, 24.91, 18.03, 13.11, 10.74, 7.74, 7.01, 10.42, 13.34, 10.58, 14.98, 11.45, 18.06, 23.97, 29.68, 18.07, 13.35, 12.01, 13.59, 17.6, 21.14, 14.1, 12.92, 15.1, 14.33, 9.67, 9.08, 5.64, 6.48, 7.88]}], \"hovertemplate\": \"%{xaxis.title.text}=%{x}<br>%{yaxis.title.text}=%{y}<br>target=%{marker.color}<extra></extra>\", \"legendgroup\": \"\", \"marker\": {\"color\": [24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15.0, 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21.0, 12.7, 14.5, 13.2, 13.1, 13.5, 18.9, 20.0, 21.0, 24.7, 30.8, 34.9, 26.6, 25.3, 24.7, 21.2, 19.3, 20.0, 16.6, 14.4, 19.4, 19.7, 20.5, 25.0, 23.4, 18.9, 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16.0, 22.2, 25.0, 33.0, 23.5, 19.4, 22.0, 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20.0, 20.8, 21.2, 20.3, 28.0, 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2, 23.6, 28.7, 22.6, 22.0, 22.9, 25.0, 20.6, 28.4, 21.4, 38.7, 43.8, 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4, 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22.0, 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18.0, 14.3, 19.2, 19.6, 23.0, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14.0, 14.4, 13.4, 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4, 17.0, 15.6, 13.1, 41.3, 24.3, 23.3, 27.0, 50.0, 50.0, 50.0, 22.7, 25.0, 50.0, 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4, 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50.0, 32.0, 29.8, 34.9, 37.0, 30.5, 36.4, 31.1, 29.1, 50.0, 33.3, 30.3, 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50.0, 22.6, 24.4, 22.5, 24.4, 20.0, 21.7, 19.3, 22.4, 28.1, 23.7, 25.0, 23.3, 28.7, 21.5, 23.0, 26.7, 21.7, 27.5, 30.1, 44.8, 50.0, 37.6, 31.6, 46.7, 31.5, 24.3, 31.7, 41.7, 48.3, 29.0, 24.0, 25.1, 31.5, 23.7, 23.3, 22.0, 20.1, 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6, 42.8, 21.9, 20.9, 44.0, 50.0, 36.0, 30.1, 33.8, 43.1, 48.8, 31.0, 36.5, 22.8, 30.7, 50.0, 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4, 32.0, 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46.0, 50.0, 32.2, 22.0, 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1, 20.3, 22.5, 29.0, 24.8, 22.0, 26.4, 33.1, 36.1, 28.4, 33.4, 28.2, 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1, 21.0, 23.8, 23.1, 20.4, 18.5, 25.0, 24.6, 23.0, 22.2, 19.3, 22.6, 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19.0, 18.7, 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1, 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25.0, 19.9, 20.8, 16.8, 21.9, 27.5, 21.9, 23.1, 50.0, 50.0, 50.0, 50.0, 50.0, 13.8, 13.8, 15.0, 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5.0, 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5.0, 11.9, 27.9, 17.2, 27.5, 15.0, 17.2, 17.9, 16.3, 7.0, 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11.0, 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13.0, 13.4, 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20.0, 16.4, 17.7, 19.5, 20.2, 21.4, 19.9, 19.0, 19.1, 19.1, 20.1, 19.9, 19.6, 23.2, 29.8, 13.8, 13.3, 16.7, 12.0, 14.6, 21.4, 23.0, 23.7, 25.0, 21.8, 20.6, 21.2, 19.1, 20.6, 15.2, 7.0, 8.1, 13.6, 20.1, 21.8, 24.5, 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22.0, 11.9], \"coloraxis\": \"coloraxis\", \"symbol\": \"circle\"}, \"name\": \"\", \"showlegend\": false, \"type\": \"splom\"}],                        {\"coloraxis\": {\"colorbar\": {\"title\": {\"text\": \"target\"}}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"dragmode\": \"select\", \"height\": 1000, \"legend\": {\"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('6b5f461d-6c8f-4614-b6bd-487972d37f10');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 209,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.scatter_matrix(df, dimensions=[\"target\", \"CRIM\", \"ZN\", \"INDUS\", \"CHAS\", \"NOX\", \"RM\", \"AGE\", \"DIS\", \"RAD\", \"TAX\", \"PTRATIO\", \"B\", \"LSTAT\", \"target\"],\n",
    "    color=\"target\", height=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>                <div id=\"1d177f24-5e5b-4221-a311-3e4a084c5321\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"1d177f24-5e5b-4221-a311-3e4a084c5321\")) {                    Plotly.newPlot(                        \"1d177f24-5e5b-4221-a311-3e4a084c5321\",                        [{\"hovertemplate\": \"RM=%{x}<br>target=%{y}<extra></extra>\", \"legendgroup\": \"\", \"marker\": {\"color\": \"#636efa\", \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"\", \"orientation\": \"v\", \"showlegend\": false, \"type\": \"scatter\", \"x\": [6.575, 6.421, 7.185, 6.998, 7.147, 6.43, 6.012, 6.172, 5.631, 6.004, 6.377, 6.009, 5.889, 5.949, 6.096, 5.834, 5.935, 5.99, 5.456, 5.727, 5.57, 5.965, 6.142, 5.813, 5.924, 5.599, 5.813, 6.047, 6.495, 6.674, 5.713, 6.072, 5.95, 5.701, 6.096, 5.933, 5.841, 5.85, 5.966, 6.595, 7.024, 6.77, 6.169, 6.211, 6.069, 5.682, 5.786, 6.03, 5.399, 5.602, 5.963, 6.115, 6.511, 5.998, 5.888, 7.249, 6.383, 6.816, 6.145, 5.927, 5.741, 5.966, 6.456, 6.762, 7.104, 6.29, 5.787, 5.878, 5.594, 5.885, 6.417, 5.961, 6.065, 6.245, 6.273, 6.286, 6.279, 6.14, 6.232, 5.874, 6.727, 6.619, 6.302, 6.167, 6.389, 6.63, 6.015, 6.121, 7.007, 7.079, 6.417, 6.405, 6.442, 6.211, 6.249, 6.625, 6.163, 8.069, 7.82, 7.416, 6.727, 6.781, 6.405, 6.137, 6.167, 5.851, 5.836, 6.127, 6.474, 6.229, 6.195, 6.715, 5.913, 6.092, 6.254, 5.928, 6.176, 6.021, 5.872, 5.731, 5.87, 6.004, 5.961, 5.856, 5.879, 5.986, 5.613, 5.693, 6.431, 5.637, 6.458, 6.326, 6.372, 5.822, 5.757, 6.335, 5.942, 6.454, 5.857, 6.151, 6.174, 5.019, 5.403, 5.468, 4.903, 6.13, 5.628, 4.926, 5.186, 5.597, 6.122, 5.404, 5.012, 5.709, 6.129, 6.152, 5.272, 6.943, 6.066, 6.51, 6.25, 7.489, 7.802, 8.375, 5.854, 6.101, 7.929, 5.877, 6.319, 6.402, 5.875, 5.88, 5.572, 6.416, 5.859, 6.546, 6.02, 6.315, 6.86, 6.98, 7.765, 6.144, 7.155, 6.563, 5.604, 6.153, 7.831, 6.782, 6.556, 7.185, 6.951, 6.739, 7.178, 6.8, 6.604, 7.875, 7.287, 7.107, 7.274, 6.975, 7.135, 6.162, 7.61, 7.853, 8.034, 5.891, 6.326, 5.783, 6.064, 5.344, 5.96, 5.404, 5.807, 6.375, 5.412, 6.182, 5.888, 6.642, 5.951, 6.373, 6.951, 6.164, 6.879, 6.618, 8.266, 8.725, 8.04, 7.163, 7.686, 6.552, 5.981, 7.412, 8.337, 8.247, 6.726, 6.086, 6.631, 7.358, 6.481, 6.606, 6.897, 6.095, 6.358, 6.393, 5.593, 5.605, 6.108, 6.226, 6.433, 6.718, 6.487, 6.438, 6.957, 8.259, 6.108, 5.876, 7.454, 8.704, 7.333, 6.842, 7.203, 7.52, 8.398, 7.327, 7.206, 5.56, 7.014, 8.297, 7.47, 5.92, 5.856, 6.24, 6.538, 7.691, 6.758, 6.854, 7.267, 6.826, 6.482, 6.812, 7.82, 6.968, 7.645, 7.923, 7.088, 6.453, 6.23, 6.209, 6.315, 6.565, 6.861, 7.148, 6.63, 6.127, 6.009, 6.678, 6.549, 5.79, 6.345, 7.041, 6.871, 6.59, 6.495, 6.982, 7.236, 6.616, 7.42, 6.849, 6.635, 5.972, 4.973, 6.122, 6.023, 6.266, 6.567, 5.705, 5.914, 5.782, 6.382, 6.113, 6.426, 6.376, 6.041, 5.708, 6.415, 6.431, 6.312, 6.083, 5.868, 6.333, 6.144, 5.706, 6.031, 6.316, 6.31, 6.037, 5.869, 5.895, 6.059, 5.985, 5.968, 7.241, 6.54, 6.696, 6.874, 6.014, 5.898, 6.516, 6.635, 6.939, 6.49, 6.579, 5.884, 6.728, 5.663, 5.936, 6.212, 6.395, 6.127, 6.112, 6.398, 6.251, 5.362, 5.803, 8.78, 3.561, 4.963, 3.863, 4.97, 6.683, 7.016, 6.216, 5.875, 4.906, 4.138, 7.313, 6.649, 6.794, 6.38, 6.223, 6.968, 6.545, 5.536, 5.52, 4.368, 5.277, 4.652, 5.0, 4.88, 5.39, 5.713, 6.051, 5.036, 6.193, 5.887, 6.471, 6.405, 5.747, 5.453, 5.852, 5.987, 6.343, 6.404, 5.349, 5.531, 5.683, 4.138, 5.608, 5.617, 6.852, 5.757, 6.657, 4.628, 5.155, 4.519, 6.434, 6.782, 5.304, 5.957, 6.824, 6.411, 6.006, 5.648, 6.103, 5.565, 5.896, 5.837, 6.202, 6.193, 6.38, 6.348, 6.833, 6.425, 6.436, 6.208, 6.629, 6.461, 6.152, 5.935, 5.627, 5.818, 6.406, 6.219, 6.485, 5.854, 6.459, 6.341, 6.251, 6.185, 6.417, 6.749, 6.655, 6.297, 7.393, 6.728, 6.525, 5.976, 5.936, 6.301, 6.081, 6.701, 6.376, 6.317, 6.513, 6.209, 5.759, 5.952, 6.003, 5.926, 5.713, 6.167, 6.229, 6.437, 6.98, 5.427, 6.162, 6.484, 5.304, 6.185, 6.229, 6.242, 6.75, 7.061, 5.762, 5.871, 6.312, 6.114, 5.905, 5.454, 5.414, 5.093, 5.983, 5.983, 5.707, 5.926, 5.67, 5.39, 5.794, 6.019, 5.569, 6.027, 6.593, 6.12, 6.976, 6.794, 6.03], \"xaxis\": \"x\", \"y\": [24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15.0, 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21.0, 12.7, 14.5, 13.2, 13.1, 13.5, 18.9, 20.0, 21.0, 24.7, 30.8, 34.9, 26.6, 25.3, 24.7, 21.2, 19.3, 20.0, 16.6, 14.4, 19.4, 19.7, 20.5, 25.0, 23.4, 18.9, 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16.0, 22.2, 25.0, 33.0, 23.5, 19.4, 22.0, 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20.0, 20.8, 21.2, 20.3, 28.0, 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2, 23.6, 28.7, 22.6, 22.0, 22.9, 25.0, 20.6, 28.4, 21.4, 38.7, 43.8, 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4, 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22.0, 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18.0, 14.3, 19.2, 19.6, 23.0, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14.0, 14.4, 13.4, 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4, 17.0, 15.6, 13.1, 41.3, 24.3, 23.3, 27.0, 50.0, 50.0, 50.0, 22.7, 25.0, 50.0, 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4, 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50.0, 32.0, 29.8, 34.9, 37.0, 30.5, 36.4, 31.1, 29.1, 50.0, 33.3, 30.3, 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50.0, 22.6, 24.4, 22.5, 24.4, 20.0, 21.7, 19.3, 22.4, 28.1, 23.7, 25.0, 23.3, 28.7, 21.5, 23.0, 26.7, 21.7, 27.5, 30.1, 44.8, 50.0, 37.6, 31.6, 46.7, 31.5, 24.3, 31.7, 41.7, 48.3, 29.0, 24.0, 25.1, 31.5, 23.7, 23.3, 22.0, 20.1, 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6, 42.8, 21.9, 20.9, 44.0, 50.0, 36.0, 30.1, 33.8, 43.1, 48.8, 31.0, 36.5, 22.8, 30.7, 50.0, 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4, 32.0, 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46.0, 50.0, 32.2, 22.0, 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1, 20.3, 22.5, 29.0, 24.8, 22.0, 26.4, 33.1, 36.1, 28.4, 33.4, 28.2, 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1, 21.0, 23.8, 23.1, 20.4, 18.5, 25.0, 24.6, 23.0, 22.2, 19.3, 22.6, 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19.0, 18.7, 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1, 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25.0, 19.9, 20.8, 16.8, 21.9, 27.5, 21.9, 23.1, 50.0, 50.0, 50.0, 50.0, 50.0, 13.8, 13.8, 15.0, 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5.0, 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5.0, 11.9, 27.9, 17.2, 27.5, 15.0, 17.2, 17.9, 16.3, 7.0, 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11.0, 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13.0, 13.4, 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20.0, 16.4, 17.7, 19.5, 20.2, 21.4, 19.9, 19.0, 19.1, 19.1, 20.1, 19.9, 19.6, 23.2, 29.8, 13.8, 13.3, 16.7, 12.0, 14.6, 21.4, 23.0, 23.7, 25.0, 21.8, 20.6, 21.2, 19.1, 20.6, 15.2, 7.0, 8.1, 13.6, 20.1, 21.8, 24.5, 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22.0, 11.9], \"yaxis\": \"y\"}, {\"hovertemplate\": \"<b>OLS trendline</b><br>target = 9.10211 * RM + -34.6706<br>R<sup>2</sup>=0.483525<br><br>RM=%{x}<br>target=%{y} <b>(trend)</b><extra></extra>\", \"legendgroup\": \"\", \"marker\": {\"color\": \"#636efa\", \"symbol\": \"circle\"}, \"mode\": \"lines\", \"name\": \"\", \"showlegend\": false, \"type\": \"scatter\", \"x\": [3.561, 3.863, 4.138, 4.138, 4.368, 4.519, 4.628, 4.652, 4.88, 4.903, 4.906, 4.926, 4.963, 4.97, 4.973, 5.0, 5.012, 5.019, 5.036, 5.093, 5.155, 5.186, 5.272, 5.277, 5.304, 5.304, 5.344, 5.349, 5.362, 5.39, 5.39, 5.399, 5.403, 5.404, 5.404, 5.412, 5.414, 5.427, 5.453, 5.454, 5.456, 5.468, 5.52, 5.531, 5.536, 5.56, 5.565, 5.569, 5.57, 5.572, 5.593, 5.594, 5.597, 5.599, 5.602, 5.604, 5.605, 5.608, 5.613, 5.617, 5.627, 5.628, 5.631, 5.637, 5.648, 5.663, 5.67, 5.682, 5.683, 5.693, 5.701, 5.705, 5.706, 5.707, 5.708, 5.709, 5.713, 5.713, 5.713, 5.727, 5.731, 5.741, 5.747, 5.757, 5.757, 5.759, 5.762, 5.782, 5.783, 5.786, 5.787, 5.79, 5.794, 5.803, 5.807, 5.813, 5.813, 5.818, 5.822, 5.834, 5.836, 5.837, 5.841, 5.85, 5.851, 5.852, 5.854, 5.854, 5.856, 5.856, 5.857, 5.859, 5.868, 5.869, 5.87, 5.871, 5.872, 5.874, 5.875, 5.875, 5.876, 5.877, 5.878, 5.879, 5.88, 5.884, 5.885, 5.887, 5.888, 5.888, 5.889, 5.891, 5.895, 5.896, 5.898, 5.905, 5.913, 5.914, 5.92, 5.924, 5.926, 5.926, 5.927, 5.928, 5.933, 5.935, 5.935, 5.936, 5.936, 5.942, 5.949, 5.95, 5.951, 5.952, 5.957, 5.96, 5.961, 5.961, 5.963, 5.965, 5.966, 5.966, 5.968, 5.972, 5.976, 5.981, 5.983, 5.983, 5.985, 5.986, 5.987, 5.99, 5.998, 6.003, 6.004, 6.004, 6.006, 6.009, 6.009, 6.012, 6.014, 6.015, 6.019, 6.02, 6.021, 6.023, 6.027, 6.03, 6.03, 6.031, 6.037, 6.041, 6.047, 6.051, 6.059, 6.064, 6.065, 6.066, 6.069, 6.072, 6.081, 6.083, 6.086, 6.092, 6.095, 6.096, 6.096, 6.101, 6.103, 6.108, 6.108, 6.112, 6.113, 6.114, 6.115, 6.12, 6.121, 6.122, 6.122, 6.127, 6.127, 6.127, 6.129, 6.13, 6.137, 6.14, 6.142, 6.144, 6.144, 6.145, 6.151, 6.152, 6.152, 6.153, 6.162, 6.162, 6.163, 6.164, 6.167, 6.167, 6.167, 6.169, 6.172, 6.174, 6.176, 6.182, 6.185, 6.185, 6.193, 6.193, 6.195, 6.202, 6.208, 6.209, 6.209, 6.211, 6.211, 6.212, 6.216, 6.219, 6.223, 6.226, 6.229, 6.229, 6.229, 6.23, 6.232, 6.24, 6.242, 6.245, 6.249, 6.25, 6.251, 6.251, 6.254, 6.266, 6.273, 6.279, 6.286, 6.29, 6.297, 6.301, 6.302, 6.31, 6.312, 6.312, 6.315, 6.315, 6.316, 6.317, 6.319, 6.326, 6.326, 6.333, 6.335, 6.341, 6.343, 6.345, 6.348, 6.358, 6.372, 6.373, 6.375, 6.376, 6.376, 6.377, 6.38, 6.38, 6.382, 6.383, 6.389, 6.393, 6.395, 6.398, 6.402, 6.404, 6.405, 6.405, 6.405, 6.406, 6.411, 6.415, 6.416, 6.417, 6.417, 6.417, 6.421, 6.425, 6.426, 6.43, 6.431, 6.431, 6.433, 6.434, 6.436, 6.437, 6.438, 6.442, 6.453, 6.454, 6.456, 6.458, 6.459, 6.461, 6.471, 6.474, 6.481, 6.482, 6.484, 6.485, 6.487, 6.49, 6.495, 6.495, 6.51, 6.511, 6.513, 6.516, 6.525, 6.538, 6.54, 6.545, 6.546, 6.549, 6.552, 6.556, 6.563, 6.565, 6.567, 6.575, 6.579, 6.59, 6.593, 6.595, 6.604, 6.606, 6.616, 6.618, 6.619, 6.625, 6.629, 6.63, 6.63, 6.631, 6.635, 6.635, 6.642, 6.649, 6.655, 6.657, 6.674, 6.678, 6.683, 6.696, 6.701, 6.715, 6.718, 6.726, 6.727, 6.727, 6.728, 6.728, 6.739, 6.749, 6.75, 6.758, 6.762, 6.77, 6.781, 6.782, 6.782, 6.794, 6.794, 6.8, 6.812, 6.816, 6.824, 6.826, 6.833, 6.842, 6.849, 6.852, 6.854, 6.86, 6.861, 6.871, 6.874, 6.879, 6.897, 6.939, 6.943, 6.951, 6.951, 6.957, 6.968, 6.968, 6.975, 6.976, 6.98, 6.98, 6.982, 6.998, 7.007, 7.014, 7.016, 7.024, 7.041, 7.061, 7.079, 7.088, 7.104, 7.107, 7.135, 7.147, 7.148, 7.155, 7.163, 7.178, 7.185, 7.185, 7.203, 7.206, 7.236, 7.241, 7.249, 7.267, 7.274, 7.287, 7.313, 7.327, 7.333, 7.358, 7.393, 7.412, 7.416, 7.42, 7.454, 7.47, 7.489, 7.52, 7.61, 7.645, 7.686, 7.691, 7.765, 7.802, 7.82, 7.82, 7.831, 7.853, 7.875, 7.923, 7.929, 8.034, 8.04, 8.069, 8.247, 8.259, 8.266, 8.297, 8.337, 8.375, 8.398, 8.704, 8.725, 8.78], \"xaxis\": \"x\", \"y\": [-2.2580106944553933, 0.4908262178610556, 2.9939061876856385, 2.9939061876856385, 5.0873912533571115, 6.461809709515336, 7.453939588463989, 7.672390204012316, 9.747671051721419, 9.957019558288565, 9.984325885232103, 10.166368064855718, 10.503146097159387, 10.566860860027646, 10.594167186971191, 10.839924129463057, 10.949149437237217, 11.012864200105483, 11.167600052785545, 11.686420264712822, 12.250751021546009, 12.532916399962595, 13.315697772344102, 13.361208317250004, 13.60696525974187, 13.60696525974187, 13.971049618989085, 14.016560163894987, 14.134887580650329, 14.38974663212337, 14.38974663212337, 14.471665612953998, 14.508074048878711, 14.5171761578599, 14.5171761578599, 14.58999302970934, 14.608197247671697, 14.72652466442704, 14.963179497937737, 14.972281606918912, 14.990485824881276, 15.099711132655436, 15.573020799676812, 15.673143998469797, 15.718654543375692, 15.93710515892402, 15.982615703829929, 16.01902413975465, 16.02812624873583, 16.046330466698187, 16.237474755302976, 16.246576864284158, 16.273883191227704, 16.29208740919006, 16.3193937361336, 16.337597954095962, 16.346700063077144, 16.374006390020675, 16.419516934926584, 16.455925370851304, 16.5469464606631, 16.55604856964429, 16.583354896587828, 16.637967550474904, 16.73809074926789, 16.874622383985596, 16.93833714685386, 17.04756245462803, 17.056664563609203, 17.147685653421, 17.220502525270447, 17.256910961195167, 17.266013070176356, 17.27511517915753, 17.284217288138713, 17.293319397119888, 17.329727833044615, 17.329727833044615, 17.329727833044615, 17.45715735878114, 17.49356579470586, 17.584586884517655, 17.63919953840474, 17.730220628216543, 17.730220628216543, 17.748424846178906, 17.775731173122445, 17.957773352746052, 17.966875461727234, 17.994181788670772, 18.003283897651954, 18.030590224595493, 18.066998660520213, 18.148917641350835, 18.185326077275562, 18.23993873116264, 18.23993873116264, 18.28544927606854, 18.321857711993268, 18.431083019767428, 18.44928723772979, 18.458389346710966, 18.494797782635693, 18.576716763466308, 18.58581887244749, 18.59492098142868, 18.613125199391035, 18.613125199391035, 18.631329417353392, 18.631329417353392, 18.640431526334574, 18.658635744296937, 18.74055472512756, 18.749656834108734, 18.758758943089923, 18.767861052071105, 18.77696316105228, 18.795167379014636, 18.804269487995818, 18.804269487995818, 18.813371596977007, 18.82247370595818, 18.831575814939363, 18.840677923920538, 18.84978003290172, 18.886188468826447, 18.89529057780762, 18.913494795769978, 18.922596904751167, 18.922596904751167, 18.93169901373235, 18.949903231694705, 18.986311667619425, 18.995413776600607, 19.013617994562964, 19.07733275743123, 19.150149629280676, 19.15925173826185, 19.213864392148935, 19.250272828073662, 19.26847704603602, 19.26847704603602, 19.277579155017193, 19.286681263998375, 19.332191808904277, 19.350396026866633, 19.350396026866633, 19.359498135847815, 19.359498135847815, 19.4141107897349, 19.477825552603164, 19.486927661584346, 19.49602977056552, 19.505131879546703, 19.550642424452604, 19.577948751396143, 19.58705086037733, 19.58705086037733, 19.605255078339688, 19.623459296302045, 19.632561405283226, 19.632561405283226, 19.65076562324559, 19.68717405917031, 19.72358249509503, 19.769093040000932, 19.78729725796329, 19.78729725796329, 19.80550147592566, 19.814603584906834, 19.823705693888016, 19.851012020831554, 19.923828892681, 19.969339437586903, 19.978441546568078, 19.978441546568078, 19.99664576453044, 20.023952091473987, 20.023952091473987, 20.051258418417518, 20.069462636379882, 20.078564745361057, 20.114973181285784, 20.12407529026696, 20.13317739924814, 20.151381617210504, 20.187790053135224, 20.21509638007877, 20.21509638007877, 20.224198489059944, 20.278811142947028, 20.315219578871755, 20.36983223275883, 20.40624066868355, 20.479057540533, 20.5245680854389, 20.533670194420083, 20.542772303401257, 20.570078630344796, 20.59738495728834, 20.679303938118963, 20.697508156081327, 20.724814483024865, 20.77942713691194, 20.806733463855487, 20.81583557283667, 20.81583557283667, 20.86134611774257, 20.879550335704927, 20.92506088061083, 20.92506088061083, 20.96146931653555, 20.970571425516738, 20.979673534497913, 20.988775643479094, 21.034286188384996, 21.043388297366178, 21.052490406347353, 21.052490406347353, 21.098000951253255, 21.098000951253255, 21.098000951253255, 21.11620516921561, 21.125307278196793, 21.18902204106506, 21.216328368008597, 21.23453258597096, 21.252736803933324, 21.252736803933324, 21.2618389129145, 21.316451566801582, 21.325553675782764, 21.325553675782764, 21.33465578476394, 21.416574765594568, 21.416574765594568, 21.42567687457575, 21.434778983556924, 21.46208531050047, 21.46208531050047, 21.46208531050047, 21.480289528462826, 21.507595855406365, 21.525800073368735, 21.544004291331092, 21.598616945218176, 21.625923272161707, 21.625923272161707, 21.698740144011154, 21.698740144011154, 21.716944361973518, 21.780659124841776, 21.83527177872886, 21.844373887710034, 21.844373887710034, 21.862578105672405, 21.862578105672405, 21.87168021465358, 21.908088650578307, 21.935394977521845, 21.971803413446565, 21.999109740390104, 22.02641606733365, 22.02641606733365, 22.02641606733365, 22.03551817631483, 22.053722394277187, 22.126539266126635, 22.14474348408899, 22.17204981103253, 22.20845824695725, 22.21756035593843, 22.226662464919613, 22.226662464919613, 22.25396879186315, 22.36319409963732, 22.426908862505577, 22.48152151639266, 22.54523627926092, 22.581644715185647, 22.645359478053905, 22.681767913978632, 22.690870022959807, 22.763686894809247, 22.781891112771618, 22.781891112771618, 22.809197439715156, 22.809197439715156, 22.81829954869633, 22.827401657677513, 22.845605875639876, 22.909320638508134, 22.909320638508134, 22.9730354013764, 22.991239619338756, 23.04585227322584, 23.064056491188204, 23.08226070915056, 23.1095670360941, 23.200588125905902, 23.328017651642426, 23.337119760623615, 23.35532397858597, 23.364426087567153, 23.364426087567153, 23.373528196548328, 23.400834523491874, 23.400834523491874, 23.41903874145423, 23.428140850435412, 23.482753504322496, 23.519161940247216, 23.537366158209572, 23.564672485153118, 23.601080921077838, 23.6192851390402, 23.628387248021383, 23.628387248021383, 23.628387248021383, 23.637489357002558, 23.68299990190846, 23.71940833783318, 23.72851044681437, 23.737612555795543, 23.737612555795543, 23.737612555795543, 23.77402099172027, 23.810429427644983, 23.819531536626165, 23.855939972550885, 23.865042081532067, 23.865042081532067, 23.883246299494424, 23.892348408475613, 23.91055262643797, 23.91965473541915, 23.928756844400326, 23.965165280325053, 24.06528847911804, 24.074390588099213, 24.092594806061577, 24.11079902402394, 24.119901133005115, 24.13810535096748, 24.229126440779282, 24.25643276772282, 24.32014753059108, 24.329249639572268, 24.347453857534624, 24.356555966515806, 24.374760184478163, 24.402066511421708, 24.44757705632761, 24.44757705632761, 24.58410869104531, 24.59321080002649, 24.611415017988854, 24.638721344932392, 24.72064032576302, 24.838967742518363, 24.85717196048072, 24.902682505386622, 24.911784614367804, 24.93909094131135, 24.96639726825488, 25.002805704179607, 25.066520467047866, 25.08472468501023, 25.102928902972593, 25.175745774822033, 25.212154210746746, 25.312277409539732, 25.339583736483277, 25.357787954445634, 25.439706935276263, 25.45791115323862, 25.548932243050423, 25.567136461012787, 25.57623856999396, 25.630851223881045, 25.667259659805765, 25.676361768786947, 25.676361768786947, 25.68546387776813, 25.72187231369285, 25.72187231369285, 25.785587076561114, 25.849301839429373, 25.903914493316456, 25.922118711278813, 26.076854563958882, 26.113262999883602, 26.158773544789504, 26.277100961544846, 26.322611506450748, 26.450041032187272, 26.47734735913081, 26.550164230980258, 26.55926633996144, 26.55926633996144, 26.568368448942614, 26.568368448942614, 26.6684916477356, 26.759512737547404, 26.768614846528585, 26.841431718378026, 26.877840154302746, 26.950657026152186, 27.05078022494517, 27.059882333926353, 27.059882333926353, 27.169107641700514, 27.169107641700514, 27.223720295587597, 27.332945603361765, 27.369354039286485, 27.442170911135925, 27.46037512909828, 27.524089891966547, 27.60600887279717, 27.669723635665434, 27.69702996260898, 27.715234180571336, 27.76984683445842, 27.778948943439595, 27.869970033251406, 27.897276360194937, 27.94278690510084, 28.10662486676209, 28.48891344397166, 28.52532187989638, 28.59813875174582, 28.59813875174582, 28.652751405632905, 28.75287460442589, 28.75287460442589, 28.81658936729415, 28.82569147627533, 28.86209991220006, 28.86209991220006, 28.880304130162415, 29.025937873861302, 29.107856854691917, 29.171571617560183, 29.189775835522546, 29.262592707371986, 29.41732856005205, 29.599370739675656, 29.7632087013369, 29.845127682167522, 29.990761425866417, 30.018067752809948, 30.272926804282996, 30.382152112057163, 30.391254221038345, 30.454968983906603, 30.527785855756044, 30.664317490473756, 30.728032253342015, 30.728032253342015, 30.89187021500326, 30.919176541946804, 31.1922398113822, 31.23775035628811, 31.31056722813755, 31.47440518979881, 31.538119952667067, 31.656447369422402, 31.893102202933086, 32.02053172866962, 32.075144382556694, 32.30269710708621, 32.62127092142752, 32.79421099206994, 32.83061942799467, 32.86702786391938, 33.17649956927951, 33.3221333129784, 33.49507338362083, 33.777238762037406, 34.59642857034364, 34.915002384684946, 35.28818885291334, 35.33369939781924, 36.00725546242659, 36.34403349473026, 36.507871456391506, 36.507871456391506, 36.60799465518449, 36.80824105277045, 37.00848745035642, 37.445388681453075, 37.500001335340166, 38.4557227783641, 38.51033543225116, 38.774296592705404, 40.394471991355495, 40.50369729912966, 40.56741206199792, 40.849577440414514, 41.213661799661715, 41.559541940946566, 41.76889044751372, 44.554135795754895, 44.74528008435967, 45.245896078324584], \"yaxis\": \"y\"}],                        {\"legend\": {\"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"RM\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"target\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('1d177f24-5e5b-4221-a311-3e4a084c5321');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 140,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.scatter(df, x=\"RM\", y=\"target\", trendline=\"ols\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>                <div id=\"147aec3a-37aa-4d2f-a3ea-2b8113e00a45\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"147aec3a-37aa-4d2f-a3ea-2b8113e00a45\")) {                    Plotly.newPlot(                        \"147aec3a-37aa-4d2f-a3ea-2b8113e00a45\",                        [{\"hovertemplate\": \"variable=CRIM<br>value=%{x}<br>target=%{y}<extra></extra>\", \"legendgroup\": \"CRIM\", \"marker\": {\"color\": \"#636efa\", \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"CRIM\", \"showlegend\": true, \"type\": \"scattergl\", \"x\": [0.00632, 0.02731, 0.02729, 0.03237, 0.06905, 0.02985, 0.08829, 0.14455, 0.21124, 0.17004, 0.22489, 0.11747, 0.09378, 0.62976, 0.63796, 0.62739, 1.05393, 0.7842, 0.80271, 0.7258, 1.25179, 0.85204, 1.23247, 0.98843, 0.75026, 0.84054, 0.67191, 0.95577, 0.77299, 1.00245, 1.13081, 1.35472, 1.38799, 1.15172, 1.61282, 0.06417, 0.09744, 0.08014, 0.17505, 0.02763, 0.03359, 0.12744, 0.1415, 0.15936, 0.12269, 0.17142, 0.18836, 0.22927, 0.25387, 0.21977, 0.08873, 0.04337, 0.0536, 0.04981, 0.0136, 0.01311, 0.02055, 0.01432, 0.15445, 0.10328, 0.14932, 0.17171, 0.11027, 0.1265, 0.01951, 0.03584, 0.04379, 0.05789, 0.13554, 0.12816, 0.08826, 0.15876, 0.09164, 0.19539, 0.07896, 0.09512, 0.10153, 0.08707, 0.05646, 0.08387, 0.04113, 0.04462, 0.03659, 0.03551, 0.05059, 0.05735, 0.05188, 0.07151, 0.0566, 0.05302, 0.04684, 0.03932, 0.04203, 0.02875, 0.04294, 0.12204, 0.11504, 0.12083, 0.08187, 0.0686, 0.14866, 0.11432, 0.22876, 0.21161, 0.1396, 0.13262, 0.1712, 0.13117, 0.12802, 0.26363, 0.10793, 0.10084, 0.12329, 0.22212, 0.14231, 0.17134, 0.13158, 0.15098, 0.13058, 0.14476, 0.06899, 0.07165, 0.09299, 0.15038, 0.09849, 0.16902, 0.38735, 0.25915, 0.32543, 0.88125, 0.34006, 1.19294, 0.59005, 0.32982, 0.97617, 0.55778, 0.32264, 0.35233, 0.2498, 0.54452, 0.2909, 1.62864, 3.32105, 4.0974, 2.77974, 2.37934, 2.15505, 2.36862, 2.33099, 2.73397, 1.6566, 1.49632, 1.12658, 2.14918, 1.41385, 3.53501, 2.44668, 1.22358, 1.34284, 1.42502, 1.27346, 1.46336, 1.83377, 1.51902, 2.24236, 2.924, 2.01019, 1.80028, 2.3004, 2.44953, 1.20742, 2.3139, 0.13914, 0.09178, 0.08447, 0.06664, 0.07022, 0.05425, 0.06642, 0.0578, 0.06588, 0.06888, 0.09103, 0.10008, 0.08308, 0.06047, 0.05602, 0.07875, 0.12579, 0.0837, 0.09068, 0.06911, 0.08664, 0.02187, 0.01439, 0.01381, 0.04011, 0.04666, 0.03768, 0.0315, 0.01778, 0.03445, 0.02177, 0.0351, 0.02009, 0.13642, 0.22969, 0.25199, 0.13587, 0.43571, 0.17446, 0.37578, 0.21719, 0.14052, 0.28955, 0.19802, 0.0456, 0.07013, 0.11069, 0.11425, 0.35809, 0.40771, 0.62356, 0.6147, 0.31533, 0.52693, 0.38214, 0.41238, 0.29819, 0.44178, 0.537, 0.46296, 0.57529, 0.33147, 0.44791, 0.33045, 0.52058, 0.51183, 0.08244, 0.09252, 0.11329, 0.10612, 0.1029, 0.12757, 0.20608, 0.19133, 0.33983, 0.19657, 0.16439, 0.19073, 0.1403, 0.21409, 0.08221, 0.36894, 0.04819, 0.03548, 0.01538, 0.61154, 0.66351, 0.65665, 0.54011, 0.53412, 0.52014, 0.82526, 0.55007, 0.76162, 0.7857, 0.57834, 0.5405, 0.09065, 0.29916, 0.16211, 0.1146, 0.22188, 0.05644, 0.09604, 0.10469, 0.06127, 0.07978, 0.21038, 0.03578, 0.03705, 0.06129, 0.01501, 0.00906, 0.01096, 0.01965, 0.03871, 0.0459, 0.04297, 0.03502, 0.07886, 0.03615, 0.08265, 0.08199, 0.12932, 0.05372, 0.14103, 0.06466, 0.05561, 0.04417, 0.03537, 0.09266, 0.1, 0.05515, 0.05479, 0.07503, 0.04932, 0.49298, 0.3494, 2.63548, 0.79041, 0.26169, 0.26938, 0.3692, 0.25356, 0.31827, 0.24522, 0.40202, 0.47547, 0.1676, 0.18159, 0.35114, 0.28392, 0.34109, 0.19186, 0.30347, 0.24103, 0.06617, 0.06724, 0.04544, 0.05023, 0.03466, 0.05083, 0.03738, 0.03961, 0.03427, 0.03041, 0.03306, 0.05497, 0.06151, 0.01301, 0.02498, 0.02543, 0.03049, 0.03113, 0.06162, 0.0187, 0.01501, 0.02899, 0.06211, 0.0795, 0.07244, 0.01709, 0.04301, 0.10659, 8.98296, 3.8497, 5.20177, 4.26131, 4.54192, 3.83684, 3.67822, 4.22239, 3.47428, 4.55587, 3.69695, 13.5222, 4.89822, 5.66998, 6.53876, 9.2323, 8.26725, 11.1081, 18.4982, 19.6091, 15.288, 9.82349, 23.6482, 17.8667, 88.9762, 15.8744, 9.18702, 7.99248, 20.0849, 16.8118, 24.3938, 22.5971, 14.3337, 8.15174, 6.96215, 5.29305, 11.5779, 8.64476, 13.3598, 8.71675, 5.87205, 7.67202, 38.3518, 9.91655, 25.0461, 14.2362, 9.59571, 24.8017, 41.5292, 67.9208, 20.7162, 11.9511, 7.40389, 14.4383, 51.1358, 14.0507, 18.811, 28.6558, 45.7461, 18.0846, 10.8342, 25.9406, 73.5341, 11.8123, 11.0874, 7.02259, 12.0482, 7.05042, 8.79212, 15.8603, 12.2472, 37.6619, 7.36711, 9.33889, 8.49213, 10.0623, 6.44405, 5.58107, 13.9134, 11.1604, 14.4208, 15.1772, 13.6781, 9.39063, 22.0511, 9.72418, 5.66637, 9.96654, 12.8023, 10.6718, 6.28807, 9.92485, 9.32909, 7.52601, 6.71772, 5.44114, 5.09017, 8.24809, 9.51363, 4.75237, 4.66883, 8.20058, 7.75223, 6.80117, 4.81213, 3.69311, 6.65492, 5.82115, 7.83932, 3.1636, 3.77498, 4.42228, 15.5757, 13.0751, 4.34879, 4.03841, 3.56868, 4.64689, 8.05579, 6.39312, 4.87141, 15.0234, 10.233, 14.3337, 5.82401, 5.70818, 5.73116, 2.81838, 2.37857, 3.67367, 5.69175, 4.83567, 0.15086, 0.18337, 0.20746, 0.10574, 0.11132, 0.17331, 0.27957, 0.17899, 0.2896, 0.26838, 0.23912, 0.17783, 0.22438, 0.06263, 0.04527, 0.06076, 0.10959, 0.04741], \"xaxis\": \"x\", \"y\": [24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15.0, 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21.0, 12.7, 14.5, 13.2, 13.1, 13.5, 18.9, 20.0, 21.0, 24.7, 30.8, 34.9, 26.6, 25.3, 24.7, 21.2, 19.3, 20.0, 16.6, 14.4, 19.4, 19.7, 20.5, 25.0, 23.4, 18.9, 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16.0, 22.2, 25.0, 33.0, 23.5, 19.4, 22.0, 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20.0, 20.8, 21.2, 20.3, 28.0, 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2, 23.6, 28.7, 22.6, 22.0, 22.9, 25.0, 20.6, 28.4, 21.4, 38.7, 43.8, 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4, 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22.0, 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18.0, 14.3, 19.2, 19.6, 23.0, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14.0, 14.4, 13.4, 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4, 17.0, 15.6, 13.1, 41.3, 24.3, 23.3, 27.0, 50.0, 50.0, 50.0, 22.7, 25.0, 50.0, 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4, 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50.0, 32.0, 29.8, 34.9, 37.0, 30.5, 36.4, 31.1, 29.1, 50.0, 33.3, 30.3, 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50.0, 22.6, 24.4, 22.5, 24.4, 20.0, 21.7, 19.3, 22.4, 28.1, 23.7, 25.0, 23.3, 28.7, 21.5, 23.0, 26.7, 21.7, 27.5, 30.1, 44.8, 50.0, 37.6, 31.6, 46.7, 31.5, 24.3, 31.7, 41.7, 48.3, 29.0, 24.0, 25.1, 31.5, 23.7, 23.3, 22.0, 20.1, 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6, 42.8, 21.9, 20.9, 44.0, 50.0, 36.0, 30.1, 33.8, 43.1, 48.8, 31.0, 36.5, 22.8, 30.7, 50.0, 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4, 32.0, 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46.0, 50.0, 32.2, 22.0, 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1, 20.3, 22.5, 29.0, 24.8, 22.0, 26.4, 33.1, 36.1, 28.4, 33.4, 28.2, 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1, 21.0, 23.8, 23.1, 20.4, 18.5, 25.0, 24.6, 23.0, 22.2, 19.3, 22.6, 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19.0, 18.7, 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1, 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25.0, 19.9, 20.8, 16.8, 21.9, 27.5, 21.9, 23.1, 50.0, 50.0, 50.0, 50.0, 50.0, 13.8, 13.8, 15.0, 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5.0, 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5.0, 11.9, 27.9, 17.2, 27.5, 15.0, 17.2, 17.9, 16.3, 7.0, 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11.0, 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13.0, 13.4, 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20.0, 16.4, 17.7, 19.5, 20.2, 21.4, 19.9, 19.0, 19.1, 19.1, 20.1, 19.9, 19.6, 23.2, 29.8, 13.8, 13.3, 16.7, 12.0, 14.6, 21.4, 23.0, 23.7, 25.0, 21.8, 20.6, 21.2, 19.1, 20.6, 15.2, 7.0, 8.1, 13.6, 20.1, 21.8, 24.5, 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22.0, 11.9], \"yaxis\": \"y\"}, {\"hovertemplate\": \"<b>OLS trendline</b><br>target = -0.41519 * value + 24.0331<br>R<sup>2</sup>=0.150780<br><br>variable=CRIM<br>value=%{x}<br>target=%{y} <b>(trend)</b><extra></extra>\", \"legendgroup\": \"CRIM\", \"marker\": {\"color\": \"#636efa\", \"symbol\": \"circle\"}, \"mode\": \"lines\", \"name\": \"CRIM\", \"showlegend\": false, \"type\": \"scattergl\", \"x\": [0.00632, 0.00906, 0.01096, 0.01301, 0.01311, 0.0136, 0.01381, 0.01432, 0.01439, 0.01501, 0.01501, 0.01538, 0.01709, 0.01778, 0.0187, 0.01951, 0.01965, 0.02009, 0.02055, 0.02177, 0.02187, 0.02498, 0.02543, 0.02729, 0.02731, 0.02763, 0.02875, 0.02899, 0.02985, 0.03041, 0.03049, 0.03113, 0.0315, 0.03237, 0.03306, 0.03359, 0.03427, 0.03445, 0.03466, 0.03502, 0.0351, 0.03537, 0.03548, 0.03551, 0.03578, 0.03584, 0.03615, 0.03659, 0.03705, 0.03738, 0.03768, 0.03871, 0.03932, 0.03961, 0.04011, 0.04113, 0.04203, 0.04294, 0.04297, 0.04301, 0.04337, 0.04379, 0.04417, 0.04462, 0.04527, 0.04544, 0.0456, 0.0459, 0.04666, 0.04684, 0.04741, 0.04819, 0.04932, 0.04981, 0.05023, 0.05059, 0.05083, 0.05188, 0.05302, 0.0536, 0.05372, 0.05425, 0.05479, 0.05497, 0.05515, 0.05561, 0.05602, 0.05644, 0.05646, 0.0566, 0.05735, 0.0578, 0.05789, 0.06047, 0.06076, 0.06127, 0.06129, 0.06151, 0.06162, 0.06211, 0.06263, 0.06417, 0.06466, 0.06588, 0.06617, 0.06642, 0.06664, 0.06724, 0.0686, 0.06888, 0.06899, 0.06905, 0.06911, 0.07013, 0.07022, 0.07151, 0.07165, 0.07244, 0.07503, 0.07875, 0.07886, 0.07896, 0.0795, 0.07978, 0.08014, 0.08187, 0.08199, 0.08221, 0.08244, 0.08265, 0.08308, 0.0837, 0.08387, 0.08447, 0.08664, 0.08707, 0.08826, 0.08829, 0.08873, 0.09065, 0.09068, 0.09103, 0.09164, 0.09178, 0.09252, 0.09266, 0.09299, 0.09378, 0.09512, 0.09604, 0.09744, 0.09849, 0.1, 0.10008, 0.10084, 0.10153, 0.1029, 0.10328, 0.10469, 0.10574, 0.10612, 0.10659, 0.10793, 0.10959, 0.11027, 0.11069, 0.11132, 0.11329, 0.11425, 0.11432, 0.1146, 0.11504, 0.11747, 0.12083, 0.12204, 0.12269, 0.12329, 0.12579, 0.1265, 0.12744, 0.12757, 0.12802, 0.12816, 0.12932, 0.13058, 0.13117, 0.13158, 0.13262, 0.13554, 0.13587, 0.13642, 0.13914, 0.1396, 0.1403, 0.14052, 0.14103, 0.1415, 0.14231, 0.14455, 0.14476, 0.14866, 0.14932, 0.15038, 0.15086, 0.15098, 0.15445, 0.15876, 0.15936, 0.16211, 0.16439, 0.1676, 0.16902, 0.17004, 0.1712, 0.17134, 0.17142, 0.17171, 0.17331, 0.17446, 0.17505, 0.17783, 0.17899, 0.18159, 0.18337, 0.18836, 0.19073, 0.19133, 0.19186, 0.19539, 0.19657, 0.19802, 0.20608, 0.20746, 0.21038, 0.21124, 0.21161, 0.21409, 0.21719, 0.21977, 0.22188, 0.22212, 0.22438, 0.22489, 0.22876, 0.22927, 0.22969, 0.23912, 0.24103, 0.24522, 0.2498, 0.25199, 0.25356, 0.25387, 0.25915, 0.26169, 0.26363, 0.26838, 0.26938, 0.27957, 0.28392, 0.28955, 0.2896, 0.2909, 0.29819, 0.29916, 0.30347, 0.31533, 0.31827, 0.32264, 0.32543, 0.32982, 0.33045, 0.33147, 0.33983, 0.34006, 0.34109, 0.3494, 0.35114, 0.35233, 0.35809, 0.36894, 0.3692, 0.37578, 0.38214, 0.38735, 0.40202, 0.40771, 0.41238, 0.43571, 0.44178, 0.44791, 0.46296, 0.47547, 0.49298, 0.51183, 0.52014, 0.52058, 0.52693, 0.53412, 0.537, 0.54011, 0.5405, 0.54452, 0.55007, 0.55778, 0.57529, 0.57834, 0.59005, 0.61154, 0.6147, 0.62356, 0.62739, 0.62976, 0.63796, 0.65665, 0.66351, 0.67191, 0.7258, 0.75026, 0.76162, 0.77299, 0.7842, 0.7857, 0.79041, 0.80271, 0.82526, 0.84054, 0.85204, 0.88125, 0.95577, 0.97617, 0.98843, 1.00245, 1.05393, 1.12658, 1.13081, 1.15172, 1.19294, 1.20742, 1.22358, 1.23247, 1.25179, 1.27346, 1.34284, 1.35472, 1.38799, 1.41385, 1.42502, 1.46336, 1.49632, 1.51902, 1.61282, 1.62864, 1.6566, 1.80028, 1.83377, 2.01019, 2.14918, 2.15505, 2.24236, 2.3004, 2.3139, 2.33099, 2.36862, 2.37857, 2.37934, 2.44668, 2.44953, 2.63548, 2.73397, 2.77974, 2.81838, 2.924, 3.1636, 3.32105, 3.47428, 3.53501, 3.56868, 3.67367, 3.67822, 3.69311, 3.69695, 3.77498, 3.83684, 3.8497, 4.03841, 4.0974, 4.22239, 4.26131, 4.34879, 4.42228, 4.54192, 4.55587, 4.64689, 4.66883, 4.75237, 4.81213, 4.83567, 4.87141, 4.89822, 5.09017, 5.20177, 5.29305, 5.44114, 5.58107, 5.66637, 5.66998, 5.69175, 5.70818, 5.73116, 5.82115, 5.82401, 5.87205, 6.28807, 6.39312, 6.44405, 6.53876, 6.65492, 6.71772, 6.80117, 6.96215, 7.02259, 7.05042, 7.36711, 7.40389, 7.52601, 7.67202, 7.75223, 7.83932, 7.99248, 8.05579, 8.15174, 8.20058, 8.24809, 8.26725, 8.49213, 8.64476, 8.71675, 8.79212, 8.98296, 9.18702, 9.2323, 9.32909, 9.33889, 9.39063, 9.51363, 9.59571, 9.72418, 9.82349, 9.91655, 9.92485, 9.96654, 10.0623, 10.233, 10.6718, 10.8342, 11.0874, 11.1081, 11.1604, 11.5779, 11.8123, 11.9511, 12.0482, 12.2472, 12.8023, 13.0751, 13.3598, 13.5222, 13.6781, 13.9134, 14.0507, 14.2362, 14.3337, 14.3337, 14.4208, 14.4383, 15.0234, 15.1772, 15.288, 15.5757, 15.8603, 15.8744, 16.8118, 17.8667, 18.0846, 18.4982, 18.811, 19.6091, 20.0849, 20.7162, 22.0511, 22.5971, 23.6482, 24.3938, 24.8017, 25.0461, 25.9406, 28.6558, 37.6619, 38.3518, 41.5292, 45.7461, 51.1358, 67.9208, 73.5341, 88.9762], \"xaxis\": \"x\", \"y\": [24.030482171567453, 24.029344550205966, 24.028555688677926, 24.027704548608202, 24.02766302958041, 24.027459586344232, 24.027372396385868, 24.027160649344133, 24.027131586024677, 24.02687416805237, 24.02687416805237, 24.026720547649543, 24.02601057227431, 24.025724090982546, 24.025342115926865, 24.02500581180175, 24.024947685162847, 24.024765001440564, 24.024574013912723, 24.024067481773663, 24.024025962745874, 24.022734720981557, 24.022547885356495, 24.021775631439574, 24.021767327634016, 24.021634466745084, 24.021169453633817, 24.021069807967116, 24.02071274432811, 24.020480237772478, 24.020447022550247, 24.02018130077238, 24.02002768036955, 24.019666464827765, 24.019379983536002, 24.01915993268871, 24.018877603299725, 24.0188028690497, 24.01871567909134, 24.01856621059129, 24.018532995369057, 24.01842089399402, 24.018375223063448, 24.01836276735511, 24.018250665980073, 24.0182257545634, 24.018097045577246, 24.017914361854963, 24.017723374327122, 24.01758636153541, 24.017461804452036, 24.017034158465783, 24.016780892396255, 24.01666048721566, 24.016452892076703, 24.01602939799323, 24.015655726743105, 24.015277903590203, 24.015265447881866, 24.015248840270747, 24.0150993717707, 24.014924991853974, 24.014767219548368, 24.014580383923306, 24.01431051024266, 24.014239927895414, 24.014173497450948, 24.014048940367573, 24.013733395756358, 24.013658661506334, 24.01342200304792, 24.013098154631148, 24.012628989617102, 24.012425546380925, 24.0122511664642, 24.01210169796415, 24.01200205229745, 24.01156610250564, 24.01109278558882, 24.010851975227627, 24.010802152394277, 24.010582101546984, 24.010357898796908, 24.010283164546884, 24.01020843029686, 24.01001744276902, 24.009847214755073, 24.009672834838348, 24.00966453103279, 24.00960640439388, 24.009295011685445, 24.009108176060383, 24.009070808935373, 24.00799961801835, 24.007879212837754, 24.00766746579602, 24.00765916199046, 24.00756782012932, 24.00752214919875, 24.00731870596257, 24.007102807018054, 24.006463413990065, 24.006259970753888, 24.00575343861483, 24.005633033434236, 24.005529235864756, 24.005437894003613, 24.005188779836864, 24.0046241210589, 24.004507867781086, 24.004462196850515, 24.004437285433838, 24.004412374017164, 24.00398887993369, 24.00395151280868, 24.003415917350168, 24.00335779071126, 24.003029790391707, 24.001954447571908, 24.000409939738063, 24.00036426880749, 24.000322749779702, 24.000098547029626, 23.99998229375181, 23.99983282525176, 23.999114546070967, 23.999064723237616, 23.998973381376477, 23.998877887612558, 23.998790697654194, 23.99861216583469, 23.998354747862383, 23.998284165515138, 23.99803505134839, 23.997134088445314, 23.99695555662581, 23.99646148019509, 23.996449024486754, 23.99626634076447, 23.995469175430873, 23.995456719722537, 23.995311403125264, 23.995058137055736, 23.995000010416828, 23.994692769611174, 23.994634642972265, 23.99449763018055, 23.994169629861, 23.99361327488859, 23.99323129983291, 23.99265003344383, 23.99221408365202, 23.991587146332368, 23.991553931110133, 23.991238386498917, 23.990951905207158, 23.990383094526415, 23.990225322220805, 23.989639903928946, 23.989203954137135, 23.989046181831526, 23.988851042400906, 23.9882946874285, 23.98760547156716, 23.98732314217818, 23.987148762261455, 23.98688719238637, 23.986069267538877, 23.985670684872076, 23.985641621552624, 23.985525368274807, 23.985342684552524, 23.98433377217719, 23.982938732843397, 23.98243635260712, 23.982166478926473, 23.981917364759724, 23.98087938906494, 23.980584603967618, 23.98019432510638, 23.98014035037025, 23.979953514745187, 23.97989538810628, 23.979413767383896, 23.978890627633724, 23.978645665369754, 23.978475437355808, 23.978043639466776, 23.976831283855265, 23.976694271063554, 23.9764659164107, 23.97533659885477, 23.97514561132693, 23.974854978132388, 23.97476363627125, 23.97455188922951, 23.97435674979889, 23.97402044567378, 23.97309041945125, 23.97300322949289, 23.97138398740902, 23.971109961825594, 23.970669860131004, 23.970470568797605, 23.970420745964255, 23.96898003569989, 23.967190565602078, 23.96694145143533, 23.96579967817106, 23.964853044337413, 23.963520283545307, 23.96293071335067, 23.962507219267195, 23.962025598544813, 23.961967471905904, 23.961934256683673, 23.961813851503077, 23.96114954705841, 23.960672078238808, 23.960427115974838, 23.959272887002236, 23.958791266279853, 23.957711771557275, 23.956972732862585, 23.95490093337579, 23.95391693241713, 23.95366781825038, 23.953447767403087, 23.951982145722045, 23.95149222119411, 23.95089019529113, 23.947543761651133, 23.94697079906761, 23.9457584434561, 23.945401379817092, 23.945247759414265, 23.944218087525034, 23.9429309976635, 23.941859806746475, 23.940983755260074, 23.940884109593377, 23.93994577956529, 23.93973403252355, 23.93812724614802, 23.937915499106282, 23.93774111918956, 23.93382587486882, 23.933032861438, 23.931293214173536, 23.929391642700686, 23.928482375992054, 23.927830527255725, 23.927701818269572, 23.92550961360218, 23.924455030296276, 23.92364956115712, 23.921677407337025, 23.92126221705911, 23.917031428127153, 23.915225350418222, 23.91288782915356, 23.912867069639667, 23.912327322278376, 23.909300585152376, 23.908897850582797, 23.907108380484985, 23.90218422378891, 23.90096356437184, 23.89914918285735, 23.89799080198197, 23.896168116661922, 23.895906546786833, 23.895483052703362, 23.89201206197999, 23.89191656821607, 23.89148892222982, 23.888038691020345, 23.88731625993677, 23.88682218350605, 23.88443068750526, 23.879925872989883, 23.879817923517624, 23.877085971488942, 23.874445361321403, 23.872282219973467, 23.86619137859645, 23.863828945915113, 23.86189000731725, 23.85220361813349, 23.849683413146547, 23.847138296742926, 23.840889683060304, 23.835695652683587, 23.828425670917294, 23.820599334178596, 23.817149102969122, 23.81696641924684, 23.81432996098208, 23.81134474288387, 23.810148994883473, 23.808857753119156, 23.808695828910768, 23.80702676399355, 23.804722457951122, 23.801521340908398, 23.794251359142102, 23.79298502879446, 23.788123150640075, 23.779200711567682, 23.77788871028947, 23.774210124427142, 23.772619945662726, 23.77163594470407, 23.768231384425164, 23.76047147813093, 23.757623272824436, 23.754135674489948, 23.731761070413103, 23.7216055162153, 23.716888954658184, 23.712168241198288, 23.70751395818286, 23.70689117276599, 23.70493562655701, 23.699828786138653, 23.690466245371667, 23.684122137925126, 23.6793474497291, 23.667219741711204, 23.63627976220097, 23.6278098805315, 23.622719647724264, 23.616898680027894, 23.595524684520825, 23.565361110830292, 23.56360485595471, 23.554923227243506, 23.53780908398785, 23.531797128763635, 23.52508765387253, 23.521396612301864, 23.513375136132545, 23.504377962810125, 23.475572061328375, 23.470639600826743, 23.456826220280508, 23.446089399693623, 23.441451724289312, 23.42553332903405, 23.411848657473968, 23.402423838165294, 23.36347899009686, 23.356910679900242, 23.345301959729735, 23.285647420598895, 23.27174269819152, 23.19849482936174, 23.14078753263432, 23.13835036570296, 23.10210010253819, 23.078002458808, 23.072397390056146, 23.06530178820658, 23.049678178048634, 23.04554703478338, 23.045227338269385, 23.01726842495458, 23.01608513266252, 22.93888050048421, 22.897988410012353, 22.87898515099218, 22.862942198653542, 22.819089801500148, 22.719610210911693, 22.654238501653964, 22.590618895369033, 22.56540438979125, 22.55142493313385, 22.507834105855544, 22.50594499009103, 22.499762806852875, 22.49816847618568, 22.465771178799965, 22.440087508208137, 22.43474816123415, 22.35639760388879, 22.331905529394582, 22.280010896557975, 22.263851690941518, 22.227530845429506, 22.197018511905526, 22.147345147055766, 22.14155324267885, 22.103762623583016, 22.09465334888556, 22.059968353068534, 22.035156582060328, 22.025383002918208, 22.01054410238552, 21.999412851034617, 21.919717077188817, 21.87338184217349, 21.8354832736054, 21.773997745348957, 21.715900169760296, 21.68048443905414, 21.678985602150867, 21.669946909800654, 21.66312533353451, 21.653584260948023, 21.616221287838442, 21.615033843643605, 21.595088102692564, 21.422360643274327, 21.37874490457935, 21.35759926372513, 21.318276592503793, 21.270048089821177, 21.24397414036811, 21.209326511676096, 21.14248918073732, 21.117395080340135, 21.105840334905757, 20.974353725792827, 20.95908302737111, 20.90837999063212, 20.847758058153737, 20.81445564596217, 20.778296724658542, 20.714706181693067, 20.688420485198264, 20.64858297803231, 20.628305084858937, 20.60857939475519, 20.600624349030337, 20.507256359332793, 20.44388586721461, 20.413996319107504, 20.382703427861042, 20.303468515223727, 20.218744787112374, 20.199944971328378, 20.159758704328976, 20.155689839605408, 20.134207894626083, 20.083139490442527, 20.049060672431256, 19.995721177427505, 19.954488630927756, 19.915851023664978, 19.912404944358283, 19.895095661672002, 19.855337040658853, 19.784464060218745, 19.602278566269604, 19.534851665136195, 19.429725486768092, 19.42113104801525, 19.399416596480293, 19.22607465545074, 19.128754054307443, 19.071125643732827, 19.030810667747275, 18.94818780244217, 18.7177156791715, 18.604451771356267, 18.48624709923384, 18.418820198100427, 18.354092033773465, 18.256397761380043, 18.1993921362223, 18.12237433966905, 18.08189328757233, 18.08189328757233, 18.04573021436593, 18.03846438450241, 17.795536552894294, 17.731680288150955, 17.685677205357962, 17.56622696240179, 17.448063809307154, 17.44220962638855, 17.053010259870945, 16.615026035698314, 16.524556074140616, 16.352833375194933, 16.222961856263094, 15.891598495459059, 15.694050961227058, 15.43194133877926, 14.877703836790404, 14.651009945048765, 14.214603443932214, 13.905037572718722, 13.735681458357156, 13.634208954434708, 13.262821250839657, 12.135496608244603, 8.396251446313498, 8.109811673579877, 6.790586084532464, 5.039770201592319, 2.8020191607133462, -4.166949654091461, -6.497537241112241, -12.908947031704873], \"yaxis\": \"y\"}, {\"hovertemplate\": \"variable=RM<br>value=%{x}<br>target=%{y}<extra></extra>\", \"legendgroup\": \"RM\", \"marker\": {\"color\": \"#EF553B\", \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"RM\", \"showlegend\": true, \"type\": \"scattergl\", \"x\": [6.575, 6.421, 7.185, 6.998, 7.147, 6.43, 6.012, 6.172, 5.631, 6.004, 6.377, 6.009, 5.889, 5.949, 6.096, 5.834, 5.935, 5.99, 5.456, 5.727, 5.57, 5.965, 6.142, 5.813, 5.924, 5.599, 5.813, 6.047, 6.495, 6.674, 5.713, 6.072, 5.95, 5.701, 6.096, 5.933, 5.841, 5.85, 5.966, 6.595, 7.024, 6.77, 6.169, 6.211, 6.069, 5.682, 5.786, 6.03, 5.399, 5.602, 5.963, 6.115, 6.511, 5.998, 5.888, 7.249, 6.383, 6.816, 6.145, 5.927, 5.741, 5.966, 6.456, 6.762, 7.104, 6.29, 5.787, 5.878, 5.594, 5.885, 6.417, 5.961, 6.065, 6.245, 6.273, 6.286, 6.279, 6.14, 6.232, 5.874, 6.727, 6.619, 6.302, 6.167, 6.389, 6.63, 6.015, 6.121, 7.007, 7.079, 6.417, 6.405, 6.442, 6.211, 6.249, 6.625, 6.163, 8.069, 7.82, 7.416, 6.727, 6.781, 6.405, 6.137, 6.167, 5.851, 5.836, 6.127, 6.474, 6.229, 6.195, 6.715, 5.913, 6.092, 6.254, 5.928, 6.176, 6.021, 5.872, 5.731, 5.87, 6.004, 5.961, 5.856, 5.879, 5.986, 5.613, 5.693, 6.431, 5.637, 6.458, 6.326, 6.372, 5.822, 5.757, 6.335, 5.942, 6.454, 5.857, 6.151, 6.174, 5.019, 5.403, 5.468, 4.903, 6.13, 5.628, 4.926, 5.186, 5.597, 6.122, 5.404, 5.012, 5.709, 6.129, 6.152, 5.272, 6.943, 6.066, 6.51, 6.25, 7.489, 7.802, 8.375, 5.854, 6.101, 7.929, 5.877, 6.319, 6.402, 5.875, 5.88, 5.572, 6.416, 5.859, 6.546, 6.02, 6.315, 6.86, 6.98, 7.765, 6.144, 7.155, 6.563, 5.604, 6.153, 7.831, 6.782, 6.556, 7.185, 6.951, 6.739, 7.178, 6.8, 6.604, 7.875, 7.287, 7.107, 7.274, 6.975, 7.135, 6.162, 7.61, 7.853, 8.034, 5.891, 6.326, 5.783, 6.064, 5.344, 5.96, 5.404, 5.807, 6.375, 5.412, 6.182, 5.888, 6.642, 5.951, 6.373, 6.951, 6.164, 6.879, 6.618, 8.266, 8.725, 8.04, 7.163, 7.686, 6.552, 5.981, 7.412, 8.337, 8.247, 6.726, 6.086, 6.631, 7.358, 6.481, 6.606, 6.897, 6.095, 6.358, 6.393, 5.593, 5.605, 6.108, 6.226, 6.433, 6.718, 6.487, 6.438, 6.957, 8.259, 6.108, 5.876, 7.454, 8.704, 7.333, 6.842, 7.203, 7.52, 8.398, 7.327, 7.206, 5.56, 7.014, 8.297, 7.47, 5.92, 5.856, 6.24, 6.538, 7.691, 6.758, 6.854, 7.267, 6.826, 6.482, 6.812, 7.82, 6.968, 7.645, 7.923, 7.088, 6.453, 6.23, 6.209, 6.315, 6.565, 6.861, 7.148, 6.63, 6.127, 6.009, 6.678, 6.549, 5.79, 6.345, 7.041, 6.871, 6.59, 6.495, 6.982, 7.236, 6.616, 7.42, 6.849, 6.635, 5.972, 4.973, 6.122, 6.023, 6.266, 6.567, 5.705, 5.914, 5.782, 6.382, 6.113, 6.426, 6.376, 6.041, 5.708, 6.415, 6.431, 6.312, 6.083, 5.868, 6.333, 6.144, 5.706, 6.031, 6.316, 6.31, 6.037, 5.869, 5.895, 6.059, 5.985, 5.968, 7.241, 6.54, 6.696, 6.874, 6.014, 5.898, 6.516, 6.635, 6.939, 6.49, 6.579, 5.884, 6.728, 5.663, 5.936, 6.212, 6.395, 6.127, 6.112, 6.398, 6.251, 5.362, 5.803, 8.78, 3.561, 4.963, 3.863, 4.97, 6.683, 7.016, 6.216, 5.875, 4.906, 4.138, 7.313, 6.649, 6.794, 6.38, 6.223, 6.968, 6.545, 5.536, 5.52, 4.368, 5.277, 4.652, 5.0, 4.88, 5.39, 5.713, 6.051, 5.036, 6.193, 5.887, 6.471, 6.405, 5.747, 5.453, 5.852, 5.987, 6.343, 6.404, 5.349, 5.531, 5.683, 4.138, 5.608, 5.617, 6.852, 5.757, 6.657, 4.628, 5.155, 4.519, 6.434, 6.782, 5.304, 5.957, 6.824, 6.411, 6.006, 5.648, 6.103, 5.565, 5.896, 5.837, 6.202, 6.193, 6.38, 6.348, 6.833, 6.425, 6.436, 6.208, 6.629, 6.461, 6.152, 5.935, 5.627, 5.818, 6.406, 6.219, 6.485, 5.854, 6.459, 6.341, 6.251, 6.185, 6.417, 6.749, 6.655, 6.297, 7.393, 6.728, 6.525, 5.976, 5.936, 6.301, 6.081, 6.701, 6.376, 6.317, 6.513, 6.209, 5.759, 5.952, 6.003, 5.926, 5.713, 6.167, 6.229, 6.437, 6.98, 5.427, 6.162, 6.484, 5.304, 6.185, 6.229, 6.242, 6.75, 7.061, 5.762, 5.871, 6.312, 6.114, 5.905, 5.454, 5.414, 5.093, 5.983, 5.983, 5.707, 5.926, 5.67, 5.39, 5.794, 6.019, 5.569, 6.027, 6.593, 6.12, 6.976, 6.794, 6.03], \"xaxis\": \"x\", \"y\": [24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15.0, 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21.0, 12.7, 14.5, 13.2, 13.1, 13.5, 18.9, 20.0, 21.0, 24.7, 30.8, 34.9, 26.6, 25.3, 24.7, 21.2, 19.3, 20.0, 16.6, 14.4, 19.4, 19.7, 20.5, 25.0, 23.4, 18.9, 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16.0, 22.2, 25.0, 33.0, 23.5, 19.4, 22.0, 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20.0, 20.8, 21.2, 20.3, 28.0, 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2, 23.6, 28.7, 22.6, 22.0, 22.9, 25.0, 20.6, 28.4, 21.4, 38.7, 43.8, 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4, 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22.0, 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18.0, 14.3, 19.2, 19.6, 23.0, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14.0, 14.4, 13.4, 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4, 17.0, 15.6, 13.1, 41.3, 24.3, 23.3, 27.0, 50.0, 50.0, 50.0, 22.7, 25.0, 50.0, 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4, 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50.0, 32.0, 29.8, 34.9, 37.0, 30.5, 36.4, 31.1, 29.1, 50.0, 33.3, 30.3, 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50.0, 22.6, 24.4, 22.5, 24.4, 20.0, 21.7, 19.3, 22.4, 28.1, 23.7, 25.0, 23.3, 28.7, 21.5, 23.0, 26.7, 21.7, 27.5, 30.1, 44.8, 50.0, 37.6, 31.6, 46.7, 31.5, 24.3, 31.7, 41.7, 48.3, 29.0, 24.0, 25.1, 31.5, 23.7, 23.3, 22.0, 20.1, 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6, 42.8, 21.9, 20.9, 44.0, 50.0, 36.0, 30.1, 33.8, 43.1, 48.8, 31.0, 36.5, 22.8, 30.7, 50.0, 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4, 32.0, 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46.0, 50.0, 32.2, 22.0, 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1, 20.3, 22.5, 29.0, 24.8, 22.0, 26.4, 33.1, 36.1, 28.4, 33.4, 28.2, 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1, 21.0, 23.8, 23.1, 20.4, 18.5, 25.0, 24.6, 23.0, 22.2, 19.3, 22.6, 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19.0, 18.7, 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1, 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25.0, 19.9, 20.8, 16.8, 21.9, 27.5, 21.9, 23.1, 50.0, 50.0, 50.0, 50.0, 50.0, 13.8, 13.8, 15.0, 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5.0, 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5.0, 11.9, 27.9, 17.2, 27.5, 15.0, 17.2, 17.9, 16.3, 7.0, 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11.0, 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13.0, 13.4, 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20.0, 16.4, 17.7, 19.5, 20.2, 21.4, 19.9, 19.0, 19.1, 19.1, 20.1, 19.9, 19.6, 23.2, 29.8, 13.8, 13.3, 16.7, 12.0, 14.6, 21.4, 23.0, 23.7, 25.0, 21.8, 20.6, 21.2, 19.1, 20.6, 15.2, 7.0, 8.1, 13.6, 20.1, 21.8, 24.5, 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22.0, 11.9], \"yaxis\": \"y\"}, {\"hovertemplate\": \"<b>OLS trendline</b><br>target = 9.10211 * value + -34.6706<br>R<sup>2</sup>=0.483525<br><br>variable=RM<br>value=%{x}<br>target=%{y} <b>(trend)</b><extra></extra>\", \"legendgroup\": \"RM\", \"marker\": {\"color\": \"#EF553B\", \"symbol\": \"circle\"}, \"mode\": \"lines\", \"name\": \"RM\", \"showlegend\": false, \"type\": \"scattergl\", \"x\": [3.561, 3.863, 4.138, 4.138, 4.368, 4.519, 4.628, 4.652, 4.88, 4.903, 4.906, 4.926, 4.963, 4.97, 4.973, 5.0, 5.012, 5.019, 5.036, 5.093, 5.155, 5.186, 5.272, 5.277, 5.304, 5.304, 5.344, 5.349, 5.362, 5.39, 5.39, 5.399, 5.403, 5.404, 5.404, 5.412, 5.414, 5.427, 5.453, 5.454, 5.456, 5.468, 5.52, 5.531, 5.536, 5.56, 5.565, 5.569, 5.57, 5.572, 5.593, 5.594, 5.597, 5.599, 5.602, 5.604, 5.605, 5.608, 5.613, 5.617, 5.627, 5.628, 5.631, 5.637, 5.648, 5.663, 5.67, 5.682, 5.683, 5.693, 5.701, 5.705, 5.706, 5.707, 5.708, 5.709, 5.713, 5.713, 5.713, 5.727, 5.731, 5.741, 5.747, 5.757, 5.757, 5.759, 5.762, 5.782, 5.783, 5.786, 5.787, 5.79, 5.794, 5.803, 5.807, 5.813, 5.813, 5.818, 5.822, 5.834, 5.836, 5.837, 5.841, 5.85, 5.851, 5.852, 5.854, 5.854, 5.856, 5.856, 5.857, 5.859, 5.868, 5.869, 5.87, 5.871, 5.872, 5.874, 5.875, 5.875, 5.876, 5.877, 5.878, 5.879, 5.88, 5.884, 5.885, 5.887, 5.888, 5.888, 5.889, 5.891, 5.895, 5.896, 5.898, 5.905, 5.913, 5.914, 5.92, 5.924, 5.926, 5.926, 5.927, 5.928, 5.933, 5.935, 5.935, 5.936, 5.936, 5.942, 5.949, 5.95, 5.951, 5.952, 5.957, 5.96, 5.961, 5.961, 5.963, 5.965, 5.966, 5.966, 5.968, 5.972, 5.976, 5.981, 5.983, 5.983, 5.985, 5.986, 5.987, 5.99, 5.998, 6.003, 6.004, 6.004, 6.006, 6.009, 6.009, 6.012, 6.014, 6.015, 6.019, 6.02, 6.021, 6.023, 6.027, 6.03, 6.03, 6.031, 6.037, 6.041, 6.047, 6.051, 6.059, 6.064, 6.065, 6.066, 6.069, 6.072, 6.081, 6.083, 6.086, 6.092, 6.095, 6.096, 6.096, 6.101, 6.103, 6.108, 6.108, 6.112, 6.113, 6.114, 6.115, 6.12, 6.121, 6.122, 6.122, 6.127, 6.127, 6.127, 6.129, 6.13, 6.137, 6.14, 6.142, 6.144, 6.144, 6.145, 6.151, 6.152, 6.152, 6.153, 6.162, 6.162, 6.163, 6.164, 6.167, 6.167, 6.167, 6.169, 6.172, 6.174, 6.176, 6.182, 6.185, 6.185, 6.193, 6.193, 6.195, 6.202, 6.208, 6.209, 6.209, 6.211, 6.211, 6.212, 6.216, 6.219, 6.223, 6.226, 6.229, 6.229, 6.229, 6.23, 6.232, 6.24, 6.242, 6.245, 6.249, 6.25, 6.251, 6.251, 6.254, 6.266, 6.273, 6.279, 6.286, 6.29, 6.297, 6.301, 6.302, 6.31, 6.312, 6.312, 6.315, 6.315, 6.316, 6.317, 6.319, 6.326, 6.326, 6.333, 6.335, 6.341, 6.343, 6.345, 6.348, 6.358, 6.372, 6.373, 6.375, 6.376, 6.376, 6.377, 6.38, 6.38, 6.382, 6.383, 6.389, 6.393, 6.395, 6.398, 6.402, 6.404, 6.405, 6.405, 6.405, 6.406, 6.411, 6.415, 6.416, 6.417, 6.417, 6.417, 6.421, 6.425, 6.426, 6.43, 6.431, 6.431, 6.433, 6.434, 6.436, 6.437, 6.438, 6.442, 6.453, 6.454, 6.456, 6.458, 6.459, 6.461, 6.471, 6.474, 6.481, 6.482, 6.484, 6.485, 6.487, 6.49, 6.495, 6.495, 6.51, 6.511, 6.513, 6.516, 6.525, 6.538, 6.54, 6.545, 6.546, 6.549, 6.552, 6.556, 6.563, 6.565, 6.567, 6.575, 6.579, 6.59, 6.593, 6.595, 6.604, 6.606, 6.616, 6.618, 6.619, 6.625, 6.629, 6.63, 6.63, 6.631, 6.635, 6.635, 6.642, 6.649, 6.655, 6.657, 6.674, 6.678, 6.683, 6.696, 6.701, 6.715, 6.718, 6.726, 6.727, 6.727, 6.728, 6.728, 6.739, 6.749, 6.75, 6.758, 6.762, 6.77, 6.781, 6.782, 6.782, 6.794, 6.794, 6.8, 6.812, 6.816, 6.824, 6.826, 6.833, 6.842, 6.849, 6.852, 6.854, 6.86, 6.861, 6.871, 6.874, 6.879, 6.897, 6.939, 6.943, 6.951, 6.951, 6.957, 6.968, 6.968, 6.975, 6.976, 6.98, 6.98, 6.982, 6.998, 7.007, 7.014, 7.016, 7.024, 7.041, 7.061, 7.079, 7.088, 7.104, 7.107, 7.135, 7.147, 7.148, 7.155, 7.163, 7.178, 7.185, 7.185, 7.203, 7.206, 7.236, 7.241, 7.249, 7.267, 7.274, 7.287, 7.313, 7.327, 7.333, 7.358, 7.393, 7.412, 7.416, 7.42, 7.454, 7.47, 7.489, 7.52, 7.61, 7.645, 7.686, 7.691, 7.765, 7.802, 7.82, 7.82, 7.831, 7.853, 7.875, 7.923, 7.929, 8.034, 8.04, 8.069, 8.247, 8.259, 8.266, 8.297, 8.337, 8.375, 8.398, 8.704, 8.725, 8.78], \"xaxis\": \"x\", \"y\": [-2.2580106944553933, 0.4908262178610556, 2.9939061876856385, 2.9939061876856385, 5.0873912533571115, 6.461809709515336, 7.453939588463989, 7.672390204012316, 9.747671051721419, 9.957019558288565, 9.984325885232103, 10.166368064855718, 10.503146097159387, 10.566860860027646, 10.594167186971191, 10.839924129463057, 10.949149437237217, 11.012864200105483, 11.167600052785545, 11.686420264712822, 12.250751021546009, 12.532916399962595, 13.315697772344102, 13.361208317250004, 13.60696525974187, 13.60696525974187, 13.971049618989085, 14.016560163894987, 14.134887580650329, 14.38974663212337, 14.38974663212337, 14.471665612953998, 14.508074048878711, 14.5171761578599, 14.5171761578599, 14.58999302970934, 14.608197247671697, 14.72652466442704, 14.963179497937737, 14.972281606918912, 14.990485824881276, 15.099711132655436, 15.573020799676812, 15.673143998469797, 15.718654543375692, 15.93710515892402, 15.982615703829929, 16.01902413975465, 16.02812624873583, 16.046330466698187, 16.237474755302976, 16.246576864284158, 16.273883191227704, 16.29208740919006, 16.3193937361336, 16.337597954095962, 16.346700063077144, 16.374006390020675, 16.419516934926584, 16.455925370851304, 16.5469464606631, 16.55604856964429, 16.583354896587828, 16.637967550474904, 16.73809074926789, 16.874622383985596, 16.93833714685386, 17.04756245462803, 17.056664563609203, 17.147685653421, 17.220502525270447, 17.256910961195167, 17.266013070176356, 17.27511517915753, 17.284217288138713, 17.293319397119888, 17.329727833044615, 17.329727833044615, 17.329727833044615, 17.45715735878114, 17.49356579470586, 17.584586884517655, 17.63919953840474, 17.730220628216543, 17.730220628216543, 17.748424846178906, 17.775731173122445, 17.957773352746052, 17.966875461727234, 17.994181788670772, 18.003283897651954, 18.030590224595493, 18.066998660520213, 18.148917641350835, 18.185326077275562, 18.23993873116264, 18.23993873116264, 18.28544927606854, 18.321857711993268, 18.431083019767428, 18.44928723772979, 18.458389346710966, 18.494797782635693, 18.576716763466308, 18.58581887244749, 18.59492098142868, 18.613125199391035, 18.613125199391035, 18.631329417353392, 18.631329417353392, 18.640431526334574, 18.658635744296937, 18.74055472512756, 18.749656834108734, 18.758758943089923, 18.767861052071105, 18.77696316105228, 18.795167379014636, 18.804269487995818, 18.804269487995818, 18.813371596977007, 18.82247370595818, 18.831575814939363, 18.840677923920538, 18.84978003290172, 18.886188468826447, 18.89529057780762, 18.913494795769978, 18.922596904751167, 18.922596904751167, 18.93169901373235, 18.949903231694705, 18.986311667619425, 18.995413776600607, 19.013617994562964, 19.07733275743123, 19.150149629280676, 19.15925173826185, 19.213864392148935, 19.250272828073662, 19.26847704603602, 19.26847704603602, 19.277579155017193, 19.286681263998375, 19.332191808904277, 19.350396026866633, 19.350396026866633, 19.359498135847815, 19.359498135847815, 19.4141107897349, 19.477825552603164, 19.486927661584346, 19.49602977056552, 19.505131879546703, 19.550642424452604, 19.577948751396143, 19.58705086037733, 19.58705086037733, 19.605255078339688, 19.623459296302045, 19.632561405283226, 19.632561405283226, 19.65076562324559, 19.68717405917031, 19.72358249509503, 19.769093040000932, 19.78729725796329, 19.78729725796329, 19.80550147592566, 19.814603584906834, 19.823705693888016, 19.851012020831554, 19.923828892681, 19.969339437586903, 19.978441546568078, 19.978441546568078, 19.99664576453044, 20.023952091473987, 20.023952091473987, 20.051258418417518, 20.069462636379882, 20.078564745361057, 20.114973181285784, 20.12407529026696, 20.13317739924814, 20.151381617210504, 20.187790053135224, 20.21509638007877, 20.21509638007877, 20.224198489059944, 20.278811142947028, 20.315219578871755, 20.36983223275883, 20.40624066868355, 20.479057540533, 20.5245680854389, 20.533670194420083, 20.542772303401257, 20.570078630344796, 20.59738495728834, 20.679303938118963, 20.697508156081327, 20.724814483024865, 20.77942713691194, 20.806733463855487, 20.81583557283667, 20.81583557283667, 20.86134611774257, 20.879550335704927, 20.92506088061083, 20.92506088061083, 20.96146931653555, 20.970571425516738, 20.979673534497913, 20.988775643479094, 21.034286188384996, 21.043388297366178, 21.052490406347353, 21.052490406347353, 21.098000951253255, 21.098000951253255, 21.098000951253255, 21.11620516921561, 21.125307278196793, 21.18902204106506, 21.216328368008597, 21.23453258597096, 21.252736803933324, 21.252736803933324, 21.2618389129145, 21.316451566801582, 21.325553675782764, 21.325553675782764, 21.33465578476394, 21.416574765594568, 21.416574765594568, 21.42567687457575, 21.434778983556924, 21.46208531050047, 21.46208531050047, 21.46208531050047, 21.480289528462826, 21.507595855406365, 21.525800073368735, 21.544004291331092, 21.598616945218176, 21.625923272161707, 21.625923272161707, 21.698740144011154, 21.698740144011154, 21.716944361973518, 21.780659124841776, 21.83527177872886, 21.844373887710034, 21.844373887710034, 21.862578105672405, 21.862578105672405, 21.87168021465358, 21.908088650578307, 21.935394977521845, 21.971803413446565, 21.999109740390104, 22.02641606733365, 22.02641606733365, 22.02641606733365, 22.03551817631483, 22.053722394277187, 22.126539266126635, 22.14474348408899, 22.17204981103253, 22.20845824695725, 22.21756035593843, 22.226662464919613, 22.226662464919613, 22.25396879186315, 22.36319409963732, 22.426908862505577, 22.48152151639266, 22.54523627926092, 22.581644715185647, 22.645359478053905, 22.681767913978632, 22.690870022959807, 22.763686894809247, 22.781891112771618, 22.781891112771618, 22.809197439715156, 22.809197439715156, 22.81829954869633, 22.827401657677513, 22.845605875639876, 22.909320638508134, 22.909320638508134, 22.9730354013764, 22.991239619338756, 23.04585227322584, 23.064056491188204, 23.08226070915056, 23.1095670360941, 23.200588125905902, 23.328017651642426, 23.337119760623615, 23.35532397858597, 23.364426087567153, 23.364426087567153, 23.373528196548328, 23.400834523491874, 23.400834523491874, 23.41903874145423, 23.428140850435412, 23.482753504322496, 23.519161940247216, 23.537366158209572, 23.564672485153118, 23.601080921077838, 23.6192851390402, 23.628387248021383, 23.628387248021383, 23.628387248021383, 23.637489357002558, 23.68299990190846, 23.71940833783318, 23.72851044681437, 23.737612555795543, 23.737612555795543, 23.737612555795543, 23.77402099172027, 23.810429427644983, 23.819531536626165, 23.855939972550885, 23.865042081532067, 23.865042081532067, 23.883246299494424, 23.892348408475613, 23.91055262643797, 23.91965473541915, 23.928756844400326, 23.965165280325053, 24.06528847911804, 24.074390588099213, 24.092594806061577, 24.11079902402394, 24.119901133005115, 24.13810535096748, 24.229126440779282, 24.25643276772282, 24.32014753059108, 24.329249639572268, 24.347453857534624, 24.356555966515806, 24.374760184478163, 24.402066511421708, 24.44757705632761, 24.44757705632761, 24.58410869104531, 24.59321080002649, 24.611415017988854, 24.638721344932392, 24.72064032576302, 24.838967742518363, 24.85717196048072, 24.902682505386622, 24.911784614367804, 24.93909094131135, 24.96639726825488, 25.002805704179607, 25.066520467047866, 25.08472468501023, 25.102928902972593, 25.175745774822033, 25.212154210746746, 25.312277409539732, 25.339583736483277, 25.357787954445634, 25.439706935276263, 25.45791115323862, 25.548932243050423, 25.567136461012787, 25.57623856999396, 25.630851223881045, 25.667259659805765, 25.676361768786947, 25.676361768786947, 25.68546387776813, 25.72187231369285, 25.72187231369285, 25.785587076561114, 25.849301839429373, 25.903914493316456, 25.922118711278813, 26.076854563958882, 26.113262999883602, 26.158773544789504, 26.277100961544846, 26.322611506450748, 26.450041032187272, 26.47734735913081, 26.550164230980258, 26.55926633996144, 26.55926633996144, 26.568368448942614, 26.568368448942614, 26.6684916477356, 26.759512737547404, 26.768614846528585, 26.841431718378026, 26.877840154302746, 26.950657026152186, 27.05078022494517, 27.059882333926353, 27.059882333926353, 27.169107641700514, 27.169107641700514, 27.223720295587597, 27.332945603361765, 27.369354039286485, 27.442170911135925, 27.46037512909828, 27.524089891966547, 27.60600887279717, 27.669723635665434, 27.69702996260898, 27.715234180571336, 27.76984683445842, 27.778948943439595, 27.869970033251406, 27.897276360194937, 27.94278690510084, 28.10662486676209, 28.48891344397166, 28.52532187989638, 28.59813875174582, 28.59813875174582, 28.652751405632905, 28.75287460442589, 28.75287460442589, 28.81658936729415, 28.82569147627533, 28.86209991220006, 28.86209991220006, 28.880304130162415, 29.025937873861302, 29.107856854691917, 29.171571617560183, 29.189775835522546, 29.262592707371986, 29.41732856005205, 29.599370739675656, 29.7632087013369, 29.845127682167522, 29.990761425866417, 30.018067752809948, 30.272926804282996, 30.382152112057163, 30.391254221038345, 30.454968983906603, 30.527785855756044, 30.664317490473756, 30.728032253342015, 30.728032253342015, 30.89187021500326, 30.919176541946804, 31.1922398113822, 31.23775035628811, 31.31056722813755, 31.47440518979881, 31.538119952667067, 31.656447369422402, 31.893102202933086, 32.02053172866962, 32.075144382556694, 32.30269710708621, 32.62127092142752, 32.79421099206994, 32.83061942799467, 32.86702786391938, 33.17649956927951, 33.3221333129784, 33.49507338362083, 33.777238762037406, 34.59642857034364, 34.915002384684946, 35.28818885291334, 35.33369939781924, 36.00725546242659, 36.34403349473026, 36.507871456391506, 36.507871456391506, 36.60799465518449, 36.80824105277045, 37.00848745035642, 37.445388681453075, 37.500001335340166, 38.4557227783641, 38.51033543225116, 38.774296592705404, 40.394471991355495, 40.50369729912966, 40.56741206199792, 40.849577440414514, 41.213661799661715, 41.559541940946566, 41.76889044751372, 44.554135795754895, 44.74528008435967, 45.245896078324584], \"yaxis\": \"y\"}, {\"hovertemplate\": \"variable=LSTAT<br>value=%{x}<br>target=%{y}<extra></extra>\", \"legendgroup\": \"LSTAT\", \"marker\": {\"color\": \"#00cc96\", \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"LSTAT\", \"showlegend\": true, \"type\": \"scattergl\", \"x\": [4.98, 9.14, 4.03, 2.94, 5.33, 5.21, 12.43, 19.15, 29.93, 17.1, 20.45, 13.27, 15.71, 8.26, 10.26, 8.47, 6.58, 14.67, 11.69, 11.28, 21.02, 13.83, 18.72, 19.88, 16.3, 16.51, 14.81, 17.28, 12.8, 11.98, 22.6, 13.04, 27.71, 18.35, 20.34, 9.68, 11.41, 8.77, 10.13, 4.32, 1.98, 4.84, 5.81, 7.44, 9.55, 10.21, 14.15, 18.8, 30.81, 16.2, 13.45, 9.43, 5.28, 8.43, 14.8, 4.81, 5.77, 3.95, 6.86, 9.22, 13.15, 14.44, 6.73, 9.5, 8.05, 4.67, 10.24, 8.1, 13.09, 8.79, 6.72, 9.88, 5.52, 7.54, 6.78, 8.94, 11.97, 10.27, 12.34, 9.1, 5.29, 7.22, 6.72, 7.51, 9.62, 6.53, 12.86, 8.44, 5.5, 5.7, 8.81, 8.2, 8.16, 6.21, 10.59, 6.65, 11.34, 4.21, 3.57, 6.19, 9.42, 7.67, 10.63, 13.44, 12.33, 16.47, 18.66, 14.09, 12.27, 15.55, 13.0, 10.16, 16.21, 17.09, 10.45, 15.76, 12.04, 10.3, 15.37, 13.61, 14.37, 14.27, 17.93, 25.41, 17.58, 14.81, 27.26, 17.19, 15.39, 18.34, 12.6, 12.26, 11.12, 15.03, 17.31, 16.96, 16.9, 14.59, 21.32, 18.46, 24.16, 34.41, 26.82, 26.42, 29.29, 27.8, 16.65, 29.53, 28.32, 21.45, 14.1, 13.28, 12.12, 15.79, 15.12, 15.02, 16.14, 4.59, 6.43, 7.39, 5.5, 1.73, 1.92, 3.32, 11.64, 9.81, 3.7, 12.14, 11.1, 11.32, 14.43, 12.03, 14.69, 9.04, 9.64, 5.33, 10.11, 6.29, 6.92, 5.04, 7.56, 9.45, 4.82, 5.68, 13.98, 13.15, 4.45, 6.68, 4.56, 5.39, 5.1, 4.69, 2.87, 5.03, 4.38, 2.97, 4.08, 8.61, 6.62, 4.56, 4.45, 7.43, 3.11, 3.81, 2.88, 10.87, 10.97, 18.06, 14.66, 23.09, 17.27, 23.98, 16.03, 9.38, 29.55, 9.47, 13.51, 9.69, 17.92, 10.5, 9.71, 21.46, 9.93, 7.6, 4.14, 4.63, 3.13, 6.36, 3.92, 3.76, 11.65, 5.25, 2.47, 3.95, 8.05, 10.88, 9.54, 4.73, 6.36, 7.37, 11.38, 12.4, 11.22, 5.19, 12.5, 18.46, 9.16, 10.15, 9.52, 6.56, 5.9, 3.59, 3.53, 3.54, 6.57, 9.25, 3.11, 5.12, 7.79, 6.9, 9.59, 7.26, 5.91, 11.25, 8.1, 10.45, 14.79, 7.44, 3.16, 13.65, 13.0, 6.59, 7.73, 6.58, 3.53, 2.98, 6.05, 4.16, 7.19, 4.85, 3.76, 4.59, 3.01, 3.16, 7.85, 8.23, 12.93, 7.14, 7.6, 9.51, 3.33, 3.56, 4.7, 8.58, 10.4, 6.27, 7.39, 15.84, 4.97, 4.74, 6.07, 9.5, 8.67, 4.86, 6.93, 8.93, 6.47, 7.53, 4.54, 9.97, 12.64, 5.98, 11.72, 7.9, 9.28, 11.5, 18.33, 15.94, 10.36, 12.73, 7.2, 6.87, 7.7, 11.74, 6.12, 5.08, 6.15, 12.79, 9.97, 7.34, 9.09, 12.43, 7.83, 5.68, 6.75, 8.01, 9.8, 10.56, 8.51, 9.74, 9.29, 5.49, 8.65, 7.18, 4.61, 10.53, 12.67, 6.36, 5.99, 5.89, 5.98, 5.49, 7.79, 4.5, 8.05, 5.57, 17.6, 13.27, 11.48, 12.67, 7.79, 14.19, 10.19, 14.64, 5.29, 7.12, 14.0, 13.33, 3.26, 3.73, 2.96, 9.53, 8.88, 34.77, 37.97, 13.44, 23.24, 21.24, 23.69, 21.78, 17.21, 21.08, 23.6, 24.56, 30.63, 30.81, 28.28, 31.99, 30.62, 20.85, 17.11, 18.76, 25.68, 15.17, 16.35, 17.12, 19.37, 19.92, 30.59, 29.97, 26.77, 20.32, 20.31, 19.77, 27.38, 22.98, 23.34, 12.13, 26.4, 19.78, 10.11, 21.22, 34.37, 20.08, 36.98, 29.05, 25.79, 26.64, 20.62, 22.74, 15.02, 15.7, 14.1, 23.29, 17.16, 24.39, 15.69, 14.52, 21.52, 24.08, 17.64, 19.69, 12.03, 16.22, 15.17, 23.27, 18.05, 26.45, 34.02, 22.88, 22.11, 19.52, 16.59, 18.85, 23.79, 23.98, 17.79, 16.44, 18.13, 19.31, 17.44, 17.73, 17.27, 16.74, 18.71, 18.13, 19.01, 16.94, 16.23, 14.7, 16.42, 14.65, 13.99, 10.29, 13.22, 14.13, 17.15, 21.32, 18.13, 14.76, 16.29, 12.87, 14.36, 11.66, 18.14, 24.1, 18.68, 24.91, 18.03, 13.11, 10.74, 7.74, 7.01, 10.42, 13.34, 10.58, 14.98, 11.45, 18.06, 23.97, 29.68, 18.07, 13.35, 12.01, 13.59, 17.6, 21.14, 14.1, 12.92, 15.1, 14.33, 9.67, 9.08, 5.64, 6.48, 7.88], \"xaxis\": \"x\", \"y\": [24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15.0, 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6, 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21.0, 12.7, 14.5, 13.2, 13.1, 13.5, 18.9, 20.0, 21.0, 24.7, 30.8, 34.9, 26.6, 25.3, 24.7, 21.2, 19.3, 20.0, 16.6, 14.4, 19.4, 19.7, 20.5, 25.0, 23.4, 18.9, 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16.0, 22.2, 25.0, 33.0, 23.5, 19.4, 22.0, 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20.0, 20.8, 21.2, 20.3, 28.0, 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2, 23.6, 28.7, 22.6, 22.0, 22.9, 25.0, 20.6, 28.4, 21.4, 38.7, 43.8, 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4, 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22.0, 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18.0, 14.3, 19.2, 19.6, 23.0, 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14.0, 14.4, 13.4, 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4, 17.0, 15.6, 13.1, 41.3, 24.3, 23.3, 27.0, 50.0, 50.0, 50.0, 22.7, 25.0, 50.0, 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4, 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50.0, 32.0, 29.8, 34.9, 37.0, 30.5, 36.4, 31.1, 29.1, 50.0, 33.3, 30.3, 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50.0, 22.6, 24.4, 22.5, 24.4, 20.0, 21.7, 19.3, 22.4, 28.1, 23.7, 25.0, 23.3, 28.7, 21.5, 23.0, 26.7, 21.7, 27.5, 30.1, 44.8, 50.0, 37.6, 31.6, 46.7, 31.5, 24.3, 31.7, 41.7, 48.3, 29.0, 24.0, 25.1, 31.5, 23.7, 23.3, 22.0, 20.1, 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6, 42.8, 21.9, 20.9, 44.0, 50.0, 36.0, 30.1, 33.8, 43.1, 48.8, 31.0, 36.5, 22.8, 30.7, 50.0, 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4, 32.0, 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46.0, 50.0, 32.2, 22.0, 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1, 20.3, 22.5, 29.0, 24.8, 22.0, 26.4, 33.1, 36.1, 28.4, 33.4, 28.2, 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1, 21.0, 23.8, 23.1, 20.4, 18.5, 25.0, 24.6, 23.0, 22.2, 19.3, 22.6, 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19.0, 18.7, 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1, 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25.0, 19.9, 20.8, 16.8, 21.9, 27.5, 21.9, 23.1, 50.0, 50.0, 50.0, 50.0, 50.0, 13.8, 13.8, 15.0, 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8, 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1, 12.5, 8.5, 5.0, 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5.0, 11.9, 27.9, 17.2, 27.5, 15.0, 17.2, 17.9, 16.3, 7.0, 7.2, 7.5, 10.4, 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11.0, 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8, 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13.0, 13.4, 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20.0, 16.4, 17.7, 19.5, 20.2, 21.4, 19.9, 19.0, 19.1, 19.1, 20.1, 19.9, 19.6, 23.2, 29.8, 13.8, 13.3, 16.7, 12.0, 14.6, 21.4, 23.0, 23.7, 25.0, 21.8, 20.6, 21.2, 19.1, 20.6, 15.2, 7.0, 8.1, 13.6, 20.1, 21.8, 24.5, 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22.0, 11.9], \"yaxis\": \"y\"}, {\"hovertemplate\": \"<b>OLS trendline</b><br>target = -0.950049 * value + 34.5538<br>R<sup>2</sup>=0.544146<br><br>variable=LSTAT<br>value=%{x}<br>target=%{y} <b>(trend)</b><extra></extra>\", \"legendgroup\": \"LSTAT\", \"marker\": {\"color\": \"#00cc96\", \"symbol\": \"circle\"}, \"mode\": \"lines\", \"name\": \"LSTAT\", \"showlegend\": false, \"type\": \"scattergl\", \"x\": [1.73, 1.92, 1.98, 2.47, 2.87, 2.88, 2.94, 2.96, 2.97, 2.98, 3.01, 3.11, 3.11, 3.13, 3.16, 3.16, 3.26, 3.32, 3.33, 3.53, 3.53, 3.54, 3.56, 3.57, 3.59, 3.7, 3.73, 3.76, 3.76, 3.81, 3.92, 3.95, 3.95, 4.03, 4.08, 4.14, 4.16, 4.21, 4.32, 4.38, 4.45, 4.45, 4.5, 4.54, 4.56, 4.56, 4.59, 4.59, 4.61, 4.63, 4.67, 4.69, 4.7, 4.73, 4.74, 4.81, 4.82, 4.84, 4.85, 4.86, 4.97, 4.98, 5.03, 5.04, 5.08, 5.1, 5.12, 5.19, 5.21, 5.25, 5.28, 5.29, 5.29, 5.33, 5.33, 5.39, 5.49, 5.49, 5.5, 5.5, 5.52, 5.57, 5.64, 5.68, 5.68, 5.7, 5.77, 5.81, 5.89, 5.9, 5.91, 5.98, 5.98, 5.99, 6.05, 6.07, 6.12, 6.15, 6.19, 6.21, 6.27, 6.29, 6.36, 6.36, 6.36, 6.43, 6.47, 6.48, 6.53, 6.56, 6.57, 6.58, 6.58, 6.59, 6.62, 6.65, 6.68, 6.72, 6.72, 6.73, 6.75, 6.78, 6.86, 6.87, 6.9, 6.92, 6.93, 7.01, 7.12, 7.14, 7.18, 7.19, 7.2, 7.22, 7.26, 7.34, 7.37, 7.39, 7.39, 7.43, 7.44, 7.44, 7.51, 7.53, 7.54, 7.56, 7.6, 7.6, 7.67, 7.7, 7.73, 7.74, 7.79, 7.79, 7.79, 7.83, 7.85, 7.88, 7.9, 8.01, 8.05, 8.05, 8.05, 8.1, 8.1, 8.16, 8.2, 8.23, 8.26, 8.43, 8.44, 8.47, 8.51, 8.58, 8.61, 8.65, 8.67, 8.77, 8.79, 8.81, 8.88, 8.93, 8.94, 9.04, 9.08, 9.09, 9.1, 9.14, 9.16, 9.22, 9.25, 9.28, 9.29, 9.38, 9.42, 9.43, 9.45, 9.47, 9.5, 9.5, 9.51, 9.52, 9.53, 9.54, 9.55, 9.59, 9.62, 9.64, 9.67, 9.68, 9.69, 9.71, 9.74, 9.8, 9.81, 9.88, 9.93, 9.97, 9.97, 10.11, 10.11, 10.13, 10.15, 10.16, 10.19, 10.21, 10.24, 10.26, 10.27, 10.29, 10.3, 10.36, 10.4, 10.42, 10.45, 10.45, 10.5, 10.53, 10.56, 10.58, 10.59, 10.63, 10.74, 10.87, 10.88, 10.97, 11.1, 11.12, 11.22, 11.25, 11.28, 11.32, 11.34, 11.38, 11.41, 11.45, 11.48, 11.5, 11.64, 11.65, 11.66, 11.69, 11.72, 11.74, 11.97, 11.98, 12.01, 12.03, 12.03, 12.04, 12.12, 12.13, 12.14, 12.26, 12.27, 12.33, 12.34, 12.4, 12.43, 12.43, 12.5, 12.6, 12.64, 12.67, 12.67, 12.73, 12.79, 12.8, 12.86, 12.87, 12.92, 12.93, 13.0, 13.0, 13.04, 13.09, 13.11, 13.15, 13.15, 13.22, 13.27, 13.27, 13.28, 13.33, 13.34, 13.35, 13.44, 13.44, 13.45, 13.51, 13.59, 13.61, 13.65, 13.83, 13.98, 13.99, 14.0, 14.09, 14.1, 14.1, 14.1, 14.13, 14.15, 14.19, 14.27, 14.33, 14.36, 14.37, 14.43, 14.44, 14.52, 14.59, 14.64, 14.65, 14.66, 14.67, 14.69, 14.7, 14.76, 14.79, 14.8, 14.81, 14.81, 14.98, 15.02, 15.02, 15.03, 15.1, 15.12, 15.17, 15.17, 15.37, 15.39, 15.55, 15.69, 15.7, 15.71, 15.76, 15.79, 15.84, 15.94, 16.03, 16.14, 16.2, 16.21, 16.22, 16.23, 16.29, 16.3, 16.35, 16.42, 16.44, 16.47, 16.51, 16.59, 16.65, 16.74, 16.9, 16.94, 16.96, 17.09, 17.1, 17.11, 17.12, 17.15, 17.16, 17.19, 17.21, 17.27, 17.27, 17.28, 17.31, 17.44, 17.58, 17.6, 17.6, 17.64, 17.73, 17.79, 17.92, 17.93, 18.03, 18.05, 18.06, 18.06, 18.07, 18.13, 18.13, 18.13, 18.14, 18.33, 18.34, 18.35, 18.46, 18.46, 18.66, 18.68, 18.71, 18.72, 18.76, 18.8, 18.85, 19.01, 19.15, 19.31, 19.37, 19.52, 19.69, 19.77, 19.78, 19.88, 19.92, 20.08, 20.31, 20.32, 20.34, 20.45, 20.62, 20.85, 21.02, 21.08, 21.14, 21.22, 21.24, 21.32, 21.32, 21.45, 21.46, 21.52, 21.78, 22.11, 22.6, 22.74, 22.88, 22.98, 23.09, 23.24, 23.27, 23.29, 23.34, 23.6, 23.69, 23.79, 23.97, 23.98, 23.98, 24.08, 24.1, 24.16, 24.39, 24.56, 24.91, 25.41, 25.68, 25.79, 26.4, 26.42, 26.45, 26.64, 26.77, 26.82, 27.26, 27.38, 27.71, 27.8, 28.28, 28.32, 29.05, 29.29, 29.53, 29.55, 29.68, 29.93, 29.97, 30.59, 30.62, 30.63, 30.81, 30.81, 31.99, 34.02, 34.37, 34.41, 34.77, 36.98, 37.97], \"xaxis\": \"x\", \"y\": [32.91025549738176, 32.729746120167746, 32.67274315894227, 32.20721897560085, 31.82719923409765, 31.817698740560072, 31.760695779334593, 31.741694792259434, 31.732194298721854, 31.722693805184274, 31.694192324571535, 31.599187389195734, 31.599187389195734, 31.580186402120574, 31.551684921507835, 31.551684921507835, 31.456679986132038, 31.399677024906556, 31.390176531368976, 31.200166660617377, 31.200166660617377, 31.190666167079797, 31.171665180004638, 31.162164686467058, 31.1431636993919, 31.03865827047852, 31.010156789865782, 30.98165530925304, 30.98165530925304, 30.93415284156514, 30.829647412651763, 30.80114593203902, 30.80114593203902, 30.725141983738382, 30.677639516050483, 30.620636554825005, 30.601635567749845, 30.554133100061943, 30.449627671148566, 30.392624709923084, 30.326121255160025, 30.326121255160025, 30.278618787472126, 30.240616813321807, 30.221615826246648, 30.221615826246648, 30.19311434563391, 30.19311434563391, 30.17411335855875, 30.15511237148359, 30.117110397333267, 30.098109410258107, 30.088608916720528, 30.06010743610779, 30.05060694257021, 29.98410348780715, 29.974602994269567, 29.955602007194408, 29.946101513656828, 29.936601020119248, 29.83209559120587, 29.82259509766829, 29.775092629980392, 29.765592136442812, 29.727590162292493, 29.708589175217334, 29.68958818814217, 29.623084733379113, 29.604083746303953, 29.566081772153634, 29.53758029154089, 29.52807979800331, 29.52807979800331, 29.490077823852992, 29.490077823852992, 29.433074862627514, 29.338069927251716, 29.338069927251716, 29.328569433714136, 29.328569433714136, 29.309568446638977, 29.262065978951075, 29.195562524188016, 29.157560550037697, 29.157560550037697, 29.138559562962538, 29.072056108199476, 29.034054134049157, 28.95805018574852, 28.94854969221094, 28.93904919867336, 28.872545743910297, 28.872545743910297, 28.863045250372718, 28.80604228914724, 28.78704130207208, 28.73953883438418, 28.711037353771438, 28.67303537962112, 28.65403439254596, 28.59703143132048, 28.57803044424532, 28.511526989482263, 28.511526989482263, 28.511526989482263, 28.445023534719205, 28.407021560568882, 28.397521067031303, 28.350018599343404, 28.321517118730664, 28.312016625193085, 28.302516131655505, 28.302516131655505, 28.293015638117925, 28.264514157505182, 28.236012676892443, 28.207511196279704, 28.169509222129385, 28.169509222129385, 28.160008728591805, 28.141007741516646, 28.112506260903906, 28.036502312603268, 28.02700181906569, 27.998500338452946, 27.979499351377786, 27.969998857840206, 27.89399490953957, 27.789489480626187, 27.770488493551028, 27.73248651940071, 27.72298602586313, 27.71348553232555, 27.69448454525039, 27.65648257110007, 27.580478622799433, 27.55197714218669, 27.53297615511153, 27.53297615511153, 27.49497418096121, 27.48547368742363, 27.48547368742363, 27.418970232660573, 27.39996924558541, 27.39046875204783, 27.37146776497267, 27.333465790822352, 27.333465790822352, 27.266962336059294, 27.238460855446554, 27.209959374833815, 27.200458881296235, 27.152956413608337, 27.152956413608337, 27.152956413608337, 27.114954439458014, 27.095953452382854, 27.067451971770115, 27.048450984694956, 26.943945555781575, 26.905943581631256, 26.905943581631256, 26.905943581631256, 26.858441113943357, 26.858441113943357, 26.80143815271788, 26.76343617856756, 26.734934697954817, 26.70643321734208, 26.544924827203218, 26.535424333665638, 26.5069228530529, 26.46892087890258, 26.40241742413952, 26.373915943526782, 26.335913969376463, 26.316912982301304, 26.221908046925503, 26.202907059850343, 26.183906072775184, 26.11740261801212, 26.069900150324223, 26.060399656786643, 25.965394721410846, 25.927392747260527, 25.917892253722947, 25.908391760185367, 25.870389786035048, 25.85138879895989, 25.794385837734406, 25.765884357121667, 25.737382876508928, 25.727882382971348, 25.642377941133127, 25.604375966982808, 25.594875473445228, 25.57587448637007, 25.55687349929491, 25.52837201868217, 25.52837201868217, 25.51887152514459, 25.50937103160701, 25.49987053806943, 25.49037004453185, 25.48086955099427, 25.442867576843952, 25.414366096231213, 25.39536510915605, 25.36686362854331, 25.35736313500573, 25.34786264146815, 25.328861654392988, 25.30036017378025, 25.24335721255477, 25.23385671901719, 25.16735326425413, 25.119850796566233, 25.081848822415914, 25.081848822415914, 24.948841912889797, 24.948841912889797, 24.929840925814634, 24.910839938739475, 24.901339445201895, 24.872837964589156, 24.853836977513993, 24.825335496901253, 24.806334509826094, 24.796834016288514, 24.777833029213355, 24.768332535675775, 24.711329574450296, 24.673327600299977, 24.654326613224818, 24.62582513261208, 24.62582513261208, 24.57832266492418, 24.54982118431144, 24.521319703698698, 24.502318716623538, 24.49281822308596, 24.454816248935636, 24.35031082002226, 24.22680440403372, 24.217303910496142, 24.13179946865792, 24.008293052669384, 23.989292065594224, 23.894287130218423, 23.865785649605684, 23.837284168992944, 23.799282194842625, 23.780281207767466, 23.742279233617147, 23.713777753004408, 23.67577577885409, 23.647274298241346, 23.628273311166186, 23.495266401640066, 23.485765908102486, 23.476265414564907, 23.447763933952167, 23.419262453339428, 23.40026146626427, 23.181750114899927, 23.172249621362347, 23.14374814074961, 23.124747153674452, 23.124747153674452, 23.115246660136872, 23.03924271183623, 23.02974221829865, 23.02024172476107, 22.906235802310114, 22.896735308772534, 22.839732347547056, 22.830231854009476, 22.773228892783994, 22.744727412171255, 22.744727412171255, 22.678223957408193, 22.583219022032395, 22.545217047882076, 22.516715567269337, 22.516715567269337, 22.45971260604386, 22.40270964481838, 22.393209151280796, 22.336206190055318, 22.326705696517738, 22.279203228829836, 22.269702735292256, 22.203199280529198, 22.203199280529198, 22.16519730637888, 22.11769483869098, 22.09869385161582, 22.0606918774655, 22.0606918774655, 21.99418842270244, 21.94668595501454, 21.94668595501454, 21.93718546147696, 21.889682993789062, 21.880182500251482, 21.870682006713903, 21.78517756487568, 21.78517756487568, 21.7756770713381, 21.718674110112623, 21.642670161811985, 21.623669174736825, 21.585667200586506, 21.414658316910064, 21.272150913846367, 21.262650420308788, 21.253149926771208, 21.16764548493299, 21.15814499139541, 21.15814499139541, 21.15814499139541, 21.129643510782667, 21.110642523707508, 21.07264054955719, 20.99663660125655, 20.93963364003107, 20.91113215941833, 20.90163166588075, 20.84462870465527, 20.83512821111769, 20.759124262817053, 20.692620808053995, 20.645118340366093, 20.635617846828513, 20.626117353290933, 20.616616859753353, 20.597615872678194, 20.588115379140614, 20.531112417915132, 20.502610937302393, 20.493110443764813, 20.483609950227233, 20.483609950227233, 20.322101560088377, 20.28409958593806, 20.28409958593806, 20.27459909240048, 20.208095637637417, 20.189094650562257, 20.141592182874355, 20.141592182874355, 19.95158231212276, 19.9325813250476, 19.78057342844632, 19.6475665189202, 19.63806602538262, 19.62856553184504, 19.581063064157142, 19.552561583544403, 19.505059115856504, 19.410054180480707, 19.32454973864248, 19.220044309729104, 19.163041348503626, 19.153540854966046, 19.144040361428466, 19.134539867890886, 19.077536906665408, 19.068036413127828, 19.020533945439926, 18.954030490676864, 18.935029503601704, 18.90652802298897, 18.868526048838646, 18.792522100538008, 18.73551913931253, 18.65001469747431, 18.498006800873032, 18.46000482672271, 18.441003839647554, 18.317497423659013, 18.307996930121433, 18.298496436583854, 18.288995943046274, 18.260494462433535, 18.250993968895955, 18.222492488283212, 18.203491501208052, 18.146488539982574, 18.146488539982574, 18.136988046444994, 18.10848656583226, 17.984980149843715, 17.851973240317598, 17.83297225324244, 17.83297225324244, 17.79497027909212, 17.709465837253898, 17.65246287602842, 17.52895646003988, 17.519455966502303, 17.4244510311265, 17.405450044051342, 17.395949550513762, 17.395949550513762, 17.386449056976183, 17.329446095750704, 17.329446095750704, 17.329446095750704, 17.31994560221312, 17.139436224999105, 17.129935731461526, 17.120435237923942, 17.015929809010565, 17.015929809010565, 16.825919938258966, 16.806918951183807, 16.778417470571068, 16.768916977033488, 16.730915002883165, 16.692913028732846, 16.645410561044947, 16.493402664443668, 16.36039575491755, 16.208387858316275, 16.151384897090793, 16.008877494027093, 15.847369103888234, 15.771365155587596, 15.761864662050016, 15.666859726674218, 15.628857752523896, 15.47684985592262, 15.258338504558282, 15.248838011020702, 15.229837023945542, 15.125331595032161, 14.963823204893302, 14.745311853528964, 14.583803463390108, 14.52680050216463, 14.469797540939147, 14.39379359263851, 14.37479260556335, 14.298788657262708, 14.298788657262708, 14.175282241274171, 14.165781747736592, 14.108778786511113, 13.861765954534032, 13.548249667793897, 13.082725484452478, 12.949718574926365, 12.816711665400245, 12.721706730024444, 12.617201301111066, 12.474693898047367, 12.446192417434627, 12.427191430359468, 12.379688962671565, 12.132676130694488, 12.04717168885627, 11.952166753480473, 11.781157869804034, 11.77165737626645, 11.77165737626645, 11.676652440890656, 11.657651453815493, 11.600648492590015, 11.382137141225677, 11.220628751086817, 10.888111477271519, 10.413086800392524, 10.156573474877867, 10.05206804596449, 9.472537940172113, 9.45353695309695, 9.425035472484215, 9.244526095270196, 9.121019679281655, 9.073517211593757, 8.65549549594024, 8.541489573489283, 8.227973286749144, 8.142468844910923, 7.686445155107087, 7.648443180956768, 6.954907152713435, 6.7268953078115175, 6.4988834629096, 6.47988247583444, 6.3563760598459, 6.118863721406402, 6.080861747256083, 5.491831147926128, 5.463329667313388, 5.453829173775809, 5.282820290099373, 5.282820290099373, 4.16176205266494, 2.233161864536214, 1.9006445907209226, 1.8626426165706036, 1.5206248492177181, -0.5789842225874366, -1.5195330828078468], \"yaxis\": \"y\"}],                        {\"legend\": {\"title\": {\"text\": \"variable\"}, \"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"value\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"target\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('147aec3a-37aa-4d2f-a3ea-2b8113e00a45');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 141,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.scatter(df, x=[\"CRIM\",\"RM\",\"LSTAT\"], y=\"target\", trendline=\"ols\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop(columns=[\"target\"]), df.target, test_size=0.33, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mRidge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfit_intercept\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcopy_X\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Linear least squares with l2 regularization.\n",
       "\n",
       "Minimizes the objective function::\n",
       "\n",
       "||y - Xw||^2_2 + alpha * ||w||^2_2\n",
       "\n",
       "This model solves a regression model where the loss function is\n",
       "the linear least squares function and regularization is given by\n",
       "the l2-norm. Also known as Ridge Regression or Tikhonov regularization.\n",
       "This estimator has built-in support for multi-variate regression\n",
       "(i.e., when y is a 2d-array of shape (n_samples, n_targets)).\n",
       "\n",
       "Read more in the :ref:`User Guide <ridge_regression>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "alpha : {float, ndarray of shape (n_targets,)}, default=1.0\n",
       "    Regularization strength; must be a positive float. Regularization\n",
       "    improves the conditioning of the problem and reduces the variance of\n",
       "    the estimates. Larger values specify stronger regularization.\n",
       "    Alpha corresponds to ``1 / (2C)`` in other linear models such as\n",
       "    :class:`~sklearn.linear_model.LogisticRegression` or\n",
       "    :class:`~sklearn.svm.LinearSVC`. If an array is passed, penalties are\n",
       "    assumed to be specific to the targets. Hence they must correspond in\n",
       "    number.\n",
       "\n",
       "fit_intercept : bool, default=True\n",
       "    Whether to fit the intercept for this model. If set\n",
       "    to false, no intercept will be used in calculations\n",
       "    (i.e. ``X`` and ``y`` are expected to be centered).\n",
       "\n",
       "normalize : bool, default=False\n",
       "    This parameter is ignored when ``fit_intercept`` is set to False.\n",
       "    If True, the regressors X will be normalized before regression by\n",
       "    subtracting the mean and dividing by the l2-norm.\n",
       "    If you wish to standardize, please use\n",
       "    :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``\n",
       "    on an estimator with ``normalize=False``.\n",
       "\n",
       "copy_X : bool, default=True\n",
       "    If True, X will be copied; else, it may be overwritten.\n",
       "\n",
       "max_iter : int, default=None\n",
       "    Maximum number of iterations for conjugate gradient solver.\n",
       "    For 'sparse_cg' and 'lsqr' solvers, the default value is determined\n",
       "    by scipy.sparse.linalg. For 'sag' solver, the default value is 1000.\n",
       "\n",
       "tol : float, default=1e-3\n",
       "    Precision of the solution.\n",
       "\n",
       "solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'},         default='auto'\n",
       "    Solver to use in the computational routines:\n",
       "\n",
       "    - 'auto' chooses the solver automatically based on the type of data.\n",
       "\n",
       "    - 'svd' uses a Singular Value Decomposition of X to compute the Ridge\n",
       "      coefficients. More stable for singular matrices than 'cholesky'.\n",
       "\n",
       "    - 'cholesky' uses the standard scipy.linalg.solve function to\n",
       "      obtain a closed-form solution.\n",
       "\n",
       "    - 'sparse_cg' uses the conjugate gradient solver as found in\n",
       "      scipy.sparse.linalg.cg. As an iterative algorithm, this solver is\n",
       "      more appropriate than 'cholesky' for large-scale data\n",
       "      (possibility to set `tol` and `max_iter`).\n",
       "\n",
       "    - 'lsqr' uses the dedicated regularized least-squares routine\n",
       "      scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative\n",
       "      procedure.\n",
       "\n",
       "    - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses\n",
       "      its improved, unbiased version named SAGA. Both methods also use an\n",
       "      iterative procedure, and are often faster than other solvers when\n",
       "      both n_samples and n_features are large. Note that 'sag' and\n",
       "      'saga' fast convergence is only guaranteed on features with\n",
       "      approximately the same scale. You can preprocess the data with a\n",
       "      scaler from sklearn.preprocessing.\n",
       "\n",
       "    All last five solvers support both dense and sparse data. However, only\n",
       "    'sag' and 'sparse_cg' supports sparse input when `fit_intercept` is\n",
       "    True.\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "       Stochastic Average Gradient descent solver.\n",
       "    .. versionadded:: 0.19\n",
       "       SAGA solver.\n",
       "\n",
       "random_state : int, RandomState instance, default=None\n",
       "    Used when ``solver`` == 'sag' or 'saga' to shuffle the data.\n",
       "    See :term:`Glossary <random_state>` for details.\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "       `random_state` to support Stochastic Average Gradient.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "coef_ : ndarray of shape (n_features,) or (n_targets, n_features)\n",
       "    Weight vector(s).\n",
       "\n",
       "intercept_ : float or ndarray of shape (n_targets,)\n",
       "    Independent term in decision function. Set to 0.0 if\n",
       "    ``fit_intercept = False``.\n",
       "\n",
       "n_iter_ : None or ndarray of shape (n_targets,)\n",
       "    Actual number of iterations for each target. Available only for\n",
       "    sag and lsqr solvers. Other solvers will return None.\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "\n",
       "See Also\n",
       "--------\n",
       "RidgeClassifier : Ridge classifier.\n",
       "RidgeCV : Ridge regression with built-in cross validation.\n",
       ":class:`~sklearn.kernel_ridge.KernelRidge` : Kernel ridge regression\n",
       "    combines ridge regression with the kernel trick.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from sklearn.linear_model import Ridge\n",
       ">>> import numpy as np\n",
       ">>> n_samples, n_features = 10, 5\n",
       ">>> rng = np.random.RandomState(0)\n",
       ">>> y = rng.randn(n_samples)\n",
       ">>> X = rng.randn(n_samples, n_features)\n",
       ">>> clf = Ridge(alpha=1.0)\n",
       ">>> clf.fit(X, y)\n",
       "Ridge()\n",
       "\u001b[0;31mFile:\u001b[0m           /usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_ridge.py\n",
       "\u001b[0;31mType:\u001b[0m           ABCMeta\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "execution_count": 153,
     "metadata": {
     },
     "output_type": "execute_result",
     "start": 0
    }
   ],
   "source": [
    "?Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgroups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2*n_jobs'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0merror_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Evaluate a score by cross-validation\n",
       "\n",
       "Read more in the :ref:`User Guide <cross_validation>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "estimator : estimator object implementing 'fit'\n",
       "    The object to use to fit the data.\n",
       "\n",
       "X : array-like of shape (n_samples, n_features)\n",
       "    The data to fit. Can be for example a list, or an array.\n",
       "\n",
       "y : array-like of shape (n_samples,) or (n_samples, n_outputs),             default=None\n",
       "    The target variable to try to predict in the case of\n",
       "    supervised learning.\n",
       "\n",
       "groups : array-like of shape (n_samples,), default=None\n",
       "    Group labels for the samples used while splitting the dataset into\n",
       "    train/test set. Only used in conjunction with a \"Group\" :term:`cv`\n",
       "    instance (e.g., :class:`GroupKFold`).\n",
       "\n",
       "scoring : str or callable, default=None\n",
       "    A str (see model evaluation documentation) or\n",
       "    a scorer callable object / function with signature\n",
       "    ``scorer(estimator, X, y)`` which should return only\n",
       "    a single value.\n",
       "\n",
       "    Similar to :func:`cross_validate`\n",
       "    but only a single metric is permitted.\n",
       "\n",
       "    If None, the estimator's default scorer (if available) is used.\n",
       "\n",
       "cv : int, cross-validation generator or an iterable, default=None\n",
       "    Determines the cross-validation splitting strategy.\n",
       "    Possible inputs for cv are:\n",
       "\n",
       "    - None, to use the default 5-fold cross validation,\n",
       "    - int, to specify the number of folds in a `(Stratified)KFold`,\n",
       "    - :term:`CV splitter`,\n",
       "    - An iterable yielding (train, test) splits as arrays of indices.\n",
       "\n",
       "    For int/None inputs, if the estimator is a classifier and ``y`` is\n",
       "    either binary or multiclass, :class:`StratifiedKFold` is used. In all\n",
       "    other cases, :class:`KFold` is used. These splitters are instantiated\n",
       "    with `shuffle=False` so the splits will be the same across calls.\n",
       "\n",
       "    Refer :ref:`User Guide <cross_validation>` for the various\n",
       "    cross-validation strategies that can be used here.\n",
       "\n",
       "    .. versionchanged:: 0.22\n",
       "        ``cv`` default value if None changed from 3-fold to 5-fold.\n",
       "\n",
       "n_jobs : int, default=None\n",
       "    Number of jobs to run in parallel. Training the estimator and computing\n",
       "    the score are parallelized over the cross-validation splits.\n",
       "    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
       "    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
       "    for more details.\n",
       "\n",
       "verbose : int, default=0\n",
       "    The verbosity level.\n",
       "\n",
       "fit_params : dict, default=None\n",
       "    Parameters to pass to the fit method of the estimator.\n",
       "\n",
       "pre_dispatch : int or str, default='2*n_jobs'\n",
       "    Controls the number of jobs that get dispatched during parallel\n",
       "    execution. Reducing this number can be useful to avoid an\n",
       "    explosion of memory consumption when more jobs get dispatched\n",
       "    than CPUs can process. This parameter can be:\n",
       "\n",
       "        - None, in which case all the jobs are immediately\n",
       "          created and spawned. Use this for lightweight and\n",
       "          fast-running jobs, to avoid delays due to on-demand\n",
       "          spawning of the jobs\n",
       "\n",
       "        - An int, giving the exact number of total jobs that are\n",
       "          spawned\n",
       "\n",
       "        - A str, giving an expression as a function of n_jobs,\n",
       "          as in '2*n_jobs'\n",
       "\n",
       "error_score : 'raise' or numeric, default=np.nan\n",
       "    Value to assign to the score if an error occurs in estimator fitting.\n",
       "    If set to 'raise', the error is raised.\n",
       "    If a numeric value is given, FitFailedWarning is raised.\n",
       "\n",
       "    .. versionadded:: 0.20\n",
       "\n",
       "Returns\n",
       "-------\n",
       "scores : ndarray of float of shape=(len(list(cv)),)\n",
       "    Array of scores of the estimator for each run of the cross validation.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from sklearn import datasets, linear_model\n",
       ">>> from sklearn.model_selection import cross_val_score\n",
       ">>> diabetes = datasets.load_diabetes()\n",
       ">>> X = diabetes.data[:150]\n",
       ">>> y = diabetes.target[:150]\n",
       ">>> lasso = linear_model.Lasso()\n",
       ">>> print(cross_val_score(lasso, X, y, cv=3))\n",
       "[0.33150734 0.08022311 0.03531764]\n",
       "\n",
       "See Also\n",
       "---------\n",
       "cross_validate : To run cross-validation on multiple metrics and also to\n",
       "    return train scores, fit times and score times.\n",
       "\n",
       "cross_val_predict : Get predictions from each split of cross-validation for\n",
       "    diagnostic purposes.\n",
       "\n",
       "sklearn.metrics.make_scorer : Make a scorer from a performance metric or\n",
       "    loss function.\n",
       "\u001b[0;31mFile:\u001b[0m      /usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "execution_count": 152,
     "metadata": {
     },
     "output_type": "execute_result",
     "start": 0
    }
   ],
   "source": [
    "?cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.69545716, 0.52495178, 0.8066144 , 0.74325956, 0.77292517,\n",
       "       0.74540836, 0.56375301])"
      ]
     },
     "execution_count": 197,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(Ridge(alpha=1, normalize=False), X_train, y_train, cv=7, n_jobs=-1)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 198,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 198,
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 706
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.bar(range(len(scores)), height=scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6931956359040433"
      ]
     },
     "execution_count": 199,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1)"
      ]
     },
     "execution_count": 200,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Ridge(alpha=1, normalize=False)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.12263618,  0.03884861,  0.02335121,  3.02513389, -8.05136786,\n",
       "        3.90751932, -0.01962242, -1.30195575,  0.21697585, -0.00897333,\n",
       "       -0.8383582 ,  0.01200224, -0.56698624])"
      ]
     },
     "execution_count": 202,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.15476175429621"
      ]
     },
     "execution_count": 203,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 13 artists>"
      ]
     },
     "execution_count": 208,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 208,
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 705
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.bar(x=df.drop(columns=[\"target\"]).columns, height=model.coef_, color=np.where(model.coef_ > 0, \"green\", \"red\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88035377, 0.73839102, 0.86079358, 0.87758513, 0.88890465,\n",
       "       0.92623314, 0.66414304])"
      ]
     },
     "execution_count": 214,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(RandomForestRegressor(), X_train, y_train, cv=7, n_jobs=-1)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 215,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 215,
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 706
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.bar(range(len(scores)), height=scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8337720464307419"
      ]
     },
     "execution_count": 216,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 217,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__abstractmethods__',\n",
       " '__annotations__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_abc_impl',\n",
       " '_check_n_features',\n",
       " '_compute_partial_dependence_recursion',\n",
       " '_estimator_type',\n",
       " '_get_param_names',\n",
       " '_get_tags',\n",
       " '_make_estimator',\n",
       " '_more_tags',\n",
       " '_repr_html_',\n",
       " '_repr_html_inner',\n",
       " '_repr_mimebundle_',\n",
       " '_required_parameters',\n",
       " '_set_oob_score',\n",
       " '_validate_X_predict',\n",
       " '_validate_data',\n",
       " '_validate_estimator',\n",
       " '_validate_y_class_weight',\n",
       " 'apply',\n",
       " 'base_estimator',\n",
       " 'base_estimator_',\n",
       " 'bootstrap',\n",
       " 'ccp_alpha',\n",
       " 'class_weight',\n",
       " 'criterion',\n",
       " 'decision_path',\n",
       " 'estimator_params',\n",
       " 'estimators_',\n",
       " 'feature_importances_',\n",
       " 'fit',\n",
       " 'get_params',\n",
       " 'max_depth',\n",
       " 'max_features',\n",
       " 'max_leaf_nodes',\n",
       " 'max_samples',\n",
       " 'min_impurity_decrease',\n",
       " 'min_impurity_split',\n",
       " 'min_samples_leaf',\n",
       " 'min_samples_split',\n",
       " 'min_weight_fraction_leaf',\n",
       " 'n_estimators',\n",
       " 'n_features_',\n",
       " 'n_features_in_',\n",
       " 'n_jobs',\n",
       " 'n_outputs_',\n",
       " 'oob_score',\n",
       " 'predict',\n",
       " 'random_state',\n",
       " 'score',\n",
       " 'set_params',\n",
       " 'verbose',\n",
       " 'warm_start']"
      ]
     },
     "execution_count": 218,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03436444, 0.00208295, 0.00669991, 0.00128846, 0.0112709 ,\n",
       "       0.36448263, 0.01345497, 0.06337927, 0.00448257, 0.01256889,\n",
       "       0.0165997 , 0.01339141, 0.45593389])"
      ]
     },
     "execution_count": 219,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 13 artists>"
      ]
     },
     "execution_count": 221,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 221,
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 706
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.bar(x=df.drop(columns=[\"target\"]).columns, height=model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>                <div id=\"e676b5b9-3142-4fef-8666-d20b1f4de154\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e676b5b9-3142-4fef-8666-d20b1f4de154\")) {                    Plotly.newPlot(                        \"e676b5b9-3142-4fef-8666-d20b1f4de154\",                        [{\"coloraxis\": \"coloraxis\", \"hovertemplate\": \"x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>\", \"name\": \"0\", \"type\": \"heatmap\", \"x\": [\"CRIM\", \"ZN\", \"INDUS\", \"CHAS\", \"NOX\", \"RM\", \"AGE\", \"DIS\", \"RAD\", \"TAX\", \"PTRATIO\", \"B\", \"LSTAT\", \"target\"], \"xaxis\": \"x\", \"y\": [\"CRIM\", \"ZN\", \"INDUS\", \"CHAS\", \"NOX\", \"RM\", \"AGE\", \"DIS\", \"RAD\", \"TAX\", \"PTRATIO\", \"B\", \"LSTAT\", \"target\"], \"yaxis\": \"y\", \"z\": [[1.0, -0.20046921966254744, 0.4065834114062594, -0.05589158222224156, 0.4209717113924554, -0.21924670286251308, 0.3527342509013634, -0.37967008695102467, 0.6255051452626024, 0.5827643120325854, 0.2899455792795226, -0.3850639419942239, 0.4556214794479463, -0.38830460858681154], [-0.20046921966254744, 1.0, -0.5338281863044696, -0.04269671929612169, -0.5166037078279843, 0.31199058737409047, -0.5695373420992109, 0.6644082227621105, -0.3119478260185367, -0.3145633246775997, -0.3916785479362161, 0.1755203173828273, -0.41299457452700283, 0.3604453424505433], [0.4065834114062594, -0.5338281863044696, 1.0, 0.06293802748966515, 0.7636514469209139, -0.39167585265684274, 0.6447785113552554, -0.7080269887427675, 0.5951292746038485, 0.7207601799515422, 0.38324755642888936, -0.3569765351041928, 0.603799716476621, -0.48372516002837274], [-0.05589158222224156, -0.04269671929612169, 0.06293802748966515, 1.0, 0.09120280684249558, 0.09125122504345677, 0.08651777425454328, -0.09917578017472799, -0.00736824088607757, -0.03558651758591146, -0.12151517365806228, 0.048788484955166495, -0.05392929837569424, 0.17526017719029868], [0.4209717113924554, -0.5166037078279843, 0.7636514469209139, 0.09120280684249558, 1.0, -0.3021881878495924, 0.7314701037859592, -0.7692301132258282, 0.6114405634855762, 0.6680232004030217, 0.18893267711276884, -0.3800506377924, 0.5908789208808451, -0.42732077237328203], [-0.21924670286251308, 0.31199058737409047, -0.39167585265684274, 0.09125122504345677, -0.3021881878495924, 1.0, -0.24026493104775065, 0.20524621293005416, -0.20984666776610833, -0.2920478326232189, -0.35550149455908525, 0.1280686350925421, -0.6138082718663955, 0.6953599470715401], [0.3527342509013634, -0.5695373420992109, 0.6447785113552554, 0.08651777425454328, 0.7314701037859592, -0.24026493104775065, 1.0, -0.747880540868632, 0.4560224517516137, 0.5064555935507051, 0.2615150116719584, -0.273533976638513, 0.6023385287262395, -0.3769545650045961], [-0.37967008695102467, 0.6644082227621105, -0.7080269887427675, -0.09917578017472799, -0.7692301132258282, 0.20524621293005416, -0.747880540868632, 1.0, -0.4945879296720758, -0.5344315844084577, -0.23247054240825826, 0.2915116731330399, -0.4969958308636848, 0.249928734085904], [0.6255051452626024, -0.3119478260185367, 0.5951292746038485, -0.00736824088607757, 0.6114405634855762, -0.20984666776610833, 0.4560224517516137, -0.4945879296720758, 1.0, 0.9102281885331865, 0.46474117850306057, -0.44441281557512585, 0.4886763349750666, -0.38162623063977735], [0.5827643120325854, -0.3145633246775997, 0.7207601799515422, -0.03558651758591146, 0.6680232004030217, -0.2920478326232189, 0.5064555935507051, -0.5344315844084577, 0.9102281885331865, 1.0, 0.4608530350656702, -0.44180800672281423, 0.5439934120015698, -0.4685359335677667], [0.2899455792795226, -0.3916785479362161, 0.38324755642888936, -0.12151517365806228, 0.18893267711276884, -0.35550149455908525, 0.2615150116719584, -0.23247054240825826, 0.46474117850306057, 0.4608530350656702, 1.0, -0.1773833023052333, 0.3740443167146772, -0.5077866855375623], [-0.3850639419942239, 0.1755203173828273, -0.3569765351041928, 0.048788484955166495, -0.3800506377924, 0.1280686350925421, -0.273533976638513, 0.2915116731330399, -0.44441281557512585, -0.44180800672281423, -0.1773833023052333, 1.0, -0.36608690169159663, 0.3334608196570662], [0.4556214794479463, -0.41299457452700283, 0.603799716476621, -0.05392929837569424, 0.5908789208808451, -0.6138082718663955, 0.6023385287262395, -0.4969958308636848, 0.4886763349750666, 0.5439934120015698, 0.3740443167146772, -0.36608690169159663, 1.0, -0.7376627261740145], [-0.38830460858681154, 0.3604453424505433, -0.48372516002837274, 0.17526017719029868, -0.42732077237328203, 0.6953599470715401, -0.3769545650045961, 0.249928734085904, -0.38162623063977735, -0.4685359335677667, -0.5077866855375623, 0.3334608196570662, -0.7376627261740145, 1.0]]}],                        {\"coloraxis\": {\"cmax\": 1, \"cmin\": -1, \"colorscale\": [[0.0, \"rgb(103,0,31)\"], [0.1, \"rgb(178,24,43)\"], [0.2, \"rgb(214,96,77)\"], [0.3, \"rgb(244,165,130)\"], [0.4, \"rgb(253,219,199)\"], [0.5, \"rgb(247,247,247)\"], [0.6, \"rgb(209,229,240)\"], [0.7, \"rgb(146,197,222)\"], [0.8, \"rgb(67,147,195)\"], [0.9, \"rgb(33,102,172)\"], [1.0, \"rgb(5,48,97)\"]]}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"constrain\": \"domain\", \"domain\": [0.0, 1.0], \"scaleanchor\": \"y\"}, \"yaxis\": {\"anchor\": \"x\", \"autorange\": \"reversed\", \"constrain\": \"domain\", \"domain\": [0.0, 1.0]}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('e676b5b9-3142-4fef-8666-d20b1f4de154');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 234,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.imshow(df.corr(), zmin=-1, zmax=1, color_continuous_scale=\"RdBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mzmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mzmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0manimation_frame\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_col_wrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_col_spacing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_row_spacing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolor_continuous_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolor_continuous_midpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrange_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtemplate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcontrast_rescaling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbinary_string\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbinary_backend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbinary_compression_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbinary_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Display an image, i.e. data on a 2D regular raster.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "\n",
       "img: array-like image, or xarray\n",
       "    The image data. Supported array shapes are\n",
       "\n",
       "    - (M, N): an image with scalar data. The data is visualized\n",
       "      using a colormap.\n",
       "    - (M, N, 3): an image with RGB values.\n",
       "    - (M, N, 4): an image with RGBA values, i.e. including transparency.\n",
       "\n",
       "zmin, zmax : scalar or iterable, optional\n",
       "    zmin and zmax define the scalar range that the colormap covers. By default,\n",
       "    zmin and zmax correspond to the min and max values of the datatype for integer\n",
       "    datatypes (ie [0-255] for uint8 images, [0, 65535] for uint16 images, etc.). For\n",
       "    a multichannel image of floats, the max of the image is computed and zmax is the\n",
       "    smallest power of 256 (1, 255, 65535) greater than this max value,\n",
       "    with a 5% tolerance. For a single-channel image, the max of the image is used.\n",
       "    Overridden by range_color.\n",
       "\n",
       "origin : str, 'upper' or 'lower' (default 'upper')\n",
       "    position of the [0, 0] pixel of the image array, in the upper left or lower left\n",
       "    corner. The convention 'upper' is typically used for matrices and images.\n",
       "\n",
       "labels : dict with str keys and str values (default `{}`)\n",
       "    Sets names used in the figure for axis titles (keys ``x`` and ``y``),\n",
       "    colorbar title and hoverlabel (key ``color``). The values should correspond\n",
       "    to the desired label to be displayed. If ``img`` is an xarray, dimension\n",
       "    names are used for axis titles, and long name for the colorbar title\n",
       "    (unless overridden in ``labels``). Possible keys are: x, y, and color.\n",
       "\n",
       "x, y: list-like, optional\n",
       "    x and y are used to label the axes of single-channel heatmap visualizations and\n",
       "    their lengths must match the lengths of the second and first dimensions of the\n",
       "    img argument. They are auto-populated if the input is an xarray.\n",
       "\n",
       "animation_frame: int or str, optional (default None)\n",
       "    axis number along which the image array is sliced to create an animation plot.\n",
       "    If `img` is an xarray, `animation_frame` can be the name of one the dimensions.\n",
       "\n",
       "facet_col: int or str, optional (default None)\n",
       "    axis number along which the image array is sliced to create a facetted plot.\n",
       "    If `img` is an xarray, `facet_col` can be the name of one the dimensions.\n",
       "\n",
       "facet_col_wrap: int\n",
       "    Maximum number of facet columns. Wraps the column variable at this width,\n",
       "    so that the column facets span multiple rows.\n",
       "    Ignored if `facet_col` is None.\n",
       "\n",
       "facet_col_spacing: float between 0 and 1\n",
       "    Spacing between facet columns, in paper units. Default is 0.02.\n",
       "\n",
       "facet_row_spacing: float between 0 and 1\n",
       "    Spacing between facet rows created when ``facet_col_wrap`` is used, in\n",
       "    paper units. Default is 0.0.7.\n",
       "\n",
       "color_continuous_scale : str or list of str\n",
       "    colormap used to map scalar data to colors (for a 2D image). This parameter is\n",
       "    not used for RGB or RGBA images. If a string is provided, it should be the name\n",
       "    of a known color scale, and if a list is provided, it should be a list of CSS-\n",
       "    compatible colors.\n",
       "\n",
       "color_continuous_midpoint : number\n",
       "    If set, computes the bounds of the continuous color scale to have the desired\n",
       "    midpoint. Overridden by range_color or zmin and zmax.\n",
       "\n",
       "range_color : list of two numbers\n",
       "    If provided, overrides auto-scaling on the continuous color scale, including\n",
       "    overriding `color_continuous_midpoint`. Also overrides zmin and zmax. Used only\n",
       "    for single-channel images.\n",
       "\n",
       "title : str\n",
       "    The figure title.\n",
       "\n",
       "template : str or dict or plotly.graph_objects.layout.Template instance\n",
       "    The figure template name or definition.\n",
       "\n",
       "width : number\n",
       "    The figure width in pixels.\n",
       "\n",
       "height: number\n",
       "    The figure height in pixels.\n",
       "\n",
       "aspect: 'equal', 'auto', or None\n",
       "  - 'equal': Ensures an aspect ratio of 1 or pixels (square pixels)\n",
       "  - 'auto': The axes is kept fixed and the aspect ratio of pixels is\n",
       "    adjusted so that the data fit in the axes. In general, this will\n",
       "    result in non-square pixels.\n",
       "  - if None, 'equal' is used for numpy arrays and 'auto' for xarrays\n",
       "    (which have typically heterogeneous coordinates)\n",
       "\n",
       "contrast_rescaling: 'minmax', 'infer', or None\n",
       "    how to determine data values corresponding to the bounds of the color\n",
       "    range, when zmin or zmax are not passed. If `minmax`, the min and max\n",
       "    values of the image are used. If `infer`, a heuristic based on the image\n",
       "    data type is used.\n",
       "\n",
       "binary_string: bool, default None\n",
       "    if True, the image data are first rescaled and encoded as uint8 and\n",
       "    then passed to plotly.js as a b64 PNG string. If False, data are passed\n",
       "    unchanged as a numerical array. Setting to True may lead to performance\n",
       "    gains, at the cost of a loss of precision depending on the original data\n",
       "    type. If None, use_binary_string is set to True for multichannel (eg) RGB\n",
       "    arrays, and to False for single-channel (2D) arrays. 2D arrays are\n",
       "    represented as grayscale and with no colorbar if use_binary_string is\n",
       "    True.\n",
       "\n",
       "binary_backend: str, 'auto' (default), 'pil' or 'pypng'\n",
       "    Third-party package for the transformation of numpy arrays to\n",
       "    png b64 strings. If 'auto', Pillow is used if installed,  otherwise\n",
       "    pypng.\n",
       "\n",
       "binary_compression_level: int, between 0 and 9 (default 4)\n",
       "    png compression level to be passed to the backend when transforming an\n",
       "    array to a png b64 string. Increasing `binary_compression` decreases the\n",
       "    size of the png string, but the compression step takes more time. For most\n",
       "    images it is not worth using levels greater than 5, but it's possible to\n",
       "    test `len(fig.data[0].source)` and to time the execution of `imshow` to\n",
       "    tune the level of compression. 0 means no compression (not recommended).\n",
       "\n",
       "binary_format: str, 'png' (default) or 'jpg'\n",
       "    compression format used to generate b64 string. 'png' is recommended\n",
       "    since it uses lossless compression, but 'jpg' (lossy) compression can\n",
       "    result if smaller binary strings for natural images.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "fig : graph_objects.Figure containing the displayed image\n",
       "\n",
       "See also\n",
       "--------\n",
       "\n",
       "plotly.graph_objects.Image : image trace\n",
       "plotly.graph_objects.Heatmap : heatmap trace\n",
       "\n",
       "Notes\n",
       "-----\n",
       "\n",
       "In order to update and customize the returned figure, use\n",
       "`go.Figure.update_traces` or `go.Figure.update_layout`.\n",
       "\n",
       "If an xarray is passed, dimensions names and coordinates are used for\n",
       "axes labels and ticks.\n",
       "\u001b[0;31mFile:\u001b[0m      /usr/local/lib/python3.8/dist-packages/plotly/express/_imshow.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "execution_count": 229,
     "metadata": {
     },
     "output_type": "execute_result",
     "start": 0
    }
   ],
   "source": [
    "?px.imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (system-wide)",
   "language": "python",
   "metadata": {
    "cocalc": {
     "description": "Python 3 programming language",
     "priority": 100,
     "url": "https://www.python.org/"
    }
   },
   "name": "python3",
   "resource_dir": "/ext/jupyter/kernels/python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}