Assignment_1.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",
    "---"
   ]
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   "source": [
    "# Assignment 1 - Introduction to Machine Learning"
   ]
  },
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   "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)."
   ]
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      "Breast Cancer Wisconsin (Diagnostic) Database\n",
      "=============================================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry \n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
      "        13 is Radius SE, field 23 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      "References\n",
      "----------\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n",
      "\n"
     ]
    }
   ],
   "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"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The object returned by `load_breast_cancer()` is a scikit-learn Bunch object, which is similar to a dictionary."
   ]
  },
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   "source": [
    "cancer.keys()\n",
    "#cancer.items()\n",
    "cancer['target']"
   ]
  },
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   "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.*"
   ]
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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() "
   ]
  },
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   "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)"
   ]
  },
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       "<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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_one():\n",
    "    \n",
    "    # Your code here\n",
    "    cancerdf = pd.DataFrame(data = np.c_[cancer['data'],cancer['target']],\n",
    "                            columns=np.append(cancer['feature_names'],['target']))\n",
    "    return cancerdf # 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": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "benign       357\n",
       "malignant    212\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    cancerdf = answer_one()\n",
    "    \n",
    "    # Your code here\n",
    "    #malignant = sum(cancer['target']==0)\n",
    "    #benign = sum(cancer['target']==1)\n",
    "    malignant = int(cancerdf[cancerdf['target']==0]['target'].count())\n",
    "    benign = int(cancerdf[cancerdf['target']==1]['target'].count())\n",
    "    series = pd.Series({'malignant':malignant,'benign':benign})\n",
    "    return series# 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": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def answer_three():\n",
    "    cancerdf = answer_one()\n",
    "    \n",
    "    # Your code here\n",
    "    X = cancerdf.drop('target',axis=1)\n",
    "    y=cancerdf['target']\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": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def answer_four():\n",
    "    X, y = answer_three()\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y,train_size = 426, random_state=0)\n",
    "    # Your code here\n",
    "    \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": 94,
   "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",
    "    value = knn.fit(X_train,y_train)\n",
    "    \n",
    "    return value # Return your answer"
   ]
  },
  {
   "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": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def answer_six():\n",
    "    cancerdf = answer_one()\n",
    "    means = cancerdf.mean()[:-1].values.reshape(1, -1)\n",
    "    knn = answer_five()\n",
    "    predict = knn.predict(means)\n",
    "    # Your code here\n",
    "    \n",
    "    return predict# Return your answer"
   ]
  },
  {
   "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": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "    predict = knn.predict(X_test)\n",
    "    return predict# Return your answer"
   ]
  },
  {
   "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": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "    score = knn.score(X_test,y_test)\n",
    "    return score # Return your answer"
   ]
  },
  {
   "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": 98,
   "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": 99,
   "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>"
      ]
     },
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     "output_type": "display_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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}