DS_Unit_1_Sprint_Challenge_1.ipynb

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      "name": "DS Unit 1 Sprint Challenge 1.ipynb",
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        "<a href=\"https://colab.research.google.com/github/Phantasm320/DS-Sprint-01-Dealing-With-Data/blob/master/DS_Unit_1_Sprint_Challenge_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# 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."
      ]
    },
    {
      "metadata": {
        "id": "287TpoGKFRVK",
        "colab_type": "code",
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      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "outputId": "124548b1-cddb-4093-bd81-e267a12260c7"
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data', header = None, names = ['age', 'op_year', 'axi_nodes', 'survived'])\n",
        "print(df.shape)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(306, 4)\n"
          ],
          "name": "stdout"
        }
      ]
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      "cell_type": "code",
      "source": [
        "'''\n",
        "7. 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",
        "'''"
      ],
      "execution_count": 45,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "\"\\n7. 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\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
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        "outputId": "f5b3a88e-881f-4c21-e70e-51988d247568"
      },
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sum()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
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          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
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      "cell_type": "code",
      "source": [
        "survive = {2: 0, 1: 1}\n",
        "df['survived'] = df['survived'].map(survive)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        },
        "outputId": "4ae97c7b-8a0d-4330-eb12-1549550130d9"
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      "cell_type": "code",
      "source": [
        "print(df['survived'].unique())\n",
        "df.head()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0]\n"
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>op_year</th>\n",
              "      <th>axi_nodes</th>\n",
              "      <th>survived</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30</td>\n",
              "      <td>62</td>\n",
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              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>30</td>\n",
              "      <td>65</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31</td>\n",
              "      <td>65</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   age  op_year  axi_nodes  survived\n",
              "0   30       64          1         1\n",
              "1   30       62          3         1\n",
              "2   30       65          0         1\n",
              "3   31       59          2         1\n",
              "4   31       65          4         1"
            ]
          },
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            "tags": []
          },
          "execution_count": 48
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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."
      ]
    },
    {
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        "outputId": "a570b76d-761d-43e5-8f99-826b09814e41"
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      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>op_year</th>\n",
              "      <th>axi_nodes</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>306.000000</td>\n",
              "      <td>306.000000</td>\n",
              "      <td>306.000000</td>\n",
              "      <td>306.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>52.457516</td>\n",
              "      <td>62.852941</td>\n",
              "      <td>4.026144</td>\n",
              "      <td>0.735294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.803452</td>\n",
              "      <td>3.249405</td>\n",
              "      <td>7.189654</td>\n",
              "      <td>0.441899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>44.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>52.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.750000</td>\n",
              "      <td>65.750000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>83.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age     op_year   axi_nodes    survived\n",
              "count  306.000000  306.000000  306.000000  306.000000\n",
              "mean    52.457516   62.852941    4.026144    0.735294\n",
              "std     10.803452    3.249405    7.189654    0.441899\n",
              "min     30.000000   58.000000    0.000000    0.000000\n",
              "25%     44.000000   60.000000    0.000000    0.000000\n",
              "50%     52.000000   63.000000    1.000000    1.000000\n",
              "75%     60.750000   65.750000    4.000000    1.000000\n",
              "max     83.000000   69.000000   52.000000    1.000000"
            ]
          },
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          },
          "execution_count": 49
        }
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        },
        "outputId": "86b00340-4ab5-456c-9c67-f00620854df1"
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(x = df['age'], y = df['axi_nodes'], c = df['survived'], cmap = 'plasma');\n",
        "plt.xlabel('Age');\n",
        "plt.ylabel('Axillary Nodes');\n",
        "plt.title('Survival based on Age vs Axillary Nodes');\n",
        "plt.colorbar();"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vo06dOtmWk+zevTuff/45ZcuW5YEHHmDSpEnUrFkzx/NJi1MIIYRP8/Pz49NPPyUiIiLb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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfe075860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HOT7KoG2Sl05",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "survivers = df[df['survived'] == 1]\n",
        "passers = df[df['survived'] == 0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WnbmG8naSl8c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "abf01d08-9e1d-4ade-aef7-4858d14c6a50"
      },
      "cell_type": "code",
      "source": [
        "plt.bar(x = 'Survived', height = survivers['axi_nodes']);\n",
        "plt.bar(x = 'Not Survived', height = passers['axi_nodes']);"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFKCAYAAABcq1WoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntcmnQQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfd213550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "iQwWsbBPizs1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "age30_40 = df[df['age'].between(30, 40)]\n",
        "age41_51 = df[df['age'].between(41, 51)]\n",
        "age52_62 = df[df['age'].between(52, 62)]\n",
        "age63_73 = df[df['age'].between(63, 73)]\n",
        "age74_84 = df[df['age'].between(74, 84)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DjExsb0lkUt5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "b56879ca-ac1b-40c1-b0d8-e69a4121dbe5"
      },
      "cell_type": "code",
      "source": [
        "survive30_40 = age30_40[age30_40['survived'] == 1]\n",
        "passed30_40 = age30_40[age30_40['survived'] == 0]\n",
        "survive41_51 = age41_51[age41_51['survived'] == 1]\n",
        "passed41_51 = age41_51[age41_51['survived'] == 0]\n",
        "survive52_62 = age52_62[age52_62['survived'] == 1]\n",
        "passed52_62 = age52_62[age52_62['survived'] == 0]\n",
        "survive63_73 = age63_73[age63_73['survived'] == 1]\n",
        "passed63_73 = age63_73[age63_73['survived'] == 0]\n",
        "survive74_84 = age74_84[age74_84['survived'] == 1]\n",
        "passed74_84 = age74_84[age74_84['survived'] == 0]\n",
        "plt.bar(x = '30-40', height = age30_40['axi_nodes'])\n",
        "plt.bar(x = '41-51', height = age41_51['axi_nodes'])\n",
        "plt.bar(x = '52-62', height = age52_62['axi_nodes'])\n",
        "plt.bar(x = '63-73', height = age63_73['axi_nodes'])\n",
        "plt.bar(x = '74-84', height = age74_84['axi_nodes']);"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfbfd4f28>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QXjQd2vRmLr7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "9051f798-7a60-4962-90f5-68fc73dedf32"
      },
      "cell_type": "code",
      "source": [
        "plt.bar(x = 'survived30-40', height = survive30_40['axi_nodes'])\n",
        "plt.bar(x = 'not survived30-40', height = passed30_40['axi_nodes']);"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfbb6a710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2xEnYUtFoPS-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "65065dc6-9020-4b8c-eee7-769dd341333a"
      },
      "cell_type": "code",
      "source": [
        "plt.bar(x = 'survived41-51', height = survive41_51['axi_nodes'])\n",
        "plt.bar(x = 'not survived41-51', height = passed41_51['axi_nodes'])"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Container object of 31 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfbd12e80>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xZxiUtZgoPVl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "89f657a0-603e-4dce-fa8d-9a035377d8a3"
      },
      "cell_type": "code",
      "source": [
        "plt.bar(x = 'survived52-62', height = survive52_62['axi_nodes'])\n",
        "plt.bar(x = 'not survived52-62', height = passed52_62['axi_nodes'])"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Container object of 30 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfc4da438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Gn7Csy5HoPX_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "71d207f7-a1b2-4d6a-c0a9-8eaae0505e2b"
      },
      "cell_type": "code",
      "source": [
        "plt.bar(x = 'survived63-73', height = survive63_73['axi_nodes'])\n",
        "plt.bar(x = 'not survived63-73', height = passed63_73['axi_nodes'])"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Container object of 13 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfbb89c50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "YZvVZeaKoPah",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "ef663c2c-da13-4416-d1b5-68e91d368384"
      },
      "cell_type": "code",
      "source": [
        "plt.bar(x = 'survived74-84', height = survive74_84['axi_nodes'])\n",
        "plt.bar(x = 'not survived74-84', height = passed74_84['axi_nodes'])"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Container object of 3 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfc5a1748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZM8JckA2bgnp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "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 correlation with survival?\n",
        "- What is at least one feature that looks to have a negative correlation 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."
      ]
    },
    {
      "metadata": {
        "id": "euzvX5tKrc1H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "1. One feature that appears to relate to survival is the amount of axillary nodes given (higher is better)\n",
        "2. One feature that appears to have a negative correlation to survival are people aged between 41-51 and the amount of axillary nodes given\n",
        "3. It seems that the age group between 41-51 did not take well to the axillary nodes given to them vs the rest of the age groups\n"
      ]
    },
    {
      "metadata": {
        "id": "0rCz6vRUtYYx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "58cae78d-a848-4141-c9c6-804ff1405a7f"
      },
      "cell_type": "code",
      "source": [
        "plt.bar(x = '30-40', height = age30_40['axi_nodes'])\n",
        "plt.bar(x = '41-51', height = age41_51['axi_nodes'])\n",
        "plt.bar(x = '52-62', height = age52_62['axi_nodes'])\n",
        "plt.bar(x = '63-73', height = age63_73['axi_nodes'])\n",
        "plt.bar(x = '74-84', height = age74_84['axi_nodes']);\n",
        "plt.xlabel('Ages')\n",
        "plt.ylabel('Amount of Axillary Nodes')\n",
        "plt.title('Age vs Axillary Nodes');"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hjo5aNAAATsEue9pXGzx4sPX/lyxZYu/FAQDgtLgjGgAAJkFpAwBgEpQ2AAAmQWkDAGAS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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfb17fb70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "LSkQHxDhtZKZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "aca83945-482a-4bb8-ac81-fca023b1c75e"
      },
      "cell_type": "code",
      "source": [
        "plt.bar(x = 'survived41-51', height = survive41_51['axi_nodes'])\n",
        "plt.bar(x = 'not survived41-51', height = passed41_51['axi_nodes']);\n",
        "plt.xlabel('Survival based on age range')\n",
        "plt.ylabel('Amount of axillary nodes')\n",
        "plt.title('Treatment results for ages 41-51');"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qce+v72p7wL206oNX7nUJwAPP2zuvy/pyGNqfffaZVq5cqYIFC0qSzp49q+7d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\nQWgDAGARhDYAABbxfw3olHH70ZPdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7bfb53aa58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}