week_1_Assignment.ipynb

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    "---\n",
    "\n",
    "_You are currently looking at **version 1.3** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 1 - Introduction to Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this assignment, you will be using the Breast Cancer Wisconsin (Diagnostic) Database to create a classifier that can help diagnose patients. First, read through the description of the dataset (below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "#print(cancer.DESCR) # Print the data set description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object returned by `load_breast_cancer()` is a scikit-learn Bunch object, which is similar to a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 0 (Example)\n",
    "\n",
    "How many features does the breast cancer dataset have?\n",
    "\n",
    "*This function should return an integer.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
     "data": {
      "text/plain": [
       "30"
      ]
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     "execution_count": 3,
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   "source": [
    "# You should write your whole answer within the function provided. The autograder will call\n",
    "# this function and compare the return value against the correct solution value\n",
    "def answer_zero():\n",
    "    # This function returns the number of features of the breast cancer dataset, which is an integer. \n",
    "    # The assignment question description will tell you the general format the autograder is expecting\n",
    "    return len(cancer['feature_names'])\n",
    "\n",
    "# You can examine what your function returns by calling it in the cell. If you have questions\n",
    "# about the assignment formats, check out the discussion forums for any FAQs\n",
    "answer_zero() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "Scikit-learn works with lists, numpy arrays, scipy-sparse matrices, and pandas DataFrames, so converting the dataset to a DataFrame is not necessary for training this model. Using a DataFrame does however help make many things easier such as munging data, so let's practice creating a classifier with a pandas DataFrame. \n",
    "\n",
    "\n",
    "\n",
    "Convert the sklearn.dataset `cancer` to a DataFrame. \n",
    "\n",
    "*This function should return a `(569, 31)` DataFrame with * \n",
    "\n",
    "*columns = *\n",
    "\n",
    "    ['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
    "    'mean smoothness', 'mean compactness', 'mean concavity',\n",
    "    'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
    "    'radius error', 'texture error', 'perimeter error', 'area error',\n",
    "    'smoothness error', 'compactness error', 'concavity error',\n",
    "    'concave points error', 'symmetry error', 'fractal dimension error',\n",
    "    'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
    "    'worst smoothness', 'worst compactness', 'worst concavity',\n",
    "    'worst concave points', 'worst symmetry', 'worst fractal dimension',\n",
    "    'target']\n",
    "\n",
    "*and index = *\n",
    "\n",
    "    RangeIndex(start=0, stop=569, step=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.990</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.300100</td>\n",
       "      <td>0.147100</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.71190</td>\n",
       "      <td>0.26540</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.570</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.086900</td>\n",
       "      <td>0.070170</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.24160</td>\n",
       "      <td>0.18600</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.690</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.197400</td>\n",
       "      <td>0.127900</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.45040</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.420</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.241400</td>\n",
       "      <td>0.105200</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.68690</td>\n",
       "      <td>0.25750</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.290</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>0.104300</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.40000</td>\n",
       "      <td>0.16250</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.450</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.157800</td>\n",
       "      <td>0.080890</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>0.07613</td>\n",
       "      <td>...</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.17910</td>\n",
       "      <td>0.52490</td>\n",
       "      <td>0.53550</td>\n",
       "      <td>0.17410</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.250</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.112700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.1794</td>\n",
       "      <td>0.05742</td>\n",
       "      <td>...</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.14420</td>\n",
       "      <td>0.25760</td>\n",
       "      <td>0.37840</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.3063</td>\n",
       "      <td>0.08368</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.710</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.093660</td>\n",
       "      <td>0.059850</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.07451</td>\n",
       "      <td>...</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.16540</td>\n",
       "      <td>0.36820</td>\n",
       "      <td>0.26780</td>\n",
       "      <td>0.15560</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.000</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.185900</td>\n",
       "      <td>0.093530</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0.07389</td>\n",
       "      <td>...</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.17030</td>\n",
       "      <td>0.54010</td>\n",
       "      <td>0.53900</td>\n",
       "      <td>0.20600</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.460</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.227300</td>\n",
       "      <td>0.085430</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.08243</td>\n",
       "      <td>...</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.18530</td>\n",
       "      <td>1.05800</td>\n",
       "      <td>1.10500</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.020</td>\n",
       "      <td>23.24</td>\n",
       "      <td>102.70</td>\n",
       "      <td>797.8</td>\n",
       "      <td>0.08206</td>\n",
       "      <td>0.06669</td>\n",
       "      <td>0.032990</td>\n",
       "      <td>0.033230</td>\n",
       "      <td>0.1528</td>\n",
       "      <td>0.05697</td>\n",
       "      <td>...</td>\n",
       "      <td>33.88</td>\n",
       "      <td>123.80</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0.11810</td>\n",
       "      <td>0.15510</td>\n",
       "      <td>0.14590</td>\n",
       "      <td>0.09975</td>\n",
       "      <td>0.2948</td>\n",
       "      <td>0.08452</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.780</td>\n",
       "      <td>17.89</td>\n",
       "      <td>103.60</td>\n",
       "      <td>781.0</td>\n",
       "      <td>0.09710</td>\n",
       "      <td>0.12920</td>\n",
       "      <td>0.099540</td>\n",
       "      <td>0.066060</td>\n",
       "      <td>0.1842</td>\n",
       "      <td>0.06082</td>\n",
       "      <td>...</td>\n",
       "      <td>27.28</td>\n",
       "      <td>136.50</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>0.13960</td>\n",
       "      <td>0.56090</td>\n",
       "      <td>0.39650</td>\n",
       "      <td>0.18100</td>\n",
       "      <td>0.3792</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19.170</td>\n",
       "      <td>24.80</td>\n",
       "      <td>132.40</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>0.09740</td>\n",
       "      <td>0.24580</td>\n",
       "      <td>0.206500</td>\n",
       "      <td>0.111800</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07800</td>\n",
       "      <td>...</td>\n",
       "      <td>29.94</td>\n",
       "      <td>151.70</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>0.10370</td>\n",
       "      <td>0.39030</td>\n",
       "      <td>0.36390</td>\n",
       "      <td>0.17670</td>\n",
       "      <td>0.3176</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.850</td>\n",
       "      <td>23.95</td>\n",
       "      <td>103.70</td>\n",
       "      <td>782.7</td>\n",
       "      <td>0.08401</td>\n",
       "      <td>0.10020</td>\n",
       "      <td>0.099380</td>\n",
       "      <td>0.053640</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.05338</td>\n",
       "      <td>...</td>\n",
       "      <td>27.66</td>\n",
       "      <td>112.00</td>\n",
       "      <td>876.5</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.19240</td>\n",
       "      <td>0.23220</td>\n",
       "      <td>0.11190</td>\n",
       "      <td>0.2809</td>\n",
       "      <td>0.06287</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.730</td>\n",
       "      <td>22.61</td>\n",
       "      <td>93.60</td>\n",
       "      <td>578.3</td>\n",
       "      <td>0.11310</td>\n",
       "      <td>0.22930</td>\n",
       "      <td>0.212800</td>\n",
       "      <td>0.080250</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.07682</td>\n",
       "      <td>...</td>\n",
       "      <td>32.01</td>\n",
       "      <td>108.80</td>\n",
       "      <td>697.7</td>\n",
       "      <td>0.16510</td>\n",
       "      <td>0.77250</td>\n",
       "      <td>0.69430</td>\n",
       "      <td>0.22080</td>\n",
       "      <td>0.3596</td>\n",
       "      <td>0.14310</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.540</td>\n",
       "      <td>27.54</td>\n",
       "      <td>96.73</td>\n",
       "      <td>658.8</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.15950</td>\n",
       "      <td>0.163900</td>\n",
       "      <td>0.073640</td>\n",
       "      <td>0.2303</td>\n",
       "      <td>0.07077</td>\n",
       "      <td>...</td>\n",
       "      <td>37.13</td>\n",
       "      <td>124.10</td>\n",
       "      <td>943.2</td>\n",
       "      <td>0.16780</td>\n",
       "      <td>0.65770</td>\n",
       "      <td>0.70260</td>\n",
       "      <td>0.17120</td>\n",
       "      <td>0.4218</td>\n",
       "      <td>0.13410</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.680</td>\n",
       "      <td>20.13</td>\n",
       "      <td>94.74</td>\n",
       "      <td>684.5</td>\n",
       "      <td>0.09867</td>\n",
       "      <td>0.07200</td>\n",
       "      <td>0.073950</td>\n",
       "      <td>0.052590</td>\n",
       "      <td>0.1586</td>\n",
       "      <td>0.05922</td>\n",
       "      <td>...</td>\n",
       "      <td>30.88</td>\n",
       "      <td>123.40</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>0.14640</td>\n",
       "      <td>0.18710</td>\n",
       "      <td>0.29140</td>\n",
       "      <td>0.16090</td>\n",
       "      <td>0.3029</td>\n",
       "      <td>0.08216</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.130</td>\n",
       "      <td>20.68</td>\n",
       "      <td>108.10</td>\n",
       "      <td>798.8</td>\n",
       "      <td>0.11700</td>\n",
       "      <td>0.20220</td>\n",
       "      <td>0.172200</td>\n",
       "      <td>0.102800</td>\n",
       "      <td>0.2164</td>\n",
       "      <td>0.07356</td>\n",
       "      <td>...</td>\n",
       "      <td>31.48</td>\n",
       "      <td>136.80</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>0.17890</td>\n",
       "      <td>0.42330</td>\n",
       "      <td>0.47840</td>\n",
       "      <td>0.20730</td>\n",
       "      <td>0.3706</td>\n",
       "      <td>0.11420</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.810</td>\n",
       "      <td>22.15</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>0.09831</td>\n",
       "      <td>0.10270</td>\n",
       "      <td>0.147900</td>\n",
       "      <td>0.094980</td>\n",
       "      <td>0.1582</td>\n",
       "      <td>0.05395</td>\n",
       "      <td>...</td>\n",
       "      <td>30.88</td>\n",
       "      <td>186.80</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>0.15120</td>\n",
       "      <td>0.31500</td>\n",
       "      <td>0.53720</td>\n",
       "      <td>0.23880</td>\n",
       "      <td>0.2768</td>\n",
       "      <td>0.07615</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.540</td>\n",
       "      <td>14.36</td>\n",
       "      <td>87.46</td>\n",
       "      <td>566.3</td>\n",
       "      <td>0.09779</td>\n",
       "      <td>0.08129</td>\n",
       "      <td>0.066640</td>\n",
       "      <td>0.047810</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>0.05766</td>\n",
       "      <td>...</td>\n",
       "      <td>19.26</td>\n",
       "      <td>99.70</td>\n",
       "      <td>711.2</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.17730</td>\n",
       "      <td>0.23900</td>\n",
       "      <td>0.12880</td>\n",
       "      <td>0.2977</td>\n",
       "      <td>0.07259</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13.080</td>\n",
       "      <td>15.71</td>\n",
       "      <td>85.63</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.10750</td>\n",
       "      <td>0.12700</td>\n",
       "      <td>0.045680</td>\n",
       "      <td>0.031100</td>\n",
       "      <td>0.1967</td>\n",
       "      <td>0.06811</td>\n",
       "      <td>...</td>\n",
       "      <td>20.49</td>\n",
       "      <td>96.09</td>\n",
       "      <td>630.5</td>\n",
       "      <td>0.13120</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.18900</td>\n",
       "      <td>0.07283</td>\n",
       "      <td>0.3184</td>\n",
       "      <td>0.08183</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9.504</td>\n",
       "      <td>12.44</td>\n",
       "      <td>60.34</td>\n",
       "      <td>273.9</td>\n",
       "      <td>0.10240</td>\n",
       "      <td>0.06492</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020760</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>...</td>\n",
       "      <td>15.66</td>\n",
       "      <td>65.13</td>\n",
       "      <td>314.9</td>\n",
       "      <td>0.13240</td>\n",
       "      <td>0.11480</td>\n",
       "      <td>0.08867</td>\n",
       "      <td>0.06227</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>0.07773</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.340</td>\n",
       "      <td>14.26</td>\n",
       "      <td>102.50</td>\n",
       "      <td>704.4</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.21350</td>\n",
       "      <td>0.207700</td>\n",
       "      <td>0.097560</td>\n",
       "      <td>0.2521</td>\n",
       "      <td>0.07032</td>\n",
       "      <td>...</td>\n",
       "      <td>19.08</td>\n",
       "      <td>125.10</td>\n",
       "      <td>980.9</td>\n",
       "      <td>0.13900</td>\n",
       "      <td>0.59540</td>\n",
       "      <td>0.63050</td>\n",
       "      <td>0.23930</td>\n",
       "      <td>0.4667</td>\n",
       "      <td>0.09946</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21.160</td>\n",
       "      <td>23.04</td>\n",
       "      <td>137.20</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>0.09428</td>\n",
       "      <td>0.10220</td>\n",
       "      <td>0.109700</td>\n",
       "      <td>0.086320</td>\n",
       "      <td>0.1769</td>\n",
       "      <td>0.05278</td>\n",
       "      <td>...</td>\n",
       "      <td>35.59</td>\n",
       "      <td>188.00</td>\n",
       "      <td>2615.0</td>\n",
       "      <td>0.14010</td>\n",
       "      <td>0.26000</td>\n",
       "      <td>0.31550</td>\n",
       "      <td>0.20090</td>\n",
       "      <td>0.2822</td>\n",
       "      <td>0.07526</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.650</td>\n",
       "      <td>21.38</td>\n",
       "      <td>110.00</td>\n",
       "      <td>904.6</td>\n",
       "      <td>0.11210</td>\n",
       "      <td>0.14570</td>\n",
       "      <td>0.152500</td>\n",
       "      <td>0.091700</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>0.06330</td>\n",
       "      <td>...</td>\n",
       "      <td>31.56</td>\n",
       "      <td>177.00</td>\n",
       "      <td>2215.0</td>\n",
       "      <td>0.18050</td>\n",
       "      <td>0.35780</td>\n",
       "      <td>0.46950</td>\n",
       "      <td>0.20950</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.09564</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.140</td>\n",
       "      <td>16.40</td>\n",
       "      <td>116.00</td>\n",
       "      <td>912.7</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.22760</td>\n",
       "      <td>0.222900</td>\n",
       "      <td>0.140100</td>\n",
       "      <td>0.3040</td>\n",
       "      <td>0.07413</td>\n",
       "      <td>...</td>\n",
       "      <td>21.40</td>\n",
       "      <td>152.40</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>0.15450</td>\n",
       "      <td>0.39490</td>\n",
       "      <td>0.38530</td>\n",
       "      <td>0.25500</td>\n",
       "      <td>0.4066</td>\n",
       "      <td>0.10590</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14.580</td>\n",
       "      <td>21.53</td>\n",
       "      <td>97.41</td>\n",
       "      <td>644.8</td>\n",
       "      <td>0.10540</td>\n",
       "      <td>0.18680</td>\n",
       "      <td>0.142500</td>\n",
       "      <td>0.087830</td>\n",
       "      <td>0.2252</td>\n",
       "      <td>0.06924</td>\n",
       "      <td>...</td>\n",
       "      <td>33.21</td>\n",
       "      <td>122.40</td>\n",
       "      <td>896.9</td>\n",
       "      <td>0.15250</td>\n",
       "      <td>0.66430</td>\n",
       "      <td>0.55390</td>\n",
       "      <td>0.27010</td>\n",
       "      <td>0.4264</td>\n",
       "      <td>0.12750</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18.610</td>\n",
       "      <td>20.25</td>\n",
       "      <td>122.10</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>0.09440</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>0.149000</td>\n",
       "      <td>0.077310</td>\n",
       "      <td>0.1697</td>\n",
       "      <td>0.05699</td>\n",
       "      <td>...</td>\n",
       "      <td>27.26</td>\n",
       "      <td>139.90</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>0.13380</td>\n",
       "      <td>0.21170</td>\n",
       "      <td>0.34460</td>\n",
       "      <td>0.14900</td>\n",
       "      <td>0.2341</td>\n",
       "      <td>0.07421</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15.300</td>\n",
       "      <td>25.27</td>\n",
       "      <td>102.40</td>\n",
       "      <td>732.4</td>\n",
       "      <td>0.10820</td>\n",
       "      <td>0.16970</td>\n",
       "      <td>0.168300</td>\n",
       "      <td>0.087510</td>\n",
       "      <td>0.1926</td>\n",
       "      <td>0.06540</td>\n",
       "      <td>...</td>\n",
       "      <td>36.71</td>\n",
       "      <td>149.30</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>0.16410</td>\n",
       "      <td>0.61100</td>\n",
       "      <td>0.63350</td>\n",
       "      <td>0.20240</td>\n",
       "      <td>0.4027</td>\n",
       "      <td>0.09876</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>17.570</td>\n",
       "      <td>15.05</td>\n",
       "      <td>115.00</td>\n",
       "      <td>955.1</td>\n",
       "      <td>0.09847</td>\n",
       "      <td>0.11570</td>\n",
       "      <td>0.098750</td>\n",
       "      <td>0.079530</td>\n",
       "      <td>0.1739</td>\n",
       "      <td>0.06149</td>\n",
       "      <td>...</td>\n",
       "      <td>19.52</td>\n",
       "      <td>134.90</td>\n",
       "      <td>1227.0</td>\n",
       "      <td>0.12550</td>\n",
       "      <td>0.28120</td>\n",
       "      <td>0.24890</td>\n",
       "      <td>0.14560</td>\n",
       "      <td>0.2756</td>\n",
       "      <td>0.07919</td>\n",
       "      <td>0.0</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",
       "      <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>539</th>\n",
       "      <td>7.691</td>\n",
       "      <td>25.44</td>\n",
       "      <td>48.34</td>\n",
       "      <td>170.4</td>\n",
       "      <td>0.08668</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.092520</td>\n",
       "      <td>0.013640</td>\n",
       "      <td>0.2037</td>\n",
       "      <td>0.07751</td>\n",
       "      <td>...</td>\n",
       "      <td>31.89</td>\n",
       "      <td>54.49</td>\n",
       "      <td>223.6</td>\n",
       "      <td>0.15960</td>\n",
       "      <td>0.30640</td>\n",
       "      <td>0.33930</td>\n",
       "      <td>0.05000</td>\n",
       "      <td>0.2790</td>\n",
       "      <td>0.10660</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>11.540</td>\n",
       "      <td>14.44</td>\n",
       "      <td>74.65</td>\n",
       "      <td>402.9</td>\n",
       "      <td>0.09984</td>\n",
       "      <td>0.11200</td>\n",
       "      <td>0.067370</td>\n",
       "      <td>0.025940</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.06782</td>\n",
       "      <td>...</td>\n",
       "      <td>19.68</td>\n",
       "      <td>78.78</td>\n",
       "      <td>457.8</td>\n",
       "      <td>0.13450</td>\n",
       "      <td>0.21180</td>\n",
       "      <td>0.17970</td>\n",
       "      <td>0.06918</td>\n",
       "      <td>0.2329</td>\n",
       "      <td>0.08134</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>14.470</td>\n",
       "      <td>24.99</td>\n",
       "      <td>95.81</td>\n",
       "      <td>656.4</td>\n",
       "      <td>0.08837</td>\n",
       "      <td>0.12300</td>\n",
       "      <td>0.100900</td>\n",
       "      <td>0.038900</td>\n",
       "      <td>0.1872</td>\n",
       "      <td>0.06341</td>\n",
       "      <td>...</td>\n",
       "      <td>31.73</td>\n",
       "      <td>113.50</td>\n",
       "      <td>808.9</td>\n",
       "      <td>0.13400</td>\n",
       "      <td>0.42020</td>\n",
       "      <td>0.40400</td>\n",
       "      <td>0.12050</td>\n",
       "      <td>0.3187</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>14.740</td>\n",
       "      <td>25.42</td>\n",
       "      <td>94.70</td>\n",
       "      <td>668.6</td>\n",
       "      <td>0.08275</td>\n",
       "      <td>0.07214</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>0.030270</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.05680</td>\n",
       "      <td>...</td>\n",
       "      <td>32.29</td>\n",
       "      <td>107.40</td>\n",
       "      <td>826.4</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.13760</td>\n",
       "      <td>0.16110</td>\n",
       "      <td>0.10950</td>\n",
       "      <td>0.2722</td>\n",
       "      <td>0.06956</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>13.210</td>\n",
       "      <td>28.06</td>\n",
       "      <td>84.88</td>\n",
       "      <td>538.4</td>\n",
       "      <td>0.08671</td>\n",
       "      <td>0.06877</td>\n",
       "      <td>0.029870</td>\n",
       "      <td>0.032750</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.05781</td>\n",
       "      <td>...</td>\n",
       "      <td>37.17</td>\n",
       "      <td>92.48</td>\n",
       "      <td>629.6</td>\n",
       "      <td>0.10720</td>\n",
       "      <td>0.13810</td>\n",
       "      <td>0.10620</td>\n",
       "      <td>0.07958</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>0.06443</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>13.870</td>\n",
       "      <td>20.70</td>\n",
       "      <td>89.77</td>\n",
       "      <td>584.8</td>\n",
       "      <td>0.09578</td>\n",
       "      <td>0.10180</td>\n",
       "      <td>0.036880</td>\n",
       "      <td>0.023690</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.06688</td>\n",
       "      <td>...</td>\n",
       "      <td>24.75</td>\n",
       "      <td>99.17</td>\n",
       "      <td>688.6</td>\n",
       "      <td>0.12640</td>\n",
       "      <td>0.20370</td>\n",
       "      <td>0.13770</td>\n",
       "      <td>0.06845</td>\n",
       "      <td>0.2249</td>\n",
       "      <td>0.08492</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>13.620</td>\n",
       "      <td>23.23</td>\n",
       "      <td>87.19</td>\n",
       "      <td>573.2</td>\n",
       "      <td>0.09246</td>\n",
       "      <td>0.06747</td>\n",
       "      <td>0.029740</td>\n",
       "      <td>0.024430</td>\n",
       "      <td>0.1664</td>\n",
       "      <td>0.05801</td>\n",
       "      <td>...</td>\n",
       "      <td>29.09</td>\n",
       "      <td>97.58</td>\n",
       "      <td>729.8</td>\n",
       "      <td>0.12160</td>\n",
       "      <td>0.15170</td>\n",
       "      <td>0.10490</td>\n",
       "      <td>0.07174</td>\n",
       "      <td>0.2642</td>\n",
       "      <td>0.06953</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>10.320</td>\n",
       "      <td>16.35</td>\n",
       "      <td>65.31</td>\n",
       "      <td>324.9</td>\n",
       "      <td>0.09434</td>\n",
       "      <td>0.04994</td>\n",
       "      <td>0.010120</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>0.06201</td>\n",
       "      <td>...</td>\n",
       "      <td>21.77</td>\n",
       "      <td>71.12</td>\n",
       "      <td>384.9</td>\n",
       "      <td>0.12850</td>\n",
       "      <td>0.08842</td>\n",
       "      <td>0.04384</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.2681</td>\n",
       "      <td>0.07399</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>10.260</td>\n",
       "      <td>16.58</td>\n",
       "      <td>65.85</td>\n",
       "      <td>320.8</td>\n",
       "      <td>0.08877</td>\n",
       "      <td>0.08066</td>\n",
       "      <td>0.043580</td>\n",
       "      <td>0.024380</td>\n",
       "      <td>0.1669</td>\n",
       "      <td>0.06714</td>\n",
       "      <td>...</td>\n",
       "      <td>22.04</td>\n",
       "      <td>71.08</td>\n",
       "      <td>357.4</td>\n",
       "      <td>0.14610</td>\n",
       "      <td>0.22460</td>\n",
       "      <td>0.17830</td>\n",
       "      <td>0.08333</td>\n",
       "      <td>0.2691</td>\n",
       "      <td>0.09479</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>9.683</td>\n",
       "      <td>19.34</td>\n",
       "      <td>61.05</td>\n",
       "      <td>285.7</td>\n",
       "      <td>0.08491</td>\n",
       "      <td>0.05030</td>\n",
       "      <td>0.023370</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>0.06235</td>\n",
       "      <td>...</td>\n",
       "      <td>25.59</td>\n",
       "      <td>69.10</td>\n",
       "      <td>364.2</td>\n",
       "      <td>0.11990</td>\n",
       "      <td>0.09546</td>\n",
       "      <td>0.09350</td>\n",
       "      <td>0.03846</td>\n",
       "      <td>0.2552</td>\n",
       "      <td>0.07920</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>10.820</td>\n",
       "      <td>24.21</td>\n",
       "      <td>68.89</td>\n",
       "      <td>361.6</td>\n",
       "      <td>0.08192</td>\n",
       "      <td>0.06602</td>\n",
       "      <td>0.015480</td>\n",
       "      <td>0.008160</td>\n",
       "      <td>0.1976</td>\n",
       "      <td>0.06328</td>\n",
       "      <td>...</td>\n",
       "      <td>31.45</td>\n",
       "      <td>83.90</td>\n",
       "      <td>505.6</td>\n",
       "      <td>0.12040</td>\n",
       "      <td>0.16330</td>\n",
       "      <td>0.06194</td>\n",
       "      <td>0.03264</td>\n",
       "      <td>0.3059</td>\n",
       "      <td>0.07626</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>10.860</td>\n",
       "      <td>21.48</td>\n",
       "      <td>68.51</td>\n",
       "      <td>360.5</td>\n",
       "      <td>0.07431</td>\n",
       "      <td>0.04227</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1661</td>\n",
       "      <td>0.05948</td>\n",
       "      <td>...</td>\n",
       "      <td>24.77</td>\n",
       "      <td>74.08</td>\n",
       "      <td>412.3</td>\n",
       "      <td>0.10010</td>\n",
       "      <td>0.07348</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.06592</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>11.130</td>\n",
       "      <td>22.44</td>\n",
       "      <td>71.49</td>\n",
       "      <td>378.4</td>\n",
       "      <td>0.09566</td>\n",
       "      <td>0.08194</td>\n",
       "      <td>0.048240</td>\n",
       "      <td>0.022570</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.06552</td>\n",
       "      <td>...</td>\n",
       "      <td>28.26</td>\n",
       "      <td>77.80</td>\n",
       "      <td>436.6</td>\n",
       "      <td>0.10870</td>\n",
       "      <td>0.17820</td>\n",
       "      <td>0.15640</td>\n",
       "      <td>0.06413</td>\n",
       "      <td>0.3169</td>\n",
       "      <td>0.08032</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>12.770</td>\n",
       "      <td>29.43</td>\n",
       "      <td>81.35</td>\n",
       "      <td>507.9</td>\n",
       "      <td>0.08276</td>\n",
       "      <td>0.04234</td>\n",
       "      <td>0.019970</td>\n",
       "      <td>0.014990</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.05637</td>\n",
       "      <td>...</td>\n",
       "      <td>36.00</td>\n",
       "      <td>88.10</td>\n",
       "      <td>594.7</td>\n",
       "      <td>0.12340</td>\n",
       "      <td>0.10640</td>\n",
       "      <td>0.08653</td>\n",
       "      <td>0.06498</td>\n",
       "      <td>0.2407</td>\n",
       "      <td>0.06484</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>9.333</td>\n",
       "      <td>21.94</td>\n",
       "      <td>59.01</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.09240</td>\n",
       "      <td>0.05605</td>\n",
       "      <td>0.039960</td>\n",
       "      <td>0.012820</td>\n",
       "      <td>0.1692</td>\n",
       "      <td>0.06576</td>\n",
       "      <td>...</td>\n",
       "      <td>25.05</td>\n",
       "      <td>62.86</td>\n",
       "      <td>295.8</td>\n",
       "      <td>0.11030</td>\n",
       "      <td>0.08298</td>\n",
       "      <td>0.07993</td>\n",
       "      <td>0.02564</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.07393</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>12.880</td>\n",
       "      <td>28.92</td>\n",
       "      <td>82.50</td>\n",
       "      <td>514.3</td>\n",
       "      <td>0.08123</td>\n",
       "      <td>0.05824</td>\n",
       "      <td>0.061950</td>\n",
       "      <td>0.023430</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05708</td>\n",
       "      <td>...</td>\n",
       "      <td>35.74</td>\n",
       "      <td>88.84</td>\n",
       "      <td>595.7</td>\n",
       "      <td>0.12270</td>\n",
       "      <td>0.16200</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.06493</td>\n",
       "      <td>0.2372</td>\n",
       "      <td>0.07242</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>10.290</td>\n",
       "      <td>27.61</td>\n",
       "      <td>65.67</td>\n",
       "      <td>321.4</td>\n",
       "      <td>0.09030</td>\n",
       "      <td>0.07658</td>\n",
       "      <td>0.059990</td>\n",
       "      <td>0.027380</td>\n",
       "      <td>0.1593</td>\n",
       "      <td>0.06127</td>\n",
       "      <td>...</td>\n",
       "      <td>34.91</td>\n",
       "      <td>69.57</td>\n",
       "      <td>357.6</td>\n",
       "      <td>0.13840</td>\n",
       "      <td>0.17100</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.09127</td>\n",
       "      <td>0.2226</td>\n",
       "      <td>0.08283</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>10.160</td>\n",
       "      <td>19.59</td>\n",
       "      <td>64.73</td>\n",
       "      <td>311.7</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.07504</td>\n",
       "      <td>0.005025</td>\n",
       "      <td>0.011160</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.06331</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>67.88</td>\n",
       "      <td>347.3</td>\n",
       "      <td>0.12650</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.01005</td>\n",
       "      <td>0.02232</td>\n",
       "      <td>0.2262</td>\n",
       "      <td>0.06742</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>9.423</td>\n",
       "      <td>27.88</td>\n",
       "      <td>59.26</td>\n",
       "      <td>271.3</td>\n",
       "      <td>0.08123</td>\n",
       "      <td>0.04971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1742</td>\n",
       "      <td>0.06059</td>\n",
       "      <td>...</td>\n",
       "      <td>34.24</td>\n",
       "      <td>66.50</td>\n",
       "      <td>330.6</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.07158</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.06969</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>14.590</td>\n",
       "      <td>22.68</td>\n",
       "      <td>96.39</td>\n",
       "      <td>657.1</td>\n",
       "      <td>0.08473</td>\n",
       "      <td>0.13300</td>\n",
       "      <td>0.102900</td>\n",
       "      <td>0.037360</td>\n",
       "      <td>0.1454</td>\n",
       "      <td>0.06147</td>\n",
       "      <td>...</td>\n",
       "      <td>27.27</td>\n",
       "      <td>105.90</td>\n",
       "      <td>733.5</td>\n",
       "      <td>0.10260</td>\n",
       "      <td>0.31710</td>\n",
       "      <td>0.36620</td>\n",
       "      <td>0.11050</td>\n",
       "      <td>0.2258</td>\n",
       "      <td>0.08004</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>11.510</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
       "      <td>0.09261</td>\n",
       "      <td>0.10210</td>\n",
       "      <td>0.111200</td>\n",
       "      <td>0.041050</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>0.06570</td>\n",
       "      <td>...</td>\n",
       "      <td>37.16</td>\n",
       "      <td>82.28</td>\n",
       "      <td>474.2</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>0.25170</td>\n",
       "      <td>0.36300</td>\n",
       "      <td>0.09653</td>\n",
       "      <td>0.2112</td>\n",
       "      <td>0.08732</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>14.050</td>\n",
       "      <td>27.15</td>\n",
       "      <td>91.38</td>\n",
       "      <td>600.4</td>\n",
       "      <td>0.09929</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.044620</td>\n",
       "      <td>0.043040</td>\n",
       "      <td>0.1537</td>\n",
       "      <td>0.06171</td>\n",
       "      <td>...</td>\n",
       "      <td>33.17</td>\n",
       "      <td>100.20</td>\n",
       "      <td>706.7</td>\n",
       "      <td>0.12410</td>\n",
       "      <td>0.22640</td>\n",
       "      <td>0.13260</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.2250</td>\n",
       "      <td>0.08321</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>11.200</td>\n",
       "      <td>29.37</td>\n",
       "      <td>70.67</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.07449</td>\n",
       "      <td>0.03558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>0.05502</td>\n",
       "      <td>...</td>\n",
       "      <td>38.30</td>\n",
       "      <td>75.19</td>\n",
       "      <td>439.6</td>\n",
       "      <td>0.09267</td>\n",
       "      <td>0.05494</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05905</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>15.220</td>\n",
       "      <td>30.62</td>\n",
       "      <td>103.40</td>\n",
       "      <td>716.9</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.20870</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.094290</td>\n",
       "      <td>0.2128</td>\n",
       "      <td>0.07152</td>\n",
       "      <td>...</td>\n",
       "      <td>42.79</td>\n",
       "      <td>128.70</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0.14170</td>\n",
       "      <td>0.79170</td>\n",
       "      <td>1.17000</td>\n",
       "      <td>0.23560</td>\n",
       "      <td>0.4089</td>\n",
       "      <td>0.14090</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>20.920</td>\n",
       "      <td>25.09</td>\n",
       "      <td>143.00</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.22360</td>\n",
       "      <td>0.317400</td>\n",
       "      <td>0.147400</td>\n",
       "      <td>0.2149</td>\n",
       "      <td>0.06879</td>\n",
       "      <td>...</td>\n",
       "      <td>29.41</td>\n",
       "      <td>179.10</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.14070</td>\n",
       "      <td>0.41860</td>\n",
       "      <td>0.65990</td>\n",
       "      <td>0.25420</td>\n",
       "      <td>0.2929</td>\n",
       "      <td>0.09873</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.560</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.243900</td>\n",
       "      <td>0.138900</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.41070</td>\n",
       "      <td>0.22160</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.130</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.097910</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.32150</td>\n",
       "      <td>0.16280</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.600</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.092510</td>\n",
       "      <td>0.053020</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.34030</td>\n",
       "      <td>0.14180</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.600</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.351400</td>\n",
       "      <td>0.152000</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.93870</td>\n",
       "      <td>0.26500</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.760</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0         17.990         10.38          122.80     1001.0          0.11840   \n",
       "1         20.570         17.77          132.90     1326.0          0.08474   \n",
       "2         19.690         21.25          130.00     1203.0          0.10960   \n",
       "3         11.420         20.38           77.58      386.1          0.14250   \n",
       "4         20.290         14.34          135.10     1297.0          0.10030   \n",
       "5         12.450         15.70           82.57      477.1          0.12780   \n",
       "6         18.250         19.98          119.60     1040.0          0.09463   \n",
       "7         13.710         20.83           90.20      577.9          0.11890   \n",
       "8         13.000         21.82           87.50      519.8          0.12730   \n",
       "9         12.460         24.04           83.97      475.9          0.11860   \n",
       "10        16.020         23.24          102.70      797.8          0.08206   \n",
       "11        15.780         17.89          103.60      781.0          0.09710   \n",
       "12        19.170         24.80          132.40     1123.0          0.09740   \n",
       "13        15.850         23.95          103.70      782.7          0.08401   \n",
       "14        13.730         22.61           93.60      578.3          0.11310   \n",
       "15        14.540         27.54           96.73      658.8          0.11390   \n",
       "16        14.680         20.13           94.74      684.5          0.09867   \n",
       "17        16.130         20.68          108.10      798.8          0.11700   \n",
       "18        19.810         22.15          130.00     1260.0          0.09831   \n",
       "19        13.540         14.36           87.46      566.3          0.09779   \n",
       "20        13.080         15.71           85.63      520.0          0.10750   \n",
       "21         9.504         12.44           60.34      273.9          0.10240   \n",
       "22        15.340         14.26          102.50      704.4          0.10730   \n",
       "23        21.160         23.04          137.20     1404.0          0.09428   \n",
       "24        16.650         21.38          110.00      904.6          0.11210   \n",
       "25        17.140         16.40          116.00      912.7          0.11860   \n",
       "26        14.580         21.53           97.41      644.8          0.10540   \n",
       "27        18.610         20.25          122.10     1094.0          0.09440   \n",
       "28        15.300         25.27          102.40      732.4          0.10820   \n",
       "29        17.570         15.05          115.00      955.1          0.09847   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "539        7.691         25.44           48.34      170.4          0.08668   \n",
       "540       11.540         14.44           74.65      402.9          0.09984   \n",
       "541       14.470         24.99           95.81      656.4          0.08837   \n",
       "542       14.740         25.42           94.70      668.6          0.08275   \n",
       "543       13.210         28.06           84.88      538.4          0.08671   \n",
       "544       13.870         20.70           89.77      584.8          0.09578   \n",
       "545       13.620         23.23           87.19      573.2          0.09246   \n",
       "546       10.320         16.35           65.31      324.9          0.09434   \n",
       "547       10.260         16.58           65.85      320.8          0.08877   \n",
       "548        9.683         19.34           61.05      285.7          0.08491   \n",
       "549       10.820         24.21           68.89      361.6          0.08192   \n",
       "550       10.860         21.48           68.51      360.5          0.07431   \n",
       "551       11.130         22.44           71.49      378.4          0.09566   \n",
       "552       12.770         29.43           81.35      507.9          0.08276   \n",
       "553        9.333         21.94           59.01      264.0          0.09240   \n",
       "554       12.880         28.92           82.50      514.3          0.08123   \n",
       "555       10.290         27.61           65.67      321.4          0.09030   \n",
       "556       10.160         19.59           64.73      311.7          0.10030   \n",
       "557        9.423         27.88           59.26      271.3          0.08123   \n",
       "558       14.590         22.68           96.39      657.1          0.08473   \n",
       "559       11.510         23.93           74.52      403.5          0.09261   \n",
       "560       14.050         27.15           91.38      600.4          0.09929   \n",
       "561       11.200         29.37           70.67      386.0          0.07449   \n",
       "562       15.220         30.62          103.40      716.9          0.10480   \n",
       "563       20.920         25.09          143.00     1347.0          0.10990   \n",
       "564       21.560         22.39          142.00     1479.0          0.11100   \n",
       "565       20.130         28.25          131.20     1261.0          0.09780   \n",
       "566       16.600         28.08          108.30      858.1          0.08455   \n",
       "567       20.600         29.33          140.10     1265.0          0.11780   \n",
       "568        7.760         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760        0.300100             0.147100         0.2419   \n",
       "1             0.07864        0.086900             0.070170         0.1812   \n",
       "2             0.15990        0.197400             0.127900         0.2069   \n",
       "3             0.28390        0.241400             0.105200         0.2597   \n",
       "4             0.13280        0.198000             0.104300         0.1809   \n",
       "5             0.17000        0.157800             0.080890         0.2087   \n",
       "6             0.10900        0.112700             0.074000         0.1794   \n",
       "7             0.16450        0.093660             0.059850         0.2196   \n",
       "8             0.19320        0.185900             0.093530         0.2350   \n",
       "9             0.23960        0.227300             0.085430         0.2030   \n",
       "10            0.06669        0.032990             0.033230         0.1528   \n",
       "11            0.12920        0.099540             0.066060         0.1842   \n",
       "12            0.24580        0.206500             0.111800         0.2397   \n",
       "13            0.10020        0.099380             0.053640         0.1847   \n",
       "14            0.22930        0.212800             0.080250         0.2069   \n",
       "15            0.15950        0.163900             0.073640         0.2303   \n",
       "16            0.07200        0.073950             0.052590         0.1586   \n",
       "17            0.20220        0.172200             0.102800         0.2164   \n",
       "18            0.10270        0.147900             0.094980         0.1582   \n",
       "19            0.08129        0.066640             0.047810         0.1885   \n",
       "20            0.12700        0.045680             0.031100         0.1967   \n",
       "21            0.06492        0.029560             0.020760         0.1815   \n",
       "22            0.21350        0.207700             0.097560         0.2521   \n",
       "23            0.10220        0.109700             0.086320         0.1769   \n",
       "24            0.14570        0.152500             0.091700         0.1995   \n",
       "25            0.22760        0.222900             0.140100         0.3040   \n",
       "26            0.18680        0.142500             0.087830         0.2252   \n",
       "27            0.10660        0.149000             0.077310         0.1697   \n",
       "28            0.16970        0.168300             0.087510         0.1926   \n",
       "29            0.11570        0.098750             0.079530         0.1739   \n",
       "..                ...             ...                  ...            ...   \n",
       "539           0.11990        0.092520             0.013640         0.2037   \n",
       "540           0.11200        0.067370             0.025940         0.1818   \n",
       "541           0.12300        0.100900             0.038900         0.1872   \n",
       "542           0.07214        0.041050             0.030270         0.1840   \n",
       "543           0.06877        0.029870             0.032750         0.1628   \n",
       "544           0.10180        0.036880             0.023690         0.1620   \n",
       "545           0.06747        0.029740             0.024430         0.1664   \n",
       "546           0.04994        0.010120             0.005495         0.1885   \n",
       "547           0.08066        0.043580             0.024380         0.1669   \n",
       "548           0.05030        0.023370             0.009615         0.1580   \n",
       "549           0.06602        0.015480             0.008160         0.1976   \n",
       "550           0.04227        0.000000             0.000000         0.1661   \n",
       "551           0.08194        0.048240             0.022570         0.2030   \n",
       "552           0.04234        0.019970             0.014990         0.1539   \n",
       "553           0.05605        0.039960             0.012820         0.1692   \n",
       "554           0.05824        0.061950             0.023430         0.1566   \n",
       "555           0.07658        0.059990             0.027380         0.1593   \n",
       "556           0.07504        0.005025             0.011160         0.1791   \n",
       "557           0.04971        0.000000             0.000000         0.1742   \n",
       "558           0.13300        0.102900             0.037360         0.1454   \n",
       "559           0.10210        0.111200             0.041050         0.1388   \n",
       "560           0.11260        0.044620             0.043040         0.1537   \n",
       "561           0.03558        0.000000             0.000000         0.1060   \n",
       "562           0.20870        0.255000             0.094290         0.2128   \n",
       "563           0.22360        0.317400             0.147400         0.2149   \n",
       "564           0.11590        0.243900             0.138900         0.1726   \n",
       "565           0.10340        0.144000             0.097910         0.1752   \n",
       "566           0.10230        0.092510             0.053020         0.1590   \n",
       "567           0.27700        0.351400             0.152000         0.2397   \n",
       "568           0.04362        0.000000             0.000000         0.1587   \n",
       "\n",
       "     mean fractal dimension   ...    worst texture  worst perimeter  \\\n",
       "0                   0.07871   ...            17.33           184.60   \n",
       "1                   0.05667   ...            23.41           158.80   \n",
       "2                   0.05999   ...            25.53           152.50   \n",
       "3                   0.09744   ...            26.50            98.87   \n",
       "4                   0.05883   ...            16.67           152.20   \n",
       "5                   0.07613   ...            23.75           103.40   \n",
       "6                   0.05742   ...            27.66           153.20   \n",
       "7                   0.07451   ...            28.14           110.60   \n",
       "8                   0.07389   ...            30.73           106.20   \n",
       "9                   0.08243   ...            40.68            97.65   \n",
       "10                  0.05697   ...            33.88           123.80   \n",
       "11                  0.06082   ...            27.28           136.50   \n",
       "12                  0.07800   ...            29.94           151.70   \n",
       "13                  0.05338   ...            27.66           112.00   \n",
       "14                  0.07682   ...            32.01           108.80   \n",
       "15                  0.07077   ...            37.13           124.10   \n",
       "16                  0.05922   ...            30.88           123.40   \n",
       "17                  0.07356   ...            31.48           136.80   \n",
       "18                  0.05395   ...            30.88           186.80   \n",
       "19                  0.05766   ...            19.26            99.70   \n",
       "20                  0.06811   ...            20.49            96.09   \n",
       "21                  0.06905   ...            15.66            65.13   \n",
       "22                  0.07032   ...            19.08           125.10   \n",
       "23                  0.05278   ...            35.59           188.00   \n",
       "24                  0.06330   ...            31.56           177.00   \n",
       "25                  0.07413   ...            21.40           152.40   \n",
       "26                  0.06924   ...            33.21           122.40   \n",
       "27                  0.05699   ...            27.26           139.90   \n",
       "28                  0.06540   ...            36.71           149.30   \n",
       "29                  0.06149   ...            19.52           134.90   \n",
       "..                      ...   ...              ...              ...   \n",
       "539                 0.07751   ...            31.89            54.49   \n",
       "540                 0.06782   ...            19.68            78.78   \n",
       "541                 0.06341   ...            31.73           113.50   \n",
       "542                 0.05680   ...            32.29           107.40   \n",
       "543                 0.05781   ...            37.17            92.48   \n",
       "544                 0.06688   ...            24.75            99.17   \n",
       "545                 0.05801   ...            29.09            97.58   \n",
       "546                 0.06201   ...            21.77            71.12   \n",
       "547                 0.06714   ...            22.04            71.08   \n",
       "548                 0.06235   ...            25.59            69.10   \n",
       "549                 0.06328   ...            31.45            83.90   \n",
       "550                 0.05948   ...            24.77            74.08   \n",
       "551                 0.06552   ...            28.26            77.80   \n",
       "552                 0.05637   ...            36.00            88.10   \n",
       "553                 0.06576   ...            25.05            62.86   \n",
       "554                 0.05708   ...            35.74            88.84   \n",
       "555                 0.06127   ...            34.91            69.57   \n",
       "556                 0.06331   ...            22.88            67.88   \n",
       "557                 0.06059   ...            34.24            66.50   \n",
       "558                 0.06147   ...            27.27           105.90   \n",
       "559                 0.06570   ...            37.16            82.28   \n",
       "560                 0.06171   ...            33.17           100.20   \n",
       "561                 0.05502   ...            38.30            75.19   \n",
       "562                 0.07152   ...            42.79           128.70   \n",
       "563                 0.06879   ...            29.41           179.10   \n",
       "564                 0.05623   ...            26.40           166.10   \n",
       "565                 0.05533   ...            38.25           155.00   \n",
       "566                 0.05648   ...            34.12           126.70   \n",
       "567                 0.07016   ...            39.42           184.60   \n",
       "568                 0.05884   ...            30.37            59.16   \n",
       "\n",
       "     worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0        2019.0           0.16220            0.66560          0.71190   \n",
       "1        1956.0           0.12380            0.18660          0.24160   \n",
       "2        1709.0           0.14440            0.42450          0.45040   \n",
       "3         567.7           0.20980            0.86630          0.68690   \n",
       "4        1575.0           0.13740            0.20500          0.40000   \n",
       "5         741.6           0.17910            0.52490          0.53550   \n",
       "6        1606.0           0.14420            0.25760          0.37840   \n",
       "7         897.0           0.16540            0.36820          0.26780   \n",
       "8         739.3           0.17030            0.54010          0.53900   \n",
       "9         711.4           0.18530            1.05800          1.10500   \n",
       "10       1150.0           0.11810            0.15510          0.14590   \n",
       "11       1299.0           0.13960            0.56090          0.39650   \n",
       "12       1332.0           0.10370            0.39030          0.36390   \n",
       "13        876.5           0.11310            0.19240          0.23220   \n",
       "14        697.7           0.16510            0.77250          0.69430   \n",
       "15        943.2           0.16780            0.65770          0.70260   \n",
       "16       1138.0           0.14640            0.18710          0.29140   \n",
       "17       1315.0           0.17890            0.42330          0.47840   \n",
       "18       2398.0           0.15120            0.31500          0.53720   \n",
       "19        711.2           0.14400            0.17730          0.23900   \n",
       "20        630.5           0.13120            0.27760          0.18900   \n",
       "21        314.9           0.13240            0.11480          0.08867   \n",
       "22        980.9           0.13900            0.59540          0.63050   \n",
       "23       2615.0           0.14010            0.26000          0.31550   \n",
       "24       2215.0           0.18050            0.35780          0.46950   \n",
       "25       1461.0           0.15450            0.39490          0.38530   \n",
       "26        896.9           0.15250            0.66430          0.55390   \n",
       "27       1403.0           0.13380            0.21170          0.34460   \n",
       "28       1269.0           0.16410            0.61100          0.63350   \n",
       "29       1227.0           0.12550            0.28120          0.24890   \n",
       "..          ...               ...                ...              ...   \n",
       "539       223.6           0.15960            0.30640          0.33930   \n",
       "540       457.8           0.13450            0.21180          0.17970   \n",
       "541       808.9           0.13400            0.42020          0.40400   \n",
       "542       826.4           0.10600            0.13760          0.16110   \n",
       "543       629.6           0.10720            0.13810          0.10620   \n",
       "544       688.6           0.12640            0.20370          0.13770   \n",
       "545       729.8           0.12160            0.15170          0.10490   \n",
       "546       384.9           0.12850            0.08842          0.04384   \n",
       "547       357.4           0.14610            0.22460          0.17830   \n",
       "548       364.2           0.11990            0.09546          0.09350   \n",
       "549       505.6           0.12040            0.16330          0.06194   \n",
       "550       412.3           0.10010            0.07348          0.00000   \n",
       "551       436.6           0.10870            0.17820          0.15640   \n",
       "552       594.7           0.12340            0.10640          0.08653   \n",
       "553       295.8           0.11030            0.08298          0.07993   \n",
       "554       595.7           0.12270            0.16200          0.24390   \n",
       "555       357.6           0.13840            0.17100          0.20000   \n",
       "556       347.3           0.12650            0.12000          0.01005   \n",
       "557       330.6           0.10730            0.07158          0.00000   \n",
       "558       733.5           0.10260            0.31710          0.36620   \n",
       "559       474.2           0.12980            0.25170          0.36300   \n",
       "560       706.7           0.12410            0.22640          0.13260   \n",
       "561       439.6           0.09267            0.05494          0.00000   \n",
       "562       915.0           0.14170            0.79170          1.17000   \n",
       "563      1819.0           0.14070            0.41860          0.65990   \n",
       "564      2027.0           0.14100            0.21130          0.41070   \n",
       "565      1731.0           0.11660            0.19220          0.32150   \n",
       "566      1124.0           0.11390            0.30940          0.34030   \n",
       "567      1821.0           0.16500            0.86810          0.93870   \n",
       "568       268.6           0.08996            0.06444          0.00000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
       "0                 0.26540          0.4601                  0.11890     0.0  \n",
       "1                 0.18600          0.2750                  0.08902     0.0  \n",
       "2                 0.24300          0.3613                  0.08758     0.0  \n",
       "3                 0.25750          0.6638                  0.17300     0.0  \n",
       "4                 0.16250          0.2364                  0.07678     0.0  \n",
       "5                 0.17410          0.3985                  0.12440     0.0  \n",
       "6                 0.19320          0.3063                  0.08368     0.0  \n",
       "7                 0.15560          0.3196                  0.11510     0.0  \n",
       "8                 0.20600          0.4378                  0.10720     0.0  \n",
       "9                 0.22100          0.4366                  0.20750     0.0  \n",
       "10                0.09975          0.2948                  0.08452     0.0  \n",
       "11                0.18100          0.3792                  0.10480     0.0  \n",
       "12                0.17670          0.3176                  0.10230     0.0  \n",
       "13                0.11190          0.2809                  0.06287     0.0  \n",
       "14                0.22080          0.3596                  0.14310     0.0  \n",
       "15                0.17120          0.4218                  0.13410     0.0  \n",
       "16                0.16090          0.3029                  0.08216     0.0  \n",
       "17                0.20730          0.3706                  0.11420     0.0  \n",
       "18                0.23880          0.2768                  0.07615     0.0  \n",
       "19                0.12880          0.2977                  0.07259     1.0  \n",
       "20                0.07283          0.3184                  0.08183     1.0  \n",
       "21                0.06227          0.2450                  0.07773     1.0  \n",
       "22                0.23930          0.4667                  0.09946     0.0  \n",
       "23                0.20090          0.2822                  0.07526     0.0  \n",
       "24                0.20950          0.3613                  0.09564     0.0  \n",
       "25                0.25500          0.4066                  0.10590     0.0  \n",
       "26                0.27010          0.4264                  0.12750     0.0  \n",
       "27                0.14900          0.2341                  0.07421     0.0  \n",
       "28                0.20240          0.4027                  0.09876     0.0  \n",
       "29                0.14560          0.2756                  0.07919     0.0  \n",
       "..                    ...             ...                      ...     ...  \n",
       "539               0.05000          0.2790                  0.10660     1.0  \n",
       "540               0.06918          0.2329                  0.08134     1.0  \n",
       "541               0.12050          0.3187                  0.10230     1.0  \n",
       "542               0.10950          0.2722                  0.06956     1.0  \n",
       "543               0.07958          0.2473                  0.06443     1.0  \n",
       "544               0.06845          0.2249                  0.08492     1.0  \n",
       "545               0.07174          0.2642                  0.06953     1.0  \n",
       "546               0.02381          0.2681                  0.07399     1.0  \n",
       "547               0.08333          0.2691                  0.09479     1.0  \n",
       "548               0.03846          0.2552                  0.07920     1.0  \n",
       "549               0.03264          0.3059                  0.07626     1.0  \n",
       "550               0.00000          0.2458                  0.06592     1.0  \n",
       "551               0.06413          0.3169                  0.08032     1.0  \n",
       "552               0.06498          0.2407                  0.06484     1.0  \n",
       "553               0.02564          0.2435                  0.07393     1.0  \n",
       "554               0.06493          0.2372                  0.07242     1.0  \n",
       "555               0.09127          0.2226                  0.08283     1.0  \n",
       "556               0.02232          0.2262                  0.06742     1.0  \n",
       "557               0.00000          0.2475                  0.06969     1.0  \n",
       "558               0.11050          0.2258                  0.08004     1.0  \n",
       "559               0.09653          0.2112                  0.08732     1.0  \n",
       "560               0.10480          0.2250                  0.08321     1.0  \n",
       "561               0.00000          0.1566                  0.05905     1.0  \n",
       "562               0.23560          0.4089                  0.14090     0.0  \n",
       "563               0.25420          0.2929                  0.09873     0.0  \n",
       "564               0.22160          0.2060                  0.07115     0.0  \n",
       "565               0.16280          0.2572                  0.06637     0.0  \n",
       "566               0.14180          0.2218                  0.07820     0.0  \n",
       "567               0.26500          0.4087                  0.12400     0.0  \n",
       "568               0.00000          0.2871                  0.07039     1.0  \n",
       "\n",
       "[569 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_one():\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "      \n",
    "    return pd.DataFrame(np.c_[cancer['data'], cancer['target']],\n",
    "                  columns= np.append(cancer['feature_names'], ['target']))\n",
    "\n",
    "# Return your answer\n",
    "\n",
    "\n",
    "answer_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "What is the class distribution? (i.e. how many instances of `malignant` (encoded 0) and how many `benign` (encoded 1)?)\n",
    "\n",
    "*This function should return a Series named `target` of length 2 with integer values and index =* `['malignant', 'benign']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "malignant    212\n",
       "benign       357\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    cancerdf = answer_one()\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    malignant = len(cancerdf[cancerdf['target'] == 0])\n",
    "    benign = len(cancerdf[cancerdf['target'] == 1])\n",
    "    target = pd.Series(data = [malignant, benign], index = ['malignant', 'benign'])\n",
    "    return target # Return your answer\n",
    "\n",
    "\n",
    "answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "Split the DataFrame into `X` (the data) and `y` (the labels).\n",
    "\n",
    "*This function should return a tuple of length 2:* `(X, y)`*, where* \n",
    "* `X`*, a pandas DataFrame, has shape* `(569, 30)`\n",
    "* `y`*, a pandas Series, has shape* `(569,)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def answer_three():\n",
    "    cancerdf = answer_one()\n",
    "    \n",
    "    # Your code here\n",
    "    X = cancerdf.iloc[:, :30]\n",
    "    y = cancerdf.iloc[:, 30]\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "Using `train_test_split`, split `X` and `y` into training and test sets `(X_train, X_test, y_train, and y_test)`.\n",
    "\n",
    "**Set the random number generator state to 0 using `random_state=0` to make sure your results match the autograder!**\n",
    "\n",
    "*This function should return a tuple of length 4:* `(X_train, X_test, y_train, y_test)`*, where* \n",
    "* `X_train` *has shape* `(426, 30)`\n",
    "* `X_test` *has shape* `(143, 30)`\n",
    "* `y_train` *has shape* `(426,)`\n",
    "* `y_test` *has shape* `(143,)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def answer_four():\n",
    "    X, y = answer_three()\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "Using KNeighborsClassifier, fit a k-nearest neighbors (knn) classifier with `X_train`, `y_train` and using one nearest neighbor (`n_neighbors = 1`).\n",
    "\n",
    "*This function should return a * `sklearn.neighbors.classification.KNeighborsClassifier`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "def answer_five():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    \n",
    "    # Your code here\n",
    "    knn = KNeighborsClassifier(n_neighbors = 1)\n",
    "    return knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "Using your knn classifier, predict the class label using the mean value for each feature.\n",
    "\n",
    "Hint: You can use `cancerdf.mean()[:-1].values.reshape(1, -1)` which gets the mean value for each feature, ignores the target column, and reshapes the data from 1 dimension to 2 (necessary for the precict method of KNeighborsClassifier).\n",
    "\n",
    "*This function should return a numpy array either `array([ 0.])` or `array([ 1.])`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_six():\n",
    "    cancerdf = answer_one()\n",
    "    means = cancerdf.mean()[:-1].values.reshape(1, -1)\n",
    "    \n",
    "    # Your code here\n",
    "    predict = answer_five()\n",
    "    label = predict.predict(means)\n",
    "    return label\n",
    "    # Return your answer\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "Using your knn classifier, predict the class labels for the test set `X_test`.\n",
    "\n",
    "*This function should return a numpy array with shape `(143,)` and values either `0.0` or `1.0`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  1.,  1.,\n",
       "        1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,  0.,  1.,\n",
       "        1.,  1.,  1.,  0.,  1.,  0.,  1.,  0.,  1.,  0.,  1.,  0.,  1.,\n",
       "        0.,  0.,  1.,  0.,  1.,  0.,  0.,  1.,  1.,  1.,  0.,  0.,  1.,\n",
       "        0.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  0.,  0.,  1.,  1.,  0.,\n",
       "        1.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  1.,  0.,  1.,  1.,  1.,\n",
       "        1.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,  1.,  1.,  0.,  0.,  1.,\n",
       "        0.,  1.,  0.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        0.,  1.,  0.,  1.,  0.,  1.,  1.,  0.,  0.,  1.,  1.,  1.,  0.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  1.,  1.,  1.,  1.,  1.,\n",
       "        0.,  1.,  1.,  1.,  1.,  1.,  1.,  0.,  0.,  1.,  1.,  1.,  0.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_seven():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    knn = answer_five()\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return knn.predict(X_test)\n",
    "answer_seven()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8\n",
    "Find the score (mean accuracy) of your knn classifier using `X_test` and `y_test`.\n",
    "\n",
    "*This function should return a float between 0 and 1*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91608391608391604"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_eight():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    knn = answer_five()\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return knn.score(X_test, y_test)\n",
    "\n",
    "answer_eight()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional plot\n",
    "\n",
    "Try using the plotting function below to visualize the differet predicition scores between training and test sets, as well as malignant and benign cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accuracy_plot():\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    %matplotlib notebook\n",
    "\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "\n",
    "    # Find the training and testing accuracies by target value (i.e. malignant, benign)\n",
    "    mal_train_X = X_train[y_train==0]\n",
    "    mal_train_y = y_train[y_train==0]\n",
    "    ben_train_X = X_train[y_train==1]\n",
    "    ben_train_y = y_train[y_train==1]\n",
    "\n",
    "    mal_test_X = X_test[y_test==0]\n",
    "    mal_test_y = y_test[y_test==0]\n",
    "    ben_test_X = X_test[y_test==1]\n",
    "    ben_test_y = y_test[y_test==1]\n",
    "\n",
    "    knn = answer_five()\n",
    "\n",
    "    scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X, ben_train_y), \n",
    "              knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y)]\n",
    "\n",
    "\n",
    "    plt.figure()\n",
    "\n",
    "    # Plot the scores as a bar chart\n",
    "    bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868','#55a868'])\n",
    "\n",
    "    # directly label the score onto the bars\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'.format(height, 2), \n",
    "                     ha='center', color='w', fontsize=11)\n",
    "\n",
    "    # remove all the ticks (both axes), and tick labels on the Y axis\n",
    "    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
    "\n",
    "    # remove the frame of the chart\n",
    "    for spine in plt.gca().spines.values():\n",
    "        spine.set_visible(False)\n",
    "\n",
    "    plt.xticks([0,1,2,3], ['Malignant\\nTraining', 'Benign\\nTraining', 'Malignant\\nTest', 'Benign\\nTest'], alpha=0.8);\n",
    "    plt.title('Training and Test Accuracies for Malignant and Benign Cells', alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment the plotting function to see the visualization.\n",
    "\n",
    "**Comment out** the plotting function when submitting your notebook for grading. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "python-machine-learning",
   "graded_item_id": "f9SY5",
   "launcher_item_id": "oxndk",
   "part_id": "mh1Vo"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}