Kraytonian_DS_Unit_1_Sprint_Challenge_1.ipynb

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        "# Data Science Unit 1 Sprint Challenge 1\n",
        "\n",
        "## Loading, cleaning, visualizing, and analyzing data\n",
        "\n",
        "In this sprint challenge you will look at a dataset of the survival of patients who underwent surgery for breast cancer.\n",
        "\n",
        "http://archive.ics.uci.edu/ml/datasets/Haberman%27s+Survival\n",
        "\n",
        "Data Set Information:\n",
        "The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer.\n",
        "\n",
        "Attribute Information:\n",
        "1. Age of patient at time of operation (numerical)\n",
        "2. Patient's year of operation (year - 1900, numerical)\n",
        "3. Number of positive axillary nodes detected (numerical)\n",
        "4. Survival status (class attribute)\n",
        "-- 1 = the patient survived 5 years or longer\n",
        "-- 2 = the patient died within 5 year\n",
        "\n",
        "Sprint challenges are evaluated based on satisfactory completion of each part. It is suggested you work through it in order, getting each aspect reasonably working, before trying to deeply explore, iterate, or refine any given step. Once you get to the end, if you want to go back and improve things, go for it!"
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        "## Part 1 - Load and validate the data\n",
        "\n",
        "- Load the data as a `pandas` data frame.\n",
        "- Validate that it has the appropriate number of observations (you can check the raw file, and also read the dataset description from UCI).\n",
        "- Validate that you have no missing values.\n",
        "- Add informative names to the features.\n",
        "- The survival variable is encoded as 1 for surviving >5 years and 2 for not - change this to be 0 for not surviving and 1 for surviving >5 years (0/1 is a more traditional encoding of binary variables)\n",
        "\n",
        "At the end, print the first five rows of the dataset to demonstrate the above."
      ]
    },
    {
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      "metadata": {
        "id": "287TpoGKFRVK",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
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    {
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      "source": [
        "attribute_info = ['age_of_patient', 'operation_year', 'number_of_positive_axillary_nodes_detected', 'survival_status']"
      ],
      "execution_count": 0,
      "outputs": []
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      "source": [
        "haberman = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data', \n",
        "                       header=None,\n",
        "                      names=attribute_info)\n",
        "haberman.sample(20).sort_index()"
      ],
      "execution_count": 0,
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>age_of_patient</th>\n",
              "      <th>operation_year</th>\n",
              "      <th>number_of_positive_axillary_nodes_detected</th>\n",
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              "     age_of_patient  ...  survival_status\n",
              "14               35  ...                1\n",
              "27               38  ...                1\n",
              "39               39  ...                1\n",
              "76               44  ...                1\n",
              "91               46  ...                2\n",
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              "110              48  ...                1\n",
              "113              48  ...                1\n",
              "118              49  ...                1\n",
              "123              49  ...                1\n",
              "154              52  ...                1\n",
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              "206              57  ...                1\n",
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              "225              60  ...                1\n",
              "247              63  ...                1\n",
              "270              66  ...                1\n",
              "284              69  ...                1\n",
              "285              70  ...                2\n",
              "\n",
              "[20 rows x 4 columns]"
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      "source": [
        "haberman.shape"
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          "data": {
            "text/plain": [
              "(306, 4)"
            ]
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      },
      "source": [
        "print(haberman.shape)\n",
        "haberman['age_of_patient'].isnull().sum()\n",
        "haberman['operation_year'].isnull().sum()\n",
        "haberman['number_of_positive_axillary_nodes_detected'].isnull().sum()\n",
        "haberman['survival_status'].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
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          "output_type": "stream",
          "text": [
            "(306, 4)\n"
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          "name": "stdout"
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        "haberman['age_of_patient'].isnull().sum()"
      ],
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        "haberman['operation_year'].isnull().sum()"
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        "haberman['number_of_positive_axillary_nodes_detected'].isnull().sum()"
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      "source": [
        "haberman['survival_status'].value_counts()"
      ],
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            "text/plain": [
              "1    225\n",
              "2     81\n",
              "Name: survival_status, dtype: int64"
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      "source": [
        "haberman.sample(5)"
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              "     age_of_patient  ...  survival_status\n",
              "286              70  ...                2\n",
              "171              54  ...                1\n",
              "64               43  ...                2\n",
              "227              60  ...                1\n",
              "67               43  ...                1\n",
              "\n",
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      "source": [
        "haberman['nsurvival_status'] = haberman['survival_status'].map({2:0, 1:1})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        " haberman['nsurvival_status'].value_counts()"
      ],
      "execution_count": 0,
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            "text/plain": [
              "1    225\n",
              "0     81\n",
              "Name: nsurvival_status, dtype: int64"
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      },
      "source": [
        "haberman['number_of_positive_axillary_nodes_detected'].value_counts"
      ],
      "execution_count": 0,
      "outputs": [
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            "text/plain": [
              "<bound method IndexOpsMixin.value_counts of 0      1\n",
              "1      3\n",
              "2      0\n",
              "3      2\n",
              "4      4\n",
              "      ..\n",
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      "source": [
        "## Part 2 - Examine the distribution and relationships of the features\n",
        "\n",
        "Explore the data - create at least *2* tables (can be summary statistics or crosstabulations) and *2* plots illustrating the nature of the data.\n",
        "\n",
        "This is open-ended, so to remind - first *complete* this task as a baseline, then go on to the remaining sections, and *then* as time allows revisit and explore further.\n",
        "\n",
        "Hint - you may need to bin some variables depending on your chosen tables/plots."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IAkllgCIFVj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# TODO"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2xGYY-v3Yq9",
        "colab_type": "code",
        "outputId": "404e16a4-a8ce-48bb-901d-6b740873e929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pd.crosstab(haberman['age_of_patient'], haberman['nsurvival_status'], normalize='index', margins=True)\n",
        "pd.crosstab(haberman['age_of_patient'], haberman['nsurvival_status'], normalize='columns', margins=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>nsurvival_status</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_of_patient</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>44</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.017778</td>\n",
              "      <td>0.022876</td>\n",
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              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.026667</td>\n",
              "      <td>0.029412</td>\n",
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              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.049383</td>\n",
              "      <td>0.013333</td>\n",
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              "      <td>0.037037</td>\n",
              "      <td>0.035556</td>\n",
              "      <td>0.035948</td>\n",
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              "      <th>48</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.017778</td>\n",
              "      <td>0.022876</td>\n",
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              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.024691</td>\n",
              "      <td>0.035556</td>\n",
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              "      <th>52</th>\n",
              "      <td>0.049383</td>\n",
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              "      <td>0.037037</td>\n",
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              "      <th>61</th>\n",
              "      <td>0.037037</td>\n",
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              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>0.037037</td>\n",
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              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.031111</td>\n",
              "      <td>0.026144</td>\n",
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              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.016340</td>\n",
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              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>0.049383</td>\n",
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              "      <td>0.032680</td>\n",
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              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0.024691</td>\n",
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              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>0.024691</td>\n",
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              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>0.013072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008889</td>\n",
              "      <td>0.006536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.006536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "nsurvival_status         0         1       All\n",
              "age_of_patient                                \n",
              "30                0.000000  0.013333  0.009804\n",
              "31                0.000000  0.008889  0.006536\n",
              "33                0.000000  0.008889  0.006536\n",
              "34                0.024691  0.022222  0.022876\n",
              "35                0.000000  0.008889  0.006536\n",
              "36                0.000000  0.008889  0.006536\n",
              "37                0.000000  0.026667  0.019608\n",
              "38                0.012346  0.040000  0.032680\n",
              "39                0.012346  0.022222  0.019608\n",
              "40                0.000000  0.013333  0.009804\n",
              "41                0.037037  0.031111  0.032680\n",
              "42                0.024691  0.031111  0.029412\n",
              "43                0.049383  0.031111  0.035948\n",
              "44                0.037037  0.017778  0.022876\n",
              "45                0.037037  0.026667  0.029412\n",
              "46                0.049383  0.013333  0.022876\n",
              "47                0.037037  0.035556  0.035948\n",
              "48                0.037037  0.017778  0.022876\n",
              "49                0.024691  0.035556  0.032680\n",
              "50                0.024691  0.044444  0.039216\n",
              "51                0.024691  0.017778  0.019608\n",
              "52                0.049383  0.044444  0.045752\n",
              "53                0.074074  0.022222  0.035948\n",
              "54                0.049383  0.040000  0.042484\n",
              "55                0.024691  0.035556  0.032680\n",
              "56                0.024691  0.022222  0.022876\n",
              "57                0.037037  0.035556  0.035948\n",
              "58                0.000000  0.031111  0.022876\n",
              "59                0.012346  0.031111  0.026144\n",
              "60                0.024691  0.017778  0.019608\n",
              "61                0.037037  0.026667  0.029412\n",
              "62                0.037037  0.017778  0.022876\n",
              "63                0.012346  0.031111  0.026144\n",
              "64                0.000000  0.022222  0.016340\n",
              "65                0.049383  0.026667  0.032680\n",
              "66                0.024691  0.013333  0.016340\n",
              "67                0.024691  0.017778  0.019608\n",
              "68                0.000000  0.008889  0.006536\n",
              "69                0.012346  0.013333  0.013072\n",
              "70                0.024691  0.022222  0.022876\n",
              "71                0.000000  0.004444  0.003268\n",
              "72                0.012346  0.013333  0.013072\n",
              "73                0.000000  0.008889  0.006536\n",
              "74                0.012346  0.004444  0.006536\n",
              "75                0.000000  0.004444  0.003268\n",
              "76                0.000000  0.004444  0.003268\n",
              "77                0.000000  0.004444  0.003268\n",
              "78                0.012346  0.000000  0.003268\n",
              "83                0.012346  0.000000  0.003268"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApmT0w2d3Ytc",
        "colab_type": "code",
        "outputId": "b7051d1f-53f3-43b9-c917-d9dc72e81aaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "haberman_age = pd.crosstab(haberman['age_of_patient'], haberman['nsurvival_status'])\n",
        "haberman_age.plot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SqcAbBEDabyivXBtZhtYiJnvcqTcDr0hUUglFEICxO4CZJ4BCRt9xc9qa97///bjpppswNTWFwcFBfP3rX1/rQ1KlWQ3cAcAjCIIDgBfAxeYPaROQnKfZtyDUgspGl1FKBeDb7wbmjunbfuEM4HDXhhKLsdlpq1gtCaVBA1e4CDKXj1RCAagOXswAZ5/Wd9y//Drw0Kf1bcvZdDz44IO4dOkSCoUC5ubm8LGPfWytD0kV0wGcEHIBwF8AOAfgEoBlQkjDvaogCJ8QBOGYIAjHotGo+SPdSCTn6QImUAsqG91KmLhEteTZJ/Vtz86BTeEt5vLrWMSU2AgVM3AmocgE8C2vp1/ZHYEWkw8Bx76+8f9enLagGQmlB8DdAIYBbAXgEwThd6XbEUK+RgjZTwjZ39fXZ/5INxKJy4A/TP/v3SQZOAueegNbeqFR0hCj1lKWPc4WMTs8gM2hooGrZODeEH2u3sZWyXk+lo2zYWhGQnkzgBlCSJQQUgDwAwA3W3NYG5zkPOCXZOAb3UrIgqfe3yOzqD5Y2Kky1IEV7DAJRRDoeoJSIU9mkfYAd3U1/sxmA3zh+va+arBAz/uJy0LaeTzgKmD0/DYTwM8BeIMgCF5BEAQAbwLwWhP72xzk0zTYBfrp95tFQmEZOLPsaSEtrJGiFsCri5i+2mNq/VDSlddSkmv8YX0ZeKkApCuj3thYNk4Vt9uNeDzOg3iLIIQgHo/D7dZv5jPtAyeEPC8IwvcBvASgCOBlAF8zu79NA8v0WAbu7gYgbHwJhWW/ei9EmUVtCSWrcDHIpwAI1MnDUOsJrnWxCAwAyxc0D5kOogYw9Eaq9V94Cdh+vfbz2oTBwUHMzc2hbday1gC3243BwUHd2zdVyEMI+QKALzSzj00HC+AsA7fZKg2tNngGzhwgei5EpWKlMlIlqLr8wPKc/M/YMAdBEG3fpb6IqXax8PcDF17UPm6WpV/9AepaOfUID+AiOjo6MDw8vNaHwRHBS+mtJiEq4mFshmrMrAENnGXWmhq40iJmsl4+AdQz8LSODDwVU58ABNQuvn3jwPYbeSMszrqHB3CrkUooAA0uG10Dz4lcKFoaKLtYabpQFBYlc8maA4WhpoFnltQvFv5+AARIRZS3AUQX34HKWLbjfCwbZ13DA7jVJOepbc0bqj3mDW58CYUFz3JBe6K8WmENg2XgchcDcStZhrtTuZBHSwNnd0NawTg5D0Cgi55sLNupx9Sfw+GsITyAW01intrWxI4IT8/Gl1DE8oXWxUitsIbh9AHlIu2bLiWfooOPxbDJ9NKAX8xTyUUt22dFVUmNDDw5Ty+89g6gfx+tIuUyCmcdwwO41SQv1xYwGZ4gkN7gAVwsX2hdjNQKaxhqLWXzicYM3BWgBTbS7F/PxYJl4EmNDDwxX9tWPJatKHOR4XDWATyAW404CDC8QRqUNvJ8xtwKnXADaOv5VQlFQwMH5L3gShIKOw4xei4WVQlFo5hHevHdfQf9u53nY9k46xMewK2GzcIUU63G3MBZeHYF6N5B/69HQhHstTawcqgFcKVFTHYc0tcC1C8WDif9ua4MXLT4PHwbH8vGWdfwAG4lpSK1qwUG6h/fDAE8lwB6dtL/65FQPN31Pm4pTOOWWxDNp2pl9AylnuB6FkwBelFV08AJqTTgEl18XX5g6ADvJ85Zt/AAbiWpKACinIFvZCthbhnorgRwLT1fq5EVoJyBE1Ir5BGjmIFXzqmajRCggVnNhZJZpA4b6d9u7A4gNgUszqrvn8NZA3gAtxLxLEwx1Y6EGzSAE0IzcG+IBlbNDHxROyNmAVy6iFlIAyAqGrjESqhHQgGoNKLW0EquAAvgdkLOuoYHcCsRz8IUs9GHOuST1AHi7qxUlWpp4AvaGbFLYSqPXCMroJaBSzsSpheoTi3dXkqgv9IqVqEISeniGxoFeoa5Ds5Zl/AAbiVJhSxuo0soTLZwdVb6umhl4Es6JBQWwCUZeHWgscQH7lZZxPQG1fV2gP5NSnnlY1e6+AoCdaPwsWycdQifSm8lbJFMGgRcAVqdaSYDJ4Te3pOyvu1tjkYferOwhUN3Jw2WemyEmhKKQgCXDjRmdPgACPI2Qq3XAkRe8Hn5u4OkQgAHqB/8+b8DXvspsFPS8t7hAny92q9vhlKBFhVxOArwAG4lics083Q46x8XhEo1pokM/Jn/D3js/zb2nHf+HXD1+42/lhLVDLyL/h5KXQQBoJgDCin1whqATtmBoCKhSBYxbTb5fihpjU6EDCaNJC4D4Ssaf56cp68ptS8CwM5b6AXkBx+X3/eHfwIM36p9DEZYPAv89X7g3/0MGNxv7b45mwYewK1EPAtTikdH5ipH/BS10L3lS/q2/7dPA1GL52qIM3CtzorVRUWNAC4INGBKFzGlA43FyHUkzCwCQR0tTpm/W2khMyHj32d0uIEP/hCITtY/XkgDD38OiE5ZH8CjU1TymfslD+AcRXgAtxLxLEwpZvuhZBZpT47rPqxv+ye+rF1xaJScWAOv/B7lsvwEHD1VmAyXzFQe6UDjuu1lMvDMAuC5Rvu1AhoNrdQuvgCw40b6T0y5BDzyn1vTsZCtp+gdxsxpS/gippUkI/WVfGK8JnuCp3VY8sT4w9oVh0bJSjRwUlaZjsN6k+gI4HKDjaUDjcUoZeB6LhZOP53wYyYDV8JmB3x91p9voHYRXuABnKMMD+BWQYh8IyuG2aEOeix5YvwD2l33jFKXgWt42vX0JmHIzcXMqUgork466YeRTwPFrL7XEoRKNaZCAE9GjAdwoGJPtPh8A7XjjJ+2ft+cTQMP4FaRWaSapVIG7uk2p4HrKYoRo1VxaIbsCp367vSJLJEKFyMjEorcVB41CcXdWe8D11uFyQgMyMtL+RRtWmXGveMfaK2EsjxHF4Y5HBl4ALcK6SxMKd4gUMwY8xITos+SJ8Y/QAOblS1QcyvUCikIoqpShQCudxEToEFaWpiTT9LCHIercXuXREIx8lpAJQOXCbbiSTxGCahk9c3ALjSkzMv4OYrwAG4VSqXYDDMNrQoZoJQzJqEERH5nq8iuUAshIPo9VCQUu1O7MhKoLGLKaOBKz3UF6LGwakoj2T6gnIFrXXzV8A/QHjjlkvHnqpG8DARH6f/5QiZHAR7AraJaxKNiIwSMyShG9GSGvwUBPLdSq4TUagvAFhW1KiMB5UVMaRUmw91JG04Vs/XHoPcC5w9TqUT6ms1k4P4wzZJTUePPVYIQeqHZeRP9ni9kchTgAdwqqr00VCQUwFgGbjTDBFoTwLMrtV4knu76Y5NiRPJxBmQWMWWm8TCkHQmNXuCUvOBKFbR6CGj4y82QXaZ3Xn176N+eZ+AcBXgAt4rEPK3WU8oetaQHOYxmmEB9xaFV5JZrGbitMqhBLQPXe7wsAy+L2gSoSSjSnuB6e4Ezql5waQC/THV3I+eZwS4KVnrvq2X9A0BoF3eicBThAdwq1CyEgLmOhGYkFF8YgGCxhJKoZb+AekdCI64Zpw8AqbSQrSDXC5zRkIEvAg5PpSxfB9UMXHJxY2Pw9Mg+UqprDhZeMBOiu7nQKLBwxrp9czYVPIBbhZaP2ExHQr29rsXYHbS5kpUZeFakgQP0d7FCQpFrKSs3jYchnYtpJNsHlGdjal181fCF5ffZDOLGWsFRYOUC9bxzOBJ4ALcKrUo+pxdwuI1JKEYlAobW+DAjEFKzETKUqkoJMZiBy3QkzMvMw2S4ZAK4kYubN0S7NSpl4GbocAPubmvveMQBPDRC/784Y93+OZsGHsCtQquXBmC8GjOzSMu/O9zGjkXJ72yGQgYoF/VJKIW0MdujXADPJVU0cImEkl6oLarqwWajGbP04pZsIoAD9O9utYTicFPNn1sJOSrwAG4F+TTNCrWCgKdHe56kGKMZJkPJ72wGcSdChlJjLqOumepcTKmEouIDFx+T0TYDQGOlaqkApGUGURvB32+9hMI0+RAL4Hwhk9MID+BWoDSOS4rRhlZGqzAZ/n4gFal3d5hF3Auc4Q1WrG7F+m2NVkayDJz1PykVabWqU8HJI7eIaVhekszGbMZCyGhFBs7eS64APTbuBefI0FQAFwShWxCE7wuCMCkIwmuCINxk1YFtKKpBQKGVLMPTbdxGqDUYQY7AAJU90nHjz5WilIEDQHapflujvUlcEgmloDAPk2GzV3qIr4j0doMZuD9cn4Hrvfhq7lNl3qZRkvP176XgKBDnThROI81m4H8J4GFCyB4Arwdg8SSBDYLeSj7DGrjZDLzy4bdiYY11/5Nq4EDj72I4A5dIKGqtZBmsJ3huhV6kjJ6fwAC9sJUK9Hu9F181/ANU+xd3SmyG5Hz9eyk0wjNwjiymA7ggCF0AbgXwdQAghOQJIUvqz9qkVHtpaAXwiv1Ob6ZmVgNX8jvL7T8yqb4NazYll4FLrYSGNXBJBq7WSpbh7kQutYhzFy7Q741q4P5+AKRW+t5MGT1DbzVm4jKwoOEmKWTohUBsawyO0n1LG3+JWbnIm161Ic1k4MMAogC+IQjCy4Ig/C9BEBrufQVB+IQgCMcEQTgWjVrYL2I9kYwAgl07cHmDtJeHtHxcDqOWPDFKFYdSjv4Z8M23qW8j7gXOYLJOQwZu0PYoDeBqrWQZrk6cvTiPP/7eU5XXMrqIKalUTc4DEJrMwDWm/TAe+iPgnz+ovo24CpMR0uFE+f5HgX/5PfV9czYdzQRwB4BrAfwtIeQaACkAn5NuRAj5GiFkPyFkf19fXxMvt45hUofciDExRqoxmUTQTHm3VgYeOUEdGNLmTmKqi5iihUWloQ6ZJWO2R4eTlrDnpAFcPQNHdgX5REXfN+ORB2qBMnGZ+sObmf6uNwOPnABiU+qdCxMyd3OhXfSrkoySigPnngOWzuk7Xs6moZkAPgdgjhDyfOX774MG9PYjrdPOZqQa00wVJsPppRmzVgbOFsbUMsecXABXyMDTC8aPV9xSNq+xiAmAuDrRUUygB5Vgb0pCQX0G3owDBahl72rnsVSgk+ZLeWD5vPJ27KIrPqaeytBmpYXM6V+AykIR69vactY1pgM4IeQygPOCIIxXHnoTgBOWHNVGI6MzcBnpSGi2CpPhD6tnhIUMsDJH/69WtZldobY+m732mLuLSkbSC5EZyccpF8CVM/Cc3Qc/0ugWKnqw6Qy88jsn582X0TNcnbQni9r5XjoHkEpwVZNC5GyNTi/QuU05Az/1KP1KytY4jzgbhmZdKP8JwHcEQfgVgKsB/Gnzh7QB0Ru4jHQkNNOJUIzU7yxFvJimJrWIe4EzBEHeEplZMG57dPppj26gtkin4kJZKrkRQBrdqAR7owHc4aQXW/Y7J+abW8AE6PnQmswjDtpqzakSl+n4Ol9v/ePBEfnAXy4Bp39eOw+tGO/GWbc0FcAJIa9U9O2rCCHvJISYmNq7CUjrbKpkZKiDUUueFK3ZmOJsTk1qyS7XL2Ay5CyRZiQU8VAHHRJKrOiGWyig37aEjM1nTrtmlaqEWJOBA9qzMdn5tjk0MvDLtNxffMcD0IVMuWrMuWP073DVvZXnt2C8G2fdwisxrcBwBq7DbWlmmIMYloErWRZZMBBsxjNwgF6wLJFQ5AK4cgZ+OecEALzOs4gVqCx2quEP0985vUBdQc1m4NV9qmXgp2k1a98V6mXxCYULSmgXvcORXjRPPULlrKvfX3k+z8DbCR7Am6WYoxWEegKXw0mDkxEJpZkMvJBWtizGpwFfHxDYoq2By2bgkn4ozPZoVPJxBUQulATVkqXZp4gLGZpxDyKCeFnH3E05/JUMvLpg2ISFkBHQkKzi07QgJzSqXpQjLeJhVJtaSeSXU48C228EeitLUVaW9HPWPTyANwvLQvUGLr3VmJkFGjjtDnPHpTUpZuEMDQp+Dakll5DPwKW/R26FLtKZysBZAFdpZFVhNknPR7BwGbGSDyvZgrHXA2p6dcKCMnqGv5/KTYWM/M8Xpun5Do1W3CgKxy0to2cwL7g4+K9cBC7/Gth9uNLWtsu6NsKcDQEP4M1iNFP2dOvXwM1m34ConF4hOMenaVDQyhylvcAZ0qEOZiUfp7++ElNlAZMQgjMJ+pa1kwKW4MdsTMXDroR/gEon0XpSzvAAACAASURBVKnK9xZo4Gpe8GIOWJ6j5zs4Si90cp7tcolWiMpdUHqGqNwl1s9PPUa/jh2mX7UuxpxNBw/gzZIxGLj0diQ024mQoTYbM5ekgT04ov2hV5JQvD1UOirm6PdGG1kxpBq4iv4dSeQQK9SKhJaIHzNmAjjTmC8dp1+tCOBqdzyLs9TixzJwQH4hMxWl28kdj8MFdA3W6+enHqX2wvDrKseg4YThbDp4AG8Wwxm4yjxJ6X7NWggB9en0zMYWqkgo6Zj8LX0xR5s0KUko7DjFX834wItZ2kpWbR4mgJlYCgnU5l/SDNzEqDG/KIA7/erNs3TvU+WOhwXr0C71qkotSSe0q/a8Yh44c5Rm32yWZ0DDCcPZdPAA3iyGNXCVeZJi9BYHqb2O3SUfwFkWF9pVy0ZTMn1q5HqBi/cP1AJ32mTlqLilbF5lGg8qAZzUAnjZ3YOZmI6+MlJYthybsib7BkQSiowGXT3fI7Rs39Ul70SpFvEoBHDWVpYQ4Nwz9Hwx+QSoZeBWtbXlrHt4AG8Wow2cvEHaR1tr2EKzEoogKE+KYVlccER06y8ntcj0Amd4JZ52o+eBIW4pq7GIORtLIWevZcuuQAgzcRMZOLtokbI1C5gA4O2ldj6587gwTS9snp7KlB2FohwtV0xoFMgt02rLU48BdicwfGvt54EBejfD/m6cTQ8P4M2SWaSZbodX3/aeHho4ciq9o8sl6mhoRkIBan5nKfEz1D7o9NWCmVymLtcLnCHNwJuRUACaTeaS8gumFWZiKWwNVcrWAXi7w+YWMV0BoKNyobAqA7fZVM73dE37BmgmLSuhiIYZyyGej3nqUWDolnr5p9rnhevg7QIP4M3CGlkxHVILPR0Js8sASHMZOKA8G5NZ2gDzGbi0I2F6gUoDRm2PTv0Symw8haGQr3o83aEwljMFLKbyxl4TqGW5VmXggModz5na+QZoMF+eqy0AM5KX6YR7pW6O7CIw/QsgdrJePmGvz/bDaQt4AG8Wo3a/akdClQDeTCdCMUrT6VlRCUCLeQD5DLzam0QlA69KKIvGJsQzdEoo5TLBbDyN4V5v9Xh6+7YAAGbiZpwolcBtRREPQ+5859PAyoXGDJyUGwcwiGdhytG9g8o0v/w6/V4awAMa3n/OpoMH8GYxOjXHK8lc5TC6MKpEYIAenzjTyyxR1wlzQzicdGFNVkKRaSXLcPqoBluVUExMiAdqEgAra1dwoVxcziBfLGO411/NwLdu3QoAmIma8YJXslUryugZgf7GRUyx44fBzr10ITMZUZd07B3UD56O1VsSGTwDbzt4AG+W9IKxzFOPhGJ2QVCKtHUqIFrAFH34/QpSS1VCkXGhCEK9JdLsoisL2OwCohDAmV1wqNdbuaAI2NY/AJtApRXDsGzVikZWDP8AdfOIe3LLnW929yNdyExe1tbkWdCWZt8A/Ts53NwL3kbwAD77FPXTmsWoX1vPUIdm+6Aw5LzgcZmMMKAgtahl4Oz4xIuYpoZPVCQTpsEreLKZTDLc66MSiqcbTmcHBnu85op5WpWBk3K9JbPqARedb08PPVfihUxClBtZiWEXgt0yAVyojIZbLQnl5W9rz/jktBQewH/+x8BjXzD3XEKM+7U93XSMWOKS8jbNDnNgVGdjioLzwjQAoTblBVBefMutUHeNUstWb7Cm5ZuVUBoycHkNfDaWgrvDhv6AGxg9BOyhszyHen3mMvChNwKDN1BJwirkZmMuTNP2sNKLYGi0PgPPLtGiKa0LytibgR03ATsPKBzDwOpIKNll4Ef3AS99q/WvxVHEZKekTUTiMh1zZYZCmj7XSKC12WnQUOtIl1kEIFBHQjPIzcaMT9OSbLHTQVwAInbTKPUCZ3h6qMbLbI+mJJRKwNaUUKgDxWYTgP0fpf8ADIe8eOnsIgghEPQ6gQBg+/XAxx8zfrxq+GX6ocTPNGrVAM2kZ5+sfS83C1OOXW+m/5QI9APRk/qOtxnYxUdPVTGnZbR3Bs4a+kt1S72YXWwM7VKebwhUsvpu7SHJWvj6AAiNGrg0oAQqzZ2kurxSL3AGk1BYf3MzEorNTrP8hHoAn4mnqHwiYbjXh2SuiFjS5EXYSuQ89XLnG6DvgZUL1KUifk7T8zk1mpNZBVuc1VNVzGkZ7R3A2W0rKZmbJWh2sZH1hFaqxjQz2UYOu4MGcXZLTwh1PgQV3AtSL7hSIysGG+rQ7KKr01e7S5CRUIqlMs7F0xiSCeDsMVM6uNVIC2myKzSYSs83UFvIZIHQqgAe6Kfv60K2uf1owRw0ehqzcVpGewdwse5rpgmQWb92cISWPCcuKu+3WQshQ9yhLr1ApQ65DBxo1E71ZOClHM0kAfPH7PQBqRj9v8wi5oWlDIplguGQfAYOwFxFptU4XPScsPMoZyFksKDOpLRqI6tmM3CVylorqUooPICvJe0dwMUBy8wb3rSEotJSFKhIKE0uYDLEszHlLG2Acgl2LqFa2l69cLHfw+xdgzMAoNKASUZCYdm1XAa+rdsDh00wV8zTCvw6zjfQ+B5IztMWAWp3PLpeX6UvuZWw341LKGtKmwdwkTZs5g1v1u4nzb7k9muFhAJUNNHK7ylnaQOUszYtCYX93izTNFOJCdTLJjISymw1gDf2m3HYbdgR9K6PDByo3PGw8105L8GRxu1cAepOWRAF8EC//pYMSsg5j1oBz8DXBe0dwMVvclMSikntt3MbLbhQysDTTU7jERPoB1IRqrcvTNOpLt0767dx+WnmKw3guRX5Ih6GV5KBm5VQqrKJINsUbDaehs9pR5/fJfv0oV7f+tDAgcqEI1EGHtgKOBUanYVGa0E+cdmi4cqrkIGnF6jO7u0FihnlMXKcltPeAbx629plUkJZpF3tHPKBRRGbjWZlcgG8VKDDfS3TwAeAcpEu0sZP0+DtcMpsJ5nMUypQm6RqBs4C+Gl6YZDrG64HlnU7/bIZ6JlYCsN9PkWb4HDFC14ur4M+2MxTzxaM5fRvRmi0thjIMvBm8fXSv0UrAzh73w5eT79yGWXNaO8AnrhMK9cC/eYXMc0G2uCIvIRiVRUmQzwpRtrWVIx0NiZrZKW1iAnQpkzuJmyPTPdWqMJkHnAlhnp9yBbKmE+02Hmhh8AAXdjNLqmfb4BKaakIlaoS89a0trXZ651HrYBddAavo1+5jLJmtHcAT87TD5zZWYLNLDaGRmngk/rPrarCZIg71Enbmorxh+s/9Gq9wBnsGMuF5u4YWACX0b/zxTLmFtOyHnAGc6esCxmFBeHoFH1/KJ1voBbcIydof3irepO3ejYmk+K2XkO/58U8awYP4P5+7cnsSjQzOT44Sqs4l8837hOw1kYIAJd/RfttK2WE4sVOQL0XOKPDXdOsm7ngVCWUxiB9fjGNMoFGBk6PwdR8TKth5/vsM/SrVgYu3taq3uStno0Zn6atbZnezjPwNaO9A3hClIEz3dIIaZP9PwBlK6FVnQgZLCiwIKGUEQb6qfbOJsRXG1lp2NqYDt6Ma4ZJJ85Gy+KsioWQsbXLA6fDZq4nitXoPd9AzZ3CtrWqsZbYCdMK2EAQ6Vg9zqrTvgG8kKndtgYG6Gq60VmCTUkorCe0NIBbNMyB0VFZpD3/fOV1VTJwoJa56dHAgdrv31QGriyhMFlkRCWA22wChkImuxJaDcvAzz8PQACCw8rbOr3UkcT+Nla1tvUz55GJ9hBaEFLr71Idq8cD+FrRvgGcBSp/v7lZguVyc35tfz8NXNKFTKuGOdS9VphenGwdQNd25W2AmpSU05mBeysf4qY0cGUJZSaWQpenAz0+GeeMiKHQOrESugLU2ZRbAbq3azuUgiO1c22VBh4YqLS1jVmzPzHJCL1TC47S5MDh4RLKGtK+AZwFKiahAMbacOZW6IfEbOASKtmZnIRicyg2dTIFu63vGVKeWRmQZOCrKaGouFBm4ylV+YQx3OvDuXgapbW2EgpCLZNWk08Y7I5IsFNftRW0cjIPSzjYHaSnR308IKel8ADOJBTAmG5ohd1Pbjo5y+qbrcgTwz7Qagtqfsk5yFVcKLollCZa31YlFJkAHktjOKRQCCNiqNeHfKmMi0vroKiEnUu1882oDpcON999ktHK2ZjVat6Kfu8N8gx8DWn6HSMIgl0QhJcFQfipFQe0aiRkMnAjK/fVxcYmMs/QKLB4lhbNMMyOJlODfaDVMkJvkEosSVEGbndpSwDsDqQZCcUlH8CzhRIuLmd0ZeDMpbI+FjJNZOBWySfifbXCSrgwTe8Qu3bQ7z09XANfQ6y45H8KwGsW7Gd1SV6u3LaGRLMEDQTwtAUZeGgXbWW7eLb2mJWdCBnVDFymJwdDEOon82h1ImRYsogpr4GfW0iDEKh6wBnrqiuhmQzcKgsh0FoJJT5dL8WJx+pxVp2mArggCIMA3gbgf1lzOKtIYp5WrNns5mYJWuHXlmtq1cTCaKlM8MkHX8aLZyUfqGoA36W+A38YSF7GVx47ibMXL+vrjGeJBi4fwM9ERXMwNejvdMHTYceZdRHAKwvCWucbqLhUhNpzTPJPz5/D/3y88j7qcNOkROn9nIwC//Q+YEVlrJ8S0mIw1hOesyY0m4H/DwD/JwCFyQSAIAifEAThmCAIx6LRqNJmq4+094TRSSZW+LXlvOBNSCjnFtL48fGLeODZ2fof7HozcMO/B7bfqL6DwADKK5fxt0dP49L8PIieDHzsLcAb7gP695o6ZgBA9xBw8yeB8bfWPczkkJ0qRTwMQRDofMz1EMD33gPc8of1c0eVcLiAt/wxcM0Hm3rJf335Ar71rOhOTm025qs/BE4+DMw8buxFyuVKewDRhYlJKEZrKDiWYDqAC4LwdgARQsiLatsRQr5GCNlPCNnf19dn9uWsJynp/hYwWH7MMvBm5lZ6Q9SjLc3AveYCOAtej5+M1rsxfCHgrv9GbV9q+PtRXLmMQomgo5hExqYdOOEPA3f+qfLgYz3YbMDhL9FZnSJmYymEfE50efTte7jXi9n4OqjGDI0Cb/6i/kXJA58Ctt/Q1EsupvO4uJxBtlDxfgcUBlUDwKlH6VelbphKJC7RegmxFOcJ0mZp+aTxg+Y0TTMZ+AEA7xAEYRbAdwHcLgjCty05qtUgIZOBG1nETC/Q21QlW54eBIF+GFhzoEKGfkBMZuDMB72YLuCV80vGdxAYgDO3AJ+jDD8ymM81EZQtYCamz0LIGAr5cH4hjWJJ8YZw07KUKYAQehcGQPmOMp+uDVNWG6wth9yACvZe5TLKmmA6gBNC/i9CyCAhZAjAvQCOEEJ+17IjayWlIh1kLM3AjcwStGpqTlDUE7rJKszZeAqeDjtsAnB0yngpNalo5e/Y1YGgPYuzySYuThYwG1fvQihlqNeHYplgbnEdWAlXEUIIltJ0qHO1mMkfpgFcKm3MPknH+bk6jWfgcgNB2BoQX8hcE9rTB56KAiD1C0dGrVdWTc0J7aINrQrZpjsRzsRSGOv347qdPZgwEcDnCrQXye3byugU0phJ2pHIFjSe1RpSuSLmV3IY6dMfwEfW04DjVSSVL6FQooG6ugYQGKCBmnWVZJx6lPaw33sPDchGtOuFaWot7RRJXewzwK2Ea4IlAZwQcpQQ8nYr9rUqiKswGdJCFi2s8muHRgEQ2lo201wZ/Uylb/bB8TB+c2EFkRVj/bGfi1DJ5PpQFq5yGivEi6dPt6AcWwdsAdNoBg60XwBn2TcgzsBlJvMQApx8FBg5CISvoCXxKQPGgvg0Lf0Xa/tcQllT2jMDr1ZhSiQUQL931iq/tthK2ISEkiuWcHEpg+FeH27fQ+8sjk4Zc/38fI5+7c7QFrd5hx9HJlvY1U4F1hpWbg6mEiGfEwGXY30U86wiS+naXVI1gMvNxoxOAcvnqHNIqZmaGnIDKriEsqa0ZwBnb2rpIqb4Z1pkFiySUCor+vHTTUko5xdo3+zhXh/2DAQw0Ok2FHyX0nlMVAI4YicBAFv6w5iYioKsgUXMTAbOrITtl4HTAL4j6K1dvOTuKJn7ZOxwrZWt3oXMcglYnGkc0MxcWDyArwntGcBZBu4TaeBGZgmWilRbtEJC8fTQC0F8uqnioJlqxkpnRx7a04enTseQL+pzZDxxKoY8caDgDgKxUwCA0cGtiCZyePWiwTa7FjATSyEccMHnMraQOlSZj9lOLFYklGt2dGN+JYd0vlg/So9x6lGgfx/QtY3ORrU59Gfgy3N0AIk0A3c4aR93HsDXhPYM4InLNHPocNceMzJLkC0MWVXyHtpFK9wyC7SkX8uvLQNbvGLjxQ6Nh5HMFXHsrD5t8uhkBEGfE47OgWoGfsUQXayaWAMZZdaghZAxHPLiwmIGuWILemGvU5YyNAO/ZjvNhmdj6Vp7iGp3yWXg3LM0+wao/bV7p/4MXM5CyPD2cA18jWjPAM5mYUrRO8nE6qk5odFaBm5SljkTS6HH24EuL12IPLCrF067TVfwLZUJjp6M4rbdfRACA9VsqrsnhNcPdplytDTLTCylOsRBieE+H8qESkrtwlKKZuBX76Dvx5lYqtbbht1RTk/QghsWwIHa+04PVQuhTHsA3tBqzWjfAC7X/S2gUn4sxuqpOcFRIHERWL5gOquXZqw+lwM3jgQxoWMh81dzS1hI5XFwvK9+YdfViYPjYbx8nv58tVjJFhBP5U1l4EPVAcftE8AX0wX4nHbsCtNujlUJSTwb89Rj9K5z8PraE9mdn541jvg0tR/KJT4e3lJ2rWjPAJ5QycD1NLSqTs2xKgOvLAxdeMl0Vj8bTzU0fTo4HsbpSFIzG52YisImALft7qtf2HV34vY9YRACPHFy9frYVOdgGljAZKyrroSrxFImj26vE36XA+GAS2QlrNxRlsvA6ceAXW+qrxwOjgCFNC2R12KhYiGU61Pv4RLKWtF+AZyQSh8Ume5vemcJWi2hMF0xZ25hNJMv4dJytqp/M5idUEsCmZiM4NodPej2OuvvTFyduHJbF0I+56rKKCwA6elCKKXb60S3twMzbbSQuZQuoLsindU19PL30/f65eP0rlMsnwDKg7XliE8rtyPmQx3WjPYL4NklupouNwGczRJMx9X3YbWEEpLpLWGAswvyk9uHe30YCnlVdfBIIotfX1jGoUqwrwZwmwPo8MBmE3DbeF9jg6wWwjzgO3VM4pFjKOTDTLSdAngePV46M3Q4JHLhBPrp4uWJHwMQaFdKMXLtjOUoFYGls8oDKjxB+rkqt18PmrWm/QJ4QqYKk6F3Mk96gVoO9fTL1oMrUHttMxZClb7ZB8fDeGY6jkxe/q6CFfscGq8EcHZeXJ3V2+Xb94SxlC7glfOrk2XNxlPY1u2Bu8Nu6vkjbWYllGbgsWQeK9lCLUk5/iCw7TpqlRXTNQjYnbVmaoovcJYugCr1N/f00MQntyz/c07LaL8AzhYplRYxAW0veGaRvmmtmmEI1LIbE1k9kwvkFv1u3xNGrljGc2fk7yqOTkXQ3+nCFVtoH5TqeRH1An/jrj7YbQImJldHBz8TSxmqwJQy1OvDpeWs4kVrs7GYzlcD+HDlvM3GUqLZmJca5ROAWmeDI7VmakosVH6uNGGIJR1cB1912i+AW5GBW1WFKYbpiyYklNlYCr1+F/wyRS83DAfh6bDLatiFUhlPnozh0HgYAlucYudAdHfR5e3AdTvMNcgyw2zMWBdCKexCxqSlzUy5TLCcKdQklF7qRJmJpeqTlLG3yO9AbrC2FJahK0oolfcs18FXnY0RwMtlYxPj1ahm4AqLmOJtlGjF4GH24TAhoczG0oqeaXeHHQd29eLIZASpXBHpfO3fs9NxJHLFmv4N0AHDTn+DPHRoTxivXlzB2Xiqbh/pfFF3taceFlN5LGcKphYwGWwxd705UQghDecunS82daeQyBZRJqgOvWDrBrOxdO397AsDW66W30FoBFiYUdev49P0/SCVYBge3g9lrVjbhs96+fY9QDEHfPTh5veVjAAOj7x+zWYJal0sMotA59bmj0VM72761Vc/tejbz53FPz4zi4f/4FbYbTIWLlAJ5dC48rSjQ3v68PPX5rH3C480/KzDLuDALskHs3Nbg0Xy0J4+/PnDk7jty0cb9uHpsOPnf3QbtnUbryCVMmOiB4oUJr+YmY/5Jw+dwHQ0hX/4yPXaGxvkv/zrb/Cd58/J/+xtV+Djb1QZOq3AUob681kG7u6wY2uXm64B+Eaoxj32FmW5LzgKlHLAyhzQvUN+m9iUsoUQ0NeR8MV/BJ75K+D3n2tuehOnjo0RwAevB5787/QN0mz5euIyXZ1XejPqmcyTWWxuBqQcu+8E3v0P9YUWAF45v4RTkSReOb+E63Y2Zv2JbAHRRE616OW3rxlEoVhGTiZT3t0faJRe7vm7hgvcnoFOfPV9r0dkJVf3eDpfwl/+4hR+fmIeH755SOOX1KbaEsBAH3ApAXcHev0uwxl4uUzwg5cuoNSC5l3FUhk/OX4RNwwF8aYr6u/+/uHpGfxydsFUAF+sNLLq8dWCYrWhl80OfOCfgfDrlHdQtRKelg/g+TRw7nng+o8p70NPR8KzT9PXOP88MHSL8nYcQ2yMAD52GHjiy8D0EeDKdze3r+S8vIWQoWc2plXDHMTYHcC+dzU8HEnQgHl0KiIbwM9WZkBKPeBiPE47PnJAx4BdxrZrZR++55pB2cd/fPwiJqYilgVwmwBs7zG/iAlU5mMarMb89YVlxCsVp7liCS6HOReMHC+fX8JKtoh/d2AIb71yS93Pfjm7aLqDIusF3uVxVh8b6vXh335dKc4ZvV19B+K2snLbzjxBM3QlDR2gd60Q1MvpmY5+6lEewC1kY2jg266jAfPUY83vi2XgSmhl4MU8HeBqVRWmBmwog1JrWPbBN1N2bhUHx/vwrIpV0QhnYikM9njhdDT31hwK+QwX84jPcTSRU9nSOEcmI3DYBBwYa9SRh3u9OBtPo2zCZ89ayfZ4axn4SK8PS+kCFvW0PwhsATq8NaeJFDbBZ+cB5X3Y7DSIq0korFjo5KPax8TRzcYI4DY7LUI4/Zh2laQWSn1QGEqzBBlWV2FqEE3k4LAJePXiCuZlJuw0U3ZuFcyq+OyZ5qf3zMbNdSGUMtTrQzSRQzJX1P2co1M0yAK1Ox+rmJiM4PqhIDrdjfrvUK8PuWIZlwxOUAJqrWS7vaIMnPWD0XMBE4SKlVDGiUIITZpGDgIOl/p+1Kox0wu00KdrBxB9DViSXwfgGGdjBHAA2H0HrZC8+LL5feTTQG5FPYCzWYI5hR7YVldhqlAolbGQzuPNV9DjlRtUPBNPYUuXGx6ndbf7RmFWxWan9xBCMBtLY9hkBaYYoz1Rookcjs8tV8+1lRn4xaUMJi8ncGiP/EJzM66ZpXQBglBzoQC1uzHd+wuOyFsJo5O1CT5aqHUkZBeHGz9Bv57iWbhVbJwAPno7rX5s5o8vNwtTSnUyj4IO3uTgYSPEk3kQAtwy1ostXW7ZQppmPdNW4HJQq+LEZHPTe2LJPJK5ojUZeLUrob4g9nilWdf7rt8OwNoMvKHaVQJbsDWjgy+l8+h0d9Q5lHYEvbAJBgJ4aJTOZC1J7laqE3z0BHCVDJxdHHbfCfQMWSOFcgBspADuDVKHxslGK5xu5GZhStGajdnE1ByjRBL0ljoccOHgeFh2ws6MycEHVnP7njAuLGVwOpI0vQ9W/t6MB5wxJK5I1MHEVAThgAu3jPVCEICoCTlDbd+DPZ5qu1cp/QE33B02cwE8UyujZzgdNmzr8WAmrnMRN7SLlsovna1//NRjlQk+8ovXdXiDyhp4/DRNvrp3UkPCmceBgnXnt53ZOAEcoH/8S6/oa/kqR0KliIdRrcZUeI2qBr4KAbxi2Qt3unFovI9O2JmtfUiW0wUspgvV8um15GDFh96MjKLW08UoXqcDA51uXTpwoVTGEyejODjehw67DSGfy7IMPFcs4enTkmpXCTabgKGQz5SEspgu1OnfjOFeP2ZiOi+m1aZWooXM6gQfHdk3UJFQFDLw+DS1KDqc9DNczACzT+nbL0eVjRfAAeD0z809nxXoqEooLANfewmFBZFwwFWbsCPSwa0oerGKrd0e7BkINFVuPxNPwWETLCkIAmgWricovnR2EYlssdp+NxywLoC/MLOAdL5U3bfisZpwzQBUQun2NC6MDoeojVKXpCXXVrY6wecOfQfiCdJ1o1Kh8WcL0zW74tAttJCO6+CWsLEC+MCV1PZ0yqSMkrwMCHbAq1ASDNRmCapJKHYn4Gx90GQLab1+l+yEndkm+ma3gkN7wjg2u0g74ZlgNpbCjqAXDrs1b8thnRPqJ6ai1OJXqUgNd7osW8ScmIzC5bDhDSMh1e2Gen04v5BGsWSsLcFSulBnIRTvL5krIpbUYSX09dHBxOKFzFOP0c+CpLBMkWo/lKX6xwmhzbJYlt/hAYZvpZ/hFhRMtRsbK4ALAr2lm56Qv9JrkZin8olaF0E2S1BNQvH0KFdyWkgkkUXQ56x6oqUTdmZiKQgCsMMC14YVHBoPo1gmeOqUOTuh1Xr+UMiHxXQBy2n19wqz+AUqFj+agVuj0U5MRXDTaEjTJTTc60WhRHBxydjr0k6EjRJK1Ymi10oYElkJy2WaIY9KJviooVSNmYoC+UR9J8Oxt9BFU602thxNNlYAB6iMkluhJblGSV5WtxAy1GZjtqIKU4FIIoc+f81/K52wMxOjfbOtrBhshmt3dKPT7TA1xZ4QgrPxtKV3E2xfatLEhaUMpuYTdRJHX8CFWDLf9ACLmVgKM7GUpnxCj5UucJ7Rq1uDlucnssWGRUygZk3UvTAa2lULqJeP08lUu3XKJ4AoA5csZMp1MmRSKJdRmmbjBfCRg4Ctw5wbJaFRxMPwh1VshIurVsQTSeQQ7qwFcOmEHbk5mGuJw27Drbv7MDEVNVxVOL+SQ6ZQmlNq6QAAIABJREFUsjQD1+MFZ+dS7NEOB9wolUnTg5zZvg/u1g7gRl0zALCcYVWYjRn4YI8HDptgwAs+Ciyfp5XGJx8FINAMXC9KLWWr0+xFfV56dgJ9e5pzlHEAbMQA7goAO2825yVNzquX0TP8A8qLmBkLGmrpJJbIoS9QXwEnnrAzsw484FIOjYcRS+bw6kWFQigFWOap1tPFKNuDXgiCehZ6tGLxG+2rWfzClXPerA4+MRXBaJ9Pl8TV53fB57RjVq/1D9RCCEA2A3fYbdgR9BrIwEfpVJ3FWZoZb7sW8Ct3uGxAaajDwjQdz9claZQ19hbg7DNALqH/NTgNbLwADtBbO6MluaUi1ePUPOCMQD8t/ZXzqmZWJwMnhCCayCEccNc9zsrWH/r1JSSy1hS9WMlt430QBO1BylJY46lmJvFIoa1VPYpBLFso4enTcdy+p97ix+56mtHB0/kinj+zoEs+AQBBEGpdBHWyJFNGL8bQ/pjEcf554MKL+t0nDEUJZRroGW7U0sfuAMoF6gnnmGZjBnAzGloqCoDoz8CBxiyckNYMc5BhKV1AvlRuyMBZ2fo3np4BgHXhARfT63fhqsFuw37w2XgKTocNW7ussRAyhlXmYz4/s4BModRQIdnnpxfNZqyET5+OI18qK1ZfyjFkcJbnYqqSgcvYCAG6iHs2btBK+MLXABD9/m+Gq5M6vKQSysIZ+VFsO95An2PWUcYB0EQAFwRhuyAIE4IgnBAE4VVBED5l5YGpEtplvCS3OolHTwauEMALGdpac1WqMGsecDFswg6TKNji13ri9vEwjs8tIZ7UHwBnYinsDHphUxhaYZahXiojyAWxickIXA4bbhqtt/ixDLwZCWViKgK/y4H9Q/rfKyMVK6HeCUdLKho4QC/umUIJ8ys6fg9vEHB3A5d/pT7BRwlBoImNWEIpl2kGLjeKzd4BjB6in2FuJzRNMxl4EcAfEUJeB+ANAO4TBEGlc7yFCAK9BTNSksuKePQuYgKNAXwVOxGKy+ilsAU3u03AYI+1GasVHNrTB0KAJ07pH4I8G2vNguxwrx+JbFF2QfLoVAQ3j4bg7qh38bg77Ai4HdVWvkYhhODoZAS37Oo11BZ3KORDmQDnF/Xp4FUJxSefgdfNx9QDK7ZRm+CjhrQjYeISrboUL2CKGTtMt5n/jfHX4gBoYqADIeQSgEuV/ycEQXgNwDYAJyw6NnXGDgMv/E/gl3+vPnGEMfME/SqRUBZSeRBCEBLZ9apZ+uxTtFcyg/WKWAUbIcv+wp3uhp8drNyWb+/xoMOiohcr2be1C71+F3748kWEfBptSAEQAGcX0rr1YiMwieknxy9iRLRQuZjOYzaexkdvkR90EQ64EDVwByFmaj6Bi8tZfOrNBhYBUd9FULyoqsRSugC7TUBAZpg13R/93Y9MzqMgKRDyOO3Yv7Onvrw/NApcOKZbPjkXTyPc6apdAD3Beg2cFQYpDUPeVXmdk4/QIj2OYSyZyCMIwhCAawA0mLMFQfgEgE8AwI4dCjP3zDB0C+DqAh79L/qf4/A0ZOD/x/deQb5Yxj/93htqD/p6aWXa839H/0nR09ynSZQkFADY1u3B6we7MNjk1JpWYbMJeMvrwnjwhfN44qT+LHzPloDlx7K7PwBBAL74k8a8wiYodwgMB9wN4+P0wrpGHjSgfwMi37rOjHmxUkav1GNla5cHnW4H/v7JGfz9kzMNP/+nj9+Im8XzUAeuAl77KTBySPO1V7IFHP4fj+M/3T6G+w5VMndPD7A8V9uoaiFUCOCBfirVnHoUuPXTmq/JaaTpAC4Igh/AvwD4A0JIg3eMEPI1AF8DgP3791sndnW4gf/4FLBySf9zAv0Njelfu7TSqDna7MDvPyO/b6fP+nmYMkRWcvA67fApZFff+uiNsNtbXw1qlv/n7Xvx7uu2g+bX2nTYbdi7tcvy4xjs8eKxP7wNy5lGCaXb68T2oPxFsC/gwivnl2R/psXEVAR7t3aiX+buSY0ebwc63Q7dC5lL6QK6ZCyEDJtNwM/+4FZcXs7UPZ7IFvGRb/wSU/OJ+gB+47+nIws93Zqv/dSpGLKFMl67JPrIe4PA5V/Xvl+YBuwuoFMl4Rk7DDz5F9bMu21DmgrggiB0gAbv7xBCfmDNIRmge4fyJG0dpPPF6gLPsvTD0OS+myWSyMpm3wy1D+56wOO0y87wXAuU2riqwcrpCSGKGa4cy+kCXjy7iP94m0LWqYIgCLr7twB0Ir3SAiZjW7enoTkYIQR+l6OxyMfeod7oTcQRUTFZFelQh/g0HRahpqePHQae+G/WzLttQ5pxoQgAvg7gNULIV6w7pNVDPPTWTCe4ViLnAeesHuFOF7KFsqGRbADw5OkoSmWCQyb1/OFen+5hzIsp+UZWWlQvFAaKhsSUy6Q6pKKu46GnByika8aC+LSyfMLYdi3gDfGyepM0swJ2AMAHAdwuCMIrlX93WXRcq4I409HdO3mViCZy6OvUXgDktAZ28TTqBT8yGUG3twNXb9eWIeQY6vXh4nIG2YL27NflTKFuGr3R1zH7nv/NxWXEkjm8frALyVyxttgrbmhVLgGLMzQDV4PNuz1lwbzbNsR0ACeEPEUIEQghVxFCrq78+zcrD67VsNs/Wm5tLhtpFZFETlVC4bQWdu6NLGSWywSPT0Vx2+6+uhFnRhju9YEQ4NyC9vtxMZ03lYEDtF/4hcWMbs+5mInJKAQB+MiBIQCiO1lxNebyHFDKa2fgAJVRMgvAhZcMH0u7s/48aKvITCyFcMCFbd0eU9NQWkU6X0QyV2yowuSsHuzcGymn//WFZcRT+abskKy3zZmo+vsxVywhnS/J9kHR9Tq91HOu50IhZWIqgtcPduO6HTTjrn52PKIMXMtCKMaKebdtSlsH8NlK/2m1cuu1oDpKjWvgawY790aqMY9MRmATgFvHjPm/xejt4816nCv1QdH9OgYTl3gyh+NzSzg0HsbWbjc67EJt/Yhl4OkFkYVwl/ZOvUFg8AZeVm+C9g7g8RRGKgFcqdx6LWCaIpdQ1o5OjwNOh81QAD86FcE1O3rQ4zMXVAGgy9OBoM+pGVgX08qdCPUwYmTgg4jHT0ZBCK22ddht2B4Uja0Ta+DxaaDDp9vVgt2HgUvHa3NrObpo2wC+ki0glsxjqNeHoZBPsdx6LagNM+YBfK0QBMHQbMxoIofjc8s4NG4++2bosRKyMnotG6ES3V4nur0dOGMwA5+YiqLX78K+imd/OCQ61qqEskAllOCI/slV1QZ1JtpEtzFtG8BZ1jAU8hmugGs1tT4oXEJZS4yMVnv8pLnqSzmGQtqSXrMZePV1DLzni6UyHp+K4OB4X7XpGJMfy2VC513aXTUJRakHihz9+4DAVq6DG6RtAzgL1sO9vqoeuH4CeA4Om6DYJpSzOvQFXLpdKBNTEYQDLuzd2tn06w73ejG/kkNKxYPOKkvNauD0dYwF8JfPL2ElW6xrPzDU60O2UMZ8IkuzbW+Qtm5eOqtvAZPR7LzbNqVtAzizPu0MeTHY44HdJqybhcxoZRKP1a1VOcYIB9y6GloVS2U8cTKKQ+NhQ1WbSuhZyGQZuFkbIUAz8IvLWV2ec4C237XbBLxxd638vuHu1dNDtexyUZ+FUMzuO+gA5HPPGnteG9O+ATxOBwK7O+zosNuwvcejuwKu1XAP+PogHHBhKV1Arqge4F48u4hEtlg3V7MZmJVQ7f24mM7DabfB02F+oDXrVnhWZ0XmkckI9u/sQae7dtGouVmYFzwIRF6j/zeSgQPA8G103i2XUXTTtgH8TCxVN77LSA+KVhNZyaKP699rjt7BDhNTUXTYBRwQN4ZqgmEdGfhyuoBur3InQj2MGOgXfmk5g8nLiQaP+5ZON1wOW+1YPd2oNjDTYyEU4/IDQwf4QqYB2jaAz0oGArNxVuvBShiVTKPnrA16y+mPTkVw/VAQAbc1axY+lwPhgEs1sNIqTPP6N1DLwPUEcNb7RNrjxWYTMBTy1QqPmJXQ1UnbMhtl7A4gOgksnjX+3DakLQP4YiqP5UyhbgLMcK8P6XypqTmIVlAolRFP5dHn5wF8renTUU5/YYlmpkZmX+pBayCxVitZPQTcHej1a3vOASqfbOv2YEyms+NQr1eUgVcCuBELoRgz827bmLYM4KxyTBzAWTa+1jJKPEndBTwDX3vYOoTaQubRKdpW1Wz3QSWGNSx+S2lznQilDIV8mp04c8USnj4dw6E9fbKSzVCvD+fiaZTKpFaNaXQBk9G7iwZ/HsB10ZYBvOoBl2Tg4p+tFdwDvn4I+V2wCUBUZTbmxGQE24MejPZZO89zqNeHeCqPlay8pW4pk0e3yU6E0tfRes+/MLOAdL6keJcxHPIhXyrj4lKmJqEYXcAUM3aYjkAsZLS3bXMsGam20ZiJpWATgO2ikWRbuz1w2m2W9gV/7kwcz5yONTxuswl47/7t2NrdOJC41geFZ+Brjd0mIORXrsbMFkp4+nQc79k/aIl9UAyb5TkbS+GqwfrWtIQQLKYLisOMjb2OD99/cQ6pXFFx+tPEZBROhw03jYZkfy62PW5XyMAvLGXwq/NLeOuVW7QPauwtwPN/h8y//iE8oe36fxk5dt8BDO6vfksIwYMvnG+YUrQafPjmofrZuxbQtgF8sMdbNzHcbhOwI+S1LAMnhODT//s45hYzDVIgIUAsmcN/fWfjINfqLEwuoawL1MrpX5hZQKagnJk2w/gALQg6fn6pIYBnCiXki+WmFzGBeh/3vm3yI+2OTkXwhpEQvE75cCHexxvH9wFdO4DtN9Zt89XHTuL7L87hhc+/SXZQt5hzgWuBch8GX/2u0V9HAgFO/gz4D09VHzkdSeLzP6Rj3yy+5mryjqu38QBuBbPxVJ18whgKWWclPB1JYm4xgz+950p84Mb60Wwf/8djmJiMyo7rYpY1PdPcOa2nL+BStBEemYzA3aGcmTbDUMiL7UEPjk5F8cGbhup+tsTK6C2o1K16zuPyAXw2lsKZWAofummn4j7CARe8Tjv97Ny0F/jDX9f9nE7woWsFR6eieO/16ln1kdPL+GL+L7E96METnzlk/u7mya8Av/hjOtu2k2b+bBTcM5+7XfYOeKPRdho4IQSzsXS1G5uY4V4vzsbTtK9Dk0xU3rAHZZobHdrThwtLGZyONE5EiSSyCPqcdXcHnLVDrR/K0akIbh7thbuJYholBEHA7eNhPD0da6iUXEw3X0bPGBJJNXKw9/Hte/pVj1Wtrwqd4JOv258aExXL4vmFDKY1+qKrwhwtp2u+8ompCPYMBDZF8AbaMIBHkzkkc0UMhRqnkQ/1+pArlnFJZdFKLxOTUcU3Crvllnsz8yrM9UU44EYsmacOCxFnoknMxtOWdB9U4uCeMLKFMp47E697fNmCRlYMr9OB/k6X4kSqiakoRvp82CHzeRFDm1op7KMyweeOvf148lQMhZLyFKBMvoRnz8Tx1n20De1RHQFfkf69QOe2qqNlJVvAsdlFyx1Da0nbBXBW8isnoQyHrHGirGQL+OXsguIbZWu3B3sGAtXbOTGRSh8Uzvog3OlCqUwaWg2zLNGK7oNK3DQSgrvDVi2iYdT6oDSfgQPK3Q/T+SKeOxPXpfEP9XpxfiEtG5yPVCb4vOvaQSRzRfxydkFmD5RnpmPIF8v4nRt3Yrxf/jOim2qDrKNAMY+nTsVQLJOWrFmsFW0YwBs94IzhPmu84E9X3ihqo7UO7Qnj2Oxig00supLlFsJ1RFhhtNrEZARjYT+2B9Uz02Zwd9hx82gvjkxG6iqEaxKKNZWfI33yaz/PnI4jXyzrGhE3FPKhWCaYW6x3d8SSOfyqMsHnwK5eOO2NFyQxE1MReJ12XD/cg4N7+vDL2QUkFKyUuhirNciamIyg0+3AtTvMDZxej7RdAJ+Jp+CwCdgmI230B9xwd9iazsCPVN4o16hMJj80HkaxTPDUqZrNkBCCaJJn4OsJ9rcQL2SmckU8PxNflVvxQ+N9OLeQrhu8sJyxTkIBaPBdqFQni5mYisDntGP/UI/mPkb65O9en6hM8Ll9Txg+lwM3jgQVs2pCCCYmo7hlVy9cDjtuHw+jUCJ4WsaKq5vhWwG7E+TkI5iYiuLW3XSS0GZh8/wmOpmNpbAj5JX9I7K+Ds1k4OUywdGT2m+Ua3d0o9PtwITozbyULqBQIlwDX0fI9UN5+nQMhRKRXaC2GibRiN8ni6k8vE47XA5rFk/l5mPSYBrBgUow1dyHQiXzkckIev21PukHx8M4HUnivMww5VORJC4sZaoXxmt39iDgdmBiUjlj18TlB3YeQO61RxBL5jaVfAK0YQCfiaWqWrccekqL1ThxaQXRRE7zttNht+HW3X04ejJadb1wD/j6Qy4Dn5iKwO9y4PqhYMtff3vQi7Gwv27BeylTsHTYh1z3w5PzSVxczuqSTwAg6HMi4HbU7aPWJ702wYct+sotTrKLFAuyHZXPyMRUpLkmc7vvgHv5NHYI87htFS66q0lbBfBymSh6wBlDvT6cX0ijqLJSrsaRyQgEAbh1t/Yb5dB4GNFEDq9eXAHAy+jXI+4OOzrdDkQqziR2m//GsV50rNKt+KE9Ybwws4BkZULPUjpviYWQsSPohSDUZ89M5tC7SCsIQkNL5pfOVSb4iC4CI31+DIW8sjLKkckIrtjSiYGu2vv/0HgYEdFnxBQVO+HvhE6id5M1iWurAD6fyCJbKKsG8JFeHwolgotL5qyEE1MRXDXYreuNctt4HwShZidkWR6XUNYX4c7aZJ7XLiVweSW7qla0QxIteKnSC9wq3B12bO3y1AXfiakIXicJplpI5ceJqQgcNgG3jNW3lT04HsYz0/E6f/tKtoBjZxcbbJm37VbO2PUSdw1iptyPwx3HTe9jvdJWAbw6B1NNQmFlwSZklIVUHq+cX8LtOrOWXr8LVw12VwM4k1D4Iub6os9fm41ZLdDScYdlFfuHeuB31dZLrOgFLkU8H3M5U8CLZxcNTxga7vXh4lKmOsFoYjKC/UP1E3wAekeRK5bx7HTN3/7kyRhKMs6tvoALrx/sqto2zfDEqSgmytdg58qLQH59TN2yirYK4MwDPqzSOa7a5D7aWCWpxeMnIyAEht74h8b78Mr5JcSTOURWcvA57YpNhThrQ7iz1g9lYjKCfds6Nft5WEmH3YY3jvVWtWCrM3CAvu9nYnSgyZOnoiiZ8EsP9/pQJsD5hTQuqvRJv3E4CE+HvU7Xn5iKoMvTgatlnFsHx8N4+dwiFiVefL0cmYziRef1sJVywOyTpvaxXmmrAD4TS8LlsGGLyoevz++Cz2lXrCpTY2Iyil6/C/u2yjcFkuP2PWEQQrOESCK7qoGBow9WTr+UzuOlc4u677Cs5NCeMOZXcjhxaYUuYlodwEM+rGSLWEwXMDEZRbe3A9fs0LYP1u2j2tQqXfV6yy2CujvsOLArVPW3014pUdym4Ny6fU8Y5cpnxChsIdU/fhvQ4d10fcbbLICnsTPkVZ32LgiC5jQUOYqlMh4/GcVB0Yq7HvZt7UKv34kjk1FehblOCQfcyBbKeOjXl1AmtMR9tWGSzU+OX0KpTFoioQC0RcDjJyO4dawPdgPvY6C+knliik7w2SUzwQegWfXcYgbT0WSlV0pO8c71ym1dCPmcpqoyXz6/hOVMAbdeMQiMHAROPkrbgW4S2iqAz8br52AqweZjGuGVyhvF6G2nzSbgtt1hPHEyisvLWR7A1yHsb/K9X55H0OfE6wdXv5Iv3OnGvm2d+NeXLwAAuiy0EQK17Pknxy8ilswb1r8BoMvbgR5vB6bmE6oTfIDaBKOJyWi1V8qtY/KvabMJuG28D4+fjDb0pNFiYjICO1tIHXsLsHwOiE4Z+8XWMW0TwEtlgnPxtKr+zRjp9WFuMaPadEfKxP/f3r3HSFmdcRz//vYCLKjsLhcL7CKgrJRaWWWL4KXgVismFmKhilolVmtNtF7SRm3apNqkpjZt1VhbY6zVf7xhirWEoFaosXgrKHjh0qJQBEGgChTBC8vTP855YZzuzl5mYG7PJ5nM+86+8855duY9c+a8533O6vBBOa2p+xO5to4ZzI49n7H+g90+AqUAJe/J8g07mNzU/ZZprrQeO5jNcThjrlvgjXV9qawQc5ZuQILJTT37lTFyYD/mv7GJ3Z+2ZRxDPqy2Zn+uk4Wrt9DcWJsxV3brmMFs3/0Zy97d3q3yLFq9lZaj6sIXXgnOt5lVBS5pqqTVktZIuilXhToY3tu+h0/b9mUcgZIYMaAfbfus3avFOrJwVfigpJ9x74pTRw/cXyn4GPDCk3ph1aG4+rIjqV03dTmYjSdVr6oKGupq2P1pG82NtdT369kXxIg4OXivqgomjcrcmElynSS5UjI57ZjwxbmoG90om3bsYeWmnQeGfPZvgMFf8gocQFIlcDdwNjAWuEDS2FwVLNeSPu1MY8ATIwZ2L6nV5h0ff/6D0k39a6oZf1Q4YeQt8MIzKH6pVujAuOR8GNdwoGLtn4P5MNMl3YvZXG6eNJAmjRpATa/Ml+C3xnxAZp2/Zv++1YwfXtelfOKJ5ETq5/Y9+kxY/yJ8vKPL+ylk2YxXmwCsMbN3ACQ9AkwHVuSiYKl+uWBVt386pUuGgbWXhTBdss2t81fyh7+v7XT7JL1nVy87bk9rvNrOL6MvPEf0qaJ3VQVfHtY/p1dAdldlhZjcNIi5r23MyYz06UYO7Mdz/9ya1ec4afx0ZR9JrpM+1ZX7c6VkcvqYwdy2YBUX3PtSl6ZDe2frRwyrraHpyJQTqU1nweI74MFp0Kfz18yp6b+D2izn+EyTTQU+DHg3ZX0DcFL6RpKuAK4AGD58ePqfu6Rtn3WrP7o9dX2rmTm+oUst3Lq+1cz6SiNvb93Vpdc9rHcl57U0MLqDM+5d8c0ThrHivZ2My5DB0OWHJL43+WiaG7s+PPRg+c4pI+nbqzLnfeAA05qHsnffPsYO6XnFdsoxA5k2bijnHN/55MXVlRX84MwmeldXdmnk1vTmobzQzgxFHWmsr2HGiWkTTjdMgONmws6N0JZFmtoeyf3oF/U0SYykmcBUM7s8rl8MnGRmV3f0nJaWFluyZEmPXs8558qVpKVm1pL+eDYnMTcCqb8HGuJjzjnnDoFsKvB/AKMljZTUC5gFPJmbYjnnnOtMj/vAzWyvpKuBp4BK4H4zeytnJXPOOZdRVlmTzGw+MD9HZXHOOdcNZXMlpnPOlRqvwJ1zrkh5Be6cc0XKK3DnnCtSPb6Qp0cvJm0F/t3Dpw8EtuWwOIWoHGKE8oizHGKE8oizEGI8ysz+LxHPIa3AsyFpSXtXIpWScogRyiPOcogRyiPOQo7Ru1Ccc65IeQXunHNFqpgq8HvzXYBDoBxihPKIsxxihPKIs2BjLJo+cOecc59XTC1w55xzKbwCd865IlWQFbikPpJekbRc0luSbomPj5T0cpxE+dGYxraoSaqU9JqkeXG9pGKUtE7SG5KWSVoSH6uX9Iykf8X7unyXM1uSaiU9LmmVpJWSJpVSnJKOje9hctsp6bpSijEh6fpY77wp6eFYHxXkcVmQFTjwCdBqZuOAZmCqpInAbcDtZnYM8CFwWR7LmCvXAitT1ksxxtPNrDllLO1NwLNmNhp4Nq4XuzuBBWY2BhhHeE9LJk4zWx3fw2ZgPLAbmEsJxQggaRhwDdBiZscRUmXPolCPSzMr6BvQF3iVMN/mNqAqPj4JeCrf5csytgbCh74VmAeoBGNcBwxMe2w1MCQuDwFW57ucWcbYH1hLHBRQqnGmxPV1YHEpxsiBuX7rCem25wFnFepxWagt8KRrYRmwBXgGeBvYbmZ74yYbCP/sYnYHcAOQzJw8gNKL0YCnJS2NE1wDHGlmm+LyZuDI/BQtZ0YCW4E/xu6w+yT1o/TiTMwCHo7LJRWjmW0EfgWsBzYBO4ClFOhxWbAVuJm1Wfi51gBMAMbkuUg5JekcYIuZLc13WQ6yU83sROBs4CpJX039o4UmTbGPZa0CTgR+b2YnAB+R1pVQInES+36nAXPS/1YKMcY+/OmEL+WhQD9gal4LlUHBVuAJM9sOLCL8bKmVlMwiVOyTKJ8CTJO0DniE0I1yJ6UVY9Kiwcy2EPpMJwDvSxoCEO+35K+EObEB2GBmL8f1xwkVeqnFCeGL+FUzez+ul1qMZwBrzWyrmX0G/IlwrBbkcVmQFbikQZJq43INcCbhpNAiYGbcbDbw5/yUMHtm9iMzazCzEYSfpAvN7CJKKEZJ/SQdniwT+k7fJEx+PTtuVtQxApjZZuBdScfGh74GrKDE4owu4ED3CZRejOuBiZL6ShIH3suCPC4L8kpMSccDDxLOAFcAj5nZzySNIrRW64HXgG+b2Sf5K2luSJoC/NDMzimlGGMsc+NqFfCQmf1c0gDgMWA4Ib3weWb2QZ6KmROSmoH7gF7AO8ClxM8uJRJn/BJeD4wysx3xsVJ8L28Bzgf2Eo7Bywl93gV3XBZkBe6cc65zBdmF4pxzrnNegTvnXJHyCtw554qUV+DOOVekvAJ3zrki5RW4c84VKa/AXdmQ1FvSX2M61PNztM8pkk5OWb9S0iU93NcISRfmolyuPFR1volzJeMEgJhjJ1emALuAF+K+78liXyOAC4GHsi6VKwt+IY/LG0lPAI1AH+BOM7tX0mXAjcB2YDnwiZldLWkQcA/hij+A68xscQf7rQfuB0YR8lZfQciU9wIwiJD6dYaZvd3Oc9cRriw8G9gDXGhmayR9A/gJ4UrL/wAXATXAS0AbIRvh9wmXXu8ys19JOhq4O77mbuC7ZrZK0gPATqAF+AJwg5k9Lukl4IuxfA+a2e3d/Je6cpPvfLZ+K98bUB/vawg5UoYR8ofXA9XA88Bv4zYPETIbQqjEV2bY713AT+NyK7AsLk8B5nVSpnXAj+MZOTE4AAACAklEQVTyJcn2QB0HGjyXA7+OyzcT0iCQvk7I9T46Lp9EyHcD8AAhm18FMBZY09Xy+c1vqTfvQnH5dI2kc+NyI3Ax8JzFXBqS5gBN8e9nAGNDfiEAjpB0mJntame/pwIzAMxsoaQBko7oRrkeTrlPWsENwKMx414vQiu5Q5IOA04G5qSUuXfKJk+Y2T5ghaSizqHt8scrcJcXMYHXGcAkM9st6W/AKkIXQnsqgIlm9vEhKJ61s3wX8BszezKW/eZO9lFBmASgo/721ERI6mAb5zLyUSguX/oDH8bKewwwkZA8f7Kkuph7eUbK9k8T+piB/dn/OvI8oY86+aLYZmY7u1G281PuX0wpb5IDenbKtv8FDk/fQXy9tZK+FcshSeM6ed129+VcR7wCd/myAKiStBL4BeFk4EbgVuAVYDGhP3pH3P4aoEXS65JWAFdm2PfNwHhJr8d9z86wbXvq4nOvBa5P2eccSUsJ8yMm/gKcG4cmnpa2n4uAyyQtB94izPSSyetAm6Tlkq7vZFvnfBSKKyxJv3Zsgc8F7jezuZ09L4evv44wI/m2zrZ1Lt+8Be4Kzc1xMus3CScKn8hzeZwrWN4Cd0VL0qWEbo5Ui83sqi48dy5h4tpUN5rZU7kqn3MHm1fgzjlXpLwLxTnnipRX4M45V6S8AnfOuSLlFbhzzhWp/wFZJ8DtswFzdgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N82Bixbq3YwC",
        "colab_type": "code",
        "outputId": "ff65c29c-591f-4553-b5a5-e678c4e63730",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "pd.crosstab(haberman['operation_year'], haberman['nsurvival_status'], normalize='index', margins=True)\n",
        "pd.crosstab(haberman['operation_year'], haberman['nsurvival_status'], normalize='columns', margins=True)"
      ],
      "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>nsurvival_status</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>operation_year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.148148</td>\n",
              "      <td>0.106667</td>\n",
              "      <td>0.117647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.088235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.049383</td>\n",
              "      <td>0.106667</td>\n",
              "      <td>0.091503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.102222</td>\n",
              "      <td>0.084967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>0.086420</td>\n",
              "      <td>0.071111</td>\n",
              "      <td>0.075163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0.098765</td>\n",
              "      <td>0.097778</td>\n",
              "      <td>0.098039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>0.098765</td>\n",
              "      <td>0.102222</td>\n",
              "      <td>0.101307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>0.160494</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.091503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0.074074</td>\n",
              "      <td>0.097778</td>\n",
              "      <td>0.091503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>0.049383</td>\n",
              "      <td>0.093333</td>\n",
              "      <td>0.081699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.044444</td>\n",
              "      <td>0.042484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>0.049383</td>\n",
              "      <td>0.031111</td>\n",
              "      <td>0.035948</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "nsurvival_status         0         1       All\n",
              "operation_year                                \n",
              "58                0.148148  0.106667  0.117647\n",
              "59                0.111111  0.080000  0.088235\n",
              "60                0.049383  0.106667  0.091503\n",
              "61                0.037037  0.102222  0.084967\n",
              "62                0.086420  0.071111  0.075163\n",
              "63                0.098765  0.097778  0.098039\n",
              "64                0.098765  0.102222  0.101307\n",
              "65                0.160494  0.066667  0.091503\n",
              "66                0.074074  0.097778  0.091503\n",
              "67                0.049383  0.093333  0.081699\n",
              "68                0.037037  0.044444  0.042484\n",
              "69                0.049383  0.031111  0.035948"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vjq8O24S6e64",
        "colab_type": "code",
        "outputId": "c30183bd-523f-43df-df6b-bca048cf45b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "haberman_oy = pd.crosstab(haberman['operation_year'], haberman['nsurvival_status'])\n",
        "haberman_oy.plot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9FaVN7_6Xon",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLxZ0pa9pev4",
        "colab_type": "code",
        "outputId": "4e648842-b7ab-43c5-9fb0-03cd056a2430",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pd.crosstab(haberman['number_of_positive_axillary_nodes_detected'], haberman['nsurvival_status'], normalize='index', margins=True)\n",
        "pd.crosstab(haberman['number_of_positive_axillary_nodes_detected'], haberman['nsurvival_status'], normalize='columns', margins=True)"
      ],
      "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>nsurvival_status</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number_of_positive_axillary_nodes_detected</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.234568</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.098765</td>\n",
              "      <td>0.146667</td>\n",
              "      <td>0.133987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.061728</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.065359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.086420</td>\n",
              "      <td>0.057778</td>\n",
              "      <td>0.065359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.044444</td>\n",
              "      <td>0.042484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.049383</td>\n",
              "      <td>0.008889</td>\n",
              "      <td>0.019608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.017778</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.024691</td>\n",
              "      <td>0.022222</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.024691</td>\n",
              "      <td>0.022222</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.049383</td>\n",
              "      <td>0.008889</td>\n",
              "      <td>0.019608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.008889</td>\n",
              "      <td>0.009804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.013072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.006536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.049383</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.016340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>0.013072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.024691</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.009804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.024691</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.009804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.006536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.008889</td>\n",
              "      <td>0.009804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.009804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0.012346</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "nsurvival_status                                   0         1       All\n",
              "number_of_positive_axillary_nodes_detected                              \n",
              "0                                           0.234568  0.520000  0.444444\n",
              "1                                           0.098765  0.146667  0.133987\n",
              "2                                           0.061728  0.066667  0.065359\n",
              "3                                           0.086420  0.057778  0.065359\n",
              "4                                           0.037037  0.044444  0.042484\n",
              "5                                           0.049383  0.008889  0.019608\n",
              "6                                           0.037037  0.017778  0.022876\n",
              "7                                           0.024691  0.022222  0.022876\n",
              "8                                           0.024691  0.022222  0.022876\n",
              "9                                           0.049383  0.008889  0.019608\n",
              "10                                          0.012346  0.008889  0.009804\n",
              "11                                          0.037037  0.004444  0.013072\n",
              "12                                          0.012346  0.004444  0.006536\n",
              "13                                          0.049383  0.004444  0.016340\n",
              "14                                          0.012346  0.013333  0.013072\n",
              "15                                          0.024691  0.004444  0.009804\n",
              "16                                          0.000000  0.004444  0.003268\n",
              "17                                          0.012346  0.000000  0.003268\n",
              "18                                          0.000000  0.004444  0.003268\n",
              "19                                          0.024691  0.004444  0.009804\n",
              "20                                          0.012346  0.004444  0.006536\n",
              "21                                          0.012346  0.000000  0.003268\n",
              "22                                          0.012346  0.008889  0.009804\n",
              "23                                          0.037037  0.000000  0.009804\n",
              "24                                          0.012346  0.000000  0.003268\n",
              "25                                          0.000000  0.004444  0.003268\n",
              "28                                          0.000000  0.004444  0.003268\n",
              "30                                          0.000000  0.004444  0.003268\n",
              "35                                          0.012346  0.000000  0.003268\n",
              "46                                          0.000000  0.004444  0.003268\n",
              "52                                          0.012346  0.000000  0.003268"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3N9nDDyapfHW",
        "colab_type": "code",
        "outputId": "1a61de4e-f082-48ed-a3f5-3e983b097149",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "haberman_nd = pd.crosstab(haberman['number_of_positive_axillary_nodes_detected'], haberman['nsurvival_status'])\n",
        "haberman_nd.plot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RadC090pfQF",
        "colab_type": "code",
        "outputId": "293e47c9-3471-4fec-f136-04bc1eea235b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        }
      },
      "source": [
        "haberman_age.plot(); haberman_oy.plot(); haberman_nd.plot(); "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2XEBaOv9bT4",
        "colab_type": "code",
        "outputId": "0c74d3ed-1809-4edb-b5d4-d73126515b53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        }
      },
      "source": [
        "haberman_age.hist(); haberman_oy.hist(); haberman_nd.hist(); "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mHqKTMy8xoB",
        "colab_type": "code",
        "outputId": "73d941f4-0f3c-4565-bf4e-704a903b18eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        }
      },
      "source": [
        "pd.crosstab(haberman['nsurvival_status'], haberman['number_of_positive_axillary_nodes_detected'], normalize='index', margins=True)\n",
        "pd.crosstab(haberman['nsurvival_status'], haberman['number_of_positive_axillary_nodes_detected'], normalize='columns', margins=True)"
      ],
      "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>number_of_positive_axillary_nodes_detected</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>28</th>\n",
              "      <th>30</th>\n",
              "      <th>35</th>\n",
              "      <th>46</th>\n",
              "      <th>52</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nsurvival_status</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",
              "      <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",
              "      <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",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.139706</td>\n",
              "      <td>0.195122</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.230769</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.264706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.860294</td>\n",
              "      <td>0.804878</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.769231</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "number_of_positive_axillary_nodes_detected         0         1  ...   52       All\n",
              "nsurvival_status                                                ...               \n",
              "0                                           0.139706  0.195122  ...  1.0  0.264706\n",
              "1                                           0.860294  0.804878  ...  0.0  0.735294\n",
              "\n",
              "[2 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpX6wJVC7wdH",
        "colab_type": "code",
        "outputId": "655876fc-8fd4-4b2c-fe76-1ba2506c8edc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        }
      },
      "source": [
        "shaberman_nd = pd.crosstab(haberman['nsurvival_status'], haberman['number_of_positive_axillary_nodes_detected'])\n",
        "shaberman_nd.plot();\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QGHeU9S8aeG",
        "colab_type": "code",
        "outputId": "5dc1f93a-bc6e-4526-853b-aeb3d59973fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "shaberman_oy = pd.crosstab(haberman['nsurvival_status'], haberman['operation_year'])\n",
        "shaberman_oy.plot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rpJPd2q8arK",
        "colab_type": "code",
        "outputId": "b77091e2-7695-41e3-c5a7-9810a1d38730",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "shaberman_age = pd.crosstab(haberman['nsurvival_status'], ['age_of_patient'])\n",
        "shaberman_age.plot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OMAds7QfyWb9Syg0ZY1i+8wQvfRNNcloW/7qjCWN6NKRsaffuB1OYHLlaRoC5wB5jzJs5Fq0ERgDT7N9X5Bj/h4gsxPZG6gU9366UctTJ8ylMWhbJT/viaRNQlRmDg2lcq7LVZbkcR47cuwLDgUgRudLDdiK2UF8sIo8Bx4Bh9mWrsV0GeRDbpZD5+3imUqpEyc42LNh2nOlr9pKVbfj3PUGM6BLoVo2+ipMjV8tsBG700+11nfUNMLaAdSmlSpAjZy8zPiyCbUcS6dbIh9fua0ldb0+ry3JpTvsJVaWU+8vMyuaTjUd464f9lCtTihmDgxka4l/iWwcUBg13pZQlok9eZHxYBJEnLtAnqBZTB7agVhXtTVRYNNyVUsUqLTOL99Yd5MP1h6jqWZYP/tKWu1rU1qP1QqbhrpQqNtuP2Rp9HTxzifva+vHi3UFU00ZfRULDXSlV5C6nZTLz+318tukodbwq8NnI9vS4pabVZbk1DXelVJH65UA8E5ZGEnsuhUc612Nc36ZUKq/RU9T0J6yUKhIXkjN4ZXU0i8NjaeBTkcV/7UyH+t5Wl1ViaLgrpQrd/6JO8eKKKBIvp/P3Hg15qldjbfRVzDTclVKF5kxSKlNW7mZ15CmCfKvw6aPtaeHnZXVZJZKGu1KqwIwxLN1xgv+siiYlI4vn77yF0bc10EZfFtJwV0oVSOy5ZCYui2LD/nja1avG9MHBNKpZyeqySjwNd6VUvmRnG77Yeozpa/ZigJf6N2d4p3qU0kZfTkHDXSmVZ4fiLxEaFsFvR89xW5MavDqoBf7VtNGXM9FwV0o5LCMrmzkbDjPrxwNUKFuamUNbMbitn7YOcEIa7koph0SduMD4sAh2n7xIv5a1mdK/OTUra6MvZ6XhrpS6qdSMLN758QAfbThMNc9yzH64LX1b+FpdlsqFhrtS6obCjyYyLiyCw/GXGdrOnxfuDsLLs6zVZSkHaLgrpf7kUlomr/9vL/O3HKOOVwXmj+rAbU1qWF2WygMNd6XUH/y8P56JSyM5eSGFEZ0Def7OW6iojb5cTq4fHxOReSJyRkSicoy1FpEtIrJTRMJFpIN9XETkHRE5KCIRItK2KItXShWe88npPLt4JyPmbcOjbCmWjOnMlP7NNdhdlCOv2mfAe8D8HGMzgJeMMWtEpJ/9fg/gLqCx/asj8KH9u1LKia2OjOPfK6I4n5zBP25vxD96NtJGXy4u13A3xmwQkcBrh4Eq9ttewEn77QHAfGOMAbaISFUR8TXGxBVSvUqpQnTmYir/XrGb/+0+RQu/Knw+qgPN62ijL3eQ37+3nga+E5GZ2E7tdLGP+wExOdaLtY/9KdxFZDQwGiAgICCfZSil8sMYw9fbY3l5VTSpmdmM79uUJ26tTxlt9OU28hvufwOeMcaEicgwYC7QOy8bMMbMAeYAhISEmHzWoZTKo5jEZCYui+SXA2fpEOjNtMEtaVBDG325m/yG+wjgn/bbXwOf2G+fAOrmWM/fPqaUslhWtmH+5qPM+N8+SglMHdiCv3QI0EZfbiq/4X4S6A6sB3oCB+zjK4F/iMhCbG+kXtDz7UpZ7+CZJMYtiWDH8fP0uKUGrwxqiV/VClaXpYpQruEuIl9huxLGR0RigcnAE8AsESkDpGI/dw6sBvoBB4FkYGQR1KyUclBGVjYf/XyId348iGf50rx1fysGttZGXyWBI1fLPHiDRe2us64Bxha0KKVUwUXGXuD5JbvYeyqJu4N9eal/c3wqlbe6LFVM9NMJSrmZ1Iws3l57gI9/OUz1iuX4aHg77mxe2+qyVDHTcFfKjWw9nEDo0kiOnL3MA+3rMqFfM7wqaKOvkkjDXSk3kJSawfT/7eWLLcep612BBY93pGsjH6vLUhbScFfKxf209wyTlkUSdzGVx7rV5199muBZTv9pl3T6G6CUi0q8nM7UVdEs+/0EjWtWIuxvXWgbUM3qspST0HBXysUYY/g2Mo7JK3ZzISWDp3o1ZuztDSlfRht9qf+n4a6UCzl9MZUXlkfxQ/Rpgv29+OLxjjTzrZL7A1WJo+GulAswxrDotxheWb2H9MxsJvZryqiu2uhL3ZiGu1JO7nhCMqFLI9h0KIGO9b2ZPjiYQJ+KVpelnJyGu1JOKivb8OmvR5j5/T7KlCrFq4Na8kD7utroSzlEw10pJ7T/tK3R186Y8/RsWpNXBrXA10sbfSnHabgr5UTSM7P5cP0h3vvpAJU9yjLrgdb0b1VHG32pPNNwV8pJ7Io5z7glEew7nUT/VnWYfG8Q1bXRl8onDXelLJaSnsWbP+xj7sYj1KzswSePhNA7qJbVZSkXp+GulIU2H0ogdGkExxKSeahjAKF3NaWKhzb6UgWn4a6UBS6mZvDa6r18te049ap78uUTHenSUBt9qcKj4a5UMftxz2kmLYviTFIqo29rwDO9m1ChnLYOUIVLw12pYpJwKY2Xvolm5a6T3FKrMrOHt6N13apWl6XcVK6fXRaReSJyRkSirhl/UkT2ishuEZmRY3yCiBwUkX0icmdRFK2UKzHGsGLnCXq/+TNrouJ4pncTvnmymwa7KlKOHLl/BrwHzL8yICK3AwOAVsaYNBGpaR8PAh4AmgN1gLUi0sQYk1XYhSvlCuIupPDCsih+3HuG1nWrMmNIME1qVba6LFUCODJB9gYRCbxm+G/ANGNMmn2dM/bxAcBC+/gRETkIdAA2F1rFSrmA7GzDV78d57XVe8nMzuaFu5sxsmt9SmvrAFVM8nvOvQlwq4i8AqQCzxljfgP8gC051ou1jylVYhw9e5nQpRFsOZxIl4bVmXZfMAHVPa0uS5Uw+Q33MoA30AloDywWkQZ52YCIjAZGAwQEBOSzDKWcR2ZWNvN+PcIb3++nXOlSTLuvJfe3r6utA5Ql8hvuscBSY4wBtolINuADnADq5ljP3z72J8aYOcAcgJCQEJPPOpRyCntPXWT8kgh2xV6gd7NavDywBbW9PKwuS5Vg+Q335cDtwE8i0gQoB5wFVgJfisib2N5QbQxsK4xClXJGaZlZvP/TIT746SBeFcry3kNtuLulrx6tK8vlGu4i8hXQA/ARkVhgMjAPmGe/PDIdGGE/it8tIouBaCATGKtXyih3teP4OcYvieDAmUsMauPHv+8JolrFclaXpRQAYstka4WEhJjw8HCry1DKIcnpmbzx/X7m/XqE2lU8eHVQS25vWtPqslQJJCLbjTEh11umn1BVKg9+PXiW0KURxCSm8HCnAMb3bUplbfSlnJCGu1IOuJCSwWur97Dwtxjq+1Rk0ehOdGxQ3eqylLohDXelcvH97lO8sDyKs5fS+Gt3W6Mvj7La6Es5Nw13pW4gPimNKd/s5tuIOJrWrswnI0II9td+MMo1aLgrdQ1jDMt3nuClb6JJTsviuT5N+Gv3hpQtnWufPaWchoa7UjmcOJ/CpGWRrN8XT9sAW6OvRjW10ZdyPRruSmFr9LVg23Gmrd5DtoHJ9wbxSOdAbfSlXJaGuyrxDsdfIjQskm1HE+nWyIfX7mtJXW9t9KVcm4a7KrEys7L5+JcjvLV2Px5lSjFjSDBD2/lr6wDlFjTcVYkUffIi48J2EXXiInc2r8XUAS2oWUUbfSn3oeGuSpTUjCzeW3eQ2T8foqpnOT78S1vuaulrdVlKFToNd1VibD+WyLglERyKv8zgtv68eE8zqnpqoy/lnjTcldu7nJbJ69/t4/PNR6njVYHPR3Wge5MaVpelVJHScFdubcP+eCYsjeTE+RRGdK7H832bUqm8/tor96e/5cotXUjOYOq30SzZHkuDGhX5ekxn2gd6W12WUsVGw125nf9FxfHiit0kXk7n7z0a8lSvxtroS5U4Gu7KbZxJSmXyit2siTpFkG8VPn20PS38vKwuSylLaLgrl2eMIWzHCaauiiYlI4vn77yF0bc10EZfqkTTcFcuLSYxmYnLIvnlwFlC6lVj2uBgGtWsZHVZSlku10MbEZknImfsk2Ffu+xfImJExMd+X0TkHRE5KCIRItK2KIpWKjvb8NmvR7jz7Q1sP3aOl/o3Z/FfO2uwK2XnyJH7Z8B7wPycgyJSF+gDHM8xfBfQ2P7VEfjQ/l2pQnPwzCVCwyIIP3aO25rU4NVBLfCvpo2+lMop13A3xmwQkcDrLHoLGAesyDE2AJhvjDHAFhGpKiK+xpi4wihWlWwZWdnM2XCYWWsPUKFcad4Y2or72vppoy+lriNf59xFZABwwhiz65p/WH5ATI77sfaxP4W7iIwGRgMEBATkpwxVgkSduMC4JRFEx12kX8vavNS/BTUql7e6LKWcVp7DXUQ8gYnYTsnkmzFmDjAHICQkxBRkW8p9pWZkMevHA8zZcBjviuWY/XBb+rbQRl9K5SY/R+4NgfrAlaN2f2CHiHQATgB1c6zrbx9TKs9+O5rI+CURHD57maHt/Hnh7iC8PMtaXZZSLiHP4W6MiQRqXrkvIkeBEGPMWRFZCfxDRBZieyP1gp5vV3l1KS2TGf/by/zNx/CvVoH/PtaBWxtroy+l8iLXcBeRr4AegI+IxAKTjTFzb7D6aqAfcBBIBkYWUp2qhFi/7wyTlkVx8kIKI7sG8lyfW6iojb6UyjNHrpZ5MJflgTluG2BswctSJc25y+lM/TaapTtO0KhmJZaM6UK7etWsLkspl6WHRMpSxhjWRJ3i3yuiOJ+cwZM9G/GPno0oX0YbfSlVEBruyjJnLqby4ooovtt9mpZ+Xswf1ZGgOlWsLkspt6DhroqdMYavw2OZ+m006ZnZhN7VlMe71aeMNvpSqtBouKtiFZOYzISlkWw8eJYOgd5MG9ySBjW0H4xShU3DXRWLrGzD55uO8vp3+yhdSpg6sAV/6RBAqVLaOkCpoqDhrorcgdNJjA+LYMfx8/S4pQavDmpJnaoVrC5LKbem4a6KTEZWNrPXH+LddQepWL40b9/fmgGt62ijL6WKgYa7KhIRsecZtySCvaeSuCfYlyn9m+NTSRt9KVVcNNxVoUrNyOKtH/bz8S+H8alUnjnD29GneW2ry1KqxNFwV4Vmy+EEQsMiOJqQzIMd6hJ6VzO8KmijL6WsoOGuCiwpNYNpa/ayYOtxArw9+fLxjnRp5GN1WUqVaBruqkB+2nuGicsiOX0xlce71efZPk3wLKe/VkpZTf8VqnxJvJzOf77ZzfKdJ2lcsxIf/K0LbQK00ZdSzkLDXeWJMYZvIuKYsnI3F1My+Gevxvz99oba6EspJ6Phrhx26kIqLyyPYu2e0wT7ezHjiY40ra2NvpRyRhruKlfGGBb+FsOr3+4hIzubSf2aMbJroDb6UsqJabirmzqWcJnQsEg2H06gUwNvpt0XTKBPRavLUkrlQsNdXVdWtuHTX48w8/t9lC1VilcHteSB9nW10ZdSLsKROVTnAfcAZ4wxLexjrwP3AunAIWCkMea8fdkE4DEgC3jKGPNdEdWuisi+U0mMC4tgV8x5ejWtycuDWuDrpY2+lHIljpw0/Qzoe83YD0ALY0wwsB+YACAiQcADQHP7Yz4QEb2MwkWkZ2bz9tr93PPuL8QkJjPrgdZ8MiJEg10pF+TIBNkbRCTwmrHvc9zdAgyx3x4ALDTGpAFHROQg0AHYXCjVqiKzM+Y845dEsO90EgNa1+Hf9wRRXRt9KeWyCuOc+yhgkf22H7awvyLWPvYnIjIaGA0QEBBQCGWo/EhJz+LNH/Yxd+MRalb2YO6IEHo1q2V1WUqpAipQuIvIJCATWJDXxxpj5gBzAEJCQkxB6lD5s+nQWULDIjmemMxDHQMIvaspVTy00ZdS7iDf4S4ij2J7o7WXMeZKOJ8A6uZYzd8+ppzIxdQMXlu9l6+2HadedU++eqITnRtWt7ospVQhyle4i0hfYBzQ3RiTnGPRSuBLEXkTqAM0BrYVuEpVaNZGn2bS8kjik9IYfVsDnundhArl9D1vpdyNI5dCfgX0AHxEJBaYjO3qmPLAD/Yp07YYY8YYY3aLyGIgGtvpmrHGmKyiKl45LuFSGlO+ieabXSdpWrsyc4aH0KpuVavLUkoVEfn/MyrWCQkJMeHh4VaX4ZaMMazcdZIpK3dzKS2Tf9zemL/1aEi5Mto6QClXJyLbjTEh11umn5opmXQAAA8iSURBVFB1YyfPp/DC8ijW7T1D67pVmTEkmCa1KltdllKqGGi4u6HsbMNXvx3ntdV7yco2vHhPEI92CaS0tg5QqsTQcHczR85eJjQsgq1HEunaqDqvDQomoLqn1WUppYqZhrubyMzKZu7GI7z5w37KlSnF9MEtGRZSF/sb3kqpEkbD3Q3sibvI+LAIImIvcEdQLV4e2IJaVTysLkspZSENdxeWlpnF++sO8sH6Q3hVKMt7D7Xh7pa+erSulNJwd1U7jp9j/JIIDpy5xH1t/HjxniCqVSxndVlKKSeh4e5iktMzmfndfj7ddATfKh58OrI9t99S0+qylFJORsPdhWw8cJYJyyKISUxheKd6jOt7C5W10ZdS6jo03F3AhZQMXvk2msXhsdT3qcii0Z3o2EAbfSmlbkzD3cl9t/sULy6PIuFyOmO6N+Tp3o3xKKuNvpRSN6fh7qTik9KYsnI330bG0cy3CnNHtKelv5fVZSmlXISGu5MxxrDs9xP8Z1U0yWlZPH/nLYy+rQFlS2ujL6WU4zTcnciJ8ylMXBrJz/vjaRtga/TVqKY2+lJK5Z2GuxPIzjZ8sfUY09fsxQBT7g1ieGdt9KWUyj8Nd4sdir9EaFgEvx09x62NfXh1UEvqemujL6VUwWi4WyQzK5s5vxzm7bUH8ChTiteHBDOknb+2DlBKFQoNdwvsPnmB8WERRJ24SN/mtfnPgObU1EZfSqlCpOFejFIzsnh33QFm/3yYap7l+PAvbbmrpa/VZSml3JAjE2TPA+4BzhhjWtjHvIFFQCBwFBhmjDkntnMKs4B+QDLwqDFmR9GU7lrCjyYyPiyCQ/GXGdzWnxfvaUZVT230pZQqGo5cPP0Z0PeasVDgR2NMY+BH+32Au4DG9q/RwIeFU6brupyWyZSVuxn60WZSM7L5fFQH3hjWSoNdKVWkcj1yN8ZsEJHAa4YHAD3stz8H1gPj7ePzjTEG2CIiVUXE1xgTV1gFu5IN++OZsDSSkxdSeKRTPZ7v25RK5fVMmFKq6OU3aWrlCOxTQC37bT8gJsd6sfaxP4W7iIzGdnRPQEBAPstwTueT03n52z0s2R5LgxoVWfzXzrQP9La6LKVUCVLgw0hjjBERk4/HzQHmAISEhOT58c5qTWQcL67YzbnkdMbe3pAne2qjL6VU8ctvuJ++crpFRHyBM/bxE0DdHOv528fc3pmkVCav2M2aqFM0r1OFz0e1p3kdbfSllLJGfsN9JTACmGb/viLH+D9EZCHQEbjg7ufbjTEs2R7L1FXRpGZmM67vLTxxqzb6UkpZy5FLIb/C9uapj4jEApOxhfpiEXkMOAYMs6++GttlkAexXQo5sghqdhoxiclMXBbJLwfO0j6wGtMGB9OwRiWry1JKKYeulnnwBot6XWddA4wtaFHOLjvbMH/zUWZ8tw8B/jOgOQ93rEcpbfSllHISel1eHh08k8T4sEi2HztH9yY1eGVQC/yraaMvpZRz0XB3UEZWNnM2HGbW2gN4li/Nm8NaMaiNnzb6Uko5JQ13B0SduMDzSyLYE3eRu1v6MqV/c2pULm91WUopdUMa7jeRmpHF22sP8PEvh/GuWI7ZD7ejb4vaVpellFK50nC/gW1HEgkNi+Dw2csMC/FnUr8gvDzLWl2WUko5RMP9GpfSMpm+Zi//3XIM/2oV+OKxjnRr7GN1WUoplSca7jn8tO8Mk5ZGEncxlVFd6/OvPk2oqI2+lFIuSJMLOHc5namroln6+wka1azEkjFdaFevmtVlKaVUvpXocDfGsDryFJNXRnE+OYOnejZibM9GlC+jjb6UUq6txIb76YupvLg8iu+jT9PSz4v5ozoSVKeK1WUppVShKHHhboxhcXgML3+7h/TMbCbc1ZTHutWnjDb6Ukq5kRIV7scTkpmwLIJfDybQob430+5rSQNt9KWUckMlItyzsg2fbTrKzO/2UbqU8PLAFjzUIUAbfSml3Jbbh/uB00mMC4vg9+Pnuf2WGrwyqCV1qlawuiyllCpSbhvu6ZnZzP75EO+uO0Cl8mV4+/7WDGhdRxt9KaVKBLcM910x5xkfFsHeU0nc26oOk+8NwqeSNvpSSpUcbhXuKelZvL12Px//cpgalcvz8SMh3BFUy+qylFKq2LlNuG85nEBoWARHE5J5sENdQu9qhlcFbfSllCqZChTuIvIM8DhggEhsc6b6AguB6sB2YLgxJr2Add5QUmoG09bsZcHW4wR4e/Ll4x3p0kgbfSmlSrZ8f3JHRPyAp4AQY0wLoDTwADAdeMsY0wg4BzxWGIVez5bDCfR5awNfbTvO493q893Tt2mwK6UUBT8tUwaoICIZgCcQB/QEHrIv/xyYAnxYwOe5rioeZfGuWI4P/tKWNgHa6Esppa7Id7gbY06IyEzgOJACfI/tNMx5Y0ymfbVYwO96jxeR0cBogICAgHzVEFSnCque7KaXNyql1DUKclqmGjAAqA/UASoCfR19vDFmjjEmxBgTUqNGjfyWocGulFLXUZBuWb2BI8aYeGNMBrAU6ApUFZErfxH4AycKWKNSSqk8Kki4Hwc6iYin2A6fewHRwE/AEPs6I4AVBStRKaVUXuU73I0xW4ElwA5sl0GWAuYA44FnReQgtssh5xZCnUoppfKgQFfLGGMmA5OvGT4MdCjIdpVSShWMzlChlFJuSMNdKaXckIa7Ukq5ITHGWF0DIhIPHMvnw32As4VYjivQfS4ZdJ9LhoLscz1jzHU/KOQU4V4QIhJujAmxuo7ipPtcMug+lwxFtc96WkYppdyQhrtSSrkhdwj3OVYXYAHd55JB97lkKJJ9dvlz7koppf7MHY7clVJKXUPDXSml3JDLhLuI9BWRfSJyUERCr7O8vIgssi/fKiKBxV9l4XJgn58VkWgRiRCRH0WknhV1Fqbc9jnHeoNFxIiIy18258g+i8gw+2u9W0S+LO4aC5sDv9sBIvKTiPxu//3uZ0WdhUVE5onIGRGJusFyEZF37D+PCBFpW+AnNcY4/Re2+VkPAQ2AcsAuIOiadf4OzLbffgBYZHXdxbDPtwOe9tt/Kwn7bF+vMrAB2IJtDl/Lay/i17kx8DtQzX6/ptV1F8M+zwH+Zr8dBBy1uu4C7vNtQFsg6gbL+wFrAAE6AVsL+pyucuTeAThojDlsjEkHFmKbBSqnAdjmbAVbK+Je4trTNOW6z8aYn4wxyfa7W7BNjuLKHHmdAaZim4g9tTiLKyKO7PMTwPvGmHMAxpgzxVxjYXNknw1QxX7bCzhZjPUVOmPMBiDxJqsMAOYbmy3YJj3yLchzukq4+wExOe5fb27Wq+sY2xyuF7D1k3dVjuxzTo9h+5/fleW6z/Y/V+saY74tzsKKkCOvcxOgiYj8KiJbRMTh6SydlCP7PAV4WERigdXAk8VTmmXy+u89VwXq566cg4g8DIQA3a2upSiJSCngTeBRi0spbmWwnZrpge2vsw0i0tIYc97SqorWg8Bnxpg3RKQz8F8RaWGMyba6MFfhKkfuJ4C6Oe5fb27Wq+vY53D1AhKKpbqi4cg+IyK9gUlAf2NMWjHVVlRy2+fKQAtgvYgcxXZucqWLv6nqyOscC6w0xmQYY44A+7GFvatyZJ8fAxYDGGM2Ax7YGmy5K4f+veeFq4T7b0BjEakvIuWwvWG68pp1VmKbsxVsc7iuM/Z3KlxUrvssIm2Aj7AFu6ufh4Vc9tkYc8EY42OMCTTGBGJ7n6G/MSbcmnILhSO/28uxHbUjIj7YTtMcLs4iC5kj+3wc27zMiEgzbOEeX6xVFq+VwCP2q2Y6AReMMXEF2qLV7yLn4d3mftiOWA4Bk+xj/8H2jxtsL/7XwEFgG9DA6pqLYZ/XAqeBnfavlVbXXNT7fM2663Hxq2UcfJ0F2+moaGzzFT9gdc3FsM9BwK/YrqTZCfSxuuYC7u9XQByQge0vsceAMcCYHK/x+/afR2Rh/F5r+wGllHJDrnJaRimlVB5ouCullBvScFdKKTek4a6UUm5Iw10ppdyQhrtSSrkhDXdV4ojIpnw+LvBGLVtv8piqIvL3wlpPKUdpuCu3Y/+U3w1/t40xXYqxnKrY2lEX1npKOUTDXVnOfkS8R0Q+tk9G8b2IVBCRp3JMRrLQvu4UEXkux2Oj7I8PtE/+MB+IAl4UkddzrPeoiLxnv33J/n2hiNydY53PRGSIfVu/iMgO+5dD/xmISHMR2SYiO+01NwamAQ3tY6+LSCX7xCo7RCRSRK60ur12vR4isirHtt8TkUftt6fl+LnMzNcPXbk97QqpnEVj4EFjzBMishgYDIQC9Y0xaSJS1cFtjDDGbBGRGsBm4Hn7svuBV65ZfxEwDPjW3uOkF7ZJTwS4wxiTag/or7B13czNGGCWMWaBfXul7fvQwhjTGq42tRtkjLlo7xOzRURWXme9Htd7AhGpDgwCmhpjjIM/F1UC6ZG7chZHjDE77be3A4FABLDA3tI404FtHDO2iQ4wxsQDh0Wkkz0Qm2LrVZLTGuB2ESkP3AVsMMakAGWBj0UkElu/oiAH92EzMFFExgP17Nu6lgCvikgEtt5AfkAtB7cPtnkKUoG5InIfkJzL+qqE0nBXziJnu+IsbH9V3o2tmVJb4Df7UW8mf/y99chx+/I121yI7ch8MLDMXNNIyRiTiq352J3YjuwX2Rc9g60hWytsR+zlHNkBY8yXQH8gBVgtIj2vs9pfgBpAO/tR+ulr9uGK6+6nsU1E0wHbbGP3AP9zpDZV8mi4K2dVCtuMSz8B47H1568EHMUW9ldmZap/k20swzZ92YPYgv56FgEjgVv5/6D0AuKMbWKI4dhOr+RKRBoAh40x7wArgGAgCVsf+iu8gDPGmAwRuR24Mqn5tesdA4LENvF7Vf6//W0lwMsYsxrbf0KtHKlNlTx6zl05q9LAFyLihe1UxjvGmPMiEoat7/VuYCu2trHXZYw5JyJ7sE2+vO0Gq30P/BdYYWzzeQJ8AISJyCPYAv/avwhuZBgwXEQygFPAq8aYRLFNjxeF7TTQdOAb+ymfcGCvvdaEnOsZY563v/cQBRzBNkE22P4DWCEiHvafy7MO1qZKGG35q5RSbkhPyyillBvS0zJK5ZGI3Int9EpOR4wxg6yoR6nr0dMySinlhvS0jFJKuSENd6WUckMa7kop5YY03JVSyg39H7a76x1Jlk0JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hep67_iulltS",
        "colab_type": "code",
        "outputId": "f4f8bfa6-9933-4ece-9918-9e963a70e767",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        }
      },
      "source": [
        "shaberman_age.hist(); shaberman_oy.hist(); shaberman_nd.hist();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 36 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjqNXvv3pYBG",
        "colab_type": "code",
        "outputId": "ee42925e-7508-46df-f893-4c7ec6c02516",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pd.crosstab(haberman['age_of_patient'], haberman['operation_year'], normalize='index', margins=True)\n",
        "pd.crosstab(haberman['age_of_patient'], haberman['operation_year'], normalize='columns', margins=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.074074</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.064516</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.032680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.074074</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.029412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.129032</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.086957</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.086957</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.074074</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.115385</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.064516</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.039216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.019608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.130435</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.045752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.086957</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.042484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.032680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.086957</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.129032</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.035948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.129032</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.026144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.019608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.115385</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.026144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.016340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.064516</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.016340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.038462</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.019608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.013072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.013072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.003268</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "operation_year        58        59        60  ...        68        69       All\n",
              "age_of_patient                                ...                              \n",
              "30              0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.009804\n",
              "31              0.000000  0.037037  0.000000  ...  0.000000  0.000000  0.006536\n",
              "33              0.027778  0.000000  0.035714  ...  0.000000  0.000000  0.006536\n",
              "34              0.027778  0.037037  0.071429  ...  0.000000  0.000000  0.022876\n",
              "35              0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.006536\n",
              "36              0.000000  0.000000  0.035714  ...  0.000000  0.090909  0.006536\n",
              "37              0.027778  0.037037  0.071429  ...  0.000000  0.000000  0.019608\n",
              "38              0.000000  0.037037  0.107143  ...  0.000000  0.090909  0.032680\n",
              "39              0.027778  0.037037  0.000000  ...  0.000000  0.000000  0.019608\n",
              "40              0.055556  0.000000  0.000000  ...  0.000000  0.000000  0.009804\n",
              "41              0.027778  0.074074  0.035714  ...  0.000000  0.090909  0.032680\n",
              "42              0.027778  0.074074  0.035714  ...  0.000000  0.090909  0.029412\n",
              "43              0.027778  0.037037  0.035714  ...  0.000000  0.000000  0.035948\n",
              "44              0.027778  0.000000  0.000000  ...  0.000000  0.000000  0.022876\n",
              "45              0.000000  0.037037  0.035714  ...  0.076923  0.000000  0.029412\n",
              "46              0.055556  0.000000  0.000000  ...  0.000000  0.090909  0.022876\n",
              "47              0.027778  0.000000  0.035714  ...  0.076923  0.000000  0.035948\n",
              "48              0.055556  0.000000  0.000000  ...  0.000000  0.000000  0.022876\n",
              "49              0.000000  0.000000  0.035714  ...  0.000000  0.000000  0.032680\n",
              "50              0.027778  0.074074  0.000000  ...  0.000000  0.000000  0.039216\n",
              "51              0.000000  0.111111  0.000000  ...  0.000000  0.000000  0.019608\n",
              "52              0.000000  0.037037  0.071429  ...  0.076923  0.181818  0.045752\n",
              "53              0.055556  0.037037  0.107143  ...  0.000000  0.000000  0.035948\n",
              "54              0.027778  0.037037  0.071429  ...  0.076923  0.090909  0.042484\n",
              "55              0.083333  0.000000  0.000000  ...  0.076923  0.181818  0.032680\n",
              "56              0.000000  0.000000  0.071429  ...  0.000000  0.000000  0.022876\n",
              "57              0.000000  0.000000  0.000000  ...  0.000000  0.090909  0.035948\n",
              "58              0.055556  0.037037  0.035714  ...  0.000000  0.000000  0.022876\n",
              "59              0.000000  0.000000  0.035714  ...  0.000000  0.000000  0.026144\n",
              "60              0.000000  0.037037  0.000000  ...  0.000000  0.000000  0.019608\n",
              "61              0.000000  0.111111  0.000000  ...  0.153846  0.000000  0.029412\n",
              "62              0.055556  0.037037  0.000000  ...  0.000000  0.000000  0.022876\n",
              "63              0.000000  0.000000  0.035714  ...  0.000000  0.000000  0.026144\n",
              "64              0.027778  0.000000  0.000000  ...  0.076923  0.000000  0.016340\n",
              "65              0.055556  0.037037  0.000000  ...  0.000000  0.000000  0.032680\n",
              "66              0.083333  0.000000  0.000000  ...  0.076923  0.000000  0.016340\n",
              "67              0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.019608\n",
              "68              0.000000  0.000000  0.000000  ...  0.076923  0.000000  0.006536\n",
              "69              0.000000  0.000000  0.035714  ...  0.000000  0.000000  0.013072\n",
              "70              0.055556  0.037037  0.000000  ...  0.076923  0.000000  0.022876\n",
              "71              0.000000  0.000000  0.000000  ...  0.076923  0.000000  0.003268\n",
              "72              0.027778  0.000000  0.000000  ...  0.000000  0.000000  0.013072\n",
              "73              0.000000  0.000000  0.000000  ...  0.076923  0.000000  0.006536\n",
              "74              0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.006536\n",
              "75              0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.003268\n",
              "76              0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.003268\n",
              "77              0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.003268\n",
              "78              0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.003268\n",
              "83              0.027778  0.000000  0.000000  ...  0.000000  0.000000  0.003268\n",
              "\n",
              "[49 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jY9IJHxspwxO",
        "colab_type": "code",
        "outputId": "d8ea1fa4-313e-4a6f-9199-b0dc646c075e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "haberman_ao = pd.crosstab(haberman['age_of_patient'], ['operation_year'])\n",
        "haberman_ao.plot(); haberman_ao.hist(); "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kXpgDB4CRnqzXycHpADatccNszH0a5+fKCaJdIQEnGkI0kcLrc2FsXusGAASixTcy02mOiEoVCiBXmCxGwDnPVKDkM9hlg9HAOr4ShWh/SMCJhvDy8SBSaY43n9oLACUrUVYSuVaySoa9dgRjSbx8IojFcLwg/w0AJqNcC04CTrQ5JOBEQxDpkzefIgt4iQg8nGclq0Tkt/9w8AQAFFSgKI+jyTxEu0MCTjSEg9PLcFtNmYi5VA5czMN0WdUicCm//YeJ4wCAzSopFHEcReBEu0MCTjSEiekAzhjyZDool0ukUEKxXCtZJWKD8sBUAOt7HXCpROmAtNl5IhhFLFmfSSgEsRogASfqTirNcfh4EGNDHjgtJhhY6RRKJC5y4IXi7LGb4JZFe2yoq+g5hr0OcA7MLJEvONG+kIATdefoQhiReAqjgx4YDAxum7nkJmY2B16YQmGMYZ2cB1fbwBRkuzYpD060LyTgRN3Jt3z12E0l2+kjseIROIDMoAa1EkIB+YITnQAJeJvxjd9M4NHDJ+py7v/x7xN44tXK7UknZgIwGxk2Dkg14B6tEbhKGSGQFediFSgAsNYjasEri8Cffm0eux5Wn4hOEKsNEvA2IhhN4CdPHsF/vHhc93MnUmnc+dQR/Palys99cDqAjQPuzHg0j81cOgcub2LmT+QRvO+cYdx86Wnod1uLnsNkNGCou/Ja8N8emMFdTx+lzU+iJdBjoAOxSjh8PAgAmA3qv3E3H4oBAOaCsYp/dmI6gEs2Za2EPXYTjs4Xj4zD8iZmsQj8zHVdOHNd8Q1MwXB35aWE4vrmgrFMySJBrFYoAm8jRK65GpEtR0bYQpWdezYYxXwolpPu6LKXicDjSRgNDFZTbU/Papp5lAJOEKsdEvA2Qti1zlcosloQgjZfobAdFBuYig3HsjnwWAoOi7HmgbzDXgdmgzFEE9rTIXM1fNIgiEZDAt5GiHb1hXC8rN92pSgj00oG5YpPBWcoInCP3YxwPIVEkTVKToS1Z/dGeqTNzuklbWkUznnVnzQIohmQgLcJiVQarxwPwWMzgXNpfJmeCGGLp9JlrWCVTMwEcFKPAx5bdg6mxyaJc7BIKaE0D1M9/10JwxlbWW0CHoolEU1IbyoUgROtAAl4m/DabAjxVBoXbZQ2C2d1FiBlRFrJJunEdKHlq0dupy+WRgnHkkVb5Cuh0mYe5e+MBJxoBUjA2wSRqrhYrvbQW4BmA5WLWyiWxNGFcEHHpIjGi21kRuQceK2s8dhgNmr3BVdel95vgARRD0jA24SJmQBsZgPO29ADQH8BnwvFsMZjzXythZePB8B5YcNNNgIvlkLRJwduNDAMddsrFvA1HitF4ERLQALeJhycXsbmtR6s8dgA6L8JNxeMZVIhWsXt4LT60GGPXRLnohF4kWk81TDstWvuxhTXNTroIQEnWgIS8DaAcy7lmoc8sJmNcNtMugqQqM44td8Fi8mg+dwT0wF4HWasld9UBJkUSokcuNo0nmqopJlnLhSTWv7XuDEXqqzahiCaAQl4GzC1tIJANJlJVfS79U0BhOMprCRSGPBYMeC2as4PT8wEMDbUVVDPLTzBS0bgOqRQACkCn9NYCz4biKHfJV1jPJkuabhFEKuBsgLOGLuTMTbLGDuguK2HMfZHxtir8v/e+i6TKEV+s0y/S18BF+fqd1s1vzkkUmkcPh5UtXx1WIwwGphqDpxzLuXAdSgjBLLOhVqi8LlQLHONAFWiEKsfLRH4XQCuzLvtywAe4ZxvBPCI/D3RJCamAzAwYPPabASupx/KbEA6V7/LpvnN4Y25MOLJtKrlK2MMHptJdSpPNJEG5+rTeKohaytbPg8+F8wV8Hp4yhCEnpQVcM75fwNYzLv5agB3y1/fDeA9Oq+rLXhhcgm/O1CbM+CJQBQ/eeINpNPF87ETMwGc0u+CXc4bD7ht+kbgobwIXMMG6cSM1NZfzPLVU8QPRVjJ6hWBV9LMIwR8oAUj8EMzATz8wnSzl0E0mGpz4Gs45zPy18cBrCl2IGPsRsbYHsbYnrm5yr2kW5l/fvx1/M3DB8ofWILfvjSDb/zHITz9+kLRY/KbZfrdVoTjKYRj+uRw81Moi+F40TZ4wWuzIZgMDBv6nKr3F/NDEcMc9IrAB9xWWIyGss08qTTHYljKgfe75EqeFhLwO588gq8+8FKzl0E0mJo3Mbm0VV80POSc384538E539Hf31/ssLZEcuKLl4yeyyHaze8bn1S9fykSx9TSSk6uWaQA9DK1mgvGYDIwdNvNmXMvhEq36k8urmCo2w6TUf0pVmwqTyYC16kKxWCQRrCVi8AXwjGkufS789hNUrVNC/mh+CNxBKPJsm+sRHtRrYCfYIwNAoD8/6x+S2of5kIxpNIc/kj1viQiiv7dgeOqOWNhYDWmIuB6RZAitWAwMAy4pei0XH7Y549k8s9qFI3AM/Mw9bOqH9Yg4KLTtN9tA2NM943geuOPSL/LpUhxl0ei/ahWwB8GcL389fUAHtJnOe2DXs52wVgSBgbEkmn85sXCHGfG7U+ZQnHpLOBydQag/c3B518pL+AqOfCQnEJx6ZQDByQBnyqTQlHm+cX/LSXgsnlZLcEC0XpoKSP8JYBnAGxijPkYY58E8C0AlzPGXgXwNvl7QoFeznahaBIn9Thw+hoXdo/7Cu6fmA5gjceKPld2vNiAR1RR6CNAoj4a0Cbg0UQKs2Um2njsJtUyQjFOTa8cOCBtZM6H4liJF68FF9cz0KoCLgu3X2cXSmJ1o6UK5UOc80HOuZlzPsw5v4NzvsA5v4xzvpFz/jbOeX6VSsejfPHXJOCxJFw2E3ZuH8G+Y0t4bTaYc79ollHidVhgNLC6ROB9Lot0W4lzC/9t4cetRpfdjJVECvFkbs5WjFPTwwtFoKWUUFxPn6v1BDyV5pn0mp9SKB0FdWLWCWX0W0skHIpK1qpXnz0Eo4HhPkUUHk2k8OpsqKDW2mhg6HVadBGgVJpjQSHgVpMRXXZzybTQpJxvLh2BS92Ywbw0SjYHrmcKpXwp4VwwBrfVlCnF7HdZsRgpX22zGgisJCD2ySmF0lmQgNcJvSLwYCwJl9WMAbcNb93Ujwf2TmWm7bx6IoRUmqt2O2qt1y7HYjiONM+mFgDpa6W9bD4i0i2XAwdQsDEbjukfgY9ojMD7PblpqHoMxqgHStEmAe8sSMDrhBBtT43GUuFYEm55gs2120cwG4zhiVfnAZRultErBaCsAc85d4k3B59/BWZjtmJFjawjYW4ePBJPgjHAZtbvqdnnssJiMpSNwPsV+wh6bwTXE2XahKpQOgsS8DqR42xXaw5cLqm7dPMAepyWTE34wekAXFYTRlRSFXqVweVXZ4ivS53b51/Bum47jIbiQ4mLORKGYyk4LaaaBxorMRgYhrvtJZt5lHl+QP9SzHqi3LhshU8MhH6QgNcJEdGt8dSWyghFpU1MALCYDLh62xD+c2IW/nAcE9MBnDHohkFFKAc8VsyHYjU1EQG5PigC8eZQzG51cjFSMv8NKIY6qOTA9ZjGk89wT2lbWVHrLmglPxSRNnFbTViiFEpHQQJeJzLGSDVEwrFkCvFUOmc+5M7tI4in0nhw/xQOqVSgCPpdViRrbCICshF4n9uSPbfbipVEKlMxkk+5GnBAGYHnplDC8RScOjbxCEo180TiSYRiyRwB72uhFIpIm5zS76QqlA6DBLxOzCqc7ZZXEpr8qPMJyflhpYCPDnkwNuTBPz/+OsLxlKrbHyB1FAK1T+aZC8bgsppy6rJLpReiiRTmQ7HyAl5kKk8kVqcI3GvHYjiu6g8zH5Te5JQ5cJvZWPP+RaPwR+IwGRiGvQ6qA+8wSMDrRL41aTW+JOFMV2JuRHrt9uFMaaJaBQqgXw53LhjLqUABkG2nDxSmF3waSggBwG42wmxkhTlwneZh5iPWM7VUGIWLNMlA3uSgAY+tJfxQ/JE4uh0W9DgtVIXSYZCA14EcZ7sahDQYk8RN5MAFV29bB7ORwWRg2LjGpfqzWh43Ek/i/xwt3YM1F4yhL0/AM+dWETdRqleqiQcQnuCF7fThWErXGnCB+EQwuVi4kZmptHHlXafOfigLoRgOTC3rdj6BP5yA12GG12HG8koCqRr3PYjWgQS8Diid7WqxJhUpFHdeBN7jtOCqrUM452QvrCZ1sdPiaX3X00fx/v/9TMmNuvzqDKD0m4OWJh6Bx25WyYEn65IDHynRzKNWaSO+11PAf/jYa3j//34mU8evF/5IHF6HBd0OC9K8+KxRov0gAa8D2dppW8aXpJqP4qGYGG5QKGjfed8W3HvDeUV/1mk1wWExluwCfcm3DM6zI9nUmAvECiLTbrsZpiKt+j5/BBajoeBn1FCbyhOJpXSzklXS57LAajKoNvPMBWMwMOmNUUl/BfM/tXB0PoxIPIU35sO6nROQBdxpzqyf0iidAwl4HVA2v/Q4LWCsyghcFvD8FAoAmIwGmIt4bQvKRZBCuCeKCPhKPIVgXnUGINVV9xVJL/j8K1jntauWNuajNpUnHE/qamQlYIwVrUSZC8bQ67IW1K33u62I6DgYQzz2wWl90yj+SEKOwM2Z74nOgAS8DoiobcBthdloQI/DUlUkJwQ8P4WilVI53EA0gWNyPriYgM8XSS2I29SuSUsJoSDfE5xzjkg8pds4tXyGvQ7VZp7ZYOGnDEDfbkzOeUbAi/2+qz3vkryJ6XXIEThVonQMJOB1QC9nu0wZoUoEroVSLe+HZyRXQ7fNlBkKkY/yjSifgSLX5NPQxCPIn8oTS6aRSvO6ROCAtLFaLAIf8KhcYw3pr3wWwnGsyKWkxX7f1RCKJZFIcfQ4zVkBpxRKx0ACXgcKnO2qFXB5mIPdXF1EWupxxcf4q7cN4ch8OBPtK1HzQck5d56wReJJLITjVUfgkYyVbP0i8KVIosABMd8HRaBnO7144xhwW3FwOlC0i7VSRBNPt8MCr9OccxvR/pCA14ECX40qy9GCUakio1pfkAG5iSiWLGwimpgOoM9lwSWnDwAADqtEhcWqM8RtC/LIOMFUpgJFo4DbzYgl05kmJ5Fr1nOcmhKxLmUteDrNMa9SaQPom0IRm6dvH1uDpUgCM8v6tOiLaNvrsMBlNcFkYFikCLxjIAGvAwW+GrIfSqVRVyiWrDr/DZSOICdmAjhj0IOxdZ7M9/nMBaIwMKDXqS7g6Ty7Va1NPIKsJ7gk3JE6DHNQkvEFX8wK+NJKAsk0VxVwMRhDDz+USfkxLx9dC0C/PLj4/fc4zWCModthIT+UDoIEvA7M5wu4y4p4Mq06QqwUSiOraigm4PFkGq+cCGJ0yIO1Hhu8DjMOTqlH4D3OwuoMIBudKsVtUmMTj8Bjy22nD9dhmIOSTDOPYiNTrF9NwKVqG30GY/j8EXgdZuw42QvGSpduVoIyhQJIQu4PUwqlUyABrwOz+QKe6VysLJILx5MFbfSVUKyJ6LXZEBIpjtFBDxhjGBvqUo/Ag+qpBUD9zcHnX4HVpK0GHFA4Esp58Egdhjko6XVaYDcbczYyi3VhCvRq5pGqcxxwWk3Y0OvMeLnXijKFAkhCTimUzoEEXGfUnO2y1qSVCUEwmoRLdu2rhmIt70KshZPh6JAHL58IFowPU/NBEQg/lFwBj2Cd1645Z58/lSeUGWhcnwg8WwuejcAzw4w96sMnBtz6+KH4/JHMJ4Azhjy6VaL4w3EwJs0YBQCvw0wplA6CBFxn1JzttLS1q1FrDrzXpd5EdHB6GTazARv6nACA0UEP4sk0Xp8L5RxXKgIX9rJKcRNRpla68qbyiHmY9WilF+Q385SqtAH08UMRNeAjPdLvZnTQg8nFlYIu1GrwRxLospszaS7J0IpSKJ1CTQLOGPscY+wgY+wAY+yXjLHiM7Q6BJEmyc2BV+eHEooma2pqEU1E+Y87MR3A5rWezItejGRTbqxxzlV9UAQOiwkuq6kghaK1AgUonMqTmUhfpxw4IG1k5gu43WwsWrrY77ZiPhSvaTDGXCiGWDKd+d2I3/chHaJw4YMi6HZY4A/HdStTJFY3VQs4Y2wdgFsA7OCcnwnACOCDei2sVcl8JFfMg/TYTbCYDBV/FA/JA41rIRJL2E4AACAASURBVL9jknOOiZlAzhzNDX1OWE2GHAFfiiSQSPGS+WxlfjgcS2IxHFcd71aM/Kk8EeH9UqccOCBtsC6vJDKPKd6kiqV9+t1WyV2yhrSEL6+8clTlDbNaJAHPPke8DjOSaa5a10+0H7WmUEwA7IwxEwAHgOnal9TaqH0kZ4xJH8VLTHLPJ53m0iZmDVUoYh35UXIwmszxETcZDdi81p1TGVGqBjxzblf2zSFfpLRgNRlgMRoy1TkiAq+2cUkL+aWEs4HinzKA8s08/nC8rH1rfnnlgNuGfre1bB7cHy4f+UtWstkIXHxNzTydQdUCzjmfAvA9AMcAzABY5pz/Qa+FtSqzJZztKonAI4kUOK/eByXzuHk5XCHS+ZN8RuVKFPHRu1xuWNw3nxFwaWOwEgFnjMnt9NkI3GExajLCqhaxPrHeuZB6F6aglIDPBqO44NuP4hfPHyv5mMKDfF139nczOugpWUo4s7yCN3/rETywb6rkuYUPikAIOA037gxqSaF4AVwNYAOAIQBOxthHVI67kTG2hzG2Z25urvqVtgilnO0qyYHX6oOS87iKJqKJmQAMDNi8Nl/APVheSWBa7hDMpoK0pVAqbeIRSJ7g2Rx4vXxQBMN5vuDFfFAEpTagH9o3jUg8hT1lhmL4/CvodVpyNmdHhzx4bTaIeFLdG/z+vVOIJtJl8+SLkTh6nIoUilM4EpKAdwK1pFDeBuAI53yOc54AcD+A8/MP4pzfzjnfwTnf0d/fX8PDtQalfDUqEnB5Gk+tFRn9brmJSH5DmJhexin9roxPi0DkxA/KE2O0RuDBWBIr8RQmFyOwmQ3oc1mKHq+GNJUnW4VSzw1MQMoROy1GTPojiCVTWF5JlIzAM8ON8z49cc5x3/gkgPJNOcoSQsHYkAeJFMers8GC4znnuG/PpPyz6oOYAWn+aDSRVo3AKYXSGdQi4McAvIkx5mDSDtBlAA7ps6zWpVjlRr/LisVIvKDWuhjBItN4KiU/BTAxHVAdhLx5rRuMZWvE50Ix2MyGko1EynmfooSwUt+WnAg8Vv8IXKoFlypR5kNyyWeJNymn1QSnxVjw5vuibxmvnAhhXbcdb8yFsBIvPrR6SqW8UvwN1MR/z5/8OLogDcbwLRXa3wrym3iUX1MKpTOoJQf+HIDdAPYCeEk+1+06ratlKdb8MuCxgnPtL6zMQGMdUihiXf5wHNPL0ZwKFIHDYsKGPmemMkLUgJcS5GyDUhS+pcIoUwsemynbiRlP1s2JUImoBRdDmUsJuLg/vwlr97gPVpMBf/W2jUhz4OUThZE0IG1G+5YKyyvX9zrhsBhVK1F27/HBYTHiXVsHS0bgSh8UgcduBmOgZp4OoaYqFM7533DON3POz+ScX8c5X/0jvOuIFme7WY2VKCKFUksrPZDN4c4Go5noutgk+7GhrkxEOBuMlm2JV7r1VVoDLlBO5QnHU3VzIlQiujG1pInE/XMKz5doIoWH9k/hyjPX4k2n9AIoXhI4F4ohnkxjuCc3AjcYGM4YLOzIjMST+M2L03jnWYPYtMatan8ryPdBAQCjgaHbbqZmng6BOjF1ZGlFrp0uYr8KaPdDESmUWgVc2UQ0UaQCRTA66MHU0gqWI4mSXZgC8ebwxnwYS5FExRuYgPAET0rTeGKNisAdCEaTeH1Omk2pTcCzb7x/nDiBQDSJa7cPY9hrh9tmKjomrVR1zuigB4emAzmlgv/fS8cRjqfkcxcfxAyop1DE9+SH0hmQgOtIuQEIymPKkZmHWaOAK5uIJmYCWOuxobdIZJ1pMJkJyKmg0o21vS4rDAzYd2wJACpq4lGuL55KI5ZMIxyrzzzMfIRb4t5jfgDZjcpiDLhtOX+33eM+DHXZcP6pfWCMYVQlkhYI8R1RE/AhD4KxZI5A3zc+iZN7HTh3Q4+i5LGIgMspFK8zt9mrm/xQOgYScB0p5WzXV+FwAFFGWGsVSqaJKBjDwenloukTIBuZv+Bbgj+SKBuZGg0MPU5rRsCry4Fn2+nDdZyHqUREtvuOLaHHadE0HDoQTSKaSOH4chRPvDqH920fzpSKjg55cHgmqNrQI8R3XXfhm1vGwkB2JpxcjODZNxZx7TnDGeMt6RzqG5kiTdJtz43Ae5wWspTtEEjAdUSkR9Sc7WxmIzw2U0URuNVkgMVU+5+o322Fb3EFr8+Fi6ZPxHEDbiv++5W5zPdazi2GH1cj4F2KdvpInSbS5yPWOV+miUcgjpkPxXD/Ph/SHLh2+3Dm/tFBD1YSKRyZDxf87ORiBH0uS0HZJgCcvsYNo4Fl9h12j/vAGHCNfO4eFftbJf5IHC6rqeA50u2wUB14h0ACriPlNsUGPNqtSUOxJNw1VqAI+t1W7Jv0I5XmqhUoSkaHPPg/cmOKJnGTr9VuNhZ0n2pB+KHMh+JIpDhcDYjAu+zmTGpK65sUIHXZ7t7jw7nre3ByrzNzv7DlVUujlHJotJmNOLVfqvxJpzl2j/twwal9mY5NxhhGeuyZTs58/OF4QfoEkGrdScA7AxJwHSnrbOeyVlCFUtswh5zHdVuRSEkf70ulUIBsg4n4ubLnlkV+pEe7D7gSMZXnuNwB2ogIXJmeqETAf3/gON6YD+PaHcM595824ILZyFQrUdSaeJSIYRrPHlnA1NIKduadO989UYk/kijYwAQAr9OCaCJdsjadaA9IwHVktkztdCV+KLWOU8t5XFlkXVZT2Y3G0cGu7M9VIG7VVKAA2Qh8elkSqUbkwIHseiu5xnufOwaHxYh3njWYc7/FZMDGAXdBJUo6zTG1VNojfXTQg5nlKG7/7zfgtppwxdjavHXai+bA831QBELUKQpvf0jAdaRc6V0l7fTBWFI3W1Xh9TE66ClrFKVMsZSrzgCypYTV5L+B7CZmIyNwILveUl4vgl6nNBgjFEviHWcOqm4sjw15MDEdyPHhPhGMIpHiZSJw6ff9+MtzeNfWIdjynBiHvXYEoknV4Q+LkTh6HOopFIAEvBMgAdeRYj4ogn63FZF4CmENXs2hqI45cHlN5dInAHBSjwNOixFeh1nTBmp/jQIurnFGFvDGReDaUygmowG9cn4/P8UhGB3yYCEcL7DuVT6WGmcoNpXVzp2tBS+MwpfCiZIROPmhtD8dL+B3PXUEf/nzcV3ONRcq7WzXX0EpYa0DjZWskatiSlWgCAwGhtEhT9ka8PxzV1MDDkgbeVaToeERuBhvpkXApeNsOKnHgXPX96jer+ZtIkR3pKf478brtGBdtx2n9jtx9kh34TqLNPMkUmkEY0nVjWOvszF+KJ++dy/uePJIXR+DKE1jXi2rmOeOLOKJV+drPk8smcJSpLSznRD32WAM6/ucRY8D9M2Bn7WuC99875m4auuQpuO//q6xzHzKcmw/2YtvvOdMXHrGQNXr89jN2Qi8QQL+1k0D+Lv3nInzNvRqOv5vrhqFxWQomoI6Q9EE9dbN0u9CDI1Q+oCr8Z1rt8BlNanunRRr5sl2YRamULrl2+rZzJNOc/zx0AmkOccnL9xQt8chStPxAu6PxBGKJRFPpmuquV7Q4GxXSTdmUIdxagKDgeHD552s+fizhrvKHyRjNDB85E3az62Gx2bKtLU7GpRCsZgMuK6CdQvPk2J4bGac1OPIqUSZ9EfQ77YW5LXzueC0vqL3dcv2t/kpFDUfFEF2E7N+KZR52eMlUMSnhWgMHZ9CER1rtUYrs1r8szMplNJ+KLFkCvFkuiE10asB0cwDNC4CrwfSlJ1sJUq1Bl9KhP3t5GJeBJ5xIiwUcLPRALfVVNcUyqT8iUCMwyOaAwm4LNy1RitanO28DguMBla2lDBjJdsAZ77VgEch4I2KwOvB2JAHRxciGR8bn3+l6r0BJSM9haWE4nnbrZJCAYBuZ339UMR6KAJvLh0t4JzzzEfRWkuutAi4wcDQ57KUTaGIKhWXTZ8UymrHo7hORx0HGtcbUeVzeCaAVJpjWsUHvBqGvQ5M+VdyShRFwKHWyAMAPQ5LXVMovkwETgLeTDpawMPxFOLyhBx/jR83hSiXq53WUguul5Vsq+CxS9dpNRlgKmMstZoRAn5wOoDjgSiSaV51g5OSYa8dwVgyJ11RzEpWUG8/lGwEnsx5YyEaS+u+WnRAKdo1p1BCUW3Odq7C6S75iI/getWBr3ZEBN7qb1hrPTb0OC2YmA7At1jcB7xSxDkmFWkUfzgOm9mgapIF1N8PRUTgqTRHmFr2m0ZHC7iy0UGPFIoW86d8b2k19Bpo3CqIHHgr578B5HiDa2ni0YpaM08xHxSB12nBUh0tZZVljZRGaR4dLeDKqSW1plBmNUywAaQUykI4ruodLei4FIocgbdyBYpgdMiDl48HcXRBKotcp4OAqzXzLEXipQXcYUFQLo/Vm3SaY8q/gpN7pXXRRmbz6GgBV+7S61GFolXAU2leMuLvuBSKnAN3NGCcWr0ZG/Ignkrjv16ZwxqPFVZT7dfksZvgtppyBHyxiJWsQDT4LK3on0aZDcYQT6UzPi5UStg8OlrARdQ91GWrqeSKc16RgAOlm3nCOo1TaxUyEXgbXK9oqX/Rt6zLBiYgpWbW5bkSLkXUfVAEop2+Hn4oYh3iWimF0jw6WsAX5Sf3yb3OmobABmNJxJJpTc52WgQ8FE2CsfaISLUgGnna4Xo39DlhlTt69ch/C/KbefyROHrKpFCA+vihiE8CouqGUijNoyYBZ4x1M8Z2M8YOM8YOMcberNfCGsFSJI4uuxl9bmtNkYqWGnCB2OgsVYkSjCXhsqh7Y7QjYhOzHXLgJqMBm+XIVE8BF808nHOk0hxLKwlVHxRBPf1QxISgMygCbzq1RuDfB/A7zvlmAFsBHKp9SY1D2sk311xyJabsVDKCrFwErpeRVSsgpvK0ehWKQKQW9OjCFAx7HQjHJcO0wEoCnKv7oAhEi309mnl8/hX0uayZ53sgSjnwZlG1gDPGugC8BcAdAMA5j3POl/RaWCPwh6WJJt0OC5ZXEiUrQ0ohWuO1ROBOqwlOixGzJfxQ9Byn1gq426gKBcimFvTKgUvnyroSimCj1AzSUimUaCKFz/96P149EaxqLb6lCEZ67DAZDXBajKrDJojGUEsEvgHAHICfMsb2McZ+whgr8EhljN3IGNvDGNszNzdXw8Ppjz8SR4/Tgh6HGZyj6iei1i5MwdouW8b/Wo1QrLMicIvJgJsvPQ3vyBtV1qpcMbYG798xjHNOLvT3rhZlM085HxRA8lm3mQ2qKZTfHzyO+/dO4fGXq3s9Kgc1e+xmSqE0kVoE3ATgHAD/zDk/G0AYwJfzD+Kc384538E539Hf31/Dw+mPtJNvzuzYV5tGmQvGYDEaSr6glJQaVAt0XgQOAF94+yZsUxlo0IoMuG34zrVbdR1OoWzmEQ6aperAgeJ+KPft8QGA5vmsSvI9Xjw2M21iNpFaBNwHwMc5f07+fjckQW8ZFsNSM4TIJVbbzDNXZphxPqUG1QL6jlMj2oMuuxkem0lzCgWQ/VDyntNTSyt46nVpgInW+axKTgRy53x67CaqA28iVQs45/w4gEnG2Cb5pssATOiyqgYQTaSwkkjJKZTaNnzmQjH0aRzNBUjRlD+SyDTs5BPScaAx0T6IT25aUigA4HUWbs7fP+4D58BgV3lLBzXEJ0exQdtlpwi8mdRahXIzgHsZYy8C2Abgf9a+pMaQnWhizrwQqk2hzAaimipQBNkNKfUovNOqUAhtiE9u/kgCJgMrm2brdlhyymM559i914c3ndKDLcNdJTfSiyGes5RCWR3UJOCc8/1yfnsL5/w9nHO/XgurN2J33uuwZHPgVaZQ5kPaujAFYsitb7EwD845RyiehLvDcuBEeUQzjz8ch9dpKZuy63FYchrUnj+yiD8tRLBz+4gmW2M1RAQ+1C1SKGZKoTSRju3EXFL4KTstRliMhqpSKMlUGgvheEUCXioCj8RT4BwUgRMFDHvtWEmk8PpcqGQTj8DrMOeUx+4e98FlNeEdZ61Fv8sGfyRRsdnV5GIEA4o5nx6bCcFoAukqS3CJ2uhYAc9MNHGawRhDt6O6EVSL4Tg411YDLuh1WmAzGzJzBZWEMj4onTGNh9CO+OR2cDpQsolH0O2wgHOpUzIcS+I/XprBO88ahMNiyjxfF8KVReE+/0pmHYAUgac5EI5TFN4MOlbAF/Mmmngdlqp8I0RLvBYfFIEYVKsWgQsrWWebdCUS+iE+uUXiqZI+KAJRpbIYieO3L80gEk/h2h3DALLP10rTKL6lSI5FgDAio2ae5tCxAr4Uzt3J9zrNVfmhVNKFqWTEa1etBe80K1lCO0pv8VJWsgKlH8p94z5s6HNix8leANosHfJJptKYWYrmCrhsBUx58ObQsQLujyTgtBgzfs3eKmcIzlXgg6KkWDNPKEopFEIdj82ccW7UkkIRny73Ty7j+SOLuHb7cGbjUwh4ufF+Sk4EYwVzPkUETpUozaGDBTye8yKodghstRH4sNeO5ZVEwRM/1GFe4ERljPRI0W8lKZSfPnUEjAHXnLMuc1+vS7qvkghcuBAqTbqEkyS10zeHjhZwZSdbj5xCqXTC9lwwBrfNlNmV10qmNTqvlJBSKEQphrul540W2wZxjM+/ggtP68NgVzb1YTUZ0e0wVyTganM+sxE4pVCaQQcLeCLnReB1WJBMcwSLdEcWQ+sknnxEJJW/kRmKdtZAY6IyhHiW80EBpE9xJoOUMtm5Y6Tg/n5XZbXgPn8EjAGD3bbMbV0UgTeVzhXwcO5Q2Gr9UOaCsYoqUATDKoNqgWwETlUohBoZAS/jgwJI1U5epwUemwlvH11TcP+Ax1qRoZXPv4I1blvOnE/Rr0A58ObQuQKukkKRbq/siTgXiqHfbSt/YB5ehxkOixGTeRF4MJaExWTQZRgu0X68+dQ+jA56cFq/S9Pxb93Ujxvfcopqiq/fZa2ond7njxRMGTIaGNxWMrRqFh35OT2RSiMYTeakUDIReIUbmbOBKPo3DVS8BqkWvLCUMByjNnqiOJvWuvHbz16k+fjvXLu16H2inZ5zrslJc3JxBedu6Cm43UOGVk2jIyNwUe+tjMC9VaRQwrEkwvFUVTlwQNrNL0ihkJEV0SD63VZEE+mirphKkqk0jgeiBRE4IG24UyNPc+hQARdNPEoBrzyFMl9lCaFAzRe8E4c5EM2hkmaemeUoUmmuKuA0lad5dKSAZ50IsykUj80MA6ssAq9kGr0aw14HgtFkTvQSjCapAoVoCP0uae9Gi4BnSwgL53xKlrKUA28GHSngGSMrRQRuMLCKm3nmqvBBUZKZc7iYjcJDlAMnGsSAR47ANVSiiM32ETUBt5soAm8SHSngGSvZvFIsyZFQ+xNxVocIHMgtJQx32EBjonkI+4fZgLYI3MCkgdz50FCH5tGRAp51IsztZuup0JFwLhiD0cA0NVWoodbMQzlwolF02c0wG5mmCNznj2CtxwaLqVAyuuxmhGJJ8gRvAh0p4EuRBKwmA+x5tbHVpFB6nRYYDdqGGefTZTfDZTXlROBBqkIhGoTBwNCnsRvT519RzX8D0iYm56i4i5monY4UcNGFmV/76q0whTJX4Si1fLK14FIEHk+mEUum4aKBxkSD0Dpabcq/guGewgoUQJrKA1A7fTPoTAGPxFXNgHqc0gxBrYZW1fqgKFE284SFEyFF4ESD0OKHkkilMbNcOgIHqJ2+GXSogCdymngE3Q4L4sk0VhIpTeep1gdFifAF55yTlSzRcAY81rKe4DNLUaQ5VGvAAZrK00xqFnDGmJExto8x9hs9FtQI/JG46sZjJc086TSveBq9GsNeO0IxqRacrGSJRtPvsmIxHMsMPlZDpPiKCjhN5WkaekTgnwVwSIfzNAx/WD2FIsoKtTTz+CNxJNO84kk8+YiPpZOLKzTQmGg4/W4r0rz0cGOR4lOrAQdoKk8zqUnAGWPDAN4J4Cf6LKf+pNMcyyvqKRRvBYZW2Uk8lTsRKhFRjc8fyYxTIytZolFoaaef9EdgNDAMqtSAA9qm8sSSKYz/yV/DSgk1ao3A/xHAlwCkix3AGLuRMbaHMbZnbm6uxoernUA0gTRXnylYSQql1jZ6wUhPtpknSCkUosFoEfCjCxEMdtlgMqrLhdtqAmOlp/I8uG8K7/vnp3FsIVL0GKJyqhZwxti7AMxyzsdLHcc5v51zvoNzvqO/v7/ah9MNNR8UQSUpFL0EvMtuhttmyonAKYVCNAotfigT08s4Y9BT9H5DxhO8eODzxnwYAPDS1HKVKyXUqCUCvwDAuxljRwH8K4BLGWM/12VVdSTjg6JWhWIXEbh2Aa+1CgWQ8uCT/hUqIyQaTrnp9JF4Em/MhzFaQsCB8p7gYvbrxAwJuJ5ULeCc869wzoc55+sBfBDAo5zzj+i2sjqR8UFRSaGYjAa4bSZNzTyzwRgcFqMuzoGimScYS4IxwFHhgGSCqBa7xQi31VQ0An/5eBCcA2NDZQTcZi5ZhSIqWQ5OB6pfLFFAx9WBl0qhAHIzj8YUSq3pE4EY7BCMJuCymGCosjWfIKqh3118NqYQ3NFyAm43lY7A5UqWCRJwXdFFwDnnj3PO36XHuerNUokUCqDdD2UuGKu5hFAw7LUjEk9hcnGFvMCJhtNXop1+YiaALrsZ67rVa8AFUgSuLuCReBIL4Tj6XFLTkJbWfUIbHReB+yNxmORNFzW0+qHU6oOiRJQSHj4eoPw30XD63VbMFxHVg9MBjA56ys7MLDWVZ0qOvi8fXQNAelMg9KEjBbzbYS76hNRqKatnCkXpC05t9ESj6Xept9MnU2kcngmUTZ8ApafyiGEQbx+TBZzSKLrReQIeTpT07+52WDIbncWIJlJYXknoUoECIMfljWrAiUYz4LEiFEsiEs8V4KMLYcSS6bIVKICUAw/FkkimCltCRP57bNCDYa+dInAd6TwBL+KDIvA6zAjHU4glixta1TrMOB+PzYwuuYSRInCi0Yi9nPlgbuAiNjDH1mmLwAGoTrj3+VdgMRnQ57JidNCDg9NUSqgXHSngaj4oArG5WSoPrlcTjxKRB6dNTKLRZLoxQ9Gc2yemA7AYDTi131X2HF2Zdno1AY9g2GuHwcAwNtSFI/PhgmifqI4OFHB1HxSBFj+UjIC7avNBUSIEnCJwotEUa6efmAng9LUumIu00Csp5Qk+uZj1Eh8d8oBz4PDxYK3LJtBhAs45x1IkruqDIsj4oYRLROA6p1CA7EYm5cCJRqMm4JxzHJwOYGywS9M5Sk3lERE4kK0np4YefegoAQ/FkkikeNEmHkDhh1ImAmcM6HVVN8xYjRGKwIkm0eu0wsBy2+lPBGJYDMc1VaAAxSPwUCwJfySREfChLhu67GaqRNGJlhDwWDKFQzrsXJdr4gG0pVBmgzH0OCyaPlpqRUTgVAdONBqjgaE3b7Sa8CypVMDzp/JM5XmJM8YwNuTBBG1k6kJLCPhX/u0lfOQnzyGhUqJUCf4SPigCscFZbhNTz/QJAGzodwKQoiGCaDT5szEPTkkBUykXQiXZFEru5uTkYuE0n9FBDw4fD6qWHBKV0RIC/s4tg1gIx/HY4dmazlPOBwUAbGYjHBZjyWaeegj4qf0u3P+p8/G2MwZ0PS9BaCHfD2ViJoD1vQ7NKT2nxQQDK0yhZMexZaf5jA55EEumcUS2mCWqpyUE/OLT+9HvtuK+cV9N59GSQgGkCL1cDlwvHxQl55zkLWqaTxD1pN+dn0LR1oEpMBgY3Cp+KD7/CmxmA/oU+0VjQ9LGKG1k1k5LqIXJaMA1Z6/DY4dnM0001aAlhQJIaZRiKRTOua4+KASxGhACnk5zBKIJ/GkhkhFarUiOhLkpFJ9fKiFUWlec0u+ExWSgjkwdaAkBB4Brtw8jmeZ4cN9U1efwh+NgLNt0UIxSlrKBaBLxZJoEnGgrBtxWJNMcSysJHJ6RarS1tNAr6VIxtPItRQqm2ZuNBmxa46ZKFB1oGQHfuMaNrSPd2D3uA+e8qnP4Iwl02c0wlvHbLuWHMheUutVIwIl2QlkLLipEKkmhAMLQKlfApSaeQivasSGppb7a1zIh0TICDgA7tw/j8PEgDkxV985dzgdF4HWYiw42nq1DGz1BNBuxpzMXjOHgdAB9LkvFZm35U3kC0QSWVxI5G5iC0SEP/JEEjgeiBfcR2mkpAb9qyxAsJgN2j09W9fPlfFAEXocFyysJ1TInPWdhEsRqQemHMjETwBkaPMDzyZ/KI2rA1SJwkZ6hNEpttJSAdznMuGJsLR56YbqkW2Ax/OEEejRG4EBhUwJQHx8Ugmg2QsCnl6J49USo4vQJIEXgyteML6+JR8nmQQ8Yo0qUWmkpAQekNMpSJIH/nKi8JrycD4og206vIuChGCwmAzx26pgk2geX1QSb2YBnXl9APJWuuAIFkLoxI/FUpuFOrYlH+Xjre50UgddIywn4Baf1YbDLVlUaZTESR49TWwoFUG+nFzXglX68JIjVDGMMA24bnj+yCKDyChQg240ZlEsJff4V2M3Gou6fo4MeKiWskZYTcKOB4Zpz1uG/XpnDiQo2QKKJFKKJtLYIXAi4SilhPbowCWI10O+2Ip5Kw242YkOfs+KfzxhayWkU4UJYLNgZHfLg2GKk5DR7ojQtJ+AAcO32EaQ5cP9e7TXhWpt4gNJ+KCTgRLsiKlE2D7rLltqqIabyCEH2+Vcw0lOY/xaIPPshSqNUTdUCzhgbYYw9xhibYIwdZIx9Vs+FlWJDnxN/tt6L+8YnNdeRisYcLSkU8ZFvsVgKhQScaEPE87qa9AkgFRkAWUOrSX9hE4+SsUHyBq+VWiLwJIAvcM5HAbwJwKcZY6P6LKs8124fxhtzYeybXNJ0vIimtaRQHBYjLEZDQQ48kUpjMRKviw8KQTSbjIBXUYEC5EbgyysJBKPJkgI+4LGhz2WlPHgNVF1KuDzCdQAACx9JREFUwTmfATAjfx1kjB0CsA7AhE5rK8k7twxh18MT+ODtz8KiwQBK7IyXGqcmYIzB6zTjjieO4N5nj2Vu55yDc2riIdoT0dtQTQUKgExlVmAloepCqMbokAf37/XhdweOF9x3y2Wn4ca3nFrVWjoFXWrhGGPrAZwN4DmV+24EcCMAnHTSSXo8HACpDOl/vX8rxv/k1/wzPU4LTtMwoBUAvv6uMew9Vnhui8mAd5y5VvNjEkSr8I6zBhFPpbFlXZUCrojAS9WAK/nc2zZi40Dha/Kp1+bxL08cwScu2EAOnSWoWcAZYy4A/wbgrzjnBZ+FOOe3A7gdAHbs2KGr8cGfnzWIPz9rUM9TZnjnlkG8c0t9zk0Qq5EuuxkfffP6qn/eYTHCaGBYXskKeKkUCgCcfZIXZ5/kLbj9dweO46afj+OJV+fx1s3kkV+Mmt7aGGNmSOJ9L+f8fn2WRBBEK8IYg8dmQmAlicnFCJwWoybrCjUu3TyAHqcF91Vpm9Ep1FKFwgDcAeAQ5/zv9VsSQRCtisduzqRQ8n3AK8FiMuDqbUP4z4lZ1X4MQqKWCPwCANcBuJQxtl/+9+c6rYsgiBbEI0/l8fkjGOkpnT4px87tI4in0nj4hWmdVtd+VC3gnPMnOeeMc76Fc75N/vdbPRdHEERr4bGbsLySwJQcgdfC6JAHo4MeSqOUgLZ3CYLQjS67GZP+FQRjpWvAtbJzxzAOTAVwiGrFVSEBJwhCNzw2c8ZyWQ8Bv3rbOpiNDLtrHGjerpCAEwShGx7FvNlaUyiA1Ltx2eY1eHDfVKYZj8hCAk4QhG4IS1mgfBOPVnbuGMZCOI5HD1c+A6DdIQEnCEI3RATutpp0G3py8en96HNZKY2iAgk4QRC6Idrp15XwAa8Uk9GAa85Zh8cOz2I+FNPlnO0CCThBELohom498t9Kdm4fRjLN8eA+7TMAOgEScIIgdENE4LU28eSzcY0bW0e6cd8en+YZAJ0ATeYlCEI3RA5c7wgckGYAfO3BA/jA7c/CVMXEoGbzpSs3Y9tIt67nJAEnCEI31vc6ce32YVx+xhrdz/2ebUP4r5dnsbySQCLVelF4PT45sEZ+HNmxYwffs2dPwx6PIAiiHWCMjXPOd+TfTjlwgiCIFoUEnCAIokUhAScIgmhRSMAJgiBaFBJwgiCIFoUEnCAIokUhAScIgmhRSMAJgiBalIY28jDG5gD8qcof7wMwr+NyViudcJ10je1DJ1znarjGkznn/fk3NlTAa4ExtketE6nd6ITrpGtsHzrhOlfzNVIKhSAIokUhAScIgmhRWknAb2/2AhpEJ1wnXWP70AnXuWqvsWVy4ARBEEQurRSBEwRBEApIwAmCIFqUVSngjDEbY+x5xtgLjLGDjLG/lW/fwBh7jjH2GmPsV4wxS7PXWiuMMSNjbB9j7Dfy9+14jUcZYy8xxvYzxvbIt/Uwxv7IGHtV/t/b7HXWAmOsmzG2mzF2mDF2iDH25na6RsbYJvnvJ/4FGGN/1U7XKGCMfU7WnQOMsV/KerQqX5erUsABxABcyjnfCmAbgCsZY28C8G0A/8A5Pw2AH8Anm7hGvfgsgEOK79vxGgHgrZzzbYp62i8DeIRzvhHAI/L3rcz3AfyOc74ZwFZIf9O2uUbO+cvy328bgO0AIgAeQBtdIwAwxtYBuAXADs75mQCMAD6I1fq65Jyv6n8AHAD2AjgPUjeUSb79zQB+3+z11Xhtw5Ce9JcC+A0A1m7XKF/HUQB9ebe9DGBQ/noQwMvNXmcN19cF4AjkooB2vMa863o7gKfa8RoBrAMwCaAH0szg3wC4YrW+LldrBC5SC/sBzAL4I4DXASxxzpPyIT5Iv+xW5h8BfAlAWv6+F+13jQDAAfyBMTbOGLtRvm0N53xG/vo4AP2n4DaODQDmAPxUTof9hDHmRHtdo5IPAvil/HVbXSPnfArA9wAcAzADYBnAOFbp63LVCjjnPMWlj2vDAM4FsLnJS9IVxti7AMxyzsebvZYGcCHn/BwA7wDwacbYW5R3cimsaeV6VhOAcwD8M+f8bABh5KUS2uAaAQBy7vfdAO7Lv68drlHO4V8N6U15CIATwJVNXVQJVq2ACzjnSwAeg/SxpZsxZpLvGgYw1bSF1c4FAN7NGDsK4F8hpVG+j/a6RgCZqAac81lIedNzAZxgjA0CgPz/bPNWWDM+AD7O+XPy97shCXo7XaPgHQD2cs5PyN+32zW+DcARzvkc5zwB4H5Ir9VV+bpclQLOGOtnjHXLX9sBXA5pU+gxANfKh10P4KHmrLB2OOdf4ZwPc87XQ/pI+ijn/MNoo2sEAMaYkzHmFl9Dyp8eAPAwpOsDWvw6OefHAUwyxjbJN10GYAJtdI0KPoRs+gRov2s8BuBNjDEHY4wh+7dcla/LVdmJyRjbAuBuSDvABgC/5pz/D8bYKZCi1R4A+wB8hHMea95K9YExdgmAv+acv6vdrlG+ngfkb00AfsE5/yZjrBfArwGcBMli+P2c88UmLbNmGGPbAPwEgAXAGwA+Dvm5i/a5RickgTuFc74s39ZWf0cAkMuWPwAgCek1eAOknPeqe12uSgEnCIIgyrMqUygEQRBEeUjACYIgWhQScIIgiBaFBJwgCKJFIQEnCIJoUUjACYIgWhQScKJjYIxZGWP/KduhfkCnc17CGDtf8f1NjLGPVnmu9Yyxv9BjXURnYCp/CEG0DWcDgOyxoxeXAAgBeFo+949rONd6AH8B4Bc1r4roCKiRh2gajLEHAYwAsAH4Puf8dsbYJwHcCmAJwAsAYpzzzzDG+gH8GFLHHwD8Fef8qSLn7QFwJ4BTIPlW3wjJKe9pAP2QrF/fxzl/XeVnj0LqLHwHgBUAf8E5f40xdhWA/wdSp+UCgA8DsAN4FkAKkhvhzZBar0Oc8+8xxk4F8CP5MSMA/i/O+WHG2F0AAgB2AFgL4Euc892MsWcBnCGv727O+T9U+CslOo1m+9nSv879B6BH/t8OyR9lHSTv8B4AZgBPAPihfMwvILkaApKIHypx3tsA/I389aUA9stfXwLgN2XWdBTAV+WvPyqOB+BFNuC5AcD/kr/eBckGAfnfQ/J63yh/fR4kvxsAuAuSm58BwCiA17Suj/7RP+U/SqEQzeQWxth75a9HAFwH4L+47KXBGLsPwOny/W8DMCr5CwEAPIwxF+c8pHLeCwG8DwA4548yxnoZY54K1vVLxf8iCh4G8CvZcc8CKUouCmPMBeB8APcp1mxVHPIg5zwNYIIx1tIe2kTzIAEnmoJs4PU2AG/mnEcYY48DOAwphaCGAcCbOOfRBiyPq3x9G4C/55w/LK99V5lzGCANASiWb1caIbEixxBESagKhWgWXQD8snhvBvAmSOb5FzPGvLL38vsUx/8BUo4ZQMb9rxhPQMpRizeKec55oIK1fUDx/zOK9QoP6OsVxwYBuPNPID/eEcbYTnkdjDG2tczjqp6LIIpBAk40i98BMDHGDgH4FqTNwCkA/xPA8wCegpSPXpaPvwXADsbYi4yxCQA3lTj3LgDbGWMvyue+vsSxanjln/0sgM8pznkfY2wc0nxEwb8DeK9cmnhR3nk+DOCTjLEXAByENOmlFC8CSDHGXmCMfa7MsQRBVSjE6kLkteUI/AEAd3LOHyj3czo+/lFIE8nnyx1LEM2GInBitbFLHmZ9ANJG4YNNXg9BrFooAidaFsbYxyGlOZQ8xTn/tIaffQDS4Folt3LOf6/X+gii3pCAEwRBtCiUQiEIgmhRSMAJgiBaFBJwgiCIFoUEnCAIokX5/wHfejRSFf+U2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ch9sJax7pmD-",
        "colab_type": "code",
        "outputId": "1cf3c432-6652-45cd-943a-547f78c68e89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "source": [
        "pd.crosstab(haberman['operation_year'], haberman['age_of_patient'], normalize='index', margins=True)\n",
        "pd.crosstab(haberman['operation_year'], haberman['age_of_patient'], normalize='columns', margins=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>age_of_patient</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
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              "      <th>37</th>\n",
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              "      <th>41</th>\n",
              "      <th>42</th>\n",
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              "      <th>45</th>\n",
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              "      <th>49</th>\n",
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              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
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              "      <th>57</th>\n",
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              "      <th>77</th>\n",
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              "      <th>83</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>operation_year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <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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              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.090909</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.088235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.091503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.375</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.084967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.075163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.098039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.101307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.091503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.091503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.081699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.042484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.035948</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "age_of_patient        30   31   33        34   35  ...   76   77   78   83       All\n",
              "operation_year                                     ...                              \n",
              "58              0.000000  0.0  0.5  0.142857  0.0  ...  0.0  0.0  0.0  1.0  0.117647\n",
              "59              0.000000  0.5  0.0  0.142857  0.0  ...  0.0  0.0  0.0  0.0  0.088235\n",
              "60              0.000000  0.0  0.5  0.285714  0.0  ...  0.0  0.0  0.0  0.0  0.091503\n",
              "61              0.000000  0.0  0.0  0.142857  0.0  ...  0.0  0.0  0.0  0.0  0.084967\n",
              "62              0.333333  0.0  0.0  0.000000  0.0  ...  0.0  0.0  0.0  0.0  0.075163\n",
              "63              0.000000  0.0  0.0  0.000000  0.5  ...  0.0  0.0  0.0  0.0  0.098039\n",
              "64              0.333333  0.0  0.0  0.000000  0.5  ...  0.0  0.0  0.0  0.0  0.101307\n",
              "65              0.333333  0.5  0.0  0.000000  0.0  ...  0.0  1.0  1.0  0.0  0.091503\n",
              "66              0.000000  0.0  0.0  0.142857  0.0  ...  0.0  0.0  0.0  0.0  0.091503\n",
              "67              0.000000  0.0  0.0  0.142857  0.0  ...  1.0  0.0  0.0  0.0  0.081699\n",
              "68              0.000000  0.0  0.0  0.000000  0.0  ...  0.0  0.0  0.0  0.0  0.042484\n",
              "69              0.000000  0.0  0.0  0.000000  0.0  ...  0.0  0.0  0.0  0.0  0.035948\n",
              "\n",
              "[12 rows x 50 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hENnGJ5Ur808",
        "colab_type": "code",
        "outputId": "05cac9c8-971b-4549-dbdd-be8d316d83f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pd.crosstab(haberman['age_of_patient'], haberman['operation_year']).plot();\n",
        "pd.crosstab(haberman['operation_year'], haberman['age_of_patient']).plot();\n",
        "pd.crosstab(haberman['age_of_patient'], haberman['operation_year']).hist();\n",
        "pd.crosstab(haberman['operation_year'], haberman['age_of_patient']).hist();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 49 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYe_4qxvqF5c",
        "colab_type": "code",
        "outputId": "a4783ae6-7c01-48f0-eff9-7f9d40f46d0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        }
      },
      "source": [
        "haberman_oa = pd.crosstab(haberman['operation_year'], ['age_of_patient'])\n",
        "haberman_oa.plot(); haberman_oa.hist(); haberman_oa.bar();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-97-7849a1254792>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mhaberman_oa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrosstab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhaberman\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'operation_year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'age_of_patient'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhaberman_oa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mhaberman_oa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mhaberman_oa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5272\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5273\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5274\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   5275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'bar'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFZ-3WKl3pv3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "haberman_ao = pd.crosstab(haberman['age_of_patient'], ['operation_year'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVEDKFbD1nCn",
        "colab_type": "code",
        "outputId": "2de496c3-a656-4689-b36b-50194c098fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        }
      },
      "source": [
        "haberman_oa.plot(kind='bar'); haberman_ao.plot(kind='bar');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORNTz6ua14lU",
        "colab_type": "code",
        "outputId": "67364cc5-a84f-4cac-883c-7ec0016f197a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "source": [
        "haberman['age_of_patient'].plot(kind='bar');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYnpZfwP4bG8",
        "colab_type": "code",
        "outputId": "7214c91c-cf25-47d8-dbc4-9809db3d66e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "haberman.sample(5)"
      ],
      "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>age_of_patient</th>\n",
              "      <th>operation_year</th>\n",
              "      <th>number_of_positive_axillary_nodes_detected</th>\n",
              "      <th>survival_status</th>\n",
              "      <th>nsurvival_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>59</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>56</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>33</td>\n",
              "      <td>58</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>57</td>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>55</td>\n",
              "      <td>66</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age_of_patient  operation_year  ...  survival_status  nsurvival_status\n",
              "3                31              59  ...                1                 1\n",
              "194              56              66  ...                1                 1\n",
              "5                33              58  ...                1                 1\n",
              "207              57              67  ...                1                 1\n",
              "185              55              66  ...                1                 1\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2eV6VUw4jaj",
        "colab_type": "code",
        "outputId": "42c10bb7-2806-4cfd-9314-31d182bca738",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df = pd.DataFrame(haberman)\n",
        "df"
      ],
      "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>age_of_patient</th>\n",
              "      <th>operation_year</th>\n",
              "      <th>number_of_positive_axillary_nodes_detected</th>\n",
              "      <th>survival_status</th>\n",
              "      <th>nsurvival_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30</td>\n",
              "      <td>62</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>59</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31</td>\n",
              "      <td>65</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>75</td>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>76</td>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>77</td>\n",
              "      <td>65</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>78</td>\n",
              "      <td>65</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>83</td>\n",
              "      <td>58</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>306 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age_of_patient  operation_year  ...  survival_status  nsurvival_status\n",
              "0                30              64  ...                1                 1\n",
              "1                30              62  ...                1                 1\n",
              "2                30              65  ...                1                 1\n",
              "3                31              59  ...                1                 1\n",
              "4                31              65  ...                1                 1\n",
              "..              ...             ...  ...              ...               ...\n",
              "301              75              62  ...                1                 1\n",
              "302              76              67  ...                1                 1\n",
              "303              77              65  ...                1                 1\n",
              "304              78              65  ...                2                 0\n",
              "305              83              58  ...                2                 0\n",
              "\n",
              "[306 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqLbXF8K5LaQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = df['age_of_patient']\n",
        "y = df['operation_year']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbLHm6EN6zmj",
        "colab_type": "code",
        "outputId": "39bcc839-4ba0-4ff0-cd9c-d30bb1a171e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "pd.crosstab(x,y).plot(kind='bar')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7febf31452e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZM8JckA2bgnp",
        "colab_type": "text"
      },
      "source": [
        "## Part 3 - Analysis and Interpretation\n",
        "\n",
        "Now that you've looked at the data, answer the following questions:\n",
        "\n",
        "- What is at least one feature that looks to have a positive relationship with survival?\n",
        "- What is at least one feature that looks to have a negative relationship with survival?\n",
        "- How are those two features related with each other, and what might that mean?\n",
        "\n",
        "Answer with text, but feel free to intersperse example code/results or refer to it from earlier."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygtTxT8k-bxI",
        "colab_type": "text"
      },
      "source": [
        "'*   positive is relationship is when operation year becomes more current less people die which means more people are surviving \n",
        "\n",
        "*   the negative relationship is that as a person gets older the surviaval rate gap lessen untill its below the death line. \n",
        "\n",
        "* cant tell educated guess is as you get older the more likly you are to get treatment."
      ]
    }
  ]
}