Data_analysis_using_Pandas.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Data analysis using Pandas",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPnL0XdO9lJf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# You need this in colab to avoid statsmodels-scipy incompatibility\n",
        "# !pip install --upgrade git+https://github.com/statsmodels/statsmodels\n",
        "# Remember to restart kernel after this"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FzTviHTbm-f",
        "colab_type": "code",
        "outputId": "3ebc2b8f-3045-4474-a804-a7a6a217b050",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "# Downgrading scipy is faster than upgrading statsmodels above\n",
        "!pip install --user scipy==1.2\n",
        "# Remember to restart kernel after this"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: scipy==1.2 in /root/.local/lib/python3.6/site-packages (1.2.0)\n",
            "Requirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scipy==1.2) (1.16.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxFo80TGtdXz",
        "colab_type": "text"
      },
      "source": [
        "# Data analysis using Pandas!\n",
        "![Data analysis using Pandas!](https://nationalzoo.si.edu/sites/default/files/styles/1400x700_scale_and_crop/public/animals/giantpanda-003.jpg?itok=bqwa0Y6t&timestamp=1520537552)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3v7QfvestkZh",
        "colab_type": "text"
      },
      "source": [
        "# The lazy way\n",
        "- Just learning basic Python can take a week.\n",
        "- Learning to program in Python can take a year.\n",
        "- An intelligent hacker with 10+ years' experience in C/C++ could not write half-decent code in Python even after several years. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZaYbFiZaqjJ",
        "colab_type": "text"
      },
      "source": [
        "This is how I use R\n",
        "  - Look up a function and use it.\n",
        "  - Do not bother learning R syntax beyond what I need to get my work done. \n",
        "  \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIaUWYQHbFCh",
        "colab_type": "text"
      },
      "source": [
        "To do data analysis you do not need to worry (much) about Python syntax, but find the libraries and functions you will need.\n",
        "- You will learn as you go.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KILcUFgbRaw",
        "colab_type": "text"
      },
      "source": [
        "## Collecting our tools"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDl3IH_3u2R1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import requests\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.api as smapi\n",
        "import sklearn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FWaZhNUdZiG",
        "colab_type": "text"
      },
      "source": [
        "## Collecting our raw  materials "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BG05tUizw1_i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'\n",
        "attr_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.names'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnfX2SUZ2vMe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "attrs_data = requests.get(attr_url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hj6jepp26XO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "attrs = attrs_data.content.decode('utf-8')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40O8EHTsdmZU",
        "colab_type": "text"
      },
      "source": [
        "### Take a look at what we have\n",
        " Skim through it, pay attention to 5-8"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_68jjqF3MeB",
        "colab_type": "code",
        "outputId": "10b7b7b2-aa35-4830-d2b3-e513c2aeb831",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2324
        }
      },
      "source": [
        "print(attrs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Citation Request:\n",
            "   This breast cancer databases was obtained from the University of Wisconsin\n",
            "   Hospitals, Madison from Dr. William H. Wolberg.  If you publish results\n",
            "   when using this database, then please include this information in your\n",
            "   acknowledgements.  Also, please cite one or more of:\n",
            "\n",
            "   1. O. L. Mangasarian and W. H. Wolberg: \"Cancer diagnosis via linear \n",
            "      programming\", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.\n",
            "\n",
            "   2. William H. Wolberg and O.L. Mangasarian: \"Multisurface method of \n",
            "      pattern separation for medical diagnosis applied to breast cytology\", \n",
            "      Proceedings of the National Academy of Sciences, U.S.A., Volume 87, \n",
            "      December 1990, pp 9193-9196.\n",
            "\n",
            "   3. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: \"Pattern recognition \n",
            "      via linear programming: Theory and application to medical diagnosis\", \n",
            "      in: \"Large-scale numerical optimization\", Thomas F. Coleman and Yuying\n",
            "      Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30.\n",
            "\n",
            "   4. K. P. Bennett & O. L. Mangasarian: \"Robust linear programming \n",
            "      discrimination of two linearly inseparable sets\", Optimization Methods\n",
            "      and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).\n",
            "\n",
            "1. Title: Wisconsin Breast Cancer Database (January 8, 1991)\n",
            "\n",
            "2. Sources:\n",
            "   -- Dr. WIlliam H. Wolberg (physician)\n",
            "      University of Wisconsin Hospitals\n",
            "      Madison, Wisconsin\n",
            "      USA\n",
            "   -- Donor: Olvi Mangasarian (mangasarian@cs.wisc.edu)\n",
            "      Received by David W. Aha (aha@cs.jhu.edu)\n",
            "   -- Date: 15 July 1992\n",
            "\n",
            "3. Past Usage:\n",
            "\n",
            "   Attributes 2 through 10 have been used to represent instances.\n",
            "   Each instance has one of 2 possible classes: benign or malignant.\n",
            "\n",
            "   1. Wolberg,~W.~H., \\& Mangasarian,~O.~L. (1990). Multisurface method of \n",
            "      pattern separation for medical diagnosis applied to breast cytology. In\n",
            "      {\\it Proceedings of the National Academy of Sciences}, {\\it 87},\n",
            "      9193--9196.\n",
            "      -- Size of data set: only 369 instances (at that point in time)\n",
            "      -- Collected classification results: 1 trial only\n",
            "      -- Two pairs of parallel hyperplanes were found to be consistent with\n",
            "         50% of the data\n",
            "         -- Accuracy on remaining 50% of dataset: 93.5%\n",
            "      -- Three pairs of parallel hyperplanes were found to be consistent with\n",
            "         67% of data\n",
            "         -- Accuracy on remaining 33% of dataset: 95.9%\n",
            "\n",
            "   2. Zhang,~J. (1992). Selecting typical instances in instance-based\n",
            "      learning.  In {\\it Proceedings of the Ninth International Machine\n",
            "      Learning Conference} (pp. 470--479).  Aberdeen, Scotland: Morgan\n",
            "      Kaufmann.\n",
            "      -- Size of data set: only 369 instances (at that point in time)\n",
            "      -- Applied 4 instance-based learning algorithms \n",
            "      -- Collected classification results averaged over 10 trials\n",
            "      -- Best accuracy result: \n",
            "         -- 1-nearest neighbor: 93.7%\n",
            "         -- trained on 200 instances, tested on the other 169\n",
            "      -- Also of interest:\n",
            "         -- Using only typical instances: 92.2% (storing only 23.1 instances)\n",
            "         -- trained on 200 instances, tested on the other 169\n",
            "\n",
            "4. Relevant Information:\n",
            "\n",
            "   Samples arrive periodically as Dr. Wolberg reports his clinical cases.\n",
            "   The database therefore reflects this chronological grouping of the data.\n",
            "   This grouping information appears immediately below, having been removed\n",
            "   from the data itself:\n",
            "\n",
            "     Group 1: 367 instances (January 1989)\n",
            "     Group 2:  70 instances (October 1989)\n",
            "     Group 3:  31 instances (February 1990)\n",
            "     Group 4:  17 instances (April 1990)\n",
            "     Group 5:  48 instances (August 1990)\n",
            "     Group 6:  49 instances (Updated January 1991)\n",
            "     Group 7:  31 instances (June 1991)\n",
            "     Group 8:  86 instances (November 1991)\n",
            "     -----------------------------------------\n",
            "     Total:   699 points (as of the donated datbase on 15 July 1992)\n",
            "\n",
            "   Note that the results summarized above in Past Usage refer to a dataset\n",
            "   of size 369, while Group 1 has only 367 instances.  This is because it\n",
            "   originally contained 369 instances; 2 were removed.  The following\n",
            "   statements summarizes changes to the original Group 1's set of data:\n",
            "\n",
            "   #####  Group 1 : 367 points: 200B 167M (January 1989)\n",
            "   #####  Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805\n",
            "   #####  Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record\n",
            "   #####                  : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial\n",
            "   #####                  : Changed 0 to 1 in field 6 of sample 1219406\n",
            "   #####                  : Changed 0 to 1 in field 8 of following sample:\n",
            "   #####                  : 1182404,2,3,1,1,1,2,0,1,1,1\n",
            "\n",
            "5. Number of Instances: 699 (as of 15 July 1992)\n",
            "\n",
            "6. Number of Attributes: 10 plus the class attribute\n",
            "\n",
            "7. Attribute Information: (class attribute has been moved to last column)\n",
            "\n",
            "   #  Attribute                     Domain\n",
            "   -- -----------------------------------------\n",
            "   1. Sample code number            id number\n",
            "   2. Clump Thickness               1 - 10\n",
            "   3. Uniformity of Cell Size       1 - 10\n",
            "   4. Uniformity of Cell Shape      1 - 10\n",
            "   5. Marginal Adhesion             1 - 10\n",
            "   6. Single Epithelial Cell Size   1 - 10\n",
            "   7. Bare Nuclei                   1 - 10\n",
            "   8. Bland Chromatin               1 - 10\n",
            "   9. Normal Nucleoli               1 - 10\n",
            "  10. Mitoses                       1 - 10\n",
            "  11. Class:                        (2 for benign, 4 for malignant)\n",
            "\n",
            "8. Missing attribute values: 16\n",
            "\n",
            "   There are 16 instances in Groups 1 to 6 that contain a single missing \n",
            "   (i.e., unavailable) attribute value, now denoted by \"?\".  \n",
            "\n",
            "9. Class distribution:\n",
            " \n",
            "   Benign: 458 (65.5%)\n",
            "   Malignant: 241 (34.5%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlVAQMvZeMWe",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0I5yJrzeN8j",
        "colab_type": "text"
      },
      "source": [
        "# Pause here: Let us discuss the data.\n",
        "  - How many entries do we expect?\n",
        "  - How many columns do we expect?\n",
        "  - What is \"domain\"?\n",
        "  - Is there any data missing? How can we find them?\n",
        "  - What is special about the last attribute?\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "draMVO0Gfxsx",
        "colab_type": "text"
      },
      "source": [
        "Let us name the columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POgGNYNx3OSP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cols = ['sample', 'clump_thickness', 'size_uniformity',\n",
        "        'shape_uniformity', 'adhesion', 'cell_size', \n",
        "        'bare_nuclei', 'bland_chromatin', 'normal_nuclei', \n",
        "        'mitoses', 'malignant']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3E_mpAKyxMa7",
        "colab_type": "text"
      },
      "source": [
        "- `left = right_hand_side` is how you assign a variable in Python.\n",
        "- Each quoted word on the RHS is a string. You can use single or double quotes for strings. \n",
        "- The whole RHS thing is called a `list`.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eyr7hmmjgAiS",
        "colab_type": "text"
      },
      "source": [
        "Pandas can read from a URL (older versions could not) into a `DataFrame`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siyFVELf3rfT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv(data_url, names=cols)\n",
        "# data = pd.read_csv(data_url, names=cols, na_values='?')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q62jYp0XyDiT",
        "colab_type": "text"
      },
      "source": [
        "We can make `head` or `tail` of the data!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doUUj3_uCDtV",
        "colab_type": "code",
        "outputId": "33c456d1-d747-4576-9eae-da3f25d50980",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sample</th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bare_nuclei</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000025</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1002945</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1015425</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1016277</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1017023</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sample  clump_thickness  size_uniformity  ...  normal_nuclei  mitoses  malignant\n",
              "0  1000025                5                1  ...              1        1          2\n",
              "1  1002945                5                4  ...              2        1          2\n",
              "2  1015425                3                1  ...              1        1          2\n",
              "3  1016277                6                8  ...              7        1          2\n",
              "4  1017023                4                1  ...              1        1          2\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5RxrnoCCHMq",
        "colab_type": "code",
        "outputId": "bbb9d1e0-89a5-44b4-cea1-2de3c5261be7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "data.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sample</th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bare_nuclei</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>694</th>\n",
              "      <td>776715</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>841769</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>696</th>\n",
              "      <td>888820</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>897471</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>698</th>\n",
              "      <td>897471</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sample  clump_thickness  ...  mitoses  malignant\n",
              "694  776715                3  ...        1          2\n",
              "695  841769                2  ...        1          2\n",
              "696  888820                5  ...        2          4\n",
              "697  897471                4  ...        1          4\n",
              "698  897471                4  ...        1          4\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1Bm-6muBbpK",
        "colab_type": "code",
        "outputId": "1c3768c9-080a-419c-8528-6a925c2f78bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(data[data.malignant == 4])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmN_1giVyNyD",
        "colab_type": "text"
      },
      "source": [
        "If that is not good enough, ask Pandas to describe the data!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYOfTKmf3w_W",
        "colab_type": "code",
        "outputId": "3a4c53e0-663c-483f-b2c6-9bf406665dc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sample</th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.990000e+02</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.071704e+06</td>\n",
              "      <td>4.417740</td>\n",
              "      <td>3.134478</td>\n",
              "      <td>3.207439</td>\n",
              "      <td>2.806867</td>\n",
              "      <td>3.216023</td>\n",
              "      <td>3.437768</td>\n",
              "      <td>2.866953</td>\n",
              "      <td>1.589413</td>\n",
              "      <td>2.689557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.170957e+05</td>\n",
              "      <td>2.815741</td>\n",
              "      <td>3.051459</td>\n",
              "      <td>2.971913</td>\n",
              "      <td>2.855379</td>\n",
              "      <td>2.214300</td>\n",
              "      <td>2.438364</td>\n",
              "      <td>3.053634</td>\n",
              "      <td>1.715078</td>\n",
              "      <td>0.951273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.163400e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.706885e+05</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.171710e+06</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.238298e+06</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.345435e+07</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             sample  clump_thickness  ...     mitoses   malignant\n",
              "count  6.990000e+02       699.000000  ...  699.000000  699.000000\n",
              "mean   1.071704e+06         4.417740  ...    1.589413    2.689557\n",
              "std    6.170957e+05         2.815741  ...    1.715078    0.951273\n",
              "min    6.163400e+04         1.000000  ...    1.000000    2.000000\n",
              "25%    8.706885e+05         2.000000  ...    1.000000    2.000000\n",
              "50%    1.171710e+06         4.000000  ...    1.000000    2.000000\n",
              "75%    1.238298e+06         6.000000  ...    1.000000    4.000000\n",
              "max    1.345435e+07        10.000000  ...   10.000000    4.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uaaDy_YnzXTE",
        "colab_type": "text"
      },
      "source": [
        "We don't want the sample code number in our statistics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGEBFKyz4LyY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.set_index('sample', inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hC-UonlU4OGQ",
        "colab_type": "code",
        "outputId": "3dd1b089-7b57-4bb8-9a10-f687e108eac2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bare_nuclei</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sample</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1000025</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1002945</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015425</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1016277</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1017023</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         clump_thickness  size_uniformity  ...  mitoses  malignant\n",
              "sample                                     ...                    \n",
              "1000025                5                1  ...        1          2\n",
              "1002945                5                4  ...        1          2\n",
              "1015425                3                1  ...        1          2\n",
              "1016277                6                8  ...        1          2\n",
              "1017023                4                1  ...        1          2\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LT_meK-xy3P8",
        "colab_type": "text"
      },
      "source": [
        "Some people like pictures more than numbers!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUWaHib24Dmx",
        "colab_type": "code",
        "outputId": "8a46b3af-2191-4942-9eb0-08a8ea78057f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "data.boxplot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee24c50fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WVilKaUs1pVdTqW2l1U4l1uTecY9fmjU1B/Dq6mp331dTdXW1h4g7tNq7TwFLvI0ZncrU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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnyTG3byz2OW",
        "colab_type": "text"
      },
      "source": [
        "Let us make the figure a little wider!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-s3h2DszLL2",
        "colab_type": "code",
        "outputId": "d9a14650-b96f-4842-fb16-b6452feb7b98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "source": [
        "data.boxplot(figsize=(12, 5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee24b92518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QQfXuGgYIYRgAACAoBeENGzbokEMO0YYNG7oCMYYnbpMY5iZdtEpPP9OZS1ut82/PpZ39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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-fMhylB0AzZ",
        "colab_type": "text"
      },
      "source": [
        "We can choose which columns to plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVIgxbB05IYo",
        "colab_type": "code",
        "outputId": "5c5c93f5-8d5f-4e1f-e69a-36437413909e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "data.boxplot(['mitoses'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee24c09be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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39wJfioiPAUebxy4CboiIx4AKWAq8Gfg6cGNEfIpjb9E25loQBl0lOHzc9vRx+9O0/kXo\nHwBuA34d+FZE9AMB/F5mvrP5682Z+VRm/h3H/jbwErAzIt7X4jWlthh09ZqfAq8ByMz/AyYi4nea\nP7sMeCgi+oA3ZeYo8CngHGAZ8CDwiYgIgIh4V/O/vwQ8k5mfBb4KnN/F3480q9XZi1RX24E7I+I6\nYANwOfC3EXEW8AzH1twXAV9sLskE8NnmGvpfAp8B9jaj/yxwMfBR4LKImAL2Azd1+zclgf8oKknF\ncMlFkgph0CWpEAZdkgph0CWpEAZdkgph0CWpEAZdkgph0CWpEP8PK4c1P9Cy7zIAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53Jmd53K0Glh",
        "colab_type": "text"
      },
      "source": [
        "The generic `plot` command works with a `kind` parameter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ioO49PPc5Tgm",
        "colab_type": "code",
        "outputId": "c07d927e-af83-402e-9675-d69c4acef9e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "data.plot(kind='box')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee24a0ff28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uJlwhtxJ22JLMItcDvzKz8wlhsDM2xp1XRfggsTm/BSab2ZlsP4lrgWfNbBuh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\nEUmcgl5EJHH/H4u8aNqfn2spAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvVjgvsx63aA",
        "colab_type": "code",
        "outputId": "2aaee68f-889e-48d9-8db2-3204a17f2c2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "data.plot(kind='hist')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee24af5d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oKMgVa1paGt26dSMwMJARI0aQnJzs0WeWXWze4GmCOCci9TNeiEgD4IJ3QlJK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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQRmadTD7fw6",
        "colab_type": "code",
        "outputId": "3637f33d-64c2-4b8c-a355-97faf6291c66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "source": [
        "plots = data.plot(kind='hist', subplots=True, figsize=(10/2.54, 20/2.54))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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q1aoBUKVKFbKysgB45JFH6NatG++++y5paWl07do15zznDpu6UyQYOnQo//d/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\n",
            "text/plain": [
              "<Figure size 283.465x566.929 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXVmRWh0yg0K",
        "colab_type": "text"
      },
      "source": [
        "You can select some columns in a `DataFrame`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-PINmlBhgSS",
        "colab_type": "code",
        "outputId": "ce8a2cc3-c29e-4a7e-ba84-b0764d5f3432",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "data[['clump_thickness', 'mitoses']].plot(kind='hist', alpha=0.5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee24964860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QbQTwYpguCvOE5Qvc3eOqT0REjiyJ+yB+DtxoZmtJnWOYFdpnAaeE9huBiQnUJiIiQaPK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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-tc4diW5zwz",
        "colab_type": "code",
        "outputId": "fc2b374a-62d1-43f4-a81d-52177aab614a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "data[['mitoses', 'cell_size']].plot(kind='density')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fee24599160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZMZYY4oS/xpAbyXrPFev77XgGp8YARKcD+uBaZ5S1MWbYuZoYROR8EdkqImUi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\nL/8fL5i+jUDeePMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0k8Ub2E0elz",
        "colab_type": "text"
      },
      "source": [
        "It is very easy to look for correlations between columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWzUDiCe6mDT",
        "colab_type": "code",
        "outputId": "4f8a3ae3-e631-4191-f333-e28e161b7d9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>clump_thickness</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.644913</td>\n",
              "      <td>0.654589</td>\n",
              "      <td>0.486356</td>\n",
              "      <td>0.521816</td>\n",
              "      <td>0.558428</td>\n",
              "      <td>0.535835</td>\n",
              "      <td>0.350034</td>\n",
              "      <td>0.716001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size_uniformity</th>\n",
              "      <td>0.644913</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.906882</td>\n",
              "      <td>0.705582</td>\n",
              "      <td>0.751799</td>\n",
              "      <td>0.755721</td>\n",
              "      <td>0.722865</td>\n",
              "      <td>0.458693</td>\n",
              "      <td>0.817904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shape_uniformity</th>\n",
              "      <td>0.654589</td>\n",
              "      <td>0.906882</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.683079</td>\n",
              "      <td>0.719668</td>\n",
              "      <td>0.735948</td>\n",
              "      <td>0.719446</td>\n",
              "      <td>0.438911</td>\n",
              "      <td>0.818934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>adhesion</th>\n",
              "      <td>0.486356</td>\n",
              "      <td>0.705582</td>\n",
              "      <td>0.683079</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.599599</td>\n",
              "      <td>0.666715</td>\n",
              "      <td>0.603352</td>\n",
              "      <td>0.417633</td>\n",
              "      <td>0.696800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cell_size</th>\n",
              "      <td>0.521816</td>\n",
              "      <td>0.751799</td>\n",
              "      <td>0.719668</td>\n",
              "      <td>0.599599</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.616102</td>\n",
              "      <td>0.628881</td>\n",
              "      <td>0.479101</td>\n",
              "      <td>0.682785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bland_chromatin</th>\n",
              "      <td>0.558428</td>\n",
              "      <td>0.755721</td>\n",
              "      <td>0.735948</td>\n",
              "      <td>0.666715</td>\n",
              "      <td>0.616102</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.665878</td>\n",
              "      <td>0.344169</td>\n",
              "      <td>0.756616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normal_nuclei</th>\n",
              "      <td>0.535835</td>\n",
              "      <td>0.722865</td>\n",
              "      <td>0.719446</td>\n",
              "      <td>0.603352</td>\n",
              "      <td>0.628881</td>\n",
              "      <td>0.665878</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.428336</td>\n",
              "      <td>0.712244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mitoses</th>\n",
              "      <td>0.350034</td>\n",
              "      <td>0.458693</td>\n",
              "      <td>0.438911</td>\n",
              "      <td>0.417633</td>\n",
              "      <td>0.479101</td>\n",
              "      <td>0.344169</td>\n",
              "      <td>0.428336</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.423170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malignant</th>\n",
              "      <td>0.716001</td>\n",
              "      <td>0.817904</td>\n",
              "      <td>0.818934</td>\n",
              "      <td>0.696800</td>\n",
              "      <td>0.682785</td>\n",
              "      <td>0.756616</td>\n",
              "      <td>0.712244</td>\n",
              "      <td>0.423170</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  clump_thickness  size_uniformity  ...   mitoses  malignant\n",
              "clump_thickness          1.000000         0.644913  ...  0.350034   0.716001\n",
              "size_uniformity          0.644913         1.000000  ...  0.458693   0.817904\n",
              "shape_uniformity         0.654589         0.906882  ...  0.438911   0.818934\n",
              "adhesion                 0.486356         0.705582  ...  0.417633   0.696800\n",
              "cell_size                0.521816         0.751799  ...  0.479101   0.682785\n",
              "bland_chromatin          0.558428         0.755721  ...  0.344169   0.756616\n",
              "normal_nuclei            0.535835         0.722865  ...  0.428336   0.712244\n",
              "mitoses                  0.350034         0.458693  ...  1.000000   0.423170\n",
              "malignant                0.716001         0.817904  ...  0.423170   1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
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      "source": [
        "data.corr('spearman')"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "      <td>0.624515</td>\n",
              "      <td>0.634310</td>\n",
              "      <td>0.446992</td>\n",
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              "      <td>0.667809</td>\n",
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              "      <td>0.639507</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.662309</td>\n",
              "      <td>0.386944</td>\n",
              "      <td>0.740350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normal_nuclei</th>\n",
              "      <td>0.570398</td>\n",
              "      <td>0.757199</td>\n",
              "      <td>0.725268</td>\n",
              "      <td>0.634310</td>\n",
              "      <td>0.705997</td>\n",
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              "      <td>0.504140</td>\n",
              "      <td>0.743823</td>\n",
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              "    <tr>\n",
              "      <th>mitoses</th>\n",
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              "      <td>0.508780</td>\n",
              "      <td>0.472749</td>\n",
              "      <td>0.446992</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.526766</td>\n",
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              "    <tr>\n",
              "      <th>malignant</th>\n",
              "      <td>0.682452</td>\n",
              "      <td>0.855487</td>\n",
              "      <td>0.836394</td>\n",
              "      <td>0.727995</td>\n",
              "      <td>0.762731</td>\n",
              "      <td>0.740350</td>\n",
              "      <td>0.743823</td>\n",
              "      <td>0.526766</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                  clump_thickness  size_uniformity  ...   mitoses  malignant\n",
              "clump_thickness          1.000000         0.666463  ...  0.418717   0.682452\n",
              "size_uniformity          0.666463         1.000000  ...  0.508780   0.855487\n",
              "shape_uniformity         0.664121         0.891904  ...  0.472749   0.836394\n",
              "adhesion                 0.541593         0.742517  ...  0.446992   0.727995\n",
              "cell_size                0.583979         0.787159  ...  0.480255   0.762731\n",
              "bland_chromatin          0.538093         0.719330  ...  0.386944   0.740350\n",
              "normal_nuclei            0.570398         0.757199  ...  0.504140   0.743823\n",
              "mitoses                  0.418717         0.508780  ...  1.000000   0.526766\n",
              "malignant                0.682452         0.855487  ...  0.526766   1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
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      "source": [
        "data.corr('kendall')"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "data": {
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              "      <td>0.695242</td>\n",
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              "    <tr>\n",
              "      <th>bland_chromatin</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.561403</td>\n",
              "      <td>0.333247</td>\n",
              "      <td>0.654153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normal_nuclei</th>\n",
              "      <td>0.462330</td>\n",
              "      <td>0.663477</td>\n",
              "      <td>0.628145</td>\n",
              "      <td>0.547012</td>\n",
              "      <td>0.608636</td>\n",
              "      <td>0.561403</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.453633</td>\n",
              "      <td>0.688275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mitoses</th>\n",
              "      <td>0.353145</td>\n",
              "      <td>0.455333</td>\n",
              "      <td>0.418632</td>\n",
              "      <td>0.400285</td>\n",
              "      <td>0.431205</td>\n",
              "      <td>0.333247</td>\n",
              "      <td>0.453633</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.508987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malignant</th>\n",
              "      <td>0.593306</td>\n",
              "      <td>0.778374</td>\n",
              "      <td>0.753993</td>\n",
              "      <td>0.667469</td>\n",
              "      <td>0.695242</td>\n",
              "      <td>0.654153</td>\n",
              "      <td>0.688275</td>\n",
              "      <td>0.508987</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  clump_thickness  size_uniformity  ...   mitoses  malignant\n",
              "clump_thickness          1.000000         0.536527  ...  0.353145   0.593306\n",
              "size_uniformity          0.536527         1.000000  ...  0.455333   0.778374\n",
              "shape_uniformity         0.534168         0.814805  ...  0.418632   0.753993\n",
              "adhesion                 0.433447         0.639617  ...  0.400285   0.667469\n",
              "cell_size                0.470035         0.681967  ...  0.431205   0.695242\n",
              "bland_chromatin          0.417864         0.599490  ...  0.333247   0.654153\n",
              "normal_nuclei            0.462330         0.663477  ...  0.453633   0.688275\n",
              "mitoses                  0.353145         0.455333  ...  1.000000   0.508987\n",
              "malignant                0.593306         0.778374  ...  0.508987   1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DdDfGW9y0k9X",
        "colab_type": "text"
      },
      "source": [
        "The two classes malignant and benign were assigned 4 and 2. We can switch them to 1 and 0."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFjItsmyAorE",
        "colab_type": "code",
        "outputId": "975da19f-e109-47b8-b14e-e5a7d7f8957c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "data['malignant'] = (data['malignant'] == 4)\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bare_nuclei</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sample</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1016277</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1017023</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         clump_thickness  size_uniformity  ...  mitoses  malignant\n",
              "sample                                     ...                    \n",
              "1000025                5                1  ...        1      False\n",
              "1002945                5                4  ...        1      False\n",
              "1015425                3                1  ...        1      False\n",
              "1016277                6                8  ...        1      False\n",
              "1017023                4                1  ...        1      False\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDgWWUtF13Hh",
        "colab_type": "code",
        "outputId": "26d85094-32bc-4178-807e-b98f4893a2e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "data.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        clump_thickness  size_uniformity  ...  mitoses  malignant\n",
              "sample                                    ...                    \n",
              "776715                3                1  ...        1          0\n",
              "841769                2                1  ...        1          0\n",
              "888820                5               10  ...        2          1\n",
              "897471                4                8  ...        1          1\n",
              "897471                4                8  ...        1          1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRL8pXWH05Rl",
        "colab_type": "text"
      },
      "source": [
        "Booleans are not good for math. Turn them into numbers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEsh16zziKSJ",
        "colab_type": "code",
        "outputId": "43f68d4b-53ba-4ff1-9a4f-8aafe65b42f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "data['malignant'] = data['malignant'].astype(int)\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bare_nuclei</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
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              "      <th>sample</th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>1016277</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "      <th>1017023</th>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         clump_thickness  size_uniformity  ...  mitoses  malignant\n",
              "sample                                     ...                    \n",
              "1000025                5                1  ...        1          0\n",
              "1002945                5                4  ...        1          0\n",
              "1015425                3                1  ...        1          0\n",
              "1016277                6                8  ...        1          0\n",
              "1017023                4                1  ...        1          0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UMmKU8s0_3e",
        "colab_type": "text"
      },
      "source": [
        "Statsmodels tries to support R-like formulas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LSpsV_VtDq4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "formula = 'malignant ~ clump_thickness + size_uniformity + shape_uniformity + adhesion + cell_size + bland_chromatin + normal_nuclei + mitoses'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jg7rJBtR8vjY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = smf.logit(formula, data=data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhzDmOmZAsLt",
        "colab_type": "code",
        "outputId": "7769154e-2144-4d44-d10c-4aa795e5d1f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "fit = model.fit() "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimization terminated successfully.\n",
            "         Current function value: 0.100619\n",
            "         Iterations 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUrOYd-uBJpI",
        "colab_type": "code",
        "outputId": "1ad12660-90bd-4d3c-8f7d-54e6e6a3af35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "fit.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Logit Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>     <td>malignant</td>    <th>  No. Observations:  </th>   <td>   699</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>   <td>   690</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>   <td>     8</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Mon, 24 Jun 2019</td> <th>  Pseudo R-squ.:     </th>   <td>0.8438</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>04:52:58</td>     <th>  Log-Likelihood:    </th>  <td> -70.333</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th>  <td> -450.26</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>9.174e-159</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "          <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th>        <td>   -9.9456</td> <td>    1.032</td> <td>   -9.634</td> <td> 0.000</td> <td>  -11.969</td> <td>   -7.922</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>clump_thickness</th>  <td>    0.5776</td> <td>    0.119</td> <td>    4.852</td> <td> 0.000</td> <td>    0.344</td> <td>    0.811</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>size_uniformity</th>  <td>   -0.0116</td> <td>    0.176</td> <td>   -0.066</td> <td> 0.948</td> <td>   -0.356</td> <td>    0.333</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>shape_uniformity</th> <td>    0.5679</td> <td>    0.191</td> <td>    2.969</td> <td> 0.003</td> <td>    0.193</td> <td>    0.943</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>adhesion</th>         <td>    0.3137</td> <td>    0.100</td> <td>    3.125</td> <td> 0.002</td> <td>    0.117</td> <td>    0.510</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>cell_size</th>        <td>    0.1306</td> <td>    0.141</td> <td>    0.929</td> <td> 0.353</td> <td>   -0.145</td> <td>    0.406</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>bland_chromatin</th>  <td>    0.5800</td> <td>    0.146</td> <td>    3.984</td> <td> 0.000</td> <td>    0.295</td> <td>    0.865</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>normal_nuclei</th>    <td>    0.1232</td> <td>    0.099</td> <td>    1.248</td> <td> 0.212</td> <td>   -0.070</td> <td>    0.317</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>mitoses</th>          <td>    0.6079</td> <td>    0.324</td> <td>    1.875</td> <td> 0.061</td> <td>   -0.027</td> <td>    1.243</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                           Logit Regression Results                           \n",
              "==============================================================================\n",
              "Dep. Variable:              malignant   No. Observations:                  699\n",
              "Model:                          Logit   Df Residuals:                      690\n",
              "Method:                           MLE   Df Model:                            8\n",
              "Date:                Mon, 24 Jun 2019   Pseudo R-squ.:                  0.8438\n",
              "Time:                        04:52:58   Log-Likelihood:                -70.333\n",
              "converged:                       True   LL-Null:                       -450.26\n",
              "                                        LLR p-value:                9.174e-159\n",
              "====================================================================================\n",
              "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------\n",
              "Intercept           -9.9456      1.032     -9.634      0.000     -11.969      -7.922\n",
              "clump_thickness      0.5776      0.119      4.852      0.000       0.344       0.811\n",
              "size_uniformity     -0.0116      0.176     -0.066      0.948      -0.356       0.333\n",
              "shape_uniformity     0.5679      0.191      2.969      0.003       0.193       0.943\n",
              "adhesion             0.3137      0.100      3.125      0.002       0.117       0.510\n",
              "cell_size            0.1306      0.141      0.929      0.353      -0.145       0.406\n",
              "bland_chromatin      0.5800      0.146      3.984      0.000       0.295       0.865\n",
              "normal_nuclei        0.1232      0.099      1.248      0.212      -0.070       0.317\n",
              "mitoses              0.6079      0.324      1.875      0.061      -0.027       1.243\n",
              "====================================================================================\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYek1oJD1QeU",
        "colab_type": "text"
      },
      "source": [
        "That is good for statistics. We want to make predictions using our model.\n",
        "\n",
        "# Enter AI."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8-0YRTHB6gU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn.linear_model as sklm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Er_46IlIEjc_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5ZrPhjvE2km",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data.drop(columns='malignant')\n",
        "y = data['malignant']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pfj7HlaGFnlg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzclgpVHF2OV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = sklm.LogisticRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJXDYjFIGB69",
        "colab_type": "code",
        "outputId": "9e6f7c83-1233-40a1-cc29-109fad6bdbf5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "source": [
        "fit = model.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-187-f75359abc795>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order=\"C\",\n\u001b[0;32m-> 1532\u001b[0;31m                          accept_large_sparse=solver != 'liblinear')\n\u001b[0m\u001b[1;32m   1533\u001b[0m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    717\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 719\u001b[0;31m                     estimator=estimator)\n\u001b[0m\u001b[1;32m    720\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    494\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m                 \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    497\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \"\"\"\n\u001b[0;32m--> 538\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '?'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAx4QlJcjKN7",
        "colab_type": "text"
      },
      "source": [
        "# Pause here: What is this telling us?\n",
        "  - Remember missing values?\n",
        "  - So far the libraries have been too friendly and let us shoot ourselves in the foot!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qStdPrydkvED",
        "colab_type": "text"
      },
      "source": [
        "We can go back and reload the data - uncomment the commented line.\n",
        "\n",
        "Or just replace the `?` entries with a missing value representative."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ndzn7xaKlDC4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data2 = data.replace('?', np.nan)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vubkq5jCmcM7",
        "colab_type": "code",
        "outputId": "d5e389e5-e20c-4989-e0e7-26c11f193507",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "data2.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bare_nuclei</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sample</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1000025</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1002945</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015425</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1016277</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1017023</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         clump_thickness  size_uniformity  ...  mitoses  malignant\n",
              "sample                                     ...                    \n",
              "1000025                5                1  ...        1          0\n",
              "1002945                5                4  ...        1          0\n",
              "1015425                3                1  ...        1          0\n",
              "1016277                6                8  ...        1          0\n",
              "1017023                4                1  ...        1          0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hU9gD6S-2xLf",
        "colab_type": "text"
      },
      "source": [
        "`DataFrame.isnull()` and `DataFrame.isna()` functions are there to check for missing values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7NAHslCGHR5",
        "colab_type": "code",
        "outputId": "5c0303ca-9a49-4242-8857-033c320b0721",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2023
        }
      },
      "source": [
        "data2.isnull()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clump_thickness</th>\n",
              "      <th>size_uniformity</th>\n",
              "      <th>shape_uniformity</th>\n",
              "      <th>adhesion</th>\n",
              "      <th>cell_size</th>\n",
              "      <th>bare_nuclei</th>\n",
              "      <th>bland_chromatin</th>\n",
              "      <th>normal_nuclei</th>\n",
              "      <th>mitoses</th>\n",
              "      <th>malignant</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sample</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1000025</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1002945</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015425</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1016277</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1017023</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1017122</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018099</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018561</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1033078</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1033078</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1035283</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1036172</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1041801</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1043999</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1044572</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1047630</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1048672</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1049815</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1050670</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1050718</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1054590</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1054593</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1056784</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1057013</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1059552</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1065726</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1066373</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1066979</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1067444</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1070935</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1350423</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1352848</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1353092</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1354840</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1354840</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1355260</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1365075</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1365328</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368267</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368273</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368882</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1369821</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1371026</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1371920</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466906</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466906</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>534555</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536708</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566346</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>603148</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>654546</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>654546</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695091</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>714039</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763235</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>776715</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>841769</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888820</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>897471</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>897471</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>699 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         clump_thickness  size_uniformity  ...  mitoses  malignant\n",
              "sample                                     ...                    \n",
              "1000025            False            False  ...    False      False\n",
              "1002945            False            False  ...    False      False\n",
              "1015425            False            False  ...    False      False\n",
              "1016277            False            False  ...    False      False\n",
              "1017023            False            False  ...    False      False\n",
              "1017122            False            False  ...    False      False\n",
              "1018099            False            False  ...    False      False\n",
              "1018561            False            False  ...    False      False\n",
              "1033078            False            False  ...    False      False\n",
              "1033078            False            False  ...    False      False\n",
              "1035283            False            False  ...    False      False\n",
              "1036172            False            False  ...    False      False\n",
              "1041801            False            False  ...    False      False\n",
              "1043999            False            False  ...    False      False\n",
              "1044572            False            False  ...    False      False\n",
              "1047630            False            False  ...    False      False\n",
              "1048672            False            False  ...    False      False\n",
              "1049815            False            False  ...    False      False\n",
              "1050670            False            False  ...    False      False\n",
              "1050718            False            False  ...    False      False\n",
              "1054590            False            False  ...    False      False\n",
              "1054593            False            False  ...    False      False\n",
              "1056784            False            False  ...    False      False\n",
              "1057013            False            False  ...    False      False\n",
              "1059552            False            False  ...    False      False\n",
              "1065726            False            False  ...    False      False\n",
              "1066373            False            False  ...    False      False\n",
              "1066979            False            False  ...    False      False\n",
              "1067444            False            False  ...    False      False\n",
              "1070935            False            False  ...    False      False\n",
              "...                  ...              ...  ...      ...        ...\n",
              "1350423            False            False  ...    False      False\n",
              "1352848            False            False  ...    False      False\n",
              "1353092            False            False  ...    False      False\n",
              "1354840            False            False  ...    False      False\n",
              "1354840            False            False  ...    False      False\n",
              "1355260            False            False  ...    False      False\n",
              "1365075            False            False  ...    False      False\n",
              "1365328            False            False  ...    False      False\n",
              "1368267            False            False  ...    False      False\n",
              "1368273            False            False  ...    False      False\n",
              "1368882            False            False  ...    False      False\n",
              "1369821            False            False  ...    False      False\n",
              "1371026            False            False  ...    False      False\n",
              "1371920            False            False  ...    False      False\n",
              "466906             False            False  ...    False      False\n",
              "466906             False            False  ...    False      False\n",
              "534555             False            False  ...    False      False\n",
              "536708             False            False  ...    False      False\n",
              "566346             False            False  ...    False      False\n",
              "603148             False            False  ...    False      False\n",
              "654546             False            False  ...    False      False\n",
              "654546             False            False  ...    False      False\n",
              "695091             False            False  ...    False      False\n",
              "714039             False            False  ...    False      False\n",
              "763235             False            False  ...    False      False\n",
              "776715             False            False  ...    False      False\n",
              "841769             False            False  ...    False      False\n",
              "888820             False            False  ...    False      False\n",
              "897471             False            False  ...    False      False\n",
              "897471             False            False  ...    False      False\n",
              "\n",
              "[699 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-FzVSjlpuOm",
        "colab_type": "text"
      },
      "source": [
        "## Drop the rows with missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXllgkhFGqaG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data3 = data2.dropna(axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fs6Oe6VJmcN",
        "colab_type": "code",
        "outputId": "c6d7f536-01ae-4b5c-f6ff-c50bb6f8b860",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(data3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "683"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apPZPibRJnv8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data3.drop(columns='malignant')\n",
        "y = data3['malignant']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCeaw9DMJtb6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oX_7Lpk_JxSi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sk_model = sklm.LogisticRegression(fit_intercept=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nBhNTwCJ1Dz",
        "colab_type": "code",
        "outputId": "31aecb0f-658e-4e9a-c070-244a98247ad5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "sk_fit = model.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IBN_dthJ4lE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sk_pred = fit.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49iqPmQIKFD8",
        "colab_type": "code",
        "outputId": "bcf59892-8b8f-419e-de52-b5d81496bf1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "sk_pred[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sample\n",
              "1240603    0.001533\n",
              "667204     0.999784\n",
              "1295529    0.949707\n",
              "1182404    0.005461\n",
              "1183911    0.001747\n",
              "1180194    0.997140\n",
              "1105257    0.797690\n",
              "1304595    0.004862\n",
              "1116116    0.994251\n",
              "1070935    0.003049\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFxf9EF8KGOJ",
        "colab_type": "code",
        "outputId": "4059920f-b98d-45e5-cd5d-e4e02b02d34d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "y_test[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sample\n",
              "1240603    0\n",
              "667204     1\n",
              "1295529    1\n",
              "1182404    0\n",
              "1183911    0\n",
              "1180194    1\n",
              "1105257    1\n",
              "1304595    0\n",
              "1116116    1\n",
              "1070935    0\n",
              "Name: malignant, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7_xRDaXKTpa",
        "colab_type": "code",
        "outputId": "ba795def-b50e-4f58-99af-cc435f62dd44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "sk_fit.score(X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9707602339181286"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7fBlgmWKmrJ",
        "colab_type": "code",
        "outputId": "20af1077-dae6-4c5e-8e48-5481c6c78166",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(sk_pred[sk_pred == y_test])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYRzm0ufK7NS",
        "colab_type": "code",
        "outputId": "66ce433a-8d91-4363-99a2-f62f284fbd1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(sk_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "171"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPQpTgDjqgGE",
        "colab_type": "code",
        "outputId": "e566f780-7835-480a-ea92-71a33c99e6b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(y_test) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "171"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmfViOosK8VI",
        "colab_type": "code",
        "outputId": "4e4d6616-8628-4e55-a3be-5f178884e990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(y_test[y_test==0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vQIFTWYLAr5",
        "colab_type": "code",
        "outputId": "4586c8b4-971d-4b30-da74-662119a58174",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(sk_pred[sk_pred == 0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9si_-Br7LGri",
        "colab_type": "code",
        "outputId": "fe7d1ba2-4e25-46e9-c701-dadd8b8c4c6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(sk_pred[sk_pred==1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIr7OtKaqnQW",
        "colab_type": "code",
        "outputId": "9c75b664-56d0-4e45-c0f4-c7f4d93e6c5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.where(sk_pred != y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  7,  41,  62, 110, 114, 121, 141]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcGtoV2Gqxxs",
        "colab_type": "code",
        "outputId": "c0f28bb1-6aab-4330-e1c0-9723e379fe22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "sk_fit.coef_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.25828932,  0.2532876 ,  0.43079709,  0.07365567, -0.11615986,\n",
              "         0.30221469,  0.1002709 ,  0.22268638,  0.13021731]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TspPrKLqu1Io",
        "colab_type": "code",
        "outputId": "72f66f72-37ac-4d41-d6dd-7a5e910e8b94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(np.flatnonzero(sk_pred == y_test.values))*1.0 / len(y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9590643274853801"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-GoOpq0wDI5",
        "colab_type": "code",
        "outputId": "ce8be5c5-0969-46d8-9e6e-91100109b800",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "np.where(sk_pred == y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  0,   1,   2,   3,   4,   5,   6,   8,   9,  10,  11,  12,  13,\n",
              "         14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
              "         27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
              "         40,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,\n",
              "         54,  55,  56,  57,  58,  59,  60,  61,  63,  64,  65,  66,  67,\n",
              "         68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,\n",
              "         81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,  92,  93,\n",
              "         94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106,\n",
              "        107, 108, 109, 111, 112, 113, 115, 116, 117, 118, 119, 120, 122,\n",
              "        123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,\n",
              "        136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 148, 149,\n",
              "        150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,\n",
              "        163, 164, 165, 166, 167, 168, 169, 170]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwRXtj3uwYnx",
        "colab_type": "text"
      },
      "source": [
        "# Parting notes\n",
        "- Code is read more often than it is written. \n",
        "  - Your clever writing will be unintelligible to your audience (including you at a later date). \n",
        "  - **Keep your reader in mind.**\n",
        " > \"Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.\"\n",
        ">\n",
        ">        — Brian W. Kernighan and P. J. Plauger in The Elements of Programming Style.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIl69Tkfw5r9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}