Lab_1_project_Nil_Yağmur_İlba.ipynb

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      "name": "Lab 1 project - Nil Yağmur İlba",
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      "name": "python"
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        "<a href=\"https://colab.research.google.com/github/yagmurilba/cmpe343/blob/master/Lab_1_project_Nil_Ya%C4%9Fmur_%C4%B0lba.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzitTTKh_tvO"
      },
      "source": [
        "#Lab 1 project - Nil Yağmur İlba\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRFHyYOexOEW"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZnY3Xq0xUzK"
      },
      "source": [
        "from sklearn.datasets import load_wine\n",
        "data = load_wine()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "oNlJeOA0u3d8",
        "outputId": "a024c2bf-05c8-42c0-90dc-0c710ac5c76f"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n',\n",
              " 'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
              "         1.065e+03],\n",
              "        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
              "         1.050e+03],\n",
              "        [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
              "         1.185e+03],\n",
              "        ...,\n",
              "        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
              "         8.350e+02],\n",
              "        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
              "         8.400e+02],\n",
              "        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
              "         5.600e+02]]),\n",
              " 'feature_names': ['alcohol',\n",
              "  'malic_acid',\n",
              "  'ash',\n",
              "  'alcalinity_of_ash',\n",
              "  'magnesium',\n",
              "  'total_phenols',\n",
              "  'flavanoids',\n",
              "  'nonflavanoid_phenols',\n",
              "  'proanthocyanins',\n",
              "  'color_intensity',\n",
              "  'hue',\n",
              "  'od280/od315_of_diluted_wines',\n",
              "  'proline'],\n",
              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2]),\n",
              " 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7')}"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRXtZf8Bu0Bw",
        "outputId": "e269aee6-e0ff-42ee-afd7-76101cc9750d"
      },
      "source": [
        "list(data.target_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['class_0', 'class_1', 'class_2']"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wLKgo94x1XE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2-2IHn5x3E3",
        "outputId": "7cf594c4-4ae9-4b0d-b234-29019a47a7ae"
      },
      "source": [
        "print(data.DESCR)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _wine_dataset:\n",
            "\n",
            "Wine recognition dataset\n",
            "------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 178 (50 in each of three classes)\n",
            "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            " \t\t- Alcohol\n",
            " \t\t- Malic acid\n",
            " \t\t- Ash\n",
            "\t\t- Alcalinity of ash  \n",
            " \t\t- Magnesium\n",
            "\t\t- Total phenols\n",
            " \t\t- Flavanoids\n",
            " \t\t- Nonflavanoid phenols\n",
            " \t\t- Proanthocyanins\n",
            "\t\t- Color intensity\n",
            " \t\t- Hue\n",
            " \t\t- OD280/OD315 of diluted wines\n",
            " \t\t- Proline\n",
            "\n",
            "    - class:\n",
            "            - class_0\n",
            "            - class_1\n",
            "            - class_2\n",
            "\t\t\n",
            "    :Summary Statistics:\n",
            "    \n",
            "    ============================= ==== ===== ======= =====\n",
            "                                   Min   Max   Mean     SD\n",
            "    ============================= ==== ===== ======= =====\n",
            "    Alcohol:                      11.0  14.8    13.0   0.8\n",
            "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
            "    Ash:                          1.36  3.23    2.36  0.27\n",
            "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
            "    Magnesium:                    70.0 162.0    99.7  14.3\n",
            "    Total Phenols:                0.98  3.88    2.29  0.63\n",
            "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
            "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
            "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
            "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
            "    Hue:                          0.48  1.71    0.96  0.23\n",
            "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
            "    Proline:                       278  1680     746   315\n",
            "    ============================= ==== ===== ======= =====\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "This is a copy of UCI ML Wine recognition datasets.\n",
            "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
            "\n",
            "The data is the results of a chemical analysis of wines grown in the same\n",
            "region in Italy by three different cultivators. There are thirteen different\n",
            "measurements taken for different constituents found in the three types of\n",
            "wine.\n",
            "\n",
            "Original Owners: \n",
            "\n",
            "Forina, M. et al, PARVUS - \n",
            "An Extendible Package for Data Exploration, Classification and Correlation. \n",
            "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
            "Via Brigata Salerno, 16147 Genoa, Italy.\n",
            "\n",
            "Citation:\n",
            "\n",
            "Lichman, M. (2013). UCI Machine Learning Repository\n",
            "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
            "School of Information and Computer Science. \n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
            "  Comparison of Classifiers in High Dimensional Settings, \n",
            "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
            "  Mathematics and Statistics, James Cook University of North Queensland. \n",
            "  (Also submitted to Technometrics). \n",
            "\n",
            "  The data was used with many others for comparing various \n",
            "  classifiers. The classes are separable, though only RDA \n",
            "  has achieved 100% correct classification. \n",
            "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
            "  (All results using the leave-one-out technique) \n",
            "\n",
            "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
            "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
            "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
            "  Mathematics and Statistics, James Cook University of North Queensland. \n",
            "  (Also submitted to Journal of Chemometrics).\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edNvE83_x8DB",
        "outputId": "9eb7fff0-7c46-4a0d-e848-8b55cf419517"
      },
      "source": [
        "data.feature_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['alcohol',\n",
              " 'malic_acid',\n",
              " 'ash',\n",
              " 'alcalinity_of_ash',\n",
              " 'magnesium',\n",
              " 'total_phenols',\n",
              " 'flavanoids',\n",
              " 'nonflavanoid_phenols',\n",
              " 'proanthocyanins',\n",
              " 'color_intensity',\n",
              " 'hue',\n",
              " 'od280/od315_of_diluted_wines',\n",
              " 'proline']"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-ximKGpyEJA",
        "outputId": "ce9133b6-b118-4303-b4a2-5a2ec52eab1e"
      },
      "source": [
        "data.target_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEd10O1WyGwT",
        "outputId": "bd63503a-66b4-4f44-992f-79a28deee012"
      },
      "source": [
        "data.data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
              "        1.065e+03],\n",
              "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
              "        1.050e+03],\n",
              "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
              "        1.185e+03],\n",
              "       ...,\n",
              "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
              "        8.350e+02],\n",
              "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
              "        8.400e+02],\n",
              "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
              "        5.600e+02]])"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "BA_kk4pKyNt0",
        "outputId": "9be99cd2-841b-4ac7-f889-3510f40fda77"
      },
      "source": [
        "data.data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(178, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6SoalnIyQCq",
        "outputId": "68ff36ae-d15e-4f3d-962a-8fd73cb505c0"
      },
      "source": [
        "data.target.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(178,)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQ7G8Om-yRi8"
      },
      "source": [
        "mydata=pd.DataFrame(data.data, columns= data.feature_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "x3qk4LTjydMu",
        "outputId": "a6ec06e7-1e3f-4525-fa21-eddf3b6f53e4"
      },
      "source": [
        "mydata"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.41</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.56</td>\n",
              "      <td>750.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.35</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.56</td>\n",
              "      <td>835.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.37</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.46</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.62</td>\n",
              "      <td>840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
              "      <td>2.74</td>\n",
              "      <td>24.5</td>\n",
              "      <td>96.0</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.60</td>\n",
              "      <td>560.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  ...   hue  od280/od315_of_diluted_wines  proline\n",
              "0      14.23        1.71  2.43  ...  1.04                          3.92   1065.0\n",
              "1      13.20        1.78  2.14  ...  1.05                          3.40   1050.0\n",
              "2      13.16        2.36  2.67  ...  1.03                          3.17   1185.0\n",
              "3      14.37        1.95  2.50  ...  0.86                          3.45   1480.0\n",
              "4      13.24        2.59  2.87  ...  1.04                          2.93    735.0\n",
              "..       ...         ...   ...  ...   ...                           ...      ...\n",
              "173    13.71        5.65  2.45  ...  0.64                          1.74    740.0\n",
              "174    13.40        3.91  2.48  ...  0.70                          1.56    750.0\n",
              "175    13.27        4.28  2.26  ...  0.59                          1.56    835.0\n",
              "176    13.17        2.59  2.37  ...  0.60                          1.62    840.0\n",
              "177    14.13        4.10  2.74  ...  0.61                          1.60    560.0\n",
              "\n",
              "[178 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgyfAK3XyfcV",
        "outputId": "75b20633-3c3a-4f16-8e6c-91bc80172b49"
      },
      "source": [
        "mydata.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alcohol                         float64\n",
              "malic_acid                      float64\n",
              "ash                             float64\n",
              "alcalinity_of_ash               float64\n",
              "magnesium                       float64\n",
              "total_phenols                   float64\n",
              "flavanoids                      float64\n",
              "nonflavanoid_phenols            float64\n",
              "proanthocyanins                 float64\n",
              "color_intensity                 float64\n",
              "hue                             float64\n",
              "od280/od315_of_diluted_wines    float64\n",
              "proline                         float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "7HgZUaQ6ynhY",
        "outputId": "12d39f01-4ae7-413b-ec42-7404657180d8"
      },
      "source": [
        "mydata.boxplot(\"proline\", figsize=(10,10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3aa574d10>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XYWZl_mIy2MO",
        "outputId": "32fd849e-7208-4b95-ed8b-39955038a2e3"
      },
      "source": [
        "mydata.hist(figsize=(12,12))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd3aa1d9f50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9d95ad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9d51190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9d02810>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9d35e90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9c77550>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9cacc50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9c70250>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9c70290>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9c26a10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9b9f650>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9b53cd0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9b16390>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9acba10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9a8e0d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9a47750>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VX89Tr+y/6faDmRXDybWZWTkcDhwn6Y3AjsALgPOAWZK2S7XX84BN1VaOiOXAcoAFCxbEyMhIV4LulLGxMWp9hpO7MM71hiXVt91t0+0HMyuGk2szsxKIiNOB0wFSzfUHImKJpC8BbwJWAScBVxYWpJkVppWbJ21YtqgDkZjbXJuZlduHgfdLWk/WBvvCguMxMxtorrm2gSFpBnATsCkijpW0D1lt3x7AzcCJEfHzImM0a0REjAFj6fl9wKFFxmNmZs9wzbUNEo8PbGZmZh3l5NoGgscHNjMzs25wsxAbFJPjA++apj0+sJmZdUUrnQ2tvJxc58gHT2/q1fGBWxmLt9a2+2Gs2375DGZmNtjqJteS9ga+AAwBASyPiPMk7Q5cBgwDG4DFEfFI50I1a1lPjg/cyli8tcbW7YexbvvlM5hZbZJ2BK4DdiDLQS6PiI+6g7n1k0baXE8AoxFxIHAY8G5JBwJLgWsjYj/g2jRt1nMi4vSImBcRw8CbgW9FxBJgLdn4wODxgc3MuuFJYGFEHAQcDBwj6TDcwdz6SN3kOiI2R8Qt6fnjZKMtzAWOJ+sEBu4MZuXk8YHNzLooMpPtp2amR+AO5tZHmmpzLWkYOAS4ARiKiM1p1hayZiPV1ulIe9U8VWvr2Up72DIb2in7zL34/eTJ4wObmRUr3XPgZmBf4HzgXtzB3PpIw8m1pF2ALwPvi4jHspHMMhERkqLaep1qr5qnam09W2kPW2aj8yc4+47tarbpNTMzy0NEPAUcLGkWcAVwQKPrVquwK0Nn6E5W2E1WjrWi1/dbI3rx+28ouZY0kyyxviQivpJeflDSnIjYLGkOsLVTQZqZmVl/iYhtktYCr6WNDuZl6AzdyQq7ycqxVvRDhVovfv9121ynm21cCKyLiE9WzFpD1gkM3BnMzMzM6pC0Z6qxRtJOwNFkfbncwdz6RiP/6hwOnAjcIem29NoZwDJgtaRTgPuBxZ0JMR/TjUE9On9i4JqBmJmZFWAOsDK1u34esDoirpJ0F7BK0pnArbiDuZVY3eQ6Iq4HVGP2kfmGY2ZmZv0qIm4nGxhh6uvuYG59o5Fxrs3MzMzMrAFOrs3MzMzMcuLk2sysBCTtKOlGSd+XdKekj6fX95F0g6T1ki6TtH3RsZqZDbLWxm4xM7Num7xt9HgaHvV6SVcD7ye7bfQqSZ8lu230Z4oMdBBM10m+mg3LFnUoEjPrNa65NjMrAd822sysHFxzbVYitWrLag0n6dqy/tLObaOr3dmuzKa7K1sn74bXqk7t7168O53ZoHNybc/S7KVOcAJn1i3t3Da62p3tymy6u7L14n0LOnUnvF68O53ZoHOzEDOzkomIbWR3tHv6ttFpVs3bRpuZWXc4uTYzKwHfNtrMrBzcLMTMrBx822gzsxJwcm1mVgK+bbSZWTk4uTYzMzNrQiud/21wuM21mZmZmVlOnFybmZmZmeXEybWZmZmZWU6cXJuZmZmZ5cTJtZmZmZlZTpxcm5mZmZnlxMm1mZmZdYWkvSWtlXSXpDslnZZe313SNZLuSX93KzpWs1Y5uTYzM7NumQBGI+JA4DDg3ZIOBJYC10bEfsC1adqslJxcm5mZWVdExOaIuCU9fxxYB8wFjgdWpsVWAicUE6FZ+5xcm5mZWddJGgYOAW4AhiJic5q1BRgqKCyztvn252ZmZtZVknYBvgy8LyIek/T0vIgISVFjvVOBUwGGhoYYGxtjfHycsbGxLkT9jNH5E13d3nSGdmo9nm7vt04o4vuvx8m1mZmZdY2kmWSJ9SUR8ZX08oOS5kTEZklzgK3V1o2I5cBygAULFsTIyAhjY2OMjIx0I/Snnbz0q13d3nRG509w9h2tpXMblozkG0wBivj+63GzEDMzM+sKZVXUFwLrIuKTFbPWACel5ycBV3Y7NrO8OLk2MysBD2FmfeJw4ERgoaTb0uONwDLgaEn3AEelabNScrMQM7NymBzC7BZJuwI3S7oGOJlsCLNlkpaSDWH24QLjNKspIq4HVGP2kd2MxaxTnFybmZVAGklhc3r+uKTKIcxG0mIrgTGcXJtZA4abbDu+YdmiDkXSX9wsxMysZDyEmZlZ73LNtQ0ESXsDXyBLPAJYHhHnSdoduAwYBjYAiyPikaLiNKsnzyHMymy64bd6aZi0SZ+6pPn+efPnvrDuMr04DJnZoHNybYPC7VWt9PIewqzMpht+q5eGSWtHI8Ok9eIwZGaDrm6zEEkXSdoq6QcVr7l3upWKb7lrZechzMzMyqGRNtcrgGOmvLaUrLZvP+DaNG1WCm6vaiXlIczMzEqgbrOQiLguJSOV3DvdSqmV9qqdaquaZ7vQWre/LVNbzH5oOzo+Pt6x9/YQZmZm5dBqm2vX9lnptNpetVNtVfNsF1rr9rdlurVtP7QdLfs/B2Zm1r62OzRO1zsdOlPrd8emR5teZ3R+7Xm1av0GSTv7oAwJRQPtVZfh9qpmZmbWplaT64Z6p0Nnav3y7gleq9ZvkLSzD0pSOzrZXvUOSbel184gS6pXSzoFuB9YXFB8ZmZm1gdazShd22el0o32qs3e6crMzMz6TyND8V0KfAfYX9LGVMPn3ulmZmZmZlM0MlrIW2rMcu90MzMzM7MKjYxzbWZmZmZmDRjsXnxmfa7ZduAbli3qUCRmZmaDwTXXZmZmZmY5cc21mZn1tGpXYEbnT+Q+LKuZWR5cc21mZmZmlhMn12ZmZtYVki6StFXSDype213SNZLuSX93KzJGs3a5WYiZPa2VG+E02wmy1jamu8zvjpZmfWMF8GngCxWvLQWujYhlkpam6Q8XEJtZLlxzbWZmZl0REdcBP5ny8vHAyvR8JXBCV4Myy5mTazMzMyvSUERsTs+3AENFBmPWLjcLMTMrCUkXAccCWyPiFem13YHLgGFgA7A4Ih4pKkazdkRESIpa8yWdCpwKMDQ0xNjYGOPj44yNjXUrRCBrxtYrhnbqXjyfuuTKppafP/eFHYrkGUV8//U4uTYzK48VuL2q9Z8HJc2JiM2S5gBbay0YEcuB5QALFiyIkZERxsbGGBkZ6VKomV4aBnJ0/gRn39Gb6dyGJSMd30YR3389bhZiZlYSbq9qfWoNcFJ6fhLQXPWoWY/pzX91zKw0WhlhxHLVUHvVapfTy6LaJe9uXgovSiPfUS9eEp+OpEuBEWC2pI3AR4FlwGpJpwD3A4uLi9CsfU6uzcz6xHTtVatdTi+Lapfge/lSeF4auaTei5fEpxMRb6kx68iuBmLWQW4WYmZWbg+mdqrUa69qZmad5+TazKzc3F7VzKyHOLk2MyuJ1F71O8D+kjamNqrLgKMl3QMclabNzKwg/d1gzcysj7i9qplZ73PNtZmZmZlZTlxzbWZmXeOhG82s37nm2szMzMwsJ665NrOe12xt54ZlizoUiZmZ2fScXJuZmVnf8D/jVjQ3CzEzMzMzy4mTazMzMzOznDi5NjMzMzPLiZNrMzMzM7OcuEOjtc2dR8zMzMwyTq7NzMxsYPnGRr2l2e9jxTE7dyiS1jm5NrO+040fS1+ByTgx6ZxG9u3o/AlOTsu5TJr1BifXZmZmZpa7Qf3n28m1mZlZH2glkXFtt1n+2kquJR0DnAfMAC6IiGWtvM+g/mdjvSGvcmxWFJdh6wcux9YvWk6uJc0AzgeOBjYC35O0JiLuyis4s05zObayy7MMu6LDiuJzsfWTdsa5PhRYHxH3RcTPgVXA8fmEZdY1LsdWdi7D1g9cjq1vtJNczwUeqJjemF4zKxOXYys7l2HrBy7H1jc63qFR0qnAqWlyXNIPO73NZr0XZgMPFR1Hkbq5D3RWzVkv6cb2m+Uy3B1l+ww1yvFsXI67omzlpVPa3Q99cj4e6LIw6MfCEWfV/PyFleF2kutNwN4V0/PSa88SEcuB5W1sp+Mk3RQRC4qOo0gDvA/qlmOX4e7oo88w3OXN9s25uBn9UF7y0Ef7oeVy3Ef7oCX+/L33+dtpFvI9YD9J+0jaHngzsCafsMy6xuXYys5l2PqBy7H1jZZrriNiQtJ7gH8jGzbnooi4M7fIzLrA5djKzmXY+oHLsfWTttpcR8TXgK/lFEuR+uZSaRsGdh/0STnuh+/Pn6FFfVKGm9UP5SUPfbMf2ijHfbMPWuTP32MUEUXHYGZmZmbWF9ppc21mZmZmZhUGKrmWdJGkrZJ+UPHaP0i6W9Ltkq6QNKvIGLuh2n6omDcqKSTNLiI2a5ykvSWtlXSXpDslnVZ0TK2SNEPSrZKuKjqWVkiaJenydC5ZJ+m1RcfUj/qpzLdL0o6SbpT0/bQvPl50TN1Q43d8d0nXSLon/d2tyBg7qdYxMCj7oFa5Tx1hb5C0XtJlqVNsYQYquQZWAMdMee0a4BUR8Urgv4DTux1UAVbw3P2ApL2B1wP/0+2ArCUTwGhEHAgcBrxb0oEFx9Sq04B1RQfRhvOAr0fEAcBBlPuz9LJ+KvPtehJYGBEHAQcDx0g6rOCYumEFz/39WgpcGxH7Adem6X5V6xgYlH1Qq9yfBZwTEfsCjwCnFBjjYCXXEXEd8JMpr30jIibS5HfJxtbsa9X2Q3IO8CHADfFLICI2R8Qt6fnjZAld6e5oJmkesAi4oOhYWiHphcBvAhcCRMTPI2JbsVH1p34p83mIzHianJkefX/urvH7dTywMj1fCZzQ1aC6aJpjYCD2wTTlfiFweXq98M8/UMl1A94BXF10EEWQdDywKSK+X3Qs1jxJw8AhwA3FRtKSc8n+qftl0YG0aB/gx8D/SU1bLpC0c9FB9buSl/lcpOZUtwFbgWsiYlD3xVBEbE7PtwBDRQbTLVOOgYHZB1PLPXAvsK2ionQjBf/T7eQ6kfQXZJdbLik6lm6T9HzgDOCvio7FmidpF+DLwPsi4rGi42mGpGOBrRFxc9GxtGE74FXAZyLiEOAJ+veSbE8oc5nPU0Q8FREHk11xPVTSK4qOqWiRDYHW9zX40x0D/b4PppZ74ICCQ3oOJ9eApJOBY4ElMZhjE76UrPbt+5I2kBXYWyS9qNCorC5JM8lOsJdExFeKjqcFhwPHpXK3Clgo6eJiQ2raRmBjRa3h5WTJtnVAH5T53KVmSGup0pdmQDwoaQ5A+ru14Hg6qsYxMFD7AJ5V7l8LzJI0ee+WecCmwgLDyTWSjiG7JH1cRPy06HiKEBF3RMSvRMRwRAyTJQuviogtBYdm05Aksna+6yLik0XH04qIOD0i5qVy92bgWxHxtoLDako6Th6QtH966UjgrgJD6lv9UObzImnPydGtJO0EHA3cXWxUhVkDnJSenwRcWWAsHTXNMTAQ+6BGuV9HlmS/KS1W+OcfqJvISLoUGAFmAw8CHyUbHWQH4OG02Hcj4l2FBNgl1fZDRFxYMX8DsCAiHiokQGuIpNcB3wbu4Jn2ymeku5yVjqQR4AMRcWzRsTRL0sFkHTK3B+4D3h4RjxQbVf/ptzLfDkmvJOu4NYOsomx1RHyi2Kg6r8bv+L8Cq4EXA/cDiyOiWqf90qt1DJC1u+77fVCr3Ev6VbKrn7sDtwJvi4gnC4tzkJJrMzMzM7NOGvhmIWZmZmZmeXFybWZmZmaWEyfXZmZmZmY5cXJtZmZmZpYTJ9dmZmZmZjlxcm1mZmZmlhMn12ZmZmZmOXFybWZmZmaWEyfXJSPps5L+sug4rH9IWiHpzKLfo8Xtjkl6Z7e3a50jaX9Jt0l6XNJPiihXjZL0G5J+OM38Qo4Ly5ekMyU9JGmLpGFJIWm7ouOqRdLVkk6qMa/t+IvaB5JGJG3s5jZb5eS6ZCLiXRHx10XHYd0laYOko/Je1qwHfQhYGxG7AmuKDmY6EfHtiNi/6DiscyS9GBgFDoyIFxUdTyMi4g0RsbLoOAaZk2szM+slLwHuLDoIs+TFwMMRsbXoQKw8BjK5TjV7H5R0u6QnJF0oaShdSnlc0jcl7ZaW/VK6FPSopOskvbziffaQ9H8lPSbpe+nS0fUV80PSuyTdI2mbpPMlqWL+OyStk/SIpH+T9JL0uiSdI2lreu87JL0izXv6MqOkkyu3V7HNfSuW/ef0ucYl/YekF0k6N23zbkmHdHJfW/sk/QvZCf7/pu/xQ5KOk3RnKldjkn6t1rLp9ZrluJnVOQEAACAASURBVMEYRiRtlHRGujy6QdKSKYvtJumr6Ri6QdJLK9Y/QNI16TL/DyUtrpi3Ih0btdb99XR8PZr+/nqNGPeV9O9puYckXdbMZ7TiSfoWcATwaUnjwPYV83aTdJWkH6fz11WS5qV5fyjppinv9eeS1qTniyTdms6nD0j6WMVyk5e4T5L0P6ns/EXF/B3SOfNH6XGupB3SvGddppZ0iKRbUjm+DNixYt7sFPO2dBx8W9JA/gZ3SjovfUDZb/ujki6TtGOa98eS1qd9v0bSXhXrVf2tVnYF8Bpgr3Q+XVFlm29X9jv+uKT7JP1Jxbx1ko6tmN4uld9Xpenp8ouWz4uqaC4naYakf0zl+j5gUYP7ckzS30m6MR03V0rafcpiS2ocM8+TtFTSvZIelrR6ct12jrcqMX5Y0qa0f34o6chGPltXRMTAPYANwHeBIWAusBW4BTiE7GT4LeCjadl3ALsCOwDnArdVvM+q9Hg+cCDwAHB9xfwArgJmkSU8PwaOSfOOB9YDvwZsB3wE+M8077eBm9N6SsvMSfNWAGem5ydXbq9im/tWLPsQ8L8qPtd/A38EzADOJLv8Wvh34kdDZfao9PxlwBPA0cBMssvo64Htpy5bsf505fjpMjXN9keACeCT6T1+K8Wwf8V7PAwcmsrzJcCqNG/ndGy8Pc07JJXLAxtYd3fgEeDENO8taXqPNH8MeGd6finwF2SVBjsCryv6e/OjpbJe+Z1Wnu/2AH6f7Hy7K/Al4F/TvOcDjwP7VbzP94A3V5Tf+alsvBJ4EDghzRtO583PAzsBBwFPAr+W5n+C7PfiV4A9gf8E/rrifTem59sD9wN/no7LNwG/qIj/74DPpnkzgd8AVPT+7qcH2bnvRmCvdO5YB7wLWJjOOa9K569PAddVrDfdb/XT3/GU8rJdml4EvJTst/q3gJ8Cr0rz/gq4pGLdRcC6iul65+U8zovvAu4G9k7rra2Mv85xuAl4Bdk5/MvAxQ0eM6elY2Ze+myfAy7N+Xjbn+x3Za+K931p0WXw6f1XdAAFHoBLKqa/DHymYvrPSCftKevNSoXihWTJ6S9IyUWafybPTa5fVzG9Glianl8NnFIx73npoHwJ2Yngv4DDgOdNiWEFzSXXn5/yuSoP7PnAtqK/Dz8aLrOTyfVfAqunlJ1NwMjUZWu819PleGqZmmadEbLkeueK11YDf1nxHhdUzHsjcHd6/ofAt6e83+d45h/Y6dY9EbhxyrrfAU5Oz8d45kfkC8ByYF7R35cfrT+okVxXWe5g4JGK6YuBv0rP9yNLtp9fY91zgXPS8+F0PMyrmH8jzyTm9wJvrJj328CG9HyEZ37sfxP4ERUJM1liMHm+/gRw5eT52Y+OlJ0NwNsqpv+e7B+aC4G/r3h9F7Lf7+E0Pd1v9dPf8ZTyUjU5Bf4VOC0937eyHJIlyH9VY71q5+U8zovfAt5Vsdzrp4u/YrkxYFnF9IHAz8lyn3rHzDrgyIp5c9L+3i7H421fsorRo4CZRZe9qY9BviT1YMXz/1dlepd0OWVZurTxGNmBCzCb7D+q7cj+c5pU+XzSlornPyU7qCFLos9Ll6C2AT8h+893bkR8C/g0cD6wVdJySS9o5UNW+VzP+Zwtvq8VZy+yGjIAIuKXZGVvbrWF65TjZjwSEU9UTN+fYpk0XVl/zWRZT+V9CfCiBtZ91met2G61z/ohsmPoRmVNZt7RwGeykpD0fEmfk3R/KsfXAbMkzUiLfJGsBg/grWQVJD9N675G0tp0Sf5Rstq8qeW/0TI4tdxTsdymSL/8FctO+geyK0zfSM0Hljbwsa151b7HqefMcbJa4bl11qtL0hskfTc1N9lGlgTPTttZT5Zo/o6k5wPHkZXTRs/LeZwX9+LZucnU9aYzdb2ZDcb3EuCKivP9OuApstYC9dZt6HhL+/Z9wMfI8qRVlU19ijbIyXUj3krWfOMostrq4fS6yC4bTZBd9pi0dxPv/QDwJxExq+KxU0T8J0BE/FNE/C+y/xZfBnywyns8QXY5NAtKKkVPZmtJ5Q/2j8hOXkDWRp+s7G2qsixMX46bsZuknSumX5xiqecB4N+nlPVdIuJPG1j3WZ+1Yrubpi4YEVsi4o8jYi/gT4B/Vup/YH1hlOxS8Gsi4gVkNcXwTDm+BthT0sFkSfYXK9b9ItnII3tHxAvJajMbLf9Ty2Ctcr8ZmJuOx8plAYiIxyNiNCJ+lSzJen9PtRHtb1PPmTuTNTN6znmkGakt8JeBfwSGImIW8DWeXbYuJSuPxwN3paQQ2jsvN3xeJCuXe09ZrlFT1/sFWfOaeh4A3jDlnL9jRDSyvxs93oiIL0bE69LyAZzVwPt3hZPr6e1K1hboYbIk9m8nZ0TEU8BXgI+lGpUDyNoyN+qzwOmTHRgkvVDSH6Tnr041LTPJEuifAb+s8h7fB14u6WBlnTY+1uwHtNJ4EPjV9Hw1sEjSkamMjJKV0/+ssixMU45b8HFJ20v6DeBYsnav9VwFvEzSiZJmpserlTph1vG1tO5blXUG+kOyfzivmrqgpD9Q6uBG1v4wqH7cWDntSna1bVvqHPXRypkR8Quy8vgPZG1Lr5my7k8i4meSDiVLbBp1KfARSXtKmk3WjvbiKst9h6zC5b2pjP8eWXtZACQdq6zTrYBHyWryXD6741Lg7em3cgeyc+ANEbGhzffdnqxN8Y+BCUlvIGt2UWlVeu1PefY/fO2clxs+L5L9XrxX0jxlAzU0c8XkbZIOTLXunwAuT7lPPZ8F/kbPDNKwp6TjG9xmQ8ebsvHwF6bv82dk54aeOZ6cXE/vC2SXJDYBd5E1sq/0HrL/OLcA/0JWKJ5s5I0j4gqy/7JWpUtCPwDekGa/gKyx/yNp+w+T/WBMfY//Iivw3wTuAa6fuoz1jb8jO+FsA34HeBtZp5yH0vTvRMTPpy4r6QPUL8eN2kJWJn9E1nbwXRFxd72VIuJxsh+XN6d1t5CV/ao9wKes+zBZEj9Kdhx8CDg2IqrVnrwauEHZKBNryNo93tfA57JyOJesA9RDZGX461WW+SJZTeCXImKi4vX/DXxC0uNkP9arm9jumcBNwO3AHWSd359zY5h0/P0eWV+Yn5D1NfhKxSL7kZ2rx8kS8X+OiLVNxGEtiohvkvVV+TJZTe5Lyc5H7b7v48B7ycrTI2T/tK2Zssxmsu/714HKEYxaPi83eV78PPBvZJVxt/DsMlnPv5C1/d5C1kn8vQ2udx7ZfvhGOua+C7ymwXUbOt7Ifj+WkZ0PtpB1gDy9wW10nJ7dPMzaIeks4EURcVLRsZjlSdIIWU/xefWWNTOzcpM0RnbOv6DoWMrINddtUDZ27yuVORQ4Bbii6LjMzMzMrBhOrtuzK9kllifILvecTTbUklnpKLtBzHiVx9VFx2ZmZvmqcb4fT31qrA1uFmJmZmZmlhPXXJuZmZmZ5cTJtZmZmZlZTrbr5sZmz54dw8PD3dxkbp544gl23nnn+gsOgE7ti5tvvvmhiNgz9zfOUb0yXJZyUpY4oXyx3n333aUvx2VRprLRiF76PP1wPm5VL30PzXLszyiyDHc1uR4eHuamm27q5iZzMzY2xsjISNFh9IRO7QtJzdyWtRD1ynBZyklZ4oTyxXrEEUeUvhyXRZnKRiN66fP0w/m4Vb30PTTLsT+jyDLsZiFmZmZmZjlxcm1mZmZmlhMn12ZmZmZmOXFybWZmZmaWk652aMzL8NKvNr3OhmWL2trG6PwJTq6z3Wa3YTaImj1+R+dPMNKZUGyKbpxbzQaRj63B4pprMzMzM7OcOLk2MzMzM8uJk2szMzMzs5yUss21mZlZLa20b22W28OaWS0N11xLmiHpVklXpel9JN0gab2kyyRt37kwzczMzMx6XzPNQk4D1lVMnwWcExH7Ao8Ap+QZmJmZmZlZ2TSUXEuaBywCLkjTAhYCl6dFVgIndCJAMzMzM7OyaLTm+lzgQ8Av0/QewLaImEjTG4G5OcdmlhtJO0q6UdL3Jd0p6ePpdTdvslJwGTYzK4e6HRolHQtsjYibJY00uwFJpwKnAgwNDTE2NtbsWzzH6PyJ+gtN8alLrmxyG8+eHtqp/nbz+GxlMD4+XsbP+iSwMCLGJc0Erpd0NfB+suZNqyR9lqx502eKDNSsBpdhM7MSaGS0kMOB4yS9EdgReAFwHjBL0nap9noesKnayhGxHFgOsGDBghgZGWk76Hp3SuyE0fkTnH3H9Ltrw5KR7gRTsLGxMfL4HrspIgIYT5Mz0yPImje9Nb2+EvgYTkysB7kMm5mVQ93kOiJOB04HSDXXH4iIJZK+BLwJWAWcBDRXNWzWZZJmADcD+wLnA/fSQPOmZq6+lKVWP88479j0aFPLT70qVM/QTuW5KjQ+Pl5/oTa0WobTunXLcStXBYv8bmqV41Y+R7M68bnLcv4ws+m1M871h4FVks4EbgUuzCcks86IiKeAgyXNAq4ADmhwvYavvpSlVj/PODt9JWl0/gSLS7BPofOJZqtlOK1btxy38l0WecWuVjnuxtXNTnzuspw/zGx6TSXXETEGjKXn9wGH5h+SWWdFxDZJa4HX0mDzJrNe4jJsZta7fPtzGwiS9ky1fUjaCTiabNz2tWTNm8DNm6yHuQybmZWDb39ug2IOsDK1WX0esDoirpJ0F27eZOXgMmxmVgJOrm0gRMTtwCFVXnfzJisFl2Ezs3Jwcm1mZmbWhOEChgS28nCbazMzMzOznDi5NjMzMzPLiZNrMzMzM7OcOLk2MzMzM8uJk2szMzMzs5w4uTYzM7OukLSjpBslfV/SnZI+nl7fR9INktZLukzS9kXHatYqJ9dmZmbWLU8CCyPiIOBg4BhJhwFnAedExL7AI8ApBcZo1hYn12ZmZtYVkRlPkzPTI4CFwOXp9ZXACQWEZ5YLJ9dmZmbWNZJmSLoN2ApcA9wLbIuIibTIRmBuUfGZtct3aDQrkWbvCrZh2aIORWJm1pqIeAo4WNIs4ArggEbXlXQqcCrA0NAQY2Njucc3Pj5e931H509MOz8PrXy2RmLvVWWOfSon12ZmZtZ1EbFN0lrgtcAsSdul2ut5wKYa6ywHlgMsWLAgRkZGco9rbGyMeu97chduf75hyfQxVNNI7L2qzLFP5WYhZmZm1hWS9kw11kjaCTgaWAesBd6UFjsJuLKYCM3a55prMzMz65Y5wEpJM8gq+FZHxFWS7gJWSToTuBW4sMggzdrh5NrMzMy6IiJuBw6p8vp9wKHdj8gsf24WYmZmZmaWEyfXZmZmZmY5cbMQsz5Wa+i+0fkTVXu7e+g+MzOz9tStuZa0o6QbJX1f0p2SPp5e30fSDZLWS7pM0vadD9fMzMzMrHc10izkSWBhRBwEHAwcI+kw4CzgnIjYF3gEOKVzYZqZmZmZ9b66yXVkxtPkzPQIYCFweXp9JXBCRyI0MzMzMyuJhtpcp/Eobwb2Bc4H7gW2pTspAWwE5tZYN/dblXbjtqNTDe1Uf7v9ctvOevrpFqVm1l21+gFMx30BzKxMGkquI+Ip4OB0V6UrgAMa3UAnblXajduOTjU6f4Kz75h+d7Vyq9Iy6qdblJqZmZnlqamh+CJiG9ktSl8LzJI0mW3OAzblHJuZmZmZWak0MlrInqnGGkk7AUcD68iS7DelxU4CruxUkGZmZmZmZdBIs5A5wMrU7vp5wOqIuErSXcAqSWcCtwIXdjBOs77TSttTMzMz6211k+uIuB04pMrr9wGHdiIoMzMzM2vc8NKv1rxBWC3uLNwZvv25mZmZmVlOnFybmZmZmeXEybWZmZmZWU6cXNtAkLS3pLWS7pJ0p6TT0uu7S7pG0j3p725Fx2pWjcuwmVk5OLm2QTEBjEbEgcBhwLslHQgsBa6NiP2Aa9O0WS9yGTYzKwEn1zYQImJzRNySnj9ONlb7XOB4YGVabCVwQjERmk3PZdjMrBwauv25WT+RNEw2vOQNwFBEbE6ztgBDVZY/FTgVYGhoiLGxsZrvPT4+Pu38SqPzJxoPOmdDO1Xf/qcuaf5eUKPz84iotqGdaHifFm18fLwr22m2DKd16pbjVspks99NK9uoVS6Hdqo+r9NlEjpTJps5f5hZ73JybQNF0i7Al4H3RcRjkp6eFxEhKaauExHLgeUACxYsiJGRkZrvPzY2xnTzKzUzFmneRudPcPYd5Tj8R+dPsLjBfVq0biRGrZThNK9uOW6lTG5Y8tz3mU6e5b7Ictzs525EM+cPM+tdbhZiA0PSTLKk5JKI+Ep6+UFJc9L8OcDWouIzq8dl2Mys9zm5toGgrHrvQmBdRHyyYtYa4KT0/CSg+XYRZl3gMmxmVg7luC5s1r7DgROBOyTdll47A1gGrJZ0CnA/sLig+MzqcRk2MysBJ9c2ECLiekA1Zh/ZzVjMWuEybGZWDk6uczTcZEedDcsWdSgSMzMzMyuC21ybmZlZV/hOozYIXHNtZmYta/aKnQ28yTuN3iJpV+BmSdcAJ5PdaXSZpKVkdxr9cIFxmrXMNddmZmbWFb7TqA0C11ybmZlZ13XqTqPtauROmd24w24rdz+tdffdvLbRSf10h1In12ZmZtZVnbzTaLsauVNmN+6w28rdT5u9a2kn7jTaqn66Q6mbhZiZmVnX+E6j1u+cXJuZmVlX+E6jNgjqJtceNsfMzMxyMnmn0YWSbkuPN5LdafRoSfcAR6Vps1JqpGGOh80xMzOztvlOozYI6tZce9gcMzMzM7PGNNXmupVhc8zMzMzMBkXD47W0OmxOJ8ak7Mb4klM1O3ZkI8o6nmM/jUVpZmZmlqeGkuvphs2JiM3TDZvTiTEpuzG+5FTNjh3ZiF4aX7IZ/TQWpZmZmVmeGhktxMPmmJmZmZk1oJGq2Mlhc+6QdFt67QyyYXJWSzoFuB9Y3JkQzczMzMzKoW5y7WFzzMzMzMwak28jYjMzswEw3GTfnw3LFnUoEmvX1O9ydP5EIX27iuBy3Bm+/bmZmZmZWU6cXJuZmZmZ5cTJtZmZmZlZTpxcm5mZmZnlxMm1mZmZmVlOnFybmZmZmeXEQ/GZmZmZ9Zhmh8mz3uGaazMzMzOznLjmukCt/FfqAdzNzMzMepeTazMzMzOry3d0bIybhZiZmZmZ5cTJtQ0ESRdJ2irpBxWv7S7pGkn3pL+7FRmjWT0ux2Zmvc/JtQ2KFcAxU15bClwbEfsB16Zps162ApdjM7Oe5uTaBkJEXAf8ZMrLxwMr0/OVwAldDcqsSS7HZma9zx0abZANRcTm9HwLMFRtIUmnAqcCDA0NMTY2VvMNx8fHp51faXT+RBOh5mtop2K334yhneBTl1zZ1Drz576wQ9FMb3x8vIjN5laOy1ImJpWpHDdyXmjm/GFmvcvJtRkQESEpasxbDiwHWLBgQYyMjNR8n7GxMaabX+nkAm8QMDp/grPvKMfh30qsG5aMdCaYOopOjNotx0WWyVaUqRw3UiabOX+YWe9ysxAbZA9KmgOQ/m4tOB6zVrgcW2m4U64NAifXNsjWACel5ycBzbU9MOsNLsdWJitwp1zrc06ubSBIuhT4DrC/pI2STgGWAUdLugc4Kk2b9SyXYys7d8q1QVCOxmpmbYqIt9SYdWRXAzFrg8ux9amGOuVCcx3MGzW1U2yZOspO1WuxN/P99FOH3rrJtaSLgGOBrRHxivTa7sBlwDCwAVgcEY90LkwzMzPrd9N1yk3zG+5g3qipHXnL1FF2ql6LvZnO5f3UobeRZiErcPsoMzMz6wx3yrW+Uje5dvsoMzMz6yB3yrW+0uq1g55qH9UNvdKOqRfaI/VTuygzM+ue1Cl3BJgtaSPwUbJOuKtTB937gcXFRWjWvrYb5vRC+6hu6JV2TEXdHKNSP7WLMjOz7nGnXBsErQ7F5/ZRZmZmZmZTtFoVO9k+ahluH2VmPWa4yatbG5Yt6lAkZtZtzR7/1lv64fxdt+baNy0wMzMzM2tM3Zprt48yMzMzM2tM8T30zMzMzKzvNNPEY3T+RCEDVnRCqx0azczMzMxsCifXZmZmZmY5cXJtZmZmZpYTt7k2MzPrsEbank5tc9qLQ4yZWX2uuTYzMzMzy4mTazMzMzOznDi5NjMzMzPLidtcm+VkeOlX+2qcTjMzM2uek+uSaWZAdnCHGDMzM7NucrMQMzMzM7OcOLk2MzMzM8tJTzQLabapg5mZmZlZL3LNtZmZmZlZTpxcm5mZmZnlxMm1mZmZmVlOeqLNtZlZkVrp9+FhLs3MrBrXXJuZmZmZ5cQ1133ON50xMzMz6x7XXJuZmZmZ5aStmmtJxwDnATOACyJiWS5RmXWRy7GVncuwQfn7DrgcW79oObmWNAM4Hzga2Ah8T9KaiLgrr+Cs+xo5OY/On+DkNm7802Mnc5djKzWXYesHLsfWT9ppFnIosD4i7ouInwOrgOPzCcusa1yOrexchq0fuBxb32gnuZ4LPFAxvTG9ZlYmLsdWdi7D1g9cjq1vdHy0EEmnAqemyXFJP+z0NjvhvTAbeKjoOHpBu/tCZ9Wc9ZJW37OTminDZSknZYkTejfWGuV4Nn1QjsuiV8tGq6Z+nmnOlbnp5/Nxq8pcrgYx9l4sw+0k15uAvSum56XXniUilgPL29hOT5B0U0QsKDqOXtBn+6JuOW6mDJdl35QlTihlrMNd3uxAnYsrlalsNKLfPk+TeqYcl/l7cOy9oZ1mId8D9pO0j6TtgTcDa/IJy6xrXI6t7FyGrR+4HFvfaLnmOiImJL0H+DeyYXMuiog7c4vMrAtcjq3sXIatH7gcWz9pq811RHwN+FpOsfS6vrqc2qa+2hc5l+Oy7JuyxAmOta4BOxdXKlPZaES/fZ6m9FA5LvP34Nh7gCKi6BjMzMzMzPqCb39uZmZmZpYTJ9eApIskbZX0g4rXdpd0jaR70t/d0uuS9E+S1ku6XdKriou8M2rsj49J2iTptvR4Y8W809P++KGk3y4m6uJU21+9StLektZKukvSnZJOKzqmWiTtKOlGSd9PsX686JimI2mGpFslXVV0LP2sTGW4WS5D3SXpmPS7tV7S0irz35/K2e2SrpXUM8MT1ou9YrnflxSSemYUjkZil7S44hj/YrdjbJeT68wK4Jgpry0Fro2I/YBr0zTAG4D90uNU4DNdirGbVvDc/QFwTkQcnB5fA5B0IFmv7pendf453cZ2kKyg+v7qRRPAaEQcCBwGvDt9h73oSWBhRBwEHAwcI+mwgmOazmnAuqKDGABlKsPNchnqkorbrb8BOBB4S5VydCuwICJeCVwO/H13o6yuwdiRtCtZmbqhuxHW1kjskvYDTgcOj4iXA+/reqBtcnINRMR1wE+mvHw8sDI9XwmcUPH6FyLzXWCWpDndibQ7auyPWo4HVkXEkxHx38B6stvYDowm91ehImJzRNySnj9O9kPek3dBS8fYeJqcmR492UlE0jxgEXBB0bH0uzKV4Wa4DHVd3dutR8TaiPhpmvwu2djbvaDRW8X/NXAW8LNuBldHI7H/MXB+RDwCEBFbuxxj25xc1zYUEZvT8y3AUHo+yLdofU+6PHbRZDMZBnt/lJqkYeAQeqhWY6p0mfw2YCtwTUT0aqznAh8Cfll0IIOkDGW4CS5D3dXsb9cpwNUdjahxdWNPTVb3joivdjOwBjSy318GvEzSf0j6rqSyXBl+mpPrBkQ2pEpP1ph10WeAl5Jdnt8MnF1sONYOSbsAXwbeFxGPFR1PLRHxVEQcTFZjdKikVxQd01SSjgW2RsTNRccySMpShhvhMtTbJL0NWAD8Q9GxNELS84BPAqNFx9Ki7cia3o4AbwE+L2lWoRE1ycl1bQ9ONvdIfycvSzR0i9Z+ExEPpkTnl8Dneabpx0DujzKTNJMsKbkkIr5SdDyNiIhtwFp6s2374cBxkjaQXeJcKOniYkPqb2Usw3W4DHVfQ79dko4C/gI4LiKe7FJs9dSLfVfgFcBYKlOHAWt6pFNjI/t9I7AmIn6Rmpv+F1myXRpOrmtbA5yUnp8EXFnx+h+lUUMOAx6taD7St6a0K/9dYHJkjDXAmyXtIGkfsgPgxm7HZ42RJOBCYF1EfLLoeKYjac/J2gpJOwFHA3cXG9VzRcTpETEvIobJOvd+KyLeVnBYfatMZbhRLkOFqHu7dUmHAJ8jS6x7qd3vtLFHxKMRMTsihlOZ+i7ZZ7ipmHCfpZHb3P8rWa01kmaTNRO5r5tBtqutOzT2C0mXkn2RsyVtBD4KLANWSzoFuB9YnBb/GvBGso57PwXe3vWAO6zG/hiRdDBZ85gNwJ8ARMSdklYDd5H14n93RDxVRNxFqba/IuLCYqOq6XDgROCO1JYZ4IzJ0V96zBxgZepd/jxgdUR4iDIrUxm2HlXrduuSPgHcFBFryJqB7AJ8Kfufjv+JiOMKCzppMPae1GDs/wa8XtJdwFPAByPi4eKibp7v0GhmZmZmlhM3CzEzMzMzy4mTazMzMzOznDi5NjMzMzPLiZNrMzMzM7OcOLk2MzMzM8uJk2szMzMzs5w4uTYzMzMzy4mTazMzMzOznDi5zpmkkyVdX3QcjZJ0taST6i9pvU7SsKSQlOudVyWdIemCPN+zXZKWSPpG0XFYb5O0QdJRRcdhZoPFyXUbOpXMdFNEvCEiVhYdh/WuiPjbiHhnI8tK+piki7sQ0yUR8fqK7YakfTu9XTPrjLJUTElaIenMouOoRdKfSnpQ0rikPZpYb0zSO9PzZ1VedPP8muf+LbISpu+T6zInvmbd4uPEzHqFpF+RdKmkH0l6VNJ/SHrNlGX+TNJ/S3pM0k2SXlcxT5LOkvRwepwlSVPWf4ukL7YZ54Fp24+kxzclHVgx/whJa9Nn2FBl/Q36/+3de7hdVXno/+9LAKHcIoazDQEJFbygkagp4s9WNyAawQptOahFGnrwl3qOHm3NORqtP8VrY5+iaPVXGwWJlxo4qJWCN0rZWusFwuWK7QAAIABJREFUiaLhUgtiEEIgKAQIWjH4nj/m2LDY2Ze19prrur+f59nPnre15rvWGmvNd4455hgRvyyJ8PZ2E8GI2A14H/CCzNw7M38+m+eZWHnRYgx9U9HRzuto18Am16VQvikiriuF+uMRsUdEjEbErRHxxoi4Hfh4RDwqIs4pX9TbyvSjyvM8OiIuiYg7y/NcEhEHNexnLCLeWb7c90XEVyNiQVn99fJ/W/liPLvhcX9Tnu8nEfGihuUHRsTFEXFXRNwYEf9vw7p55RL8j8u+NkTEwRHx4Yg4e8Lrvzgi/qJMr254zHUR8QcN250REd+YJp7Gs9WZtj0jIm4q+/lJRJzW1oeoaZXP/nOlbP48Ij4UEbtExFsi4uaI2BoRn4iI/aZ4/HRl7ayIuCgiPhUR9wJnTBPHQ7XR8fDVmhUR8dOI+FlE/GVZtxx4M/DS8n34QVm+X0ScGxFbImJzRLwrIuaVdbMqc9FQyxUR49/DH5T9vjQiromI3294nt1KrE+fxUehwbY0In4YVYJzQVTHiZ1qSaMhKYjqmPE3pYzfEREfiYg9exP+nLQ38F3gmcD+wDrg0ojYGyCqRHsNcAqwH3Au8Pnx3xVgJXAycCTwNOD3gT+bsI8TgS+2GedtJYb9gQXAxcD6hvX3A+cB/3ua5/j9kgjvXUMiOALsAVzb5vOoTQObXBenAS8EHg88AXhLWf5YqsJ+CNWX7C+Bo4GlVF+2oxq23QX4eNn2ccAvgQ9N2M8fA38K/Bdgd+B/leXPLf/nly/Gt8r8s4AfUX3Z/ho4N+Khs+b1wK3AgVRfyvdExLFl3euBlwMnAPsC/w34BdUPy8sjYheAktw/Hxg/6/4x8HtUPzJvBz4VEQsb4p8unokm3TYi9gI+CLwoM/cB/h/g6imeQ20qB4lLgJuBxcAiqrJzRvk7BvhtqoPQxPI6brqyBnAScBEwH/h0iyH+LvBE4DjgrRHx5Mz8MvAe4ILyfTiybHs+sAM4DHg68AKgsZlJW2UuM8e/h0eW/V4AfAJ4RcNmJwBbMvP7Lb5ODb5TgeXAoVSJ1hlNPGYN1TFlKVW5XQS8tUPxDbWIeHKpxNkWEddGxEvK8seUk/97I+JKquM4AJl5U2a+LzO3ZOaDmbmW6tj7xLLJYuDazNyQmUn1fV9AdYwGWAGcnZm3ZuZm4GwaPvdyLD0e+HKZf0mJbVuJ9ckN2z49Ir5XTvAvoEpex+PclpmbSgwBPEhVXsbXX5mZnwRuquGtHI9n0srCiHgC1e8oVBV+/zLD8xwfEf9eTjo/VOIfXzdlE51oqJCbuO1kFR1l+Ysj4ury/n4zIp7W8Pgp399pYv9aRPxRmX5OOTE+scwfFxFXT/Y6ynaviogbSiwfbsyFIuK/RcT1paLnKxFxSFkeEfH+qCq07o2IjRHx1GmDzMyB/AM2Aa9qmD+BKskcBR4A9mhY92PghIb5FwKbpnjepcDdDfNjwFsa5v8H8OUyvRhIYNeG9WcANzbM/1bZ5rHAwVRfvn0a1v8VcH6Z/hFw0hRxXQ8cX6ZfA3xxmvfm6vHnmS6ehtf3yiZi3wvYBvwRsGevP/9h/wOeDdzZWLbK8suB/9Ew/0Tg18CujeWxibJ2FvD1JmM5C/hUmR7fx0EN668EXjZx2zI/AvyqscxQnUBe0U6ZK4/7RsN8Aoc1zB8I3AfsW+YvAt7Q68/Vv+7+UR0nXtEw/9fARyaWn7IuqRKjoKpxfHzDumcDP+n16xm0P2A34EaqK1q7A8eW7+UTqU7+Lyzf86cCmyd+Jg3PsxT4T2C/Mr8vsIHqxHwe8D+B7wNR1t8DPKvh8cuA+xrmjwa+VaafUD7v40u8bygx717+bgb+oqw7her39l0T4ttGVYHwGxryhYb1z2eSnKOUzzuofuu/SlVBMNN7+g7g21QnEgcA3wTeWdYtZkJOMsVzLCifwynldf1Fib8xF5j095WGnGGmbcv804GtDZ/VivK6H9Xs+zvFe/C3ZfrNVDneexvWfWCa2C6hqlB6XHnfl5d1J5XP/clUx9C3AN8s615IVd7mU/0+PBlYOF2Mg15zfUvD9M1UB1SAOzPzPxvWHVjW77RtRPxWRPx9VJfZ76Vq6jG/4fISwO0N07+gqi2czkPbZ+YvyuTeZZ93ZeZ9E2JZVKYPpiokk1nHwzVxrwA+Ob4iIv6k4axwG9UP1YKGx04VT9OxZ+b9wEuBVwFbIuLSiHjSFM+h9h0M3JyZOyYsn6ws70qVxE7cbrqyBo/8/rSq2e/EIVQ/mlsayuff83AN0yOeq64yl5m3Af8G/FFEzAdeROu18xoOrf5+H0B1krehocx+uSxXa46mer/XZOYDmfkvVMnNK6hOmt+amfdn5jVUx7idRMS+VMe7t2fmPWXxfcBngW9Qnby/DViZJRMq+7yn4WnuAfZuqKVsbBLyUuDSzLwsM38N/A2wJ9WVsqOpfr/OycxfZ+ZFVM1VHiEz51NdOX4NVZLfrNOoEuJDgCuAr5Tfq5ke847M3JqZd1JdrT69hX1CVRl5bWZeVF7zOTzye1KnlcDfZ+Z3sroKsY7qMzuaJt/fSXwNeF6Zfi5VxdH4/PPK+qmsyeqKw0+p3vOlZfmrgL/KzOvLcfc9VE3KDqFK+PcBnkR1And9Zm6ZLsBBT64Pbph+HFX7J6jOThrdRlV4J9t2FdVZ9LMyc18ebuoxVbOJRhP3M5PbgP0jYp8JsWwu07fQcGlsgk8BJ0XEkVRnTf8IUD74j1J9qR9TvuTXNBl/SzLzK5l5PLAQ+PeyX3XGLcDjYucbDScryzuoaj8mbjddWYPWy28zJj7nLVQ/pAsyc3752zczn9LUk7VX5sZPSP8rVS3V5hm219xxP1UCDUBEPLZh3c+omgc+paHM7peZMyXl2tmBwC2Z+ZuGZTdTXZnalZ0ryB4hqnbu/wR8OzP/qmHVmVRNNZ9CVfv5CuCSiBivYNtOVbs9bl9ge0PyfQIPJ9ePqLAosd5CVRFxILC54XGTxlkedz/VVZFPRMR/mWybSR7zb5n5y8z8RXl926iaeE5nysrCFhxIw3tfXl87lS3TOQRYNX6iWk5WDy4xNP3+TvAt4AkRMUKVHH8CODiqJrNH8fD9cJOZ6mT7EOADDTHeRZVHLSonhR8CPgxsjYi15aRvSoOeXL86Ig6KiP2p2lVfMMV2nwHeEhEHlDf/rVTJKlRnI7+kaqO0P9UZcLPupLoM9NvNbJyZt1BdwvmrqG6qeRrVj8R4LB8D3hkRh5c2Pk+L0pVOZt5KdUb3SeCzmfnL8pi9qBKaOwEi4k+paq5rFREjEXFSVO1gf0X14/WbGR6m2bsS2AKsiYi9Snl5DlVZ/ouIODSqm3vG2zg/ooa7ibLWKXcAi6PcH1DO7r8KnB0R+0Z1Q+bjI+J50z4LLZe5O9j5e/iPwDOA11H9+ErjfgA8JSKWRsQeVM2ZgIeSq48C7x9PkiJiUUS8sCeRDrbbqJKexlzjcVQJzg52riB7SFSdDvwj1X0jE29GXApckpn/kZm/yep+jy1Utc1Q3dB3ZMP2R5Zl4ydSC4HvNcT4UIVFqd0+mKoiYguwqLFd7sQ4J9iF6qRt0TTbTGe87fZ0pqssbNYWGt77htfcjEecmFKdKE3nFuDdDSeq8zPztzLzM7T+/gIPXeHcQPXbfk1mPkB1vHs98OPM/FmTr2VinH82Ic49M/ObZZ8fzMxnAkdQNSWa7ibVgU+u/4HqwH0TVXOKqfpGfBdwFfBDYCPVl2p823OoLgH9jKod05eb3Xn5gN8N/Fs52zm6iYe9nOoy0G3A54G3ZeY/l3Xvo2qD9lXgXqo7oBvvUF8HLKGhSUhmXkd1s8a3qBKMJVSXw+u2C1XBvY3qjO55wH/vwH4EZOaDVHe4Hwb8lOoA81KqO88/SXVm/hOqdoj/c4qnma6sdcr/Kf9/HhHjB68/oapdug64m6r988JJHjtRK2XuLGBd+R6eClBOQD9LdSPb51p+JRpamfkfVG0z/xm4gap5QaM3UrW//HZpLvjPPHwznZr3HarawTdE1WPPKNXv2qepvpNnlaaZR1C1xQUe6lLuIqqKrxUTar6hqmg6MSJ+u1REHU+V8FxT1n8CeH05KTqQ6gr1+WXdi6jumxqvLb2wPNdxZb+rqE7mv0l1XN0BvLbE/4dUNaPjcR4f1Q1580pN5vuofuOuL+t3KSdvu1WzsUdE7F7WPS6qm/F2L8v/N1VzzpmO39NVFjbrUqqTyz8sV0dfy8xJ8rirgT8sn9thVJU2jSZWdHwUeFVEPKt8VntFxInlquq07+8MvkZ1xX68CcjYhPlWfQR4U0Q8BR7q5eq/lunfKfHvRnVy8Z/MVLk4XYPsfv6jahD//F7H0eXX/FyqRCt6HYt//g3CH+XA0+s4/PNvrv5RNd34GlW75+uAPyjLD6Bqf30v1ZW6d1JuPqM6kU6qxHx7w9/vlfVBdXL0U6r219cDpzfsM6huXr2r/P01D9/seBFwyoQY/6DEdk+J9SkN65ZRtaO+j+rq+AWUG+6ompz9e4ntTqqk9WkNjx0tr6Pxb6zhfflhSdZ+TnWz+rIm3s89qHpR2lL+PkjpwIEmb2gs2y4H/qO85g+V193MDY0LqCoA76M6EThrwravKnFtA05t2Nd3y7ItVJUw+8z0/s4Q/wtLXM8r808t8y9t2GbK11Hmz2/cF1Xb9Y1UZfIW4Lyy/LjyWW2nqoj9NNV9QVPGN17YBk5UHbK/MjtfE9cXyhnTeuAHmfmOXscj9bvSzOv7VAfd6drgSZoDSi3t7cBvZ+a9vY5Hw2vQm4XMCVH1ubmN6lL6OT0OR0MoIr4UD48S1vj35l7HNhtRDZhzC/AlE2tJxf7A/2dirU4b2JprSZKkYRMRX2LyXkPek5nvafI5fg/40mTrcgB6vikVO5NV7vxrZr5okuV9xeRakiRJqonNQiRJkqSaTBygoqMWLFiQixcv7uYuuf/++9lrr726us9m9XNs0P34NmzY8LPM7OtR0HpRhuvQ72VtNvr1NQ1DOe7X93Y2huW1+Hu8s37+PR7UcjeIcU8Vcy/LcFeT68WLF3PVVVd1c5eMjY0xOjra1X02q59jg+7HFxHNjMzUU70ow3Xo97I2G/36moahHPfrezsbw/Ja/D3eWT//Hg9quRvEuKeKuZdl2GYhkiRJUk1MriVJkqSamFxLkiRJNTG5liRJkmrS1RsaB8ni1Ze2/JhNa07sQCQaVpYxqTMav1urluzgjBm+a36vNAw8pvQPa64lSZKkmphcS5IkSTWxWYjmjIiYB1wFbM7MF0fEocB64DHABuD0zHyglzFK2tlsLndLUq9Yc6255HXA9Q3z7wXen5mHAXcDZ/YkKkmSNDRMrjUnRMRBwInAx8p8AMcCF5VN1gEn9yY6SZobImKPiLgyIn4QEddGxNvL8kMj4jsRcWNEXBARu/c6Vmm2mm4W4iV1DbhzgDcA+5T5xwDbMnNHmb8VWDTZAyNiJbASYGRkhLGxsVoCWrVkx8wbTTDbfW/fvr22uPvFML4maQ74FXBsZm6PiN2Ab0TEl4DXU11JXB8RH6G6kvh3vQxUmq1W2lyPX1Lft8yPX1L3i6C+FhEvBrZm5oaIGG318Zm5FlgLsGzZshwdbfkpJjVT92CT2XTa7PY9NjZGXXH3i2F8TdKwy8wEtpfZ3cpfUl1J/OOyfB1wFuYUGlBNJdcNl9TfDby+4ZK6XwQNgucAL4mIE4A9qE4QPwDMj4hdS+31QcDmHsYoSXNCuRK+ATgM+DDwY3p8JbFuvbiyVsfV0EG8ItiPMTdbc913l9SbNds3vRuX7PuxQDTq9/ialZlvAt4EUGqu/1dmnhYR/wc4hap50wrgCz0LUpLmiMx8EFgaEfOBzwNPauGxHbmSWLdeXFmr42roIF4R7MeYZ0yu+/WSerNm+6Z345J9PxaIRv0eXw3eCKyPiHcB3wfO7XE8kjRnZOa2iLgCeDZeSdQQaabm2kvqGhqZOQaMlembgKN6GY8kzSURcQDw65JY7wkcT3UP1xV4JVFDYsbk2kvq0uAaH3xj1ZIdTV2N2bTmxE6HJGluWwisK+2udwEuzMxLIuI6vJKoIdHOCI1eUpckSU3LzB8CT59kuVcSNTRaSq69pC5JkiRNzREaJUmSpJq00yxEkiRJA2rxhHtxZro/x/tymmPNtSRJklQTk2tJkiSpJibXkiRJUk1MriVJkqSaeENjjSbeGDCT85fv1aFIJEmS1AvWXEuSJEk1MbmWpAEQEXtExJUR8YOIuDYi3l6WHxoR34mIGyPigojYvdexStJcZnItSYPhV8CxmXkksBRYHhFHA+8F3p+ZhwF3A2f2MEZJmvNMriVpAGRle5ndrfwlcCxwUVm+Dji5B+FJkgqTa0kaEBExLyKuBrYClwE/BrZl5o6yya3Aol7FJ0mytxBJGhiZ+SCwNCLmA58HntTsYyNiJbASYGRkhLGxsSm33b59+7Tru23Vkh0zbzSFkT1nfnw/vdap9NtnImlqJteSNGAyc1tEXAE8G5gfEbuW2uuDgM1TPGYtsBZg2bJlOTo6OuXzj42NMd36bjujxW5OG61asoOzN05/qNt02uisn79b+u0z0dzUapfDm9ac2KFI+pvNQiRpAETEAaXGmojYEzgeuB64AjilbLYC+EJvIpQkgTXX0kBptdZAQ2UhsC4i5lFVjFyYmZdExHXA+oh4F/B94NxeBilJc53JtSQNgMz8IfD0SZbfBBzV/YgkSZMxuZYkSXPWbK4IztW2xGqOba41Jzi6nSRJ6gaTa80Vjm4nSZI6bsZmIRGxB/B14FFl+4sy820RcSiwHngMsAE4PTMf6GSw7fBGsLktMxOYanS7Py7L1wFnAX/X7fgkSdJwaKbN9XiN3/aI2A34RkR8CXg9VY3f+oj4CFWNn0mJ+lbpZWEDcBjwYZoc3a6VwTda0c7AGK1qZiANGIzBNMY5qIYkqR/NmFxb46dhMdvR7VoZfKMV7QyM0apmBtKAwRhMY5yDakiS+lFTvYXMtsavPLYjtX7NGq/d6mYtYbP6veat3+ObrdmMbidJktSMppLr2db4lcd2pNavWeO1W92sJWzW+cv36uuat2GqGYyIA4Bfl8R6fHS79/Lw6HbrcXQ7SZLUppb6ubbGTwPM0e0kSVLHNdNbiDV+GniObidJkrqhmZpra/wkSZKkJjTTW4g1fpIkSVITHKFRkiR1RUQcHBFXRMR1EXFtRLyuLN8/Ii6LiBvK/0f3OlZptkyuJUlSt+wAVmXmEcDRwKsj4ghgNXB5Zh4OXF7mpYFkci1JkroiM7dk5vfK9H3A9VTjZJxENSAd5f/JvYlQal9LXfFJkjRsFs9iHIRNa07sQCRzS0Qsprqn6zvASGZuKatuB0ameEztA9PNZpC5mfbbi0HY6hgsb2TPep5nXDfeg34c8M7kWpIkdVVE7A18FvjzzLw3Ih5al5kZETnZ4zoxMN1sBpnbdNr0++3FIGx1DJa3askOzt5YX2o40/tUh34c8M5mIZIkqWsiYjeqxPrTmfm5sviOiFhY1i8EtvYqPqldJteSJKkroqqiPhe4PjPf17DqYqoB6cCB6TTgbBYiSZK65TnA6cDGiLi6LHszsAa4MCLOBG4GTu1RfFLbTK4lSVJXZOY3gJhi9XHdjEXqlIFMrlu5s3vVkh21NPKXJEmSZmKba0mSJKkmJteSJElSTUyuJUmSpJqYXEvSAIiIgyPiioi4LiKujYjXleX7R8RlEXFD+f/oXscqSXOZybUkDYYdwKrMPAI4Gnh1RBwBrAYuz8zDgcvLvCSpR0yuJWkAZOaWzPxemb4PuB5YBJwErCubrQNO7k2EkiQY0K74JGkui4jFwNOB7wAjmbmlrLodGJniMSuBlQAjIyOMjY1N+fzbt2+fdn07Nm6+p+XHrFoy+/2N7Fl1yVq3v/10awMILlm0X1v76+RnIqleJteSNEAiYm/gs8CfZ+a91WjSlczMiMjJHpeZa4G1AMuWLcvR0dEp9zE2NsZ069vR7XEHVi3Zwdkbe3+o23TaaFuP7+RnIqleNguRpAEREbtRJdafzszPlcV3RMTCsn4hsLVX8UmSTK41R9jTggZdVFXU5wLXZ+b7GlZdDKwo0yuA1torSJJqNWNybVKiIWFPCxp0zwFOB46NiKvL3wnAGuD4iLgBeH6ZlyT1SDMN0caTku9FxD7Ahoi4DDiDKilZExGrqZKSN3YuVGn2yg1fW8r0fRHR2NPCaNlsHTCG5Vh9KDO/AcQUq4/rZiySpKnNmFyblGjYtNrTQiu9LLSiEz0YTKXZHhMGqTcCe0+QJPWjlm6hnk33T1I/mU1PC630stCKbvaa0GyPCe32aNBN9p4gSf1t8SyOc5vWnNiBSLqr6eR6tt0/daLWr5Uav071cVqHfq956/f4WjVdTwuZucWeFiRJUruaSq7bSUo6UevXSo1fv/RxOpnzl+/V1zVvw1Qz2ERPC2uwpwVJktSmGbNOkxINifGeFjZGxNVl2Zupyu+FEXEmcDNwao/ik3pu/BLuqiU7mq7EGIZLuJJUp2aqdE1KNPDsaUGSVJeZ2hJPPEH1JHRuaaa3EJMSSZIkqQn92RhZkjQQZtMbgCQNM4c/lyRJkmpizbWkh8zVPkklDQ+vpqjXrLmWJEmSamLNdQ9t3HxPy6P0WUsoSZLUv0yupZp4KVKSJJlcS5IkdZCVL3OLba4lSZKkmphcS5IkSTUxuZYkSZJqYnItSZIk1cTkWpIkdUVEnBcRWyPimoZl+0fEZRFxQ/n/6F7GKLXL5FqSJHXL+cDyCctWA5dn5uHA5WVeGlgm15IkqSsy8+vAXRMWnwSsK9PrgJO7GpRUM/u5ltSWVvtvdZRRSROMZOaWMn07MDLVhhGxElgJMDIywtjY2E7brFqyowMhtmZkz/6Io1X9EPdkn+l0tm/f3vJjOs3kWpIk9YXMzIjIadavBdYCLFu2LEdHR3fa5ow+GLBl1ZIdnL1x8FKsfoh702mjLW0/NjbGZOWgl2wWIkmSeumOiFgIUP5v7XE8UltMriVpQNjTgobUxcCKMr0C+EIPY5HaZnItSYPjfOxpQQMsIj4DfAt4YkTcGhFnAmuA4yPiBuD5ZV4aWIPXIEiS5qjM/HpELJ6w+CRgtEyvA8aAN3YtKKkFmfnyKVYd19VApA4yudacEBHnAS8GtmbmU8uy/YELgMXAJuDUzLy7VzFKs9RUTwut9LLQDz0G1KVfXku7vRn0Y48IkiY3Y3JtUqIhcT7wIeATDcvGL6eviYjVZd4aPw2s6XpaaKWXhX7oMaAu/fJaWu0BYaJ+7BFB0uSaaXN9Prbx04Bz4AINMXtakKQ+MmNybVKiIdb0wAVSH7OnBUnqI7O9VlbraEqtaqX9XL+0t5vMbGLrZpu7udTGb7rL6c2W4X4tZ9Bf34O//XTrud+SRfvttGwulc9xpaeFUWBBRNwKvI2qZ4ULS68LNwOn9i5CSVLbDdHqGE2pVa2MvtQv7e0mM5vY2m2314o50MbvjohYmJlbpruc3mwZ7odRwabSz9+DZkxW7udA+dyJPS1IUv+bbT/XtvHTMPByuiRJqtVsk2uTEg0UBy6QJEnd0ExXfLbx08DzcrokSeqGGZNrkxJJkiSpOX1xh9PiPr4RrN+0+l5tWnNihyKRJEnSRLNtcy1JkiRpApNrSZIkqSZ90SxEnWMzEkmSpO6x5lqSJEmqicm1JEmSVBObhUjqe5M1b1q1ZMeUQ87bvEmS1Csm15Ikddhsupz1JFEaTDYLkSRJkmpici1JkiTVxORakiRJqonJtSRJklQTb2iUJElSX2j15t/zl+/VoUhmz5prSZIkqSYm15IkSVJNbBYiaei0elnR/oQlSXWx5lqSJEmqicm1JEmSVBObhahtXoKXJEmqWHMtSZIk1aStmuuIWA58AJgHfCwz19QSldRFlmMNOsuwoPWriNBfVxItxxoWs06uI2Ie8GHgeOBW4LsRcXFmXldXcOq+xh/nVUt2cMYsfqwHieVYMNhJiWVYw8ByrGHSTrOQo4AbM/OmzHwAWA+cVE9YUtdYjjXoLMMaBpZjDY12kutFwC0N87eWZdIgsRxr0FmGNQwsxxoaHe8tJCJWAivL7PaI+FGn99notbAA+Fk399msfo4NOhdfvHfKVYfUva869LoM16Hfy9ps9Po1DXM57vV7W6dBfi0Typi/xwzO7/GglrtBjPuY904Zc8/KcDvJ9Wbg4Ib5g8qyR8jMtcDaNvbTloi4KjOX9Wr/0+nn2KD/46vJjOW412W4DsP4WQ7ja5ql2n+Lh+m9HZbXMiyvYxoDkVM0a1A/r0GMux9jbqdZyHeBwyPi0IjYHXgZcHE9YUldYznWoLMMaxhYjjU0Zl1znZk7IuI1wFeous05LzOvrS0yqQssxxp0lmENA8uxhklbba4z84vAF2uKpVP6+fJRP8cG/R9fLQakHLdrGD/LYXxNs9KBMjxM7+2wvJZheR1TGrLf4kH9vAYx7r6LOTKz1zFIkiRJQ8HhzyVJkqSaDG1yHRHnRcTWiLim17FMFBEHR8QVEXFdRFwbEa/rdUzjImKPiLgyIn5QYnt7r2NSeyJiU0RsjIirI+KqXsczG5N9nyNi/4i4LCJuKP8f3csYB1FELI+IH0XEjRGxepL1Z0TEnaXsXB0Rr+xFnDOZ6fc+Kh8sr/OHEfGMbsfYjCZex2hE3NPweby12zFqev18fJ/KoB/3I2JeRHw/Ii7pdSzjhja5Bs4Hlvc6iCnsAFZl5hHA0cCrI+KIHsc07lfAsZl5JLAUWB4RR/c4JrXvmMxc2m/dFbXgfHb+Pq8GLs/Mw4HLy7ya1DDc9IuAI4CXT/E7dEEpO0sz82NdDbJ55zP97/2LgMPL30rg77oQ02ycz8zHrX9t+Dze0YWY1Jp+Pr5PZdCP+68Dru8rO2W+AAARwUlEQVR1EI2GNrnOzK8Dd/U6jslk5pbM/F6Zvo+qUPTFSFRZ2V5mdyt/NsxXT03xfT4JWFem1wEndzWowTc0w0038Xt/EvCJ8vv2bWB+RCzsTnTN6+fjlprTz8f3qQzycT8iDgJOBPrqxH9ok+tBERGLgacD3+ltJA8rl1iuBrYCl2Vm38SmWUngqxGxoYxuNixGMnNLmb4dGOllMAOo2eGm/6g0pbgoIg6eZP0gGKahtZ9dLt9/KSKe0utgNLV+PL5PZYCP++cAbwB+0+tAGplc91BE7A18FvjzzLy31/GMy8wHM3Mp1QhZR0XEU3sdk9ryu5n5DKpL46+OiOf2OqC6ZdXt0UDUtAyYfwIWZ+bTgMt4+EqBeuN7wCHl8v3fAv/Y43g0hX49vk9lEI/7EfFiYGtmbuh1LBOZXPdIROxG9cX7dGZ+rtfxTCYztwFX0L9t19WEzNxc/m8FPk/VHGAY3DF+ab/839rjeAbNjMNNZ+bPM/NXZfZjwDO7FFvdmhpau99l5r3jl+9Ln9C7RcSCHoelCQbh+D6VATvuPwd4SURsomrWdmxEfKq3IVVMrnsgIgI4F7g+M9/X63gaRcQBETG/TO8JHA/8e2+j0mxFxF4Rsc/4NPACoO960Jmli4EVZXoF8IUexjKIZhxuekK75JfQZzcNteBi4E9KryFHA/c0NCkaGBHx2HL8ICKOojqG/7y3UalRPx/fpzKox/3MfFNmHpSZi6l+v/4lM1/R47CANkdo7GcR8RlgFFgQEbcCb8vMc3sb1UOeA5wObCxtnADeXGoiem0hsK70JLALcGFm9k33NmrZCPD5cjzeFfiHzPxyb0Nq3WTfZ2ANcGFEnAncDJzauwgHz1TDTUfEO4CrMvNi4LUR8RKqHhDuAs7oWcDTmKJ87AaQmR+hGvXvBOBG4BfAn/Ym0uk18TpOAf57ROwAfgm8LB0Jrt/08/F9Kh73a+YIjZIkSVJNbBYiSZIk1cTkWpIkSaqJybUkSZJUE5NrSZIkqSYm15IkSVJNTK4lSZKkmphcS5IkSTUxuZYkSZJqYnLdIxExWkbgGp+/NiJGexiSJEmS2jS0w58Pmsx8Sq9jkCRJUnusue6QiPDERZIkaY4xuW5RRGyKiDdFxHURcXdEfDwi9hhv5hERb4yI24GPR8SjIuKciLit/J0TEY+a5nmfX6bPiogLI+ITEXFfaTKyrGHbAyPisxFxZ0T8JCJe26WXL0mSpGmYXM/OacALgccDTwDeUpY/FtgfOARYCfwlcDSwFDgSOKph25m8BFgPzAcuBj4EEBG7AP8E/ABYBBwH/HlEvLDdFyVJkqT2mFzPzocy85bMvAt4N/Dysvw3wNsy81eZ+UuqJPwdmbk1M+8E3g6c3uQ+vpGZX8zMB4FPUiXnAL8DHJCZ78jMBzLzJuCjwMtqem2SJEmaJdsFz84tDdM3AweW6Tsz8z8b1h1Y1k+27Uxub5j+BbBHacd9CHBgRGxrWD8P+Ncmn1eSJEkdYnI9Owc3TD8OuK1M54TtbqNKhq+dZNvZugX4SWYe3ubzSJIkqWY2C5mdV0fEQRGxP1W76gum2O4zwFsi4oCIWAC8FfhUm/u+Eriv3Di5Z0TMi4inRsTvtPm8kiRJapPJ9ez8A/BV4Cbgx8C7ptjuXcBVwA+BjcD3ptm2KaUN9oupbpL8CfAz4GPAfu08ryRJktoXmRNbMmg6EbEJeGVm/nOvY5EkSVJ/seZakiRJqonJtSRJklQTm4VIkiRJNbHmWpIkSaqJybUkSZJUk64OIrNgwYJcvHhxx/dz//33s9dee3V8P3Ux3sqGDRt+lpkH1P7EkiRJXdLV5Hrx4sVcddVVHd/P2NgYo6OjHd9PXYy3EhE3z7yVJElS/7JZiCRJklQTk2tJkiSpJibXkiRJUk1MriVJkqSaNH1DY0TMA64CNmfmiyPiUGA98BhgA3B6Zj7QmTDbt3j1pS1tv2nNiR2KRJIkScOqlZrr1wHXN8y/F3h/Zh4G3A2cWWdgkiRJ0qBpKrmOiIOAE4GPlfkAjgUuKpusA07uRICSJEnSoGi2Wcg5wBuAfcr8Y4BtmbmjzN8KLJrsgRGxElgJMDIywtjY2KyDbdb27dt32s+qJTsm33gK3Yhz3GTx9rNBi1eSJKlbZkyuI+LFwNbM3BARo63uIDPXAmsBli1blt0YLGWyQU7OaLXN9WmjM25TFweRkSRJGg7N1Fw/B3hJRJwA7AHsC3wAmB8Ru5ba64OAzZ0LU5IkSep/M7a5zsw3ZeZBmbkYeBnwL5l5GnAFcErZbAXwhY5FKUmSJA2Advq5fiPw+oi4kaoN9rn1hCRJkiQNpqb7uQbIzDFgrEzfBBxVf0iSJEnSYHKERkmSJKkmJteSJElSTUyuJUmSpJq01Oa6Xyyeoc/qVUt2tNyvtSRJktQua64lSZKkmphcS5IkSTUxuZYkSZJqYnItSZIk1cTkWpIkSaqJybUkSZJUE5NrSZIkqSYm15IkSVJNTK4lSZKkmphcS5IkSTUxuZYkSZJqYnItSZIk1cTkWpIkSaqJybUkSZJUE5NrSZIkqSYm15IkSVJNTK4lSZKkmphcS5IkSTWZMbmOiD0i4sqI+EFEXBsRby/LD42I70TEjRFxQUTs3vlwJUmSpP7VTM31r4BjM/NIYCmwPCKOBt4LvD8zDwPuBs7sXJiSJElS/5sxuc7K9jK7W/lL4FjgorJ8HXByRyKUJEmSBsSuzWwUEfOADcBhwIeBHwPbMnNH2eRWYNEUj10JrAQYGRlhbGyszZBh1ZId064f2XPmbWZSR5zN2r59e1f3165Bi1eSJKlbmkquM/NBYGlEzAc+Dzyp2R1k5lpgLcCyZctydHR0FmE+0hmrL512/aolOzh7Y1MvbUqbThtt6/GtGBsbo473pVsGLV5JkqRuaam3kMzcBlwBPBuYHxHjGexBwOaaY5MkSZIGSjO9hRxQaqyJiD2B44HrqZLsU8pmK4AvdCpISZIkaRA003ZiIbCutLveBbgwMy+JiOuA9RHxLuD7wLkdjFOSJEnqezMm15n5Q+Dpkyy/CTiqE0FJkiRJg8gRGiVJkqSamFxLkiRJNTG5liRJkmpici1JkiTVxORakiRJqonJtSRJklQTk2tJkiSpJibXkiRJUk1MriVJkqSamFxLkiRJNTG5liRJkmpici1JkiTVxORakiRJqonJtSRJklQTk2tJkiSpJibXkiRJUk1MriVJkqSamFxLkiRJNTG5liRJkmqya68DAFi8+tJehyBJkiS1zZprSZIkqSYm15IkSVJNZkyuI+LgiLgiIq6LiGsj4nVl+f4RcVlE3FD+P7rz4UqSJEn9q5ma6x3Aqsw8AjgaeHVEHAGsBi7PzMOBy8u8JEmSNGfNmFxn5pbM/F6Zvg+4HlgEnASsK5utA07uVJCSJEnSIIjMbH7jiMXA14GnAj/NzPlleQB3j89PeMxKYCXAyMjIM9evX7/T827cfM8sQp/ayJ5wxy/be44li/arJ5gmbN++nb333rtr+2tXp+I95phjNmTmstqfWJIkqUuaTq4jYm/ga8C7M/NzEbGtMZmOiLszc9p218uWLcurrrpqp+V1d8W3askOzt7YXi+Dm9acWFM0MxsbG2N0dLRr+2tXp+KNCJNrSZI00JrqLSQidgM+C3w6Mz9XFt8REQvL+oXA1s6EKEmSJA2GGat3S5OPc4HrM/N9DasuBlYAa8r/L3Qkwh6ZTW16q7Xd4/tYtWQHZzS5v27WqEuSJKk1zbSdeA5wOrAxIq4uy95MlVRfGBFnAjcDp3YmREmSJGkwzJhcZ+Y3gJhi9XH1hiNJkiQNLkdolCRJkmpici1JkiTVxORakiRJqonJtSRJklQTk2tJkiSpJibXkiRJUk1MriVJkqSamFxLkiRJNTG5liRJkmpici1JkiTVxORakiRJqonJtSRJklQTk2tJkiSpJibXkiRJUk1MriVJkqSamFxLkiRJNdm11wEMk8WrL+11CDtpNaZNa07sUCSSJEnDz5prSZIkqSYm15IkSVJNTK4lSZKkmphcS5IkSTUxuZYkSZJqMmNyHRHnRcTWiLimYdn+EXFZRNxQ/j+6s2FKkiRJ/a+ZmuvzgeUTlq0GLs/Mw4HLy7wkSZI0p82YXGfm14G7Jiw+CVhXptcBJ9cclyRJkjRwIjNn3ihiMXBJZj61zG/LzPllOoC7x+cneexKYCXAyMjIM9evX7/TNhs33zPL8Cc3sifc8ctan7KjWol3yaL9WnruVt/bZp5/+/bt7L333i09bzOOOeaYDZm5rPYnliRJ6pK2R2jMzIyIKTP0zFwLrAVYtmxZjo6O7rTNGTWPbLhqyQ7O3jg4g0+2Eu+m00Zbeu5W39tmnn9sbIzJPkdJkqS5bra9hdwREQsByv+t9YUkSZIkDabZJtcXAyvK9ArgC/WEI0mSJA2uZrri+wzwLeCJEXFrRJwJrAGOj4gbgOeXeUmSJGlOm7Ghb2a+fIpVx9UciyRJkjTQBueuPwGwuOabPyVJklQfhz+XJEmSamJyLUmSJNXE5FqSJEmqicm1JEmSVBOTa0mSJKkmJteSJElSTUyuJUmSpJqYXEuSJEk1MbmWJEmSamJyLUmSJNXE5FqSJEmqya69DkD9ZfHqS2fcZtWSHZzRsN2mNSd2MiRJkqSBYc21JEmSVBOTa0mSJKkmJteSJElSTUyuJUmSpJqYXEuSJEk1MbmWJEmSamJXfGpbM933NbLrPkmSNKysuZYkSZJqYnItSZIk1aSt5DoilkfEjyLixohYXVdQkiRJ0iCadXIdEfOADwMvAo4AXh4RR9QVmCRJkjRo2qm5Pgq4MTNvyswHgPXASfWEJUmSJA2eyMzZPTDiFGB5Zr6yzJ8OPCszXzNhu5XAyjL7ROBHsw+3aQuAn3VhP3Ux3sohmXlAB55XkiSpKzreFV9mrgXWdno/jSLiqsxc1s19tsN4JUmShkM7zUI2Awc3zB9UlkmSJElzUjvJ9XeBwyPi0IjYHXgZcHE9YUmSJEmDZ9bNQjJzR0S8BvgKMA84LzOvrS2y9nS1GUoNjFeSJGkIzPqGRkmSJEmP5AiNkiRJUk1MriVJkqSaDGRyHRGbImJjRFwdEVeVZftHxGURcUP5/+iyPCLig2WI9h9GxDO6FON5EbE1Iq5pWNZyjBGxomx/Q0Ss6HK8Z0XE5vI+Xx0RJzSse1OJ90cR8cKG5cvLshsjYnWn4pUkSepHA5lcF8dk5tKG/pZXA5dn5uHA5WUequHZDy9/K4G/61J85wPLJyxrKcaI2B94G/AsqhEx3zaekHcpXoD3l/d5aWZ+scR1BFXvME8pj/n/I2JeRMwDPlxezxHAy8u2kiRJc8IgJ9cTnQSsK9PrgJMbln8iK98G5kfEwk4Hk5lfB+5qM8YXApdl5l2ZeTdwGZMnwJ2KdyonAesz81eZ+RPgRqrk/yjgxsy8KTMfANaXbSVJkuaEQU2uE/hqRGwow6sDjGTmljJ9OzBSphcBtzQ89tayrBdajbEfYn9NaapyXkOteT/HK0mS1DODmlz/bmY+g6r5wasj4rmNK7PqX7Cv+xgchBipmqc8HlgKbAHO7m04kiRJ/W0gk+vM3Fz+bwU+T9Uc4Y7x5h7l/9ayeT8N095qjD2NPTPvyMwHM/M3wEep3memiauf3mtJkqSuG7jkOiL2ioh9xqeBFwDXUA29Pt6bxgrgC2X6YuBPSo8cRwP3NDTN6LZWY/wK8IKIeHRpkvGCsqwrJrRN/wOq93k83pdFxKMi4lCqGzGvBL4LHB4Rh0bE7lQ3PV7crXglSZJ6bdbDn/fQCPD5iIAq/n/IzC9HxHeBCyPiTOBm4NSy/ReBE6huuvsF8KfdCDIiPgOMAgsi4laqXj/WtBJjZt4VEe+kSloB3pGZzd50WEe8oxGxlKr5yibgz0pc10bEhcB1wA7g1Zn5YHme11CdAMwDzsvMazsRryRJUj9y+HNJkiSpJgPXLESSJEnqVybXkiRJUk1MriVJkqSamFxLkiRJNTG5liRJkmpici1JkiTVxORakiRJqsn/BQcPkvsCSpMxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "FVhcK3YXzJs4",
        "outputId": "9156d161-dbf0-4127-f553-4335537ff5e3"
      },
      "source": [
        "mydata.hist(\"alcohol\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a9754c50>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AtNCS4HzMhh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "7Kr5tU-jzaAh",
        "outputId": "c9179f57-c76f-4773-db7a-3c8d3f2ebf09"
      },
      "source": [
        "mydata.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3a9880c10>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "H4kHYbw9zf58",
        "outputId": "46a3e05a-ccb0-4641-8fad-02168fb2810b"
      },
      "source": [
        "mydata[\"alcohol\"].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3a95f5ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19l5sSq126Uv"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "pQzpxS4Qzko1",
        "outputId": "6675be6e-f3d5-40fe-ebb2-7ee7f72eafe1"
      },
      "source": [
        "mydata.corr()"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.094397</td>\n",
              "      <td>0.211545</td>\n",
              "      <td>-0.310235</td>\n",
              "      <td>0.270798</td>\n",
              "      <td>0.289101</td>\n",
              "      <td>0.236815</td>\n",
              "      <td>-0.155929</td>\n",
              "      <td>0.136698</td>\n",
              "      <td>0.546364</td>\n",
              "      <td>-0.071747</td>\n",
              "      <td>0.072343</td>\n",
              "      <td>0.643720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malic_acid</th>\n",
              "      <td>0.094397</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.164045</td>\n",
              "      <td>0.288500</td>\n",
              "      <td>-0.054575</td>\n",
              "      <td>-0.335167</td>\n",
              "      <td>-0.411007</td>\n",
              "      <td>0.292977</td>\n",
              "      <td>-0.220746</td>\n",
              "      <td>0.248985</td>\n",
              "      <td>-0.561296</td>\n",
              "      <td>-0.368710</td>\n",
              "      <td>-0.192011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ash</th>\n",
              "      <td>0.211545</td>\n",
              "      <td>0.164045</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.443367</td>\n",
              "      <td>0.286587</td>\n",
              "      <td>0.128980</td>\n",
              "      <td>0.115077</td>\n",
              "      <td>0.186230</td>\n",
              "      <td>0.009652</td>\n",
              "      <td>0.258887</td>\n",
              "      <td>-0.074667</td>\n",
              "      <td>0.003911</td>\n",
              "      <td>0.223626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <td>-0.310235</td>\n",
              "      <td>0.288500</td>\n",
              "      <td>0.443367</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>-0.321113</td>\n",
              "      <td>-0.351370</td>\n",
              "      <td>0.361922</td>\n",
              "      <td>-0.197327</td>\n",
              "      <td>0.018732</td>\n",
              "      <td>-0.273955</td>\n",
              "      <td>-0.276769</td>\n",
              "      <td>-0.440597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>magnesium</th>\n",
              "      <td>0.270798</td>\n",
              "      <td>-0.054575</td>\n",
              "      <td>0.286587</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.214401</td>\n",
              "      <td>0.195784</td>\n",
              "      <td>-0.256294</td>\n",
              "      <td>0.236441</td>\n",
              "      <td>0.199950</td>\n",
              "      <td>0.055398</td>\n",
              "      <td>0.066004</td>\n",
              "      <td>0.393351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_phenols</th>\n",
              "      <td>0.289101</td>\n",
              "      <td>-0.335167</td>\n",
              "      <td>0.128980</td>\n",
              "      <td>-0.321113</td>\n",
              "      <td>0.214401</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.864564</td>\n",
              "      <td>-0.449935</td>\n",
              "      <td>0.612413</td>\n",
              "      <td>-0.055136</td>\n",
              "      <td>0.433681</td>\n",
              "      <td>0.699949</td>\n",
              "      <td>0.498115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flavanoids</th>\n",
              "      <td>0.236815</td>\n",
              "      <td>-0.411007</td>\n",
              "      <td>0.115077</td>\n",
              "      <td>-0.351370</td>\n",
              "      <td>0.195784</td>\n",
              "      <td>0.864564</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.537900</td>\n",
              "      <td>0.652692</td>\n",
              "      <td>-0.172379</td>\n",
              "      <td>0.543479</td>\n",
              "      <td>0.787194</td>\n",
              "      <td>0.494193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <td>-0.155929</td>\n",
              "      <td>0.292977</td>\n",
              "      <td>0.186230</td>\n",
              "      <td>0.361922</td>\n",
              "      <td>-0.256294</td>\n",
              "      <td>-0.449935</td>\n",
              "      <td>-0.537900</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.365845</td>\n",
              "      <td>0.139057</td>\n",
              "      <td>-0.262640</td>\n",
              "      <td>-0.503270</td>\n",
              "      <td>-0.311385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proanthocyanins</th>\n",
              "      <td>0.136698</td>\n",
              "      <td>-0.220746</td>\n",
              "      <td>0.009652</td>\n",
              "      <td>-0.197327</td>\n",
              "      <td>0.236441</td>\n",
              "      <td>0.612413</td>\n",
              "      <td>0.652692</td>\n",
              "      <td>-0.365845</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.025250</td>\n",
              "      <td>0.295544</td>\n",
              "      <td>0.519067</td>\n",
              "      <td>0.330417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>color_intensity</th>\n",
              "      <td>0.546364</td>\n",
              "      <td>0.248985</td>\n",
              "      <td>0.258887</td>\n",
              "      <td>0.018732</td>\n",
              "      <td>0.199950</td>\n",
              "      <td>-0.055136</td>\n",
              "      <td>-0.172379</td>\n",
              "      <td>0.139057</td>\n",
              "      <td>-0.025250</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.521813</td>\n",
              "      <td>-0.428815</td>\n",
              "      <td>0.316100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hue</th>\n",
              "      <td>-0.071747</td>\n",
              "      <td>-0.561296</td>\n",
              "      <td>-0.074667</td>\n",
              "      <td>-0.273955</td>\n",
              "      <td>0.055398</td>\n",
              "      <td>0.433681</td>\n",
              "      <td>0.543479</td>\n",
              "      <td>-0.262640</td>\n",
              "      <td>0.295544</td>\n",
              "      <td>-0.521813</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.565468</td>\n",
              "      <td>0.236183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <td>0.072343</td>\n",
              "      <td>-0.368710</td>\n",
              "      <td>0.003911</td>\n",
              "      <td>-0.276769</td>\n",
              "      <td>0.066004</td>\n",
              "      <td>0.699949</td>\n",
              "      <td>0.787194</td>\n",
              "      <td>-0.503270</td>\n",
              "      <td>0.519067</td>\n",
              "      <td>-0.428815</td>\n",
              "      <td>0.565468</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.312761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proline</th>\n",
              "      <td>0.643720</td>\n",
              "      <td>-0.192011</td>\n",
              "      <td>0.223626</td>\n",
              "      <td>-0.440597</td>\n",
              "      <td>0.393351</td>\n",
              "      <td>0.498115</td>\n",
              "      <td>0.494193</td>\n",
              "      <td>-0.311385</td>\n",
              "      <td>0.330417</td>\n",
              "      <td>0.316100</td>\n",
              "      <td>0.236183</td>\n",
              "      <td>0.312761</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               alcohol  ...   proline\n",
              "alcohol                       1.000000  ...  0.643720\n",
              "malic_acid                    0.094397  ... -0.192011\n",
              "ash                           0.211545  ...  0.223626\n",
              "alcalinity_of_ash            -0.310235  ... -0.440597\n",
              "magnesium                     0.270798  ...  0.393351\n",
              "total_phenols                 0.289101  ...  0.498115\n",
              "flavanoids                    0.236815  ...  0.494193\n",
              "nonflavanoid_phenols         -0.155929  ... -0.311385\n",
              "proanthocyanins               0.136698  ...  0.330417\n",
              "color_intensity               0.546364  ...  0.316100\n",
              "hue                          -0.071747  ...  0.236183\n",
              "od280/od315_of_diluted_wines  0.072343  ...  0.312761\n",
              "proline                       0.643720  ...  1.000000\n",
              "\n",
              "[13 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "AwdOf4dj1Z2b",
        "outputId": "79f23e56-b75b-4220-e716-9b9559592f75"
      },
      "source": [
        "mydata[[\"alcohol\",\"proline\"]].corr()"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.64372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proline</th>\n",
              "      <td>0.64372</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         alcohol  proline\n",
              "alcohol  1.00000  0.64372\n",
              "proline  0.64372  1.00000"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "JvHNm3r51lBr",
        "outputId": "b7d7f8a1-a8cd-4e6f-c7e2-d117daaccc3b"
      },
      "source": [
        "mydata[[\"alcohol\",\"proline\"]].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3a9c41690>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tXte630zofK"
      },
      "source": [
        "# I will do it later (normalization)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jkDFL8ZwzrnA"
      },
      "source": [
        "# to get helper document\n",
        "mydata.boxplot?"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkVReJW0z3Om"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NADvItGD0Eq7",
        "outputId": "7c7ad916-8c1a-4544-8808-2260c985d72f"
      },
      "source": [
        "mydata.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alcohol  malic_acid  ash   alcalinity_of_ash  magnesium  total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity  hue   od280/od315_of_diluted_wines  proline\n",
              "14.83    1.64        2.17  14.0               97.0       2.80           2.98        0.29                  1.98             5.20             1.08  2.85                          1045.0     1\n",
              "12.36    3.83        2.38  21.0               88.0       2.30           0.92        0.50                  1.04             7.65             0.56  1.58                          520.0      1\n",
              "12.64    1.36        2.02  16.8               100.0      2.02           1.41        0.53                  0.62             5.75             0.98  1.59                          450.0      1\n",
              "12.60    2.46        2.20  18.5               94.0       1.62           0.66        0.63                  0.94             7.10             0.73  1.58                          695.0      1\n",
              "         1.34        1.90  18.5               88.0       1.45           1.36        0.29                  1.35             2.45             1.04  2.77                          562.0      1\n",
              "                                                                                                                                                                                          ..\n",
              "13.45    3.70        2.60  23.0               111.0      1.70           0.92        0.43                  1.46             10.68            0.85  1.56                          695.0      1\n",
              "13.41    3.84        2.12  18.8               90.0       2.45           2.68        0.27                  1.48             4.28             0.91  3.00                          1035.0     1\n",
              "13.40    4.60        2.86  25.0               112.0      1.98           0.96        0.27                  1.11             8.50             0.67  1.92                          630.0      1\n",
              "         3.91        2.48  23.0               102.0      1.80           0.75        0.43                  1.41             7.30             0.70  1.56                          750.0      1\n",
              "11.03    1.51        2.20  21.5               85.0       2.46           2.17        0.52                  2.01             1.90             1.71  2.87                          407.0      1\n",
              "Length: 178, dtype: int64"
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          "execution_count": 113
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          "metadata": {},
          "execution_count": 114
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ACSuPIih0Qr8"
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      "source": [
        "mydata[\"target\"]=data.target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      "metadata": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
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              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
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              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
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              "      <td>14.37</td>\n",
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              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
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              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
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              "      <td>2.93</td>\n",
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            "text/plain": [
              "   alcohol  malic_acid   ash  ...  od280/od315_of_diluted_wines  proline  target\n",
              "0    14.23        1.71  2.43  ...                          3.92   1065.0       0\n",
              "1    13.20        1.78  2.14  ...                          3.40   1050.0       0\n",
              "2    13.16        2.36  2.67  ...                          3.17   1185.0       0\n",
              "3    14.37        1.95  2.50  ...                          3.45   1480.0       0\n",
              "4    13.24        2.59  2.87  ...                          2.93    735.0       0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      },
      "source": [
        "mydata.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alcohol                         float64\n",
              "malic_acid                      float64\n",
              "ash                             float64\n",
              "alcalinity_of_ash               float64\n",
              "magnesium                       float64\n",
              "total_phenols                   float64\n",
              "flavanoids                      float64\n",
              "nonflavanoid_phenols            float64\n",
              "proanthocyanins                 float64\n",
              "color_intensity                 float64\n",
              "hue                             float64\n",
              "od280/od315_of_diluted_wines    float64\n",
              "proline                         float64\n",
              "target                            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
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      "outputs": [
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              "0      0\n",
              "1      0\n",
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              "      ..\n",
              "173    2\n",
              "174    2\n",
              "175    2\n",
              "176    2\n",
              "177    2\n",
              "Name: target, Length: 178, dtype: category\n",
              "Categories (3, int64): [0, 1, 2]"
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          "execution_count": 118
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          "data": {
            "text/plain": [
              "alcohol                         float64\n",
              "malic_acid                      float64\n",
              "ash                             float64\n",
              "alcalinity_of_ash               float64\n",
              "magnesium                       float64\n",
              "total_phenols                   float64\n",
              "flavanoids                      float64\n",
              "nonflavanoid_phenols            float64\n",
              "proanthocyanins                 float64\n",
              "color_intensity                 float64\n",
              "hue                             float64\n",
              "od280/od315_of_diluted_wines    float64\n",
              "proline                         float64\n",
              "target                            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoRKI1N20-xe"
      },
      "source": [
        "mydata.target=mydata.target.astype('category')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "4OgdHjoG1Dn1",
        "outputId": "61b4d5f0-c8e6-4b50-f22c-48814e7b0bd5"
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      "source": [
        "mydata.target.value_counts()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    71\n",
              "0    59\n",
              "2    48\n",
              "Name: target, dtype: int64"
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          },
          "metadata": {},
          "execution_count": 121
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcAL975R2v_5"
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      "source": [
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      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAVXGVZv1Nlt"
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      "source": [
        "mydata[\"magnesiumCAT\"]= mydata.magnesium.map(lambda x: \"High\" if x> 120 else \"Low\" )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "height": 437
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              "      <td>1065.0</td>\n",
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              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
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              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
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              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
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              "      <td>113.0</td>\n",
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              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Low</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
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              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
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              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
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              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
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              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>1.80</td>\n",
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              "      <td>1.41</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.56</td>\n",
              "      <td>750.0</td>\n",
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              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
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              "      <td>0.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.35</td>\n",
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              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.37</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.46</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.62</td>\n",
              "      <td>840.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
              "      <td>2.74</td>\n",
              "      <td>24.5</td>\n",
              "      <td>96.0</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.60</td>\n",
              "      <td>560.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  ...  proline  target  magnesiumCAT\n",
              "0      14.23        1.71  2.43  ...   1065.0       0          High\n",
              "1      13.20        1.78  2.14  ...   1050.0       0           Low\n",
              "2      13.16        2.36  2.67  ...   1185.0       0           Low\n",
              "3      14.37        1.95  2.50  ...   1480.0       0           Low\n",
              "4      13.24        2.59  2.87  ...    735.0       0           Low\n",
              "..       ...         ...   ...  ...      ...     ...           ...\n",
              "173    13.71        5.65  2.45  ...    740.0       2           Low\n",
              "174    13.40        3.91  2.48  ...    750.0       2           Low\n",
              "175    13.27        4.28  2.26  ...    835.0       2           Low\n",
              "176    13.17        2.59  2.37  ...    840.0       2           Low\n",
              "177    14.13        4.10  2.74  ...    560.0       2           Low\n",
              "\n",
              "[178 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TvjdUw463F2m",
        "outputId": "b2a1857c-f240-4182-9a2e-9340e0732fc4"
      },
      "source": [
        "mydata.magnesiumCAT.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low     165\n",
              "High     13\n",
              "Name: magnesiumCAT, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "krmw7HPN3Nfi",
        "outputId": "80356734-676d-4635-cf00-d08531a9c846"
      },
      "source": [
        "mydata.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alcohol                          float64\n",
              "malic_acid                       float64\n",
              "ash                              float64\n",
              "alcalinity_of_ash                float64\n",
              "magnesium                        float64\n",
              "total_phenols                    float64\n",
              "flavanoids                       float64\n",
              "nonflavanoid_phenols             float64\n",
              "proanthocyanins                  float64\n",
              "color_intensity                  float64\n",
              "hue                              float64\n",
              "od280/od315_of_diluted_wines     float64\n",
              "proline                          float64\n",
              "target                          category\n",
              "magnesiumCAT                      object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqy4dt883Nzq"
      },
      "source": [
        "Row Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "4fiu-W6o1kz6",
        "outputId": "5faacc61-ff95-4b2e-9df0-f619f11f677f"
      },
      "source": [
        "mydata[mydata.magnesiumCAT==\"High\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>target</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
              "      <td>0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>14.06</td>\n",
              "      <td>2.15</td>\n",
              "      <td>2.61</td>\n",
              "      <td>17.6</td>\n",
              "      <td>121.0</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.51</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.25</td>\n",
              "      <td>5.05</td>\n",
              "      <td>1.06</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1295.0</td>\n",
              "      <td>0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>14.06</td>\n",
              "      <td>1.63</td>\n",
              "      <td>2.28</td>\n",
              "      <td>16.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.17</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.10</td>\n",
              "      <td>5.65</td>\n",
              "      <td>1.09</td>\n",
              "      <td>3.71</td>\n",
              "      <td>780.0</td>\n",
              "      <td>0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>13.05</td>\n",
              "      <td>2.05</td>\n",
              "      <td>3.22</td>\n",
              "      <td>25.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>2.63</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1.13</td>\n",
              "      <td>3.20</td>\n",
              "      <td>830.0</td>\n",
              "      <td>0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>13.76</td>\n",
              "      <td>1.53</td>\n",
              "      <td>2.70</td>\n",
              "      <td>19.5</td>\n",
              "      <td>132.0</td>\n",
              "      <td>2.95</td>\n",
              "      <td>2.74</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.35</td>\n",
              "      <td>5.40</td>\n",
              "      <td>1.25</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1235.0</td>\n",
              "      <td>0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>14.22</td>\n",
              "      <td>3.99</td>\n",
              "      <td>2.51</td>\n",
              "      <td>13.2</td>\n",
              "      <td>128.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.04</td>\n",
              "      <td>0.20</td>\n",
              "      <td>2.08</td>\n",
              "      <td>5.10</td>\n",
              "      <td>0.89</td>\n",
              "      <td>3.53</td>\n",
              "      <td>760.0</td>\n",
              "      <td>0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>12.21</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.75</td>\n",
              "      <td>16.8</td>\n",
              "      <td>151.0</td>\n",
              "      <td>1.85</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.14</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1.28</td>\n",
              "      <td>3.07</td>\n",
              "      <td>718.0</td>\n",
              "      <td>1</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>12.99</td>\n",
              "      <td>1.67</td>\n",
              "      <td>2.60</td>\n",
              "      <td>30.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>3.30</td>\n",
              "      <td>2.89</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.96</td>\n",
              "      <td>3.35</td>\n",
              "      <td>1.31</td>\n",
              "      <td>3.50</td>\n",
              "      <td>985.0</td>\n",
              "      <td>1</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>12.33</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1.95</td>\n",
              "      <td>14.8</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1.90</td>\n",
              "      <td>1.85</td>\n",
              "      <td>0.35</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1.06</td>\n",
              "      <td>2.31</td>\n",
              "      <td>750.0</td>\n",
              "      <td>1</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>12.47</td>\n",
              "      <td>1.52</td>\n",
              "      <td>2.20</td>\n",
              "      <td>19.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.27</td>\n",
              "      <td>0.32</td>\n",
              "      <td>3.28</td>\n",
              "      <td>2.60</td>\n",
              "      <td>1.16</td>\n",
              "      <td>2.63</td>\n",
              "      <td>937.0</td>\n",
              "      <td>1</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>11.81</td>\n",
              "      <td>2.12</td>\n",
              "      <td>2.74</td>\n",
              "      <td>21.5</td>\n",
              "      <td>134.0</td>\n",
              "      <td>1.60</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.14</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.50</td>\n",
              "      <td>0.95</td>\n",
              "      <td>2.26</td>\n",
              "      <td>625.0</td>\n",
              "      <td>1</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>12.86</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2.32</td>\n",
              "      <td>18.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>1.51</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.94</td>\n",
              "      <td>4.10</td>\n",
              "      <td>0.76</td>\n",
              "      <td>1.29</td>\n",
              "      <td>630.0</td>\n",
              "      <td>2</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>13.50</td>\n",
              "      <td>3.12</td>\n",
              "      <td>2.62</td>\n",
              "      <td>24.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>1.40</td>\n",
              "      <td>1.57</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.25</td>\n",
              "      <td>8.60</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.30</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  ...  proline  target  magnesiumCAT\n",
              "0      14.23        1.71  2.43  ...   1065.0       0          High\n",
              "7      14.06        2.15  2.61  ...   1295.0       0          High\n",
              "20     14.06        1.63  2.28  ...    780.0       0          High\n",
              "25     13.05        2.05  3.22  ...    830.0       0          High\n",
              "33     13.76        1.53  2.70  ...   1235.0       0          High\n",
              "39     14.22        3.99  2.51  ...    760.0       0          High\n",
              "69     12.21        1.19  1.75  ...    718.0       1          High\n",
              "73     12.99        1.67  2.60  ...    985.0       1          High\n",
              "78     12.33        0.99  1.95  ...    750.0       1          High\n",
              "95     12.47        1.52  2.20  ...    937.0       1          High\n",
              "96     11.81        2.12  2.74  ...    625.0       1          High\n",
              "130    12.86        1.35  2.32  ...    630.0       2          High\n",
              "150    13.50        3.12  2.62  ...    500.0       2          High\n",
              "\n",
              "[13 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "ydjXXrlR1p_M",
        "outputId": "f0b813ad-623d-433c-f96d-5283a53ebd5d"
      },
      "source": [
        "mydata[mydata.magnesiumCAT==\"Low\"]"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>target</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>14.20</td>\n",
              "      <td>1.76</td>\n",
              "      <td>2.45</td>\n",
              "      <td>15.2</td>\n",
              "      <td>112.0</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.97</td>\n",
              "      <td>6.75</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.41</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.56</td>\n",
              "      <td>750.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.35</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.56</td>\n",
              "      <td>835.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.37</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.46</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.62</td>\n",
              "      <td>840.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
              "      <td>2.74</td>\n",
              "      <td>24.5</td>\n",
              "      <td>96.0</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.60</td>\n",
              "      <td>560.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>165 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  ...  proline  target  magnesiumCAT\n",
              "1      13.20        1.78  2.14  ...   1050.0       0           Low\n",
              "2      13.16        2.36  2.67  ...   1185.0       0           Low\n",
              "3      14.37        1.95  2.50  ...   1480.0       0           Low\n",
              "4      13.24        2.59  2.87  ...    735.0       0           Low\n",
              "5      14.20        1.76  2.45  ...   1450.0       0           Low\n",
              "..       ...         ...   ...  ...      ...     ...           ...\n",
              "173    13.71        5.65  2.45  ...    740.0       2           Low\n",
              "174    13.40        3.91  2.48  ...    750.0       2           Low\n",
              "175    13.27        4.28  2.26  ...    835.0       2           Low\n",
              "176    13.17        2.59  2.37  ...    840.0       2           Low\n",
              "177    14.13        4.10  2.74  ...    560.0       2           Low\n",
              "\n",
              "[165 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "kghq6UDO1uq0",
        "outputId": "cdf7db66-b2be-422e-b07a-bf5faaec7238"
      },
      "source": [
        "mydata.hist(\"magnesium\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd3a95af3d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GvmP4TnT1y8t"
      },
      "source": [
        "def mapping(x):\n",
        "    if x< 40:\n",
        "      return \"Very Low\"\n",
        "    if x<80:\n",
        "      return \"Low\"\n",
        "    if x<120:\n",
        "      return \"Normal\"\n",
        "    if x <140:\n",
        "      return \"High\"\n",
        "    return \"Very High\"\n",
        "\n",
        "mydata[\"magnesiumCAT\"]= mydata.magnesium.map(lambda x: mapping(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "OmN5QAJt2eLR",
        "outputId": "ebf2f43c-c765-49c4-f74b-fdee2ba12b4f"
      },
      "source": [
        "mydata"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>target</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
              "      <td>0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.41</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.56</td>\n",
              "      <td>750.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.35</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.56</td>\n",
              "      <td>835.0</td>\n",
              "      <td>2</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.37</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.46</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.62</td>\n",
              "      <td>840.0</td>\n",
              "      <td>2</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
              "      <td>2.74</td>\n",
              "      <td>24.5</td>\n",
              "      <td>96.0</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.60</td>\n",
              "      <td>560.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  ...  proline  target  magnesiumCAT\n",
              "0      14.23        1.71  2.43  ...   1065.0       0          High\n",
              "1      13.20        1.78  2.14  ...   1050.0       0        Normal\n",
              "2      13.16        2.36  2.67  ...   1185.0       0        Normal\n",
              "3      14.37        1.95  2.50  ...   1480.0       0        Normal\n",
              "4      13.24        2.59  2.87  ...    735.0       0        Normal\n",
              "..       ...         ...   ...  ...      ...     ...           ...\n",
              "173    13.71        5.65  2.45  ...    740.0       2        Normal\n",
              "174    13.40        3.91  2.48  ...    750.0       2        Normal\n",
              "175    13.27        4.28  2.26  ...    835.0       2          High\n",
              "176    13.17        2.59  2.37  ...    840.0       2          High\n",
              "177    14.13        4.10  2.74  ...    560.0       2        Normal\n",
              "\n",
              "[178 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VeDp_iIY2jog",
        "outputId": "d00168e7-373a-4f64-c559-f00cc18ff827"
      },
      "source": [
        "mydata.magnesiumCAT"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        High\n",
              "1      Normal\n",
              "2      Normal\n",
              "3      Normal\n",
              "4      Normal\n",
              "        ...  \n",
              "173    Normal\n",
              "174    Normal\n",
              "175      High\n",
              "176      High\n",
              "177    Normal\n",
              "Name: magnesiumCAT, Length: 178, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BciLCJAH2mcB",
        "outputId": "9c28cbe2-b4da-4a40-f6d8-149dfb6fb748"
      },
      "source": [
        "mydata.magnesiumCAT.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Normal       158\n",
              "High          14\n",
              "Low            4\n",
              "Very High      2\n",
              "Name: magnesiumCAT, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "M-gxgobe3o1D",
        "outputId": "0f49f740-ea5e-47b8-f855-22c21cb5d5c4"
      },
      "source": [
        "mydata.magnesiumCAT.value_counts().plot(kind=\"bar\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3a9471b10>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "hYVXPQ9d2qSo",
        "outputId": "78d44671-20d0-4730-a821-c1588ef0a559"
      },
      "source": [
        "mydata[(mydata.magnesiumCAT==\"Normal\") & (mydata.color_intensity>10.0)]"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>target</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>12.79</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2.48</td>\n",
              "      <td>22.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.26</td>\n",
              "      <td>10.80</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.47</td>\n",
              "      <td>480.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>13.23</td>\n",
              "      <td>3.30</td>\n",
              "      <td>2.28</td>\n",
              "      <td>18.5</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.87</td>\n",
              "      <td>10.52</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.51</td>\n",
              "      <td>675.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>14.34</td>\n",
              "      <td>1.68</td>\n",
              "      <td>2.70</td>\n",
              "      <td>25.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>1.31</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2.70</td>\n",
              "      <td>13.00</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.96</td>\n",
              "      <td>660.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>13.48</td>\n",
              "      <td>1.67</td>\n",
              "      <td>2.64</td>\n",
              "      <td>22.5</td>\n",
              "      <td>89.0</td>\n",
              "      <td>2.60</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.52</td>\n",
              "      <td>2.29</td>\n",
              "      <td>11.75</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.78</td>\n",
              "      <td>620.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>13.45</td>\n",
              "      <td>3.70</td>\n",
              "      <td>2.60</td>\n",
              "      <td>23.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>1.70</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.46</td>\n",
              "      <td>10.68</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.56</td>\n",
              "      <td>695.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>12.82</td>\n",
              "      <td>3.37</td>\n",
              "      <td>2.30</td>\n",
              "      <td>19.5</td>\n",
              "      <td>88.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.97</td>\n",
              "      <td>10.26</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.75</td>\n",
              "      <td>685.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  ...  proline  target  magnesiumCAT\n",
              "151    12.79        2.67  2.48  ...    480.0       2        Normal\n",
              "153    13.23        3.30  2.28  ...    675.0       2        Normal\n",
              "158    14.34        1.68  2.70  ...    660.0       2        Normal\n",
              "159    13.48        1.67  2.64  ...    620.0       2        Normal\n",
              "166    13.45        3.70  2.60  ...    695.0       2        Normal\n",
              "167    12.82        3.37  2.30  ...    685.0       2        Normal\n",
              "\n",
              "[6 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQxEalvJ3dPW"
      },
      "source": [
        "# sorting"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "Mk9hbRb87LbD",
        "outputId": "21ff6461-fe3e-4cbd-e1de-b39cecf0769a"
      },
      "source": [
        "mydata.sort_values(\"hue\")"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>target</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>12.79</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2.48</td>\n",
              "      <td>22.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.26</td>\n",
              "      <td>10.80</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.47</td>\n",
              "      <td>480.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>12.87</td>\n",
              "      <td>4.61</td>\n",
              "      <td>2.48</td>\n",
              "      <td>21.5</td>\n",
              "      <td>86.0</td>\n",
              "      <td>1.70</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.86</td>\n",
              "      <td>7.65</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.86</td>\n",
              "      <td>625.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>13.32</td>\n",
              "      <td>3.24</td>\n",
              "      <td>2.38</td>\n",
              "      <td>21.5</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.93</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.25</td>\n",
              "      <td>8.42</td>\n",
              "      <td>0.55</td>\n",
              "      <td>1.62</td>\n",
              "      <td>650.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>13.23</td>\n",
              "      <td>3.30</td>\n",
              "      <td>2.28</td>\n",
              "      <td>18.5</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.87</td>\n",
              "      <td>10.52</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.51</td>\n",
              "      <td>675.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>12.36</td>\n",
              "      <td>3.83</td>\n",
              "      <td>2.38</td>\n",
              "      <td>21.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.30</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.04</td>\n",
              "      <td>7.65</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.58</td>\n",
              "      <td>520.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>11.65</td>\n",
              "      <td>1.67</td>\n",
              "      <td>2.62</td>\n",
              "      <td>26.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>1.92</td>\n",
              "      <td>1.61</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.34</td>\n",
              "      <td>2.60</td>\n",
              "      <td>1.36</td>\n",
              "      <td>3.21</td>\n",
              "      <td>562.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>12.00</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2.00</td>\n",
              "      <td>19.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2.42</td>\n",
              "      <td>2.26</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.43</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1.38</td>\n",
              "      <td>3.12</td>\n",
              "      <td>278.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>12.29</td>\n",
              "      <td>3.17</td>\n",
              "      <td>2.21</td>\n",
              "      <td>18.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.85</td>\n",
              "      <td>2.99</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2.81</td>\n",
              "      <td>2.30</td>\n",
              "      <td>1.42</td>\n",
              "      <td>2.83</td>\n",
              "      <td>406.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>12.17</td>\n",
              "      <td>1.45</td>\n",
              "      <td>2.53</td>\n",
              "      <td>19.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1.89</td>\n",
              "      <td>1.75</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.03</td>\n",
              "      <td>2.95</td>\n",
              "      <td>1.45</td>\n",
              "      <td>2.23</td>\n",
              "      <td>355.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>11.03</td>\n",
              "      <td>1.51</td>\n",
              "      <td>2.20</td>\n",
              "      <td>21.5</td>\n",
              "      <td>85.0</td>\n",
              "      <td>2.46</td>\n",
              "      <td>2.17</td>\n",
              "      <td>0.52</td>\n",
              "      <td>2.01</td>\n",
              "      <td>1.90</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.87</td>\n",
              "      <td>407.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  ...  proline  target  magnesiumCAT\n",
              "151    12.79        2.67  2.48  ...    480.0       2        Normal\n",
              "147    12.87        4.61  2.48  ...    625.0       2        Normal\n",
              "148    13.32        3.24  2.38  ...    650.0       2        Normal\n",
              "153    13.23        3.30  2.28  ...    675.0       2        Normal\n",
              "160    12.36        3.83  2.38  ...    520.0       2        Normal\n",
              "..       ...         ...   ...  ...      ...     ...           ...\n",
              "87     11.65        1.67  2.62  ...    562.0       1        Normal\n",
              "80     12.00        0.92  2.00  ...    278.0       1        Normal\n",
              "99     12.29        3.17  2.21  ...    406.0       1        Normal\n",
              "64     12.17        1.45  2.53  ...    355.0       1        Normal\n",
              "115    11.03        1.51  2.20  ...    407.0       1        Normal\n",
              "\n",
              "[178 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "k8Hm_wHJ7p89",
        "outputId": "9daf3016-8d2b-489a-bcb6-7944c8598406"
      },
      "source": [
        "mydata.sort_values(\"hue\", ascending=False)"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>target</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>11.03</td>\n",
              "      <td>1.51</td>\n",
              "      <td>2.20</td>\n",
              "      <td>21.5</td>\n",
              "      <td>85.0</td>\n",
              "      <td>2.46</td>\n",
              "      <td>2.17</td>\n",
              "      <td>0.52</td>\n",
              "      <td>2.01</td>\n",
              "      <td>1.90</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.87</td>\n",
              "      <td>407.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>12.17</td>\n",
              "      <td>1.45</td>\n",
              "      <td>2.53</td>\n",
              "      <td>19.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1.89</td>\n",
              "      <td>1.75</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.03</td>\n",
              "      <td>2.95</td>\n",
              "      <td>1.45</td>\n",
              "      <td>2.23</td>\n",
              "      <td>355.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>12.29</td>\n",
              "      <td>3.17</td>\n",
              "      <td>2.21</td>\n",
              "      <td>18.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.85</td>\n",
              "      <td>2.99</td>\n",
              "      <td>0.45</td>\n",
              "      <td>2.81</td>\n",
              "      <td>2.30</td>\n",
              "      <td>1.42</td>\n",
              "      <td>2.83</td>\n",
              "      <td>406.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>12.00</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2.00</td>\n",
              "      <td>19.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2.42</td>\n",
              "      <td>2.26</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.43</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1.38</td>\n",
              "      <td>3.12</td>\n",
              "      <td>278.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>11.65</td>\n",
              "      <td>1.67</td>\n",
              "      <td>2.62</td>\n",
              "      <td>26.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>1.92</td>\n",
              "      <td>1.61</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.34</td>\n",
              "      <td>2.60</td>\n",
              "      <td>1.36</td>\n",
              "      <td>3.21</td>\n",
              "      <td>562.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>13.23</td>\n",
              "      <td>3.30</td>\n",
              "      <td>2.28</td>\n",
              "      <td>18.5</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.87</td>\n",
              "      <td>10.52</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.51</td>\n",
              "      <td>675.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>12.36</td>\n",
              "      <td>3.83</td>\n",
              "      <td>2.38</td>\n",
              "      <td>21.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>2.30</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.04</td>\n",
              "      <td>7.65</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.58</td>\n",
              "      <td>520.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>13.32</td>\n",
              "      <td>3.24</td>\n",
              "      <td>2.38</td>\n",
              "      <td>21.5</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.93</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.25</td>\n",
              "      <td>8.42</td>\n",
              "      <td>0.55</td>\n",
              "      <td>1.62</td>\n",
              "      <td>650.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>12.87</td>\n",
              "      <td>4.61</td>\n",
              "      <td>2.48</td>\n",
              "      <td>21.5</td>\n",
              "      <td>86.0</td>\n",
              "      <td>1.70</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.86</td>\n",
              "      <td>7.65</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.86</td>\n",
              "      <td>625.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>12.79</td>\n",
              "      <td>2.67</td>\n",
              "      <td>2.48</td>\n",
              "      <td>22.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.26</td>\n",
              "      <td>10.80</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.47</td>\n",
              "      <td>480.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  ...  proline  target  magnesiumCAT\n",
              "115    11.03        1.51  2.20  ...    407.0       1        Normal\n",
              "64     12.17        1.45  2.53  ...    355.0       1        Normal\n",
              "99     12.29        3.17  2.21  ...    406.0       1        Normal\n",
              "80     12.00        0.92  2.00  ...    278.0       1        Normal\n",
              "87     11.65        1.67  2.62  ...    562.0       1        Normal\n",
              "..       ...         ...   ...  ...      ...     ...           ...\n",
              "153    13.23        3.30  2.28  ...    675.0       2        Normal\n",
              "160    12.36        3.83  2.38  ...    520.0       2        Normal\n",
              "148    13.32        3.24  2.38  ...    650.0       2        Normal\n",
              "147    12.87        4.61  2.48  ...    625.0       2        Normal\n",
              "151    12.79        2.67  2.48  ...    480.0       2        Normal\n",
              "\n",
              "[178 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32eL7lAx7w9M"
      },
      "source": [
        "mydata=mydata.drop(columns='target')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "ahRafcJk76u_",
        "outputId": "9c694a55-8861-494f-85cb-a3646f74844c"
      },
      "source": [
        "mydata"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113.0</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.41</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.56</td>\n",
              "      <td>750.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.35</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.56</td>\n",
              "      <td>835.0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.37</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.46</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.62</td>\n",
              "      <td>840.0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
              "      <td>2.74</td>\n",
              "      <td>24.5</td>\n",
              "      <td>96.0</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.60</td>\n",
              "      <td>560.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid  ...  proline  magnesiumCAT\n",
              "0      14.23        1.71  ...   1065.0          High\n",
              "1      13.20        1.78  ...   1050.0        Normal\n",
              "2      13.16        2.36  ...   1185.0        Normal\n",
              "3      14.37        1.95  ...   1480.0        Normal\n",
              "4      13.24        2.59  ...    735.0        Normal\n",
              "..       ...         ...  ...      ...           ...\n",
              "173    13.71        5.65  ...    740.0        Normal\n",
              "174    13.40        3.91  ...    750.0        Normal\n",
              "175    13.27        4.28  ...    835.0          High\n",
              "176    13.17        2.59  ...    840.0          High\n",
              "177    14.13        4.10  ...    560.0        Normal\n",
              "\n",
              "[178 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "4ifxeynY79_T",
        "outputId": "17e1fed4-8389-4357-b9cc-38755b950ca8"
      },
      "source": [
        "mydata.filter(regex='^m')"
      ],
      "execution_count": null,
      "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>malic_acid</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.71</td>\n",
              "      <td>127.0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.78</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.36</td>\n",
              "      <td>101.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.95</td>\n",
              "      <td>113.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.59</td>\n",
              "      <td>118.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>5.65</td>\n",
              "      <td>95.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>3.91</td>\n",
              "      <td>102.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>4.28</td>\n",
              "      <td>120.0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>2.59</td>\n",
              "      <td>120.0</td>\n",
              "      <td>High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>4.10</td>\n",
              "      <td>96.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     malic_acid  magnesium magnesiumCAT\n",
              "0          1.71      127.0         High\n",
              "1          1.78      100.0       Normal\n",
              "2          2.36      101.0       Normal\n",
              "3          1.95      113.0       Normal\n",
              "4          2.59      118.0       Normal\n",
              "..          ...        ...          ...\n",
              "173        5.65       95.0       Normal\n",
              "174        3.91      102.0       Normal\n",
              "175        4.28      120.0         High\n",
              "176        2.59      120.0         High\n",
              "177        4.10       96.0       Normal\n",
              "\n",
              "[178 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "YFBV_rhG8QQU",
        "outputId": "a4f06429-e537-4290-9405-9fa246988247"
      },
      "source": [
        "mydata.filter(regex='_')"
      ],
      "execution_count": null,
      "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>malic_acid</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.71</td>\n",
              "      <td>15.6</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.28</td>\n",
              "      <td>5.64</td>\n",
              "      <td>3.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.78</td>\n",
              "      <td>11.2</td>\n",
              "      <td>2.65</td>\n",
              "      <td>0.26</td>\n",
              "      <td>4.38</td>\n",
              "      <td>3.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.36</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.30</td>\n",
              "      <td>5.68</td>\n",
              "      <td>3.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.95</td>\n",
              "      <td>16.8</td>\n",
              "      <td>3.85</td>\n",
              "      <td>0.24</td>\n",
              "      <td>7.80</td>\n",
              "      <td>3.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.59</td>\n",
              "      <td>21.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.39</td>\n",
              "      <td>4.32</td>\n",
              "      <td>2.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>5.65</td>\n",
              "      <td>20.5</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.52</td>\n",
              "      <td>7.70</td>\n",
              "      <td>1.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>3.91</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.43</td>\n",
              "      <td>7.30</td>\n",
              "      <td>1.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>4.28</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.43</td>\n",
              "      <td>10.20</td>\n",
              "      <td>1.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>2.59</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.53</td>\n",
              "      <td>9.30</td>\n",
              "      <td>1.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>4.10</td>\n",
              "      <td>24.5</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.20</td>\n",
              "      <td>1.60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     malic_acid  ...  od280/od315_of_diluted_wines\n",
              "0          1.71  ...                          3.92\n",
              "1          1.78  ...                          3.40\n",
              "2          2.36  ...                          3.17\n",
              "3          1.95  ...                          3.45\n",
              "4          2.59  ...                          2.93\n",
              "..          ...  ...                           ...\n",
              "173        5.65  ...                          1.74\n",
              "174        3.91  ...                          1.56\n",
              "175        4.28  ...                          1.56\n",
              "176        2.59  ...                          1.62\n",
              "177        4.10  ...                          1.60\n",
              "\n",
              "[178 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "ofi5gl1d8aMu",
        "outputId": "e498892c-2b38-4627-9d99-21da35456b6a"
      },
      "source": [
        "mydata[  [\"hue\",\"ash\",\"proline\"]   ]"
      ],
      "execution_count": null,
      "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>hue</th>\n",
              "      <th>ash</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.04</td>\n",
              "      <td>2.43</td>\n",
              "      <td>1065.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.05</td>\n",
              "      <td>2.14</td>\n",
              "      <td>1050.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.03</td>\n",
              "      <td>2.67</td>\n",
              "      <td>1185.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.86</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.04</td>\n",
              "      <td>2.87</td>\n",
              "      <td>735.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>0.64</td>\n",
              "      <td>2.45</td>\n",
              "      <td>740.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>0.70</td>\n",
              "      <td>2.48</td>\n",
              "      <td>750.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>0.59</td>\n",
              "      <td>2.26</td>\n",
              "      <td>835.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>0.60</td>\n",
              "      <td>2.37</td>\n",
              "      <td>840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>0.61</td>\n",
              "      <td>2.74</td>\n",
              "      <td>560.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      hue   ash  proline\n",
              "0    1.04  2.43   1065.0\n",
              "1    1.05  2.14   1050.0\n",
              "2    1.03  2.67   1185.0\n",
              "3    0.86  2.50   1480.0\n",
              "4    1.04  2.87    735.0\n",
              "..    ...   ...      ...\n",
              "173  0.64  2.45    740.0\n",
              "174  0.70  2.48    750.0\n",
              "175  0.59  2.26    835.0\n",
              "176  0.60  2.37    840.0\n",
              "177  0.61  2.74    560.0\n",
              "\n",
              "[178 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "i9Ortfdd8frh",
        "outputId": "c8cc1886-a2c3-4270-f516-02041862a897"
      },
      "source": [
        "mydata.iloc[20:45,4:9]"
      ],
      "execution_count": null,
      "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>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>126.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.17</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>102.0</td>\n",
              "      <td>2.41</td>\n",
              "      <td>2.41</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>101.0</td>\n",
              "      <td>2.61</td>\n",
              "      <td>2.88</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>95.0</td>\n",
              "      <td>2.48</td>\n",
              "      <td>2.37</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>96.0</td>\n",
              "      <td>2.53</td>\n",
              "      <td>2.61</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>124.0</td>\n",
              "      <td>2.63</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>93.0</td>\n",
              "      <td>2.85</td>\n",
              "      <td>2.94</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>94.0</td>\n",
              "      <td>2.40</td>\n",
              "      <td>2.19</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>107.0</td>\n",
              "      <td>2.95</td>\n",
              "      <td>2.97</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>96.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.33</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>101.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.25</td>\n",
              "      <td>0.29</td>\n",
              "      <td>2.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>106.0</td>\n",
              "      <td>2.86</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>104.0</td>\n",
              "      <td>2.42</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>132.0</td>\n",
              "      <td>2.95</td>\n",
              "      <td>2.74</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>110.0</td>\n",
              "      <td>2.35</td>\n",
              "      <td>2.53</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>100.0</td>\n",
              "      <td>2.70</td>\n",
              "      <td>2.98</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>110.0</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>98.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>98.0</td>\n",
              "      <td>2.40</td>\n",
              "      <td>2.64</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>128.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.04</td>\n",
              "      <td>0.20</td>\n",
              "      <td>2.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>117.0</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0.34</td>\n",
              "      <td>2.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>90.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>101.0</td>\n",
              "      <td>3.25</td>\n",
              "      <td>3.56</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>103.0</td>\n",
              "      <td>2.64</td>\n",
              "      <td>2.63</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>107.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    magnesium  total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins\n",
              "20      126.0           3.00        3.17                  0.24             2.10\n",
              "21      102.0           2.41        2.41                  0.25             1.98\n",
              "22      101.0           2.61        2.88                  0.27             1.69\n",
              "23       95.0           2.48        2.37                  0.26             1.46\n",
              "24       96.0           2.53        2.61                  0.28             1.66\n",
              "25      124.0           2.63        2.68                  0.47             1.92\n",
              "26       93.0           2.85        2.94                  0.34             1.45\n",
              "27       94.0           2.40        2.19                  0.27             1.35\n",
              "28      107.0           2.95        2.97                  0.37             1.76\n",
              "29       96.0           2.65        2.33                  0.26             1.98\n",
              "30      101.0           3.00        3.25                  0.29             2.38\n",
              "31      106.0           2.86        3.19                  0.22             1.95\n",
              "32      104.0           2.42        2.69                  0.42             1.97\n",
              "33      132.0           2.95        2.74                  0.50             1.35\n",
              "34      110.0           2.35        2.53                  0.29             1.54\n",
              "35      100.0           2.70        2.98                  0.26             1.86\n",
              "36      110.0           2.60        2.68                  0.34             1.36\n",
              "37       98.0           2.45        2.43                  0.29             1.44\n",
              "38       98.0           2.40        2.64                  0.28             1.37\n",
              "39      128.0           3.00        3.04                  0.20             2.08\n",
              "40      117.0           3.15        3.29                  0.34             2.34\n",
              "41       90.0           2.45        2.68                  0.27             1.48\n",
              "42      101.0           3.25        3.56                  0.17             1.70\n",
              "43      103.0           2.64        2.63                  0.32             1.66\n",
              "44      107.0           3.00        3.00                  0.28             2.03"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SdwVYeBT5nTf"
      },
      "source": [
        "Sampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "LiB0S_zi8htQ",
        "outputId": "cee6c6d5-3e96-4bca-faac-ff71e205f296"
      },
      "source": [
        "mydata.sample(frac=0.01)"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>12.82</td>\n",
              "      <td>3.37</td>\n",
              "      <td>2.30</td>\n",
              "      <td>19.5</td>\n",
              "      <td>88.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.97</td>\n",
              "      <td>10.26</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.75</td>\n",
              "      <td>685.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>13.94</td>\n",
              "      <td>1.73</td>\n",
              "      <td>2.27</td>\n",
              "      <td>17.4</td>\n",
              "      <td>108.0</td>\n",
              "      <td>2.88</td>\n",
              "      <td>3.54</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.08</td>\n",
              "      <td>8.90</td>\n",
              "      <td>1.12</td>\n",
              "      <td>3.10</td>\n",
              "      <td>1260.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid  ...  proline  magnesiumCAT\n",
              "167    12.82        3.37  ...    685.0        Normal\n",
              "49     13.94        1.73  ...   1260.0        Normal\n",
              "\n",
              "[2 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "VFq3nYf08qj_",
        "outputId": "2340fdc7-0205-4309-edb6-93ff79ea3fb4"
      },
      "source": [
        "mydata.sample(frac=1.0)"
      ],
      "execution_count": null,
      "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>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>14.22</td>\n",
              "      <td>1.70</td>\n",
              "      <td>2.30</td>\n",
              "      <td>16.3</td>\n",
              "      <td>118.0</td>\n",
              "      <td>3.20</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.26</td>\n",
              "      <td>2.03</td>\n",
              "      <td>6.38</td>\n",
              "      <td>0.94</td>\n",
              "      <td>3.31</td>\n",
              "      <td>970.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>13.87</td>\n",
              "      <td>1.90</td>\n",
              "      <td>2.80</td>\n",
              "      <td>19.4</td>\n",
              "      <td>107.0</td>\n",
              "      <td>2.95</td>\n",
              "      <td>2.97</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.76</td>\n",
              "      <td>4.50</td>\n",
              "      <td>1.25</td>\n",
              "      <td>3.40</td>\n",
              "      <td>915.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>12.53</td>\n",
              "      <td>5.51</td>\n",
              "      <td>2.64</td>\n",
              "      <td>25.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>1.79</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.63</td>\n",
              "      <td>1.10</td>\n",
              "      <td>5.00</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.69</td>\n",
              "      <td>515.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>12.08</td>\n",
              "      <td>1.33</td>\n",
              "      <td>2.30</td>\n",
              "      <td>23.6</td>\n",
              "      <td>70.0</td>\n",
              "      <td>2.20</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.38</td>\n",
              "      <td>1.74</td>\n",
              "      <td>1.07</td>\n",
              "      <td>3.21</td>\n",
              "      <td>625.0</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>12.29</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.98</td>\n",
              "      <td>16.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>2.55</td>\n",
              "      <td>2.50</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.77</td>\n",
              "      <td>2.90</td>\n",
              "      <td>1.23</td>\n",
              "      <td>2.74</td>\n",
              "      <td>428.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>12.34</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.46</td>\n",
              "      <td>21.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>2.56</td>\n",
              "      <td>2.11</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.31</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.80</td>\n",
              "      <td>3.38</td>\n",
              "      <td>438.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>12.47</td>\n",
              "      <td>1.52</td>\n",
              "      <td>2.20</td>\n",
              "      <td>19.0</td>\n",
              "      <td>162.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.27</td>\n",
              "      <td>0.32</td>\n",
              "      <td>3.28</td>\n",
              "      <td>2.60</td>\n",
              "      <td>1.16</td>\n",
              "      <td>2.63</td>\n",
              "      <td>937.0</td>\n",
              "      <td>Very High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>11.87</td>\n",
              "      <td>4.31</td>\n",
              "      <td>2.39</td>\n",
              "      <td>21.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2.86</td>\n",
              "      <td>3.03</td>\n",
              "      <td>0.21</td>\n",
              "      <td>2.91</td>\n",
              "      <td>2.80</td>\n",
              "      <td>0.75</td>\n",
              "      <td>3.64</td>\n",
              "      <td>380.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>14.83</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.17</td>\n",
              "      <td>14.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.98</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.98</td>\n",
              "      <td>5.20</td>\n",
              "      <td>1.08</td>\n",
              "      <td>2.85</td>\n",
              "      <td>1045.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid  ...  proline  magnesiumCAT\n",
              "56     14.22        1.70  ...    970.0        Normal\n",
              "28     13.87        1.90  ...    915.0        Normal\n",
              "137    12.53        5.51  ...    515.0        Normal\n",
              "173    13.71        5.65  ...    740.0        Normal\n",
              "89     12.08        1.33  ...    625.0           Low\n",
              "..       ...         ...  ...      ...           ...\n",
              "97     12.29        1.41  ...    428.0        Normal\n",
              "102    12.34        2.45  ...    438.0        Normal\n",
              "95     12.47        1.52  ...    937.0     Very High\n",
              "124    11.87        4.31  ...    380.0        Normal\n",
              "8      14.83        1.64  ...   1045.0        Normal\n",
              "\n",
              "[178 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "6wJix8LV8yaB",
        "outputId": "8cad0725-bd60-4220-9ecc-222b4ed20f5e"
      },
      "source": [
        "mydata.sample(frac=1).reset_index()"
      ],
      "execution_count": null,
      "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>index</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>od280/od315_of_diluted_wines</th>\n",
              "      <th>proline</th>\n",
              "      <th>magnesiumCAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>161</td>\n",
              "      <td>13.69</td>\n",
              "      <td>3.26</td>\n",
              "      <td>2.54</td>\n",
              "      <td>20.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>1.83</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.80</td>\n",
              "      <td>5.88</td>\n",
              "      <td>0.96</td>\n",
              "      <td>1.82</td>\n",
              "      <td>680.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>166</td>\n",
              "      <td>13.45</td>\n",
              "      <td>3.70</td>\n",
              "      <td>2.60</td>\n",
              "      <td>23.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>1.70</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.46</td>\n",
              "      <td>10.68</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.56</td>\n",
              "      <td>695.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>13.41</td>\n",
              "      <td>3.84</td>\n",
              "      <td>2.12</td>\n",
              "      <td>18.8</td>\n",
              "      <td>90.0</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.68</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.48</td>\n",
              "      <td>4.28</td>\n",
              "      <td>0.91</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1035.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>136</td>\n",
              "      <td>12.25</td>\n",
              "      <td>4.72</td>\n",
              "      <td>2.54</td>\n",
              "      <td>21.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.80</td>\n",
              "      <td>3.85</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.27</td>\n",
              "      <td>720.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>12.33</td>\n",
              "      <td>1.10</td>\n",
              "      <td>2.28</td>\n",
              "      <td>16.0</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.05</td>\n",
              "      <td>1.09</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.41</td>\n",
              "      <td>3.27</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.67</td>\n",
              "      <td>680.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>126</td>\n",
              "      <td>12.43</td>\n",
              "      <td>1.53</td>\n",
              "      <td>2.29</td>\n",
              "      <td>21.5</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2.74</td>\n",
              "      <td>3.15</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.77</td>\n",
              "      <td>3.94</td>\n",
              "      <td>0.69</td>\n",
              "      <td>2.84</td>\n",
              "      <td>352.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>165</td>\n",
              "      <td>13.73</td>\n",
              "      <td>4.36</td>\n",
              "      <td>2.26</td>\n",
              "      <td>22.5</td>\n",
              "      <td>88.0</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.15</td>\n",
              "      <td>6.62</td>\n",
              "      <td>0.78</td>\n",
              "      <td>1.75</td>\n",
              "      <td>520.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>17</td>\n",
              "      <td>13.83</td>\n",
              "      <td>1.57</td>\n",
              "      <td>2.62</td>\n",
              "      <td>20.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>2.95</td>\n",
              "      <td>3.40</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.72</td>\n",
              "      <td>6.60</td>\n",
              "      <td>1.13</td>\n",
              "      <td>2.57</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>48</td>\n",
              "      <td>14.10</td>\n",
              "      <td>2.02</td>\n",
              "      <td>2.40</td>\n",
              "      <td>18.8</td>\n",
              "      <td>103.0</td>\n",
              "      <td>2.75</td>\n",
              "      <td>2.92</td>\n",
              "      <td>0.32</td>\n",
              "      <td>2.38</td>\n",
              "      <td>6.20</td>\n",
              "      <td>1.07</td>\n",
              "      <td>2.75</td>\n",
              "      <td>1060.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>120</td>\n",
              "      <td>11.45</td>\n",
              "      <td>2.40</td>\n",
              "      <td>2.42</td>\n",
              "      <td>20.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>2.90</td>\n",
              "      <td>2.79</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.83</td>\n",
              "      <td>3.25</td>\n",
              "      <td>0.80</td>\n",
              "      <td>3.39</td>\n",
              "      <td>625.0</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     index  alcohol  ...  proline  magnesiumCAT\n",
              "0      161    13.69  ...    680.0        Normal\n",
              "1      166    13.45  ...    695.0        Normal\n",
              "2       41    13.41  ...   1035.0        Normal\n",
              "3      136    12.25  ...    720.0        Normal\n",
              "4       60    12.33  ...    680.0        Normal\n",
              "..     ...      ...  ...      ...           ...\n",
              "173    126    12.43  ...    352.0        Normal\n",
              "174    165    13.73  ...    520.0        Normal\n",
              "175     17    13.83  ...   1130.0        Normal\n",
              "176     48    14.10  ...   1060.0        Normal\n",
              "177    120    11.45  ...    625.0        Normal\n",
              "\n",
              "[178 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqT5R3kJ85yf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9suH-swm9bkJ"
      },
      "source": [
        "# Lab 1 project\n",
        "\n",
        "1) How many rows satisfy the condition \"color_intensiy>7 AND hue < 0.7\" for the wine dataset\n",
        "\n",
        "2) How many rows satisfy the condition \"ash>2.5 AND magnesium > 80\"\n",
        "\n",
        "3) What is the correlation between alcohol and proline\n",
        "\n",
        "4) When Sorting mydata by **hue** in ascending order, what would be the value of *magnesium* at the first row ?\n",
        "\n",
        "5) Change mapping function above so that **\"Very Low\"** will be defined as if x< 55 (instead of x<40). \n",
        "So now, how many category of \"very low\" we will have.\n",
        "     \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xz2nYTc4_sOj"
      },
      "source": [
        "# Solution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uorigp0coOp",
        "outputId": "1901d65c-3b8b-4213-ba34-0913c857c912"
      },
      "source": [
        "# 1) How many rows satisfy the condition \"color_intensiy>7 AND hue < 0.7\"\n",
        "mydata[  (mydata.color_intensity>7)   &  (mydata.hue <0.7) ].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zB7UqJ8Zcpda",
        "outputId": "abb8b771-11e6-418d-caf0-c0e92c2159f8"
      },
      "source": [
        "#2) How many rows satisfy the condition \"ash>2.5 AND magnesium > 80\"\n",
        "mydata[ (mydata.ash>2.5) & (mydata.magnesium>80)].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(51, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twl-kv5Dd2oE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "2fd6c778-abfa-4e46-d1a8-875afad5c278"
      },
      "source": [
        "#3) What is the correlation between alcohol and proline\n",
        "\n",
        "mydata[[\"alcohol\",\"proline\"]].corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.64372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proline</th>\n",
              "      <td>0.64372</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         alcohol  proline\n",
              "alcohol  1.00000  0.64372\n",
              "proline  0.64372  1.00000"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y58mGcUId-s_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e37c5aa-71b1-4dea-ed4f-b7038fe45d0f"
      },
      "source": [
        "#4) When Sorting mydata by hue in ascending order, what would be the value of magnesium at the first row ?\n",
        "\n",
        "mydata.sort_values(\"hue\",  ascending=True)['magnesium']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "151    112.0\n",
              "147     86.0\n",
              "148     92.0\n",
              "153     98.0\n",
              "160     88.0\n",
              "       ...  \n",
              "87      88.0\n",
              "80      86.0\n",
              "99      88.0\n",
              "64     104.0\n",
              "115     85.0\n",
              "Name: magnesium, Length: 178, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lseyCcyNeh2-"
      },
      "source": [
        "# 5) Change mapping function above so that \"Very Low\" will be defined as if x< 55 (instead of x<40). So now, how many category of \"very low\" we will have.\n",
        "\n",
        "def mapping(x):\n",
        "    if x< 55:\n",
        "      return \"Very Low\"\n",
        "    if x<85:\n",
        "      return \"Low\"\n",
        "    if x<120:\n",
        "      return \"Normal\"\n",
        "    if x <140:\n",
        "      return \"High\"\n",
        "    return \"Very High\"\n",
        "\n",
        "mydata[\"magnesiumCAT\"]= mydata.magnesium.map(lambda x: mapping(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8YQvajkfLBY",
        "outputId": "7f556cca-0739-4f2a-e655-5bb19d71e58b"
      },
      "source": [
        "mydata.magnesiumCAT.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Normal       148\n",
              "Low           14\n",
              "High          14\n",
              "Very High      2\n",
              "Name: magnesiumCAT, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    }
  ]
}