EDALabData.ipynb

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        "<a href=\"https://colab.research.google.com/github/OmdenaAI/SanJoseUSAChapter_ChronicDiseaseRisk/blob/Lab_data/EDALabData.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "WKkiNNjermUu"
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      "source": [
        "#importing the libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Reading the data\n",
        "lab_data = pd.read_csv('/content/Merged_Lab_Data.csv')"
      ],
      "metadata": {
        "id": "N_l20F6OrxRn"
      },
      "execution_count": 2,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "#info about the data\n",
        "lab_data.info()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "_qYIgXIsr15P",
        "outputId": "66b08e84-b50f-4b4d-ee69-b2f666879927"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13772 entries, 0 to 13771\n",
            "Columns: 300 entries, SEQN to LBDTCSI\n",
            "dtypes: float64(300)\n",
            "memory usage: 31.5 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#See the head of the data\n",
        "lab_data.head()"
      ],
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        },
        "id": "u5_gT8uitfS5",
        "outputId": "08e70cb5-31f4-4160-deca-e4a8771eeba3"
      },
      "execution_count": 4,
      "outputs": [
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              "       SEQN  LBXHSCRP      LBDHRPLC  LBXHBS      WTSAFPRP  LBXGLU  LBDGLUSI  \\\n",
              "0  109263.0       NaN           NaN     NaN           NaN     NaN       NaN   \n",
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              "4  109269.0      0.73  5.397605e-79     1.0           NaN     NaN       NaN   \n",
              "\n",
              "     WTSAFPRP_1  LBXIN  LBDINSI  ...  LBDIHGSI      LBDIHGLC  LBXBGE  \\\n",
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              "3           NaN    NaN      NaN  ...      1.20  5.397605e-79    0.05   \n",
              "4           NaN    NaN      NaN  ...      0.75  1.000000e+00    0.05   \n",
              "\n",
              "   LBDBGESI  LBDBGELC  LBXBGM  LBDBGMSI  LBDBGMLC  LBXTC  LBDTCSI  \n",
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      "cell_type": "code",
      "source": [
        "#see the tail or the end of the data\n",
        "lab_data.tail()"
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        "id": "rdo6xe9avF0Y",
        "outputId": "bc820835-ce9b-42a6-ae55-cf9a6c9c9a3c"
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      "execution_count": 5,
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              "           SEQN  LBXHSCRP      LBDHRPLC  LBXHBS      WTSAFPRP  LBXGLU  \\\n",
              "13767  124818.0      2.04  5.397605e-79     2.0           NaN     NaN   \n",
              "13768  124819.0       NaN           NaN     NaN           NaN     NaN   \n",
              "13769  124820.0       NaN           NaN     2.0           NaN     NaN   \n",
              "13770  124821.0      0.51  5.397605e-79     2.0  10704.467871   125.0   \n",
              "13771  124822.0      0.82  5.397605e-79     2.0  36585.328133    96.0   \n",
              "\n",
              "       LBDGLUSI    WTSAFPRP_1  LBXIN  LBDINSI  ...  LBDIHGSI      LBDIHGLC  \\\n",
              "13767       NaN           NaN    NaN      NaN  ...      0.75  1.000000e+00   \n",
              "13768       NaN           NaN    NaN      NaN  ...       NaN           NaN   \n",
              "13769       NaN           NaN    NaN      NaN  ...      0.75  1.000000e+00   \n",
              "13770      6.94  10704.467871   7.75     46.5  ...      1.30  5.397605e-79   \n",
              "13771      5.33  36585.328133   4.45     26.7  ...      2.00  5.397605e-79   \n",
              "\n",
              "       LBXBGE  LBDBGESI  LBDBGELC  LBXBGM  LBDBGMSI      LBDBGMLC  LBXTC  \\\n",
              "13767    0.05      0.25       1.0    0.61      3.04  5.397605e-79  234.0   \n",
              "13768     NaN       NaN       NaN     NaN       NaN           NaN    NaN   \n",
              "13769    0.05      0.25       1.0    0.18      0.90  1.000000e+00  179.0   \n",
              "13770    0.05      0.25       1.0    0.67      3.34  5.397605e-79  155.0   \n",
              "13771    0.05      0.25       1.0    2.33     11.63  5.397605e-79  171.0   \n",
              "\n",
              "       LBDTCSI  \n",
              "13767     6.05  \n",
              "13768      NaN  \n",
              "13769     4.63  \n",
              "13770     4.01  \n",
              "13771     4.42  \n",
              "\n",
              "[5 rows x 300 columns]"
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>13769</th>\n",
              "      <td>124820.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>179.0</td>\n",
              "      <td>4.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13770</th>\n",
              "      <td>124821.0</td>\n",
              "      <td>0.51</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10704.467871</td>\n",
              "      <td>125.0</td>\n",
              "      <td>6.94</td>\n",
              "      <td>10704.467871</td>\n",
              "      <td>7.75</td>\n",
              "      <td>46.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1.30</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.67</td>\n",
              "      <td>3.34</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>155.0</td>\n",
              "      <td>4.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13771</th>\n",
              "      <td>124822.0</td>\n",
              "      <td>0.82</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>2.0</td>\n",
              "      <td>36585.328133</td>\n",
              "      <td>96.0</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36585.328133</td>\n",
              "      <td>4.45</td>\n",
              "      <td>26.7</td>\n",
              "      <td>...</td>\n",
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              "      <td>5.397605e-79</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>2.33</td>\n",
              "      <td>11.63</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>171.0</td>\n",
              "      <td>4.42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 300 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-350f49cd-1b03-4318-8b9a-427b212cb523 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-350f49cd-1b03-4318-8b9a-427b212cb523');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-629c6a78-2a88-487d-a24a-479dddc2b5de button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#what type to know\n",
        "lab_data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "8LaOa4W8vIQe",
        "outputId": "65f01c2c-a7e4-43fd-8782-4bb2949d6821"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEQN        float64\n",
              "LBXHSCRP    float64\n",
              "LBDHRPLC    float64\n",
              "LBXHBS      float64\n",
              "WTSAFPRP    float64\n",
              "             ...   \n",
              "LBXBGM      float64\n",
              "LBDBGMSI    float64\n",
              "LBDBGMLC    float64\n",
              "LBXTC       float64\n",
              "LBDTCSI     float64\n",
              "Length: 300, dtype: object"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHRPLC</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHBS</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WTSAFPRP</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXBGM</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTC</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTCSI</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>300 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Knowing the unique data for every variable\n",
        "lab_data.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "Zy2kZ9gHvO_H",
        "outputId": "731c737f-2702-4ed9-d2f6-0bc830f3eabd"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEQN        13772\n",
              "LBXHSCRP     1513\n",
              "LBDHRPLC        2\n",
              "LBXHBS          2\n",
              "WTSAFPRP     4351\n",
              "            ...  \n",
              "LBXBGM        702\n",
              "LBDBGMSI      702\n",
              "LBDBGMLC        2\n",
              "LBXTC         272\n",
              "LBDTCSI       272\n",
              "Length: 300, dtype: int64"
            ],
            "text/html": [
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              "<style scoped>\n",
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              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <td>13772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <td>1513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHRPLC</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHBS</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WTSAFPRP</th>\n",
              "      <td>4351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXBGM</th>\n",
              "      <td>702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <td>702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTC</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTCSI</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>300 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Finding null data\n",
        "lab_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "JDvui5s8vWrp",
        "outputId": "85f8de8c-b15c-487d-dcdf-9aa9c738b01d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEQN           0\n",
              "LBXHSCRP    2158\n",
              "LBDHRPLC    2158\n",
              "LBXHBS      2162\n",
              "WTSAFPRP    8682\n",
              "            ... \n",
              "LBXBGM      1743\n",
              "LBDBGMSI    1743\n",
              "LBDBGMLC    1743\n",
              "LBXTC       2944\n",
              "LBDTCSI     2944\n",
              "Length: 300, dtype: int64"
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <td>2158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHRPLC</th>\n",
              "      <td>2158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHBS</th>\n",
              "      <td>2162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WTSAFPRP</th>\n",
              "      <td>8682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXBGM</th>\n",
              "      <td>1743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <td>1743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <td>1743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTC</th>\n",
              "      <td>2944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTCSI</th>\n",
              "      <td>2944</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>300 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the percentage of missing values for each column\n",
        "missing_percentage = (lab_data.isnull().sum() / len(lab_data)) * 100\n",
        "\n",
        "# Filter columns where the percentage of missing values is greater than 70\n",
        "columns_with_high_missing = missing_percentage[missing_percentage > 70]\n",
        "\n",
        "columns_with_high_missing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EJ1jIsJo4710",
        "outputId": "d5405b4e-6579-4c2a-93ad-d4ee6c4af0a4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "LBDNFOSL    77.693872\n",
              "LBXMFOS     77.693872\n",
              "LBDMFOSL    77.693872\n",
              "WTSAPRP     70.512634\n",
              "LBXEOA      73.591345\n",
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              "LBXHCG      99.368283\n",
              "LBDHBG      95.352890\n",
              "LBDHD       95.360151\n",
              "LBXIGG      93.051118\n",
              "LBXIGM      93.051118\n",
              "LBXIGGA     98.061284\n",
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              "      <th>LBXMFOS</th>\n",
              "      <td>77.693872</td>\n",
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              "      <th>LBDMFOSL</th>\n",
              "      <td>77.693872</td>\n",
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              "    <tr>\n",
              "      <th>WTSAPRP</th>\n",
              "      <td>70.512634</td>\n",
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              "    <tr>\n",
              "      <th>LBXEOA</th>\n",
              "      <td>73.591345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDEOALC</th>\n",
              "      <td>73.591345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHCG</th>\n",
              "      <td>99.368283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHBG</th>\n",
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              "    <tr>\n",
              "      <th>LBDHD</th>\n",
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              "    <tr>\n",
              "      <th>LBXIGG</th>\n",
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              "    <tr>\n",
              "      <th>LBXIGM</th>\n",
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              "    <tr>\n",
              "      <th>LBXIGGA</th>\n",
              "      <td>98.061284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTFR</th>\n",
              "      <td>73.700261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTFRSI</th>\n",
              "      <td>73.700261</td>\n",
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              "    <tr>\n",
              "      <th>URXUPB</th>\n",
              "      <td>71.413012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URDUPBLC</th>\n",
              "      <td>71.413012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WTSSAGPP</th>\n",
              "      <td>72.240778</td>\n",
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              "    <tr>\n",
              "      <th>SSAGP</th>\n",
              "      <td>80.271565</td>\n",
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              "    <tr>\n",
              "      <th>SSIPPP</th>\n",
              "      <td>71.587279</td>\n",
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              "    <tr>\n",
              "      <th>SSIPPPL</th>\n",
              "      <td>71.587279</td>\n",
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              "    <tr>\n",
              "      <th>SSBPPP</th>\n",
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              "    <tr>\n",
              "      <th>SSBPPPL</th>\n",
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              "    <tr>\n",
              "      <th>PHACOFHR</th>\n",
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              "      <th>PHACOFMN</th>\n",
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              "      <th>PHAALCMN</th>\n",
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              "      <th>PHAGUMHR</th>\n",
              "      <td>97.044728</td>\n",
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              "      <th>PHAGUMMN</th>\n",
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              "      <td>99.201278</td>\n",
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              "    <tr>\n",
              "      <th>PHAANTMN</th>\n",
              "      <td>99.201278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHASUPHR</th>\n",
              "      <td>98.017717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHASUPMN</th>\n",
              "      <td>98.017717</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "col_miss_high_missing_ind = columns_with_high_missing.index"
      ],
      "metadata": {
        "id": "Ax0zb9qx6d1L"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter columns where the percentage of missing values is lesser than 70\n",
        "columns_with_low_missing = missing_percentage[missing_percentage > 70]\n",
        "\n",
        "columns_with_low_missing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fMZWcRW653f2",
        "outputId": "8eb7ac05-d0ba-48a8-bfd2-e746935c8468"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "WTSBAPRP    74.876561\n",
              "LBXPFDE     77.693872\n",
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              "LBXMFOS     77.693872\n",
              "LBDMFOSL    77.693872\n",
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              "LBXEOA      73.591345\n",
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              "LBDHBG      95.352890\n",
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              "LBXIGG      93.051118\n",
              "LBXIGM      93.051118\n",
              "LBXIGGA     98.061284\n",
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              "LBDTFRSI    73.700261\n",
              "URXUPB      71.413012\n",
              "URDUPBLC    71.413012\n",
              "WTSSAGPP    72.240778\n",
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              "    <tr>\n",
              "      <th>LBXMPAH</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDMPAHL</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXPFNA</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDPFNAL</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXPFUA</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDPFUAL</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXNFOA</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDNFOAL</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXBFOA</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBFOAL</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXNFOS</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDNFOSL</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXMFOS</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDMFOSL</th>\n",
              "      <td>77.693872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WTSAPRP</th>\n",
              "      <td>70.512634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXEOA</th>\n",
              "      <td>73.591345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDEOALC</th>\n",
              "      <td>73.591345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHCG</th>\n",
              "      <td>99.368283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHBG</th>\n",
              "      <td>95.352890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHD</th>\n",
              "      <td>95.360151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXIGG</th>\n",
              "      <td>93.051118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXIGM</th>\n",
              "      <td>93.051118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXIGGA</th>\n",
              "      <td>98.061284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTFR</th>\n",
              "      <td>73.700261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTFRSI</th>\n",
              "      <td>73.700261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URXUPB</th>\n",
              "      <td>71.413012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>URDUPBLC</th>\n",
              "      <td>71.413012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WTSSAGPP</th>\n",
              "      <td>72.240778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SSAGP</th>\n",
              "      <td>80.271565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SSIPPP</th>\n",
              "      <td>71.587279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SSIPPPL</th>\n",
              "      <td>71.587279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SSBPPP</th>\n",
              "      <td>71.514667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SSBPPPL</th>\n",
              "      <td>71.514667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHACOFHR</th>\n",
              "      <td>98.845484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHACOFMN</th>\n",
              "      <td>98.845484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHAALCHR</th>\n",
              "      <td>99.520767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHAALCMN</th>\n",
              "      <td>99.520767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHAGUMHR</th>\n",
              "      <td>97.044728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHAGUMMN</th>\n",
              "      <td>97.044728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHAANTHR</th>\n",
              "      <td>99.201278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHAANTMN</th>\n",
              "      <td>99.201278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHASUPHR</th>\n",
              "      <td>98.017717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHASUPMN</th>\n",
              "      <td>98.017717</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. The laboratoary data has 300 columns and 13771\n",
        "2. There are many (NaN) values in the data\n",
        "3. The weight problem percentages is more than 70%\n",
        "4. The na values have to be dropped"
      ],
      "metadata": {
        "id": "kmGp5GmSwg1m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping the higher missing variables\n",
        "lab_data_dropped = lab_data.drop(col_miss_high_missing_ind,axis=1)\n",
        "#Yes."
      ],
      "metadata": {
        "id": "p3-wRiCy2sJF"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the missing and uniqueness\n",
        "\n",
        "lab_data_dropped_uniq = lab_data_dropped.nunique()\n",
        "lab_data_dropped_uniq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "PWOlhdO91Pr_",
        "outputId": "4aab952d-dbac-4ad4-db2d-07ddc4f4c554"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEQN        13772\n",
              "LBXHSCRP     1513\n",
              "LBDHRPLC        2\n",
              "LBXHBS          2\n",
              "WTSAFPRP     4351\n",
              "            ...  \n",
              "LBXBGM        702\n",
              "LBDBGMSI      702\n",
              "LBDBGMLC        2\n",
              "LBXTC         272\n",
              "LBDTCSI       272\n",
              "Length: 252, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
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              "    }\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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <td>13772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <td>1513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHRPLC</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHBS</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WTSAFPRP</th>\n",
              "      <td>4351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXBGM</th>\n",
              "      <td>702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <td>702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTC</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTCSI</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets take out the varibales which is coming from the urine labaoratory data as well as taking out the variabes which is not about the cholesterol\n",
        "lab_data_dropped_new = lab_data_dropped.drop(['URXUMA','URXUMS','URXUCR','URXCRS','URDACT','URDUCRLC','PHQ020','PHQ030','PHQ040','PHQ050','PHQ060','PHAFSTHR','PHAFSTMN','PHDSESN','WTFOLPRP','WTFOLPRP_1'],axis=1)\n",
        "lab_data_dropped_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "QUZNUF189j5S",
        "outputId": "9783f76e-265c-4291-9a78-18015e9b32e4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           SEQN  LBXHSCRP      LBDHRPLC  LBXHBS      WTSAFPRP  LBXGLU  \\\n",
              "0      109263.0       NaN           NaN     NaN           NaN     NaN   \n",
              "1      109264.0      0.11  1.000000e+00     2.0  27533.174559    97.0   \n",
              "2      109265.0      0.31  5.397605e-79     NaN           NaN     NaN   \n",
              "3      109266.0      0.72  5.397605e-79     1.0           NaN     NaN   \n",
              "4      109269.0      0.73  5.397605e-79     1.0           NaN     NaN   \n",
              "...         ...       ...           ...     ...           ...     ...   \n",
              "13767  124818.0      2.04  5.397605e-79     2.0           NaN     NaN   \n",
              "13768  124819.0       NaN           NaN     NaN           NaN     NaN   \n",
              "13769  124820.0       NaN           NaN     2.0           NaN     NaN   \n",
              "13770  124821.0      0.51  5.397605e-79     2.0  10704.467871   125.0   \n",
              "13771  124822.0      0.82  5.397605e-79     2.0  36585.328133    96.0   \n",
              "\n",
              "       LBDGLUSI    WTSAFPRP_1  LBXIN  LBDINSI  ...  LBDIHGSI      LBDIHGLC  \\\n",
              "0           NaN           NaN    NaN      NaN  ...       NaN           NaN   \n",
              "1          5.38  27533.174559   6.05     36.3  ...      0.75  1.000000e+00   \n",
              "2           NaN           NaN    NaN      NaN  ...      0.75  1.000000e+00   \n",
              "3           NaN           NaN    NaN      NaN  ...      1.20  5.397605e-79   \n",
              "4           NaN           NaN    NaN      NaN  ...      0.75  1.000000e+00   \n",
              "...         ...           ...    ...      ...  ...       ...           ...   \n",
              "13767       NaN           NaN    NaN      NaN  ...      0.75  1.000000e+00   \n",
              "13768       NaN           NaN    NaN      NaN  ...       NaN           NaN   \n",
              "13769       NaN           NaN    NaN      NaN  ...      0.75  1.000000e+00   \n",
              "13770      6.94  10704.467871   7.75     46.5  ...      1.30  5.397605e-79   \n",
              "13771      5.33  36585.328133   4.45     26.7  ...      2.00  5.397605e-79   \n",
              "\n",
              "       LBXBGE  LBDBGESI  LBDBGELC  LBXBGM  LBDBGMSI      LBDBGMLC  LBXTC  \\\n",
              "0         NaN       NaN       NaN     NaN       NaN           NaN    NaN   \n",
              "1        0.05      0.25       1.0    0.18      0.90  1.000000e+00  166.0   \n",
              "2        0.05      0.25       1.0    0.18      0.90  1.000000e+00    NaN   \n",
              "3        0.05      0.25       1.0    0.18      0.90  1.000000e+00  195.0   \n",
              "4        0.05      0.25       1.0    0.18      0.90  1.000000e+00    NaN   \n",
              "...       ...       ...       ...     ...       ...           ...    ...   \n",
              "13767    0.05      0.25       1.0    0.61      3.04  5.397605e-79  234.0   \n",
              "13768     NaN       NaN       NaN     NaN       NaN           NaN    NaN   \n",
              "13769    0.05      0.25       1.0    0.18      0.90  1.000000e+00  179.0   \n",
              "13770    0.05      0.25       1.0    0.67      3.34  5.397605e-79  155.0   \n",
              "13771    0.05      0.25       1.0    2.33     11.63  5.397605e-79  171.0   \n",
              "\n",
              "       LBDTCSI  \n",
              "0          NaN  \n",
              "1         4.29  \n",
              "2          NaN  \n",
              "3         5.04  \n",
              "4          NaN  \n",
              "...        ...  \n",
              "13767     6.05  \n",
              "13768      NaN  \n",
              "13769     4.63  \n",
              "13770     4.01  \n",
              "13771     4.42  \n",
              "\n",
              "[13772 rows x 236 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1942c29-852c-4fc0-8448-024a15911b9c\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEQN</th>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <th>LBDHRPLC</th>\n",
              "      <th>LBXHBS</th>\n",
              "      <th>WTSAFPRP</th>\n",
              "      <th>LBXGLU</th>\n",
              "      <th>LBDGLUSI</th>\n",
              "      <th>WTSAFPRP_1</th>\n",
              "      <th>LBXIN</th>\n",
              "      <th>LBDINSI</th>\n",
              "      <th>...</th>\n",
              "      <th>LBDIHGSI</th>\n",
              "      <th>LBDIHGLC</th>\n",
              "      <th>LBXBGE</th>\n",
              "      <th>LBDBGESI</th>\n",
              "      <th>LBDBGELC</th>\n",
              "      <th>LBXBGM</th>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <th>LBXTC</th>\n",
              "      <th>LBDTCSI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>109263.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>109264.0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>27533.174559</td>\n",
              "      <td>97.0</td>\n",
              "      <td>5.38</td>\n",
              "      <td>27533.174559</td>\n",
              "      <td>6.05</td>\n",
              "      <td>36.3</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>166.0</td>\n",
              "      <td>4.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>109265.0</td>\n",
              "      <td>0.31</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>109266.0</td>\n",
              "      <td>0.72</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1.20</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>195.0</td>\n",
              "      <td>5.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>109269.0</td>\n",
              "      <td>0.73</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>13767</th>\n",
              "      <td>124818.0</td>\n",
              "      <td>2.04</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.61</td>\n",
              "      <td>3.04</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>234.0</td>\n",
              "      <td>6.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13768</th>\n",
              "      <td>124819.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13769</th>\n",
              "      <td>124820.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>179.0</td>\n",
              "      <td>4.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13770</th>\n",
              "      <td>124821.0</td>\n",
              "      <td>0.51</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10704.467871</td>\n",
              "      <td>125.0</td>\n",
              "      <td>6.94</td>\n",
              "      <td>10704.467871</td>\n",
              "      <td>7.75</td>\n",
              "      <td>46.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1.30</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.67</td>\n",
              "      <td>3.34</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>155.0</td>\n",
              "      <td>4.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13771</th>\n",
              "      <td>124822.0</td>\n",
              "      <td>0.82</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>2.0</td>\n",
              "      <td>36585.328133</td>\n",
              "      <td>96.0</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36585.328133</td>\n",
              "      <td>4.45</td>\n",
              "      <td>26.7</td>\n",
              "      <td>...</td>\n",
              "      <td>2.00</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.33</td>\n",
              "      <td>11.63</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>171.0</td>\n",
              "      <td>4.42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13772 rows × 236 columns</p>\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "lab_data_dropped_new"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUrOLadJXWv8",
        "outputId": "51871d28-533a-4511-bad6-e552905255ec"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13772 entries, 0 to 13771\n",
            "Columns: 236 entries, SEQN to LBDTCSI\n",
            "dtypes: float64(236)\n",
            "memory usage: 24.8 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "geEDEvhQXZGO",
        "outputId": "18a1ccfa-8ea1-4fdc-d39b-6aa6dcf72729"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEQN           0\n",
              "LBXHSCRP    2158\n",
              "LBDHRPLC    2158\n",
              "LBXHBS      2162\n",
              "WTSAFPRP    8682\n",
              "            ... \n",
              "LBXBGM      1743\n",
              "LBDBGMSI    1743\n",
              "LBDBGMLC    1743\n",
              "LBXTC       2944\n",
              "LBDTCSI     2944\n",
              "Length: 236, dtype: int64"
            ],
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              "      <th>LBXBGM</th>\n",
              "      <td>1743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <td>1743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <td>1743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTC</th>\n",
              "      <td>2944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTCSI</th>\n",
              "      <td>2944</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>236 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the percentage of missing values for each column\n",
        "missing_percentage = (lab_data_dropped_new.isnull().sum() / len(lab_data_dropped_new)) * 100\n",
        "\n",
        "# Filter columns where the percentage of missing values is greater than 50\n",
        "columns_with_between_missing = missing_percentage[(missing_percentage >=50)]\n",
        "\n",
        "columns_with_between_missing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "pgdlsm7NXbW0",
        "outputId": "387c8ae5-6f96-436b-d7ed-fc3452acbd0b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WTSAFPRP      63.040953\n",
              "LBXGLU        65.553297\n",
              "LBDGLUSI      65.553297\n",
              "WTSAFPRP_1    63.040953\n",
              "LBXIN         66.417369\n",
              "                ...    \n",
              "LBDLDLSI      66.475457\n",
              "LBDLDLM       66.475457\n",
              "LBDLDMSI      66.475457\n",
              "LBDLDLN       66.293930\n",
              "LBDLDNSI      66.293930\n",
              "Length: 105, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>WTSAFPRP</th>\n",
              "      <td>63.040953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXGLU</th>\n",
              "      <td>65.553297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDGLUSI</th>\n",
              "      <td>65.553297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WTSAFPRP_1</th>\n",
              "      <td>63.040953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXIN</th>\n",
              "      <td>66.417369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDLDLSI</th>\n",
              "      <td>66.475457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDLDLM</th>\n",
              "      <td>66.475457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDLDMSI</th>\n",
              "      <td>66.475457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDLDLN</th>\n",
              "      <td>66.293930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDLDNSI</th>\n",
              "      <td>66.293930</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>105 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gE55q_AdYdG4",
        "outputId": "9fcd2152-c805-4534-e3f6-77a156497419"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new_dup = lab_data_dropped_new.drop(columns_with_between_missing.index,axis=1)"
      ],
      "metadata": {
        "id": "AAMNuE21YvbM"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_columns = lab_data_dropped_new_dup.select_dtypes(include=['number']).columns\n",
        "lab_data_dropped_new_dup[numeric_columns] = lab_data_dropped_new_dup[numeric_columns].fillna(lab_data_dropped_new_dup[numeric_columns].median())"
      ],
      "metadata": {
        "id": "LBSbcWwfWreP"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new_dup.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "FNsxwlNxZS0S",
        "outputId": "78135bf7-e6ce-4464-abb8-f8d4fce68e1b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEQN        0\n",
              "LBXHSCRP    0\n",
              "LBDHRPLC    0\n",
              "LBXHBS      0\n",
              "LBXHA       0\n",
              "           ..\n",
              "LBXBGM      0\n",
              "LBDBGMSI    0\n",
              "LBDBGMLC    0\n",
              "LBXTC       0\n",
              "LBDTCSI     0\n",
              "Length: 131, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SEQN</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHRPLC</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHBS</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHA</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXBGM</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTC</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTCSI</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new_dup.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gnYOwFlJZUdi",
        "outputId": "34b00397-02a8-4d19-c04f-2ec70689201b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13772 entries, 0 to 13771\n",
            "Columns: 131 entries, SEQN to LBDTCSI\n",
            "dtypes: float64(131)\n",
            "memory usage: 13.8 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new_dup.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "e-mIV-fsZW4a",
        "outputId": "96f1d776-43f2-4ac0-eb5d-a8fe0efe6642"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEQN        13772\n",
              "LBXHSCRP     1513\n",
              "LBDHRPLC        2\n",
              "LBXHBS          2\n",
              "LBXHA           3\n",
              "            ...  \n",
              "LBXBGM        702\n",
              "LBDBGMSI      702\n",
              "LBDBGMLC        2\n",
              "LBXTC         272\n",
              "LBDTCSI       272\n",
              "Length: 131, dtype: int64"
            ],
            "text/html": [
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              "<style scoped>\n",
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              "  <tbody>\n",
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              "      <th>SEQN</th>\n",
              "      <td>13772</td>\n",
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              "    <tr>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <td>1513</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
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              "      <th>LBXHBS</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXHA</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXBGM</th>\n",
              "      <td>702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <td>702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBXTC</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTCSI</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>131 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new_dup.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "ZfWSTzf5ZkBG",
        "outputId": "38c0eb9c-78ca-4d48-b45a-616f72d1c579"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                SEQN      LBXHSCRP      LBDHRPLC        LBXHBS         LBXHA  \\\n",
              "count   13772.000000  13772.000000  1.377200e+04  13772.000000  13772.000000   \n",
              "mean   117079.858844      3.092706  2.889922e-02      1.742884      1.330090   \n",
              "std      4498.549164      7.196760  1.675294e-01      0.437060      0.472112   \n",
              "min    109263.000000      0.110000  5.397605e-79      1.000000      1.000000   \n",
              "25%    113184.750000      0.620000  5.397605e-79      1.000000      1.000000   \n",
              "50%    117104.500000      1.320000  5.397605e-79      2.000000      1.000000   \n",
              "75%    120981.250000      3.000000  5.397605e-79      2.000000      2.000000   \n",
              "max    124822.000000    246.860000  1.000000e+00      2.000000      3.000000   \n",
              "\n",
              "             LBXHCR        LBDHCI        LBDHDD      LBDHDDSI        LBXHBC  \\\n",
              "count  13772.000000  13772.000000  13772.000000  13772.000000  13772.000000   \n",
              "mean       2.974877      2.988455     52.945033      1.369516      1.953529   \n",
              "std        0.193442      0.196396     13.372292      0.345735      0.210855   \n",
              "min        1.000000      1.000000      5.000000      0.130000      1.000000   \n",
              "25%        3.000000      3.000000     45.000000      1.160000      2.000000   \n",
              "50%        3.000000      3.000000     51.000000      1.320000      2.000000   \n",
              "75%        3.000000      3.000000     58.000000      1.500000      2.000000   \n",
              "max        3.000000      4.000000    189.000000      4.890000      3.000000   \n",
              "\n",
              "       ...      LBDIHGSI      LBDIHGLC        LBXBGE      LBDBGESI  \\\n",
              "count  ...  13772.000000  1.377200e+04  13772.000000  13772.000000   \n",
              "mean   ...      1.038636  8.292187e-01      0.050372      0.251858   \n",
              "std    ...      2.679797  3.763314e-01      0.009610      0.047980   \n",
              "min    ...      0.750000  5.397605e-79      0.050000      0.250000   \n",
              "25%    ...      0.750000  1.000000e+00      0.050000      0.250000   \n",
              "50%    ...      0.750000  1.000000e+00      0.050000      0.250000   \n",
              "75%    ...      0.750000  1.000000e+00      0.050000      0.250000   \n",
              "max    ...    169.410000  1.000000e+00      1.010000      5.040000   \n",
              "\n",
              "           LBDBGELC        LBXBGM      LBDBGMSI      LBDBGMLC         LBXTC  \\\n",
              "count  1.377200e+04  13772.000000  13772.000000  1.377200e+04  13772.000000   \n",
              "mean   9.942637e-01      0.820902      4.097657  3.825879e-01    176.509367   \n",
              "std    7.552341e-02      1.673779      8.351694  4.860366e-01     35.829756   \n",
              "min    5.397605e-79      0.180000      0.900000  5.397605e-79     71.000000   \n",
              "25%    1.000000e+00      0.180000      0.900000  5.397605e-79    155.000000   \n",
              "50%    1.000000e+00      0.320000      1.600000  5.397605e-79    173.000000   \n",
              "75%    1.000000e+00      0.740000      3.690000  1.000000e+00    192.000000   \n",
              "max    1.000000e+00     55.900000    278.940000  1.000000e+00    446.000000   \n",
              "\n",
              "            LBDTCSI  \n",
              "count  13772.000000  \n",
              "mean       4.563753  \n",
              "std        0.926661  \n",
              "min        1.840000  \n",
              "25%        4.010000  \n",
              "50%        4.470000  \n",
              "75%        4.970000  \n",
              "max       11.530000  \n",
              "\n",
              "[8 rows x 131 columns]"
            ],
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              "\n",
              "  <div id=\"df-a8030ac5-3eb9-4827-996a-9fa8c2783738\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEQN</th>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <th>LBDHRPLC</th>\n",
              "      <th>LBXHBS</th>\n",
              "      <th>LBXHA</th>\n",
              "      <th>LBXHCR</th>\n",
              "      <th>LBDHCI</th>\n",
              "      <th>LBDHDD</th>\n",
              "      <th>LBDHDDSI</th>\n",
              "      <th>LBXHBC</th>\n",
              "      <th>...</th>\n",
              "      <th>LBDIHGSI</th>\n",
              "      <th>LBDIHGLC</th>\n",
              "      <th>LBXBGE</th>\n",
              "      <th>LBDBGESI</th>\n",
              "      <th>LBDBGELC</th>\n",
              "      <th>LBXBGM</th>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <th>LBXTC</th>\n",
              "      <th>LBDTCSI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>1.377200e+04</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>1.377200e+04</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>1.377200e+04</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>1.377200e+04</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>117079.858844</td>\n",
              "      <td>3.092706</td>\n",
              "      <td>2.889922e-02</td>\n",
              "      <td>1.742884</td>\n",
              "      <td>1.330090</td>\n",
              "      <td>2.974877</td>\n",
              "      <td>2.988455</td>\n",
              "      <td>52.945033</td>\n",
              "      <td>1.369516</td>\n",
              "      <td>1.953529</td>\n",
              "      <td>...</td>\n",
              "      <td>1.038636</td>\n",
              "      <td>8.292187e-01</td>\n",
              "      <td>0.050372</td>\n",
              "      <td>0.251858</td>\n",
              "      <td>9.942637e-01</td>\n",
              "      <td>0.820902</td>\n",
              "      <td>4.097657</td>\n",
              "      <td>3.825879e-01</td>\n",
              "      <td>176.509367</td>\n",
              "      <td>4.563753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4498.549164</td>\n",
              "      <td>7.196760</td>\n",
              "      <td>1.675294e-01</td>\n",
              "      <td>0.437060</td>\n",
              "      <td>0.472112</td>\n",
              "      <td>0.193442</td>\n",
              "      <td>0.196396</td>\n",
              "      <td>13.372292</td>\n",
              "      <td>0.345735</td>\n",
              "      <td>0.210855</td>\n",
              "      <td>...</td>\n",
              "      <td>2.679797</td>\n",
              "      <td>3.763314e-01</td>\n",
              "      <td>0.009610</td>\n",
              "      <td>0.047980</td>\n",
              "      <td>7.552341e-02</td>\n",
              "      <td>1.673779</td>\n",
              "      <td>8.351694</td>\n",
              "      <td>4.860366e-01</td>\n",
              "      <td>35.829756</td>\n",
              "      <td>0.926661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>109263.000000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.180000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>1.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>113184.750000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.160000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.180000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>4.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>117104.500000</td>\n",
              "      <td>1.320000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>1.320000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>173.000000</td>\n",
              "      <td>4.470000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>120981.250000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.740000</td>\n",
              "      <td>3.690000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>192.000000</td>\n",
              "      <td>4.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>124822.000000</td>\n",
              "      <td>246.860000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>189.000000</td>\n",
              "      <td>4.890000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>169.410000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.010000</td>\n",
              "      <td>5.040000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>55.900000</td>\n",
              "      <td>278.940000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>446.000000</td>\n",
              "      <td>11.530000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 131 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            style=\"display:none;\">\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-a8030ac5-3eb9-4827-996a-9fa8c2783738 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a8030ac5-3eb9-4827-996a-9fa8c2783738');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b05e5424-840c-49f6-8328-4fc46d8115b2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b05e5424-840c-49f6-8328-4fc46d8115b2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_names = lab_data_dropped_new_dup.columns.tolist()\n",
        "print(column_names)\n",
        "\n",
        "# Method 2: Using list comprehension\n",
        "column_names = [col for col in lab_data_dropped_new_dup]\n",
        "column_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-zrmrVKZpoS",
        "outputId": "a710ca91-3c44-4b66-9e27-bf0f7f859210"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['SEQN', 'LBXHSCRP', 'LBDHRPLC', 'LBXHBS', 'LBXHA', 'LBXHCR', 'LBDHCI', 'LBDHDD', 'LBDHDDSI', 'LBXHBC', 'LBDRFO', 'LBDRFOSI', 'LBDFOTSI', 'LBDFOT', 'LBXSF1SI', 'LBDSF1LC', 'LBXSF2SI', 'LBDSF2LC', 'LBXSF3SI', 'LBDSF3LC', 'LBXSF4SI', 'LBDSF4LC', 'LBXSF5SI', 'LBDSF5LC', 'LBXSF6SI', 'LBDSF6LC', 'LBXFER', 'LBDFERSI', 'LBXIRN', 'LBDIRNSI', 'LBXUIB', 'LBDUIBLC', 'LBDUIBSI', 'LBDTIB', 'LBDTIBSI', 'LBDPCT', 'URDUMALC', 'LBXWBCSI', 'LBXLYPCT', 'LBXMOPCT', 'LBXNEPCT', 'LBXEOPCT', 'LBXBAPCT', 'LBDLYMNO', 'LBDMONO', 'LBDNENO', 'LBDEONO', 'LBDBANO', 'LBXRBCSI', 'LBXHGB', 'LBXHCT', 'LBXMCVSI', 'LBXMC', 'LBXMCHSI', 'LBXRDW', 'LBXPLTSI', 'LBXMPSI', 'LBXNRBC', 'LBXCOT', 'LBDCOTLC', 'LBXHCOT', 'LBDHCOLC', 'LBXGH', 'LBDHEG', 'LBDHEM', 'LBXBPB', 'LBDBPBSI', 'LBDBPBLC', 'LBXBCD', 'LBDBCDSI', 'LBDBCDLC', 'LBXTHG', 'LBDTHGSI', 'LBDTHGLC', 'LBXBSE', 'LBDBSESI', 'LBDBSELC', 'LBXBMN', 'LBDBMNSI', 'LBDBMNLC', 'LBXSATSI', 'LBDSATLC', 'LBXSAL', 'LBDSALSI', 'LBXSAPSI', 'LBXSASSI', 'LBXSC3SI', 'LBXSBU', 'LBDSBUSI', 'LBXSCLSI', 'LBXSCK', 'LBXSCR', 'LBDSCRSI', 'LBXSGB', 'LBDSGBSI', 'LBXSGL', 'LBDSGLSI', 'LBXSGTSI', 'LBDSGTLC', 'LBXSIR', 'LBDSIRSI', 'LBXSLDSI', 'LBXSOSSI', 'LBXSPH', 'LBDSPHSI', 'LBXSKSI', 'LBXSNASI', 'LBXSTB', 'LBDSTBSI', 'LBDSTBLC', 'LBXSCA', 'LBDSCASI', 'LBXSCH', 'LBDSCHSI', 'LBXSTP', 'LBDSTPSI', 'LBXSTR', 'LBDSTRSI', 'LBXSUA', 'LBDSUASI', 'LBXIHG', 'LBDIHGSI', 'LBDIHGLC', 'LBXBGE', 'LBDBGESI', 'LBDBGELC', 'LBXBGM', 'LBDBGMSI', 'LBDBGMLC', 'LBXTC', 'LBDTCSI']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SEQN',\n",
              " 'LBXHSCRP',\n",
              " 'LBDHRPLC',\n",
              " 'LBXHBS',\n",
              " 'LBXHA',\n",
              " 'LBXHCR',\n",
              " 'LBDHCI',\n",
              " 'LBDHDD',\n",
              " 'LBDHDDSI',\n",
              " 'LBXHBC',\n",
              " 'LBDRFO',\n",
              " 'LBDRFOSI',\n",
              " 'LBDFOTSI',\n",
              " 'LBDFOT',\n",
              " 'LBXSF1SI',\n",
              " 'LBDSF1LC',\n",
              " 'LBXSF2SI',\n",
              " 'LBDSF2LC',\n",
              " 'LBXSF3SI',\n",
              " 'LBDSF3LC',\n",
              " 'LBXSF4SI',\n",
              " 'LBDSF4LC',\n",
              " 'LBXSF5SI',\n",
              " 'LBDSF5LC',\n",
              " 'LBXSF6SI',\n",
              " 'LBDSF6LC',\n",
              " 'LBXFER',\n",
              " 'LBDFERSI',\n",
              " 'LBXIRN',\n",
              " 'LBDIRNSI',\n",
              " 'LBXUIB',\n",
              " 'LBDUIBLC',\n",
              " 'LBDUIBSI',\n",
              " 'LBDTIB',\n",
              " 'LBDTIBSI',\n",
              " 'LBDPCT',\n",
              " 'URDUMALC',\n",
              " 'LBXWBCSI',\n",
              " 'LBXLYPCT',\n",
              " 'LBXMOPCT',\n",
              " 'LBXNEPCT',\n",
              " 'LBXEOPCT',\n",
              " 'LBXBAPCT',\n",
              " 'LBDLYMNO',\n",
              " 'LBDMONO',\n",
              " 'LBDNENO',\n",
              " 'LBDEONO',\n",
              " 'LBDBANO',\n",
              " 'LBXRBCSI',\n",
              " 'LBXHGB',\n",
              " 'LBXHCT',\n",
              " 'LBXMCVSI',\n",
              " 'LBXMC',\n",
              " 'LBXMCHSI',\n",
              " 'LBXRDW',\n",
              " 'LBXPLTSI',\n",
              " 'LBXMPSI',\n",
              " 'LBXNRBC',\n",
              " 'LBXCOT',\n",
              " 'LBDCOTLC',\n",
              " 'LBXHCOT',\n",
              " 'LBDHCOLC',\n",
              " 'LBXGH',\n",
              " 'LBDHEG',\n",
              " 'LBDHEM',\n",
              " 'LBXBPB',\n",
              " 'LBDBPBSI',\n",
              " 'LBDBPBLC',\n",
              " 'LBXBCD',\n",
              " 'LBDBCDSI',\n",
              " 'LBDBCDLC',\n",
              " 'LBXTHG',\n",
              " 'LBDTHGSI',\n",
              " 'LBDTHGLC',\n",
              " 'LBXBSE',\n",
              " 'LBDBSESI',\n",
              " 'LBDBSELC',\n",
              " 'LBXBMN',\n",
              " 'LBDBMNSI',\n",
              " 'LBDBMNLC',\n",
              " 'LBXSATSI',\n",
              " 'LBDSATLC',\n",
              " 'LBXSAL',\n",
              " 'LBDSALSI',\n",
              " 'LBXSAPSI',\n",
              " 'LBXSASSI',\n",
              " 'LBXSC3SI',\n",
              " 'LBXSBU',\n",
              " 'LBDSBUSI',\n",
              " 'LBXSCLSI',\n",
              " 'LBXSCK',\n",
              " 'LBXSCR',\n",
              " 'LBDSCRSI',\n",
              " 'LBXSGB',\n",
              " 'LBDSGBSI',\n",
              " 'LBXSGL',\n",
              " 'LBDSGLSI',\n",
              " 'LBXSGTSI',\n",
              " 'LBDSGTLC',\n",
              " 'LBXSIR',\n",
              " 'LBDSIRSI',\n",
              " 'LBXSLDSI',\n",
              " 'LBXSOSSI',\n",
              " 'LBXSPH',\n",
              " 'LBDSPHSI',\n",
              " 'LBXSKSI',\n",
              " 'LBXSNASI',\n",
              " 'LBXSTB',\n",
              " 'LBDSTBSI',\n",
              " 'LBDSTBLC',\n",
              " 'LBXSCA',\n",
              " 'LBDSCASI',\n",
              " 'LBXSCH',\n",
              " 'LBDSCHSI',\n",
              " 'LBXSTP',\n",
              " 'LBDSTPSI',\n",
              " 'LBXSTR',\n",
              " 'LBDSTRSI',\n",
              " 'LBXSUA',\n",
              " 'LBDSUASI',\n",
              " 'LBXIHG',\n",
              " 'LBDIHGSI',\n",
              " 'LBDIHGLC',\n",
              " 'LBXBGE',\n",
              " 'LBDBGESI',\n",
              " 'LBDBGELC',\n",
              " 'LBXBGM',\n",
              " 'LBDBGMSI',\n",
              " 'LBDBGMLC',\n",
              " 'LBXTC',\n",
              " 'LBDTCSI']"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dropping the dietary columns which is for the infant part\n",
        "lab_data_dropped_new_dup = lab_data_dropped_new_dup.drop(['URDUMALC'],axis=1)"
      ],
      "metadata": {
        "id": "M8IqCeoeZ76Z"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new_dup"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "Jw4YVejKaccH",
        "outputId": "8515f066-fd7f-4a2a-abc1-43be19b0b235"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           SEQN  LBXHSCRP      LBDHRPLC  LBXHBS  LBXHA  LBXHCR  LBDHCI  \\\n",
              "0      109263.0      1.32  5.397605e-79     2.0    1.0     3.0     3.0   \n",
              "1      109264.0      0.11  1.000000e+00     2.0    1.0     3.0     3.0   \n",
              "2      109265.0      0.31  5.397605e-79     2.0    1.0     3.0     3.0   \n",
              "3      109266.0      0.72  5.397605e-79     1.0    1.0     3.0     3.0   \n",
              "4      109269.0      0.73  5.397605e-79     1.0    1.0     3.0     3.0   \n",
              "...         ...       ...           ...     ...    ...     ...     ...   \n",
              "13767  124818.0      2.04  5.397605e-79     2.0    2.0     3.0     3.0   \n",
              "13768  124819.0      1.32  5.397605e-79     2.0    1.0     3.0     3.0   \n",
              "13769  124820.0      1.32  5.397605e-79     2.0    1.0     3.0     3.0   \n",
              "13770  124821.0      0.51  5.397605e-79     2.0    1.0     3.0     3.0   \n",
              "13771  124822.0      0.82  5.397605e-79     2.0    1.0     3.0     3.0   \n",
              "\n",
              "       LBDHDD  LBDHDDSI  LBXHBC  ...  LBDIHGSI      LBDIHGLC  LBXBGE  \\\n",
              "0        51.0      1.32     2.0  ...      0.75  1.000000e+00    0.05   \n",
              "1        72.0      1.86     2.0  ...      0.75  1.000000e+00    0.05   \n",
              "2        51.0      1.32     2.0  ...      0.75  1.000000e+00    0.05   \n",
              "3        56.0      1.45     2.0  ...      1.20  5.397605e-79    0.05   \n",
              "4        51.0      1.32     2.0  ...      0.75  1.000000e+00    0.05   \n",
              "...       ...       ...     ...  ...       ...           ...     ...   \n",
              "13767    50.0      1.29     2.0  ...      0.75  1.000000e+00    0.05   \n",
              "13768    51.0      1.32     2.0  ...      0.75  1.000000e+00    0.05   \n",
              "13769    64.0      1.66     2.0  ...      0.75  1.000000e+00    0.05   \n",
              "13770    44.0      1.14     2.0  ...      1.30  5.397605e-79    0.05   \n",
              "13771    65.0      1.68     2.0  ...      2.00  5.397605e-79    0.05   \n",
              "\n",
              "       LBDBGESI  LBDBGELC  LBXBGM  LBDBGMSI      LBDBGMLC  LBXTC  LBDTCSI  \n",
              "0          0.25       1.0    0.32      1.60  5.397605e-79  173.0     4.47  \n",
              "1          0.25       1.0    0.18      0.90  1.000000e+00  166.0     4.29  \n",
              "2          0.25       1.0    0.18      0.90  1.000000e+00  173.0     4.47  \n",
              "3          0.25       1.0    0.18      0.90  1.000000e+00  195.0     5.04  \n",
              "4          0.25       1.0    0.18      0.90  1.000000e+00  173.0     4.47  \n",
              "...         ...       ...     ...       ...           ...    ...      ...  \n",
              "13767      0.25       1.0    0.61      3.04  5.397605e-79  234.0     6.05  \n",
              "13768      0.25       1.0    0.32      1.60  5.397605e-79  173.0     4.47  \n",
              "13769      0.25       1.0    0.18      0.90  1.000000e+00  179.0     4.63  \n",
              "13770      0.25       1.0    0.67      3.34  5.397605e-79  155.0     4.01  \n",
              "13771      0.25       1.0    2.33     11.63  5.397605e-79  171.0     4.42  \n",
              "\n",
              "[13772 rows x 130 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEQN</th>\n",
              "      <th>LBXHSCRP</th>\n",
              "      <th>LBDHRPLC</th>\n",
              "      <th>LBXHBS</th>\n",
              "      <th>LBXHA</th>\n",
              "      <th>LBXHCR</th>\n",
              "      <th>LBDHCI</th>\n",
              "      <th>LBDHDD</th>\n",
              "      <th>LBDHDDSI</th>\n",
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              "      <th>...</th>\n",
              "      <th>LBDIHGSI</th>\n",
              "      <th>LBDIHGLC</th>\n",
              "      <th>LBXBGE</th>\n",
              "      <th>LBDBGESI</th>\n",
              "      <th>LBDBGELC</th>\n",
              "      <th>LBXBGM</th>\n",
              "      <th>LBDBGMSI</th>\n",
              "      <th>LBDBGMLC</th>\n",
              "      <th>LBXTC</th>\n",
              "      <th>LBDTCSI</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>109263.0</td>\n",
              "      <td>1.32</td>\n",
              "      <td>5.397605e-79</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.32</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.60</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>173.0</td>\n",
              "      <td>4.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>109264.0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>1.86</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>166.0</td>\n",
              "      <td>4.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>109265.0</td>\n",
              "      <td>0.31</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>1.32</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>173.0</td>\n",
              "      <td>4.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>109266.0</td>\n",
              "      <td>0.72</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>1.45</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.20</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>195.0</td>\n",
              "      <td>5.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>109269.0</td>\n",
              "      <td>0.73</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>51.0</td>\n",
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              "      <td>0.18</td>\n",
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              "      <td>50.0</td>\n",
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              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.61</td>\n",
              "      <td>3.04</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>234.0</td>\n",
              "      <td>6.05</td>\n",
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              "      <th>13768</th>\n",
              "      <td>124819.0</td>\n",
              "      <td>1.32</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>13769</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>1.66</td>\n",
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              "      <td>0.75</td>\n",
              "      <td>1.000000e+00</td>\n",
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              "      <td>1.000000e+00</td>\n",
              "      <td>179.0</td>\n",
              "      <td>4.63</td>\n",
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              "      <th>13770</th>\n",
              "      <td>124821.0</td>\n",
              "      <td>0.51</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1.14</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>1.30</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.05</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.67</td>\n",
              "      <td>3.34</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>155.0</td>\n",
              "      <td>4.01</td>\n",
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              "      <td>124822.0</td>\n",
              "      <td>0.82</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>2.00</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.33</td>\n",
              "      <td>11.63</td>\n",
              "      <td>5.397605e-79</td>\n",
              "      <td>171.0</td>\n",
              "      <td>4.42</td>\n",
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              "        document.querySelector('#df-93d63e1e-47d4-4e77-85da-f9032935d44a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-93d63e1e-47d4-4e77-85da-f9032935d44a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "</svg>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
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              "      border-color: transparent;\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4b5a285b-4b8f-410c-9af7-d22162c1ba00 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_1ae66622-a54b-4e83-85c4-6efcc3bde6e4\">\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('lab_data_dropped_new_dup')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('lab_data_dropped_new_dup');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lab_data_dropped_new_dup"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cholesterol = ['LBDHDD','LBDHDDSI','LBXTC','LBDTCSI']"
      ],
      "metadata": {
        "id": "NHRCkcfoai90"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_chol = lab_data_dropped_new_dup[cholesterol]"
      ],
      "metadata": {
        "id": "hAY9IExPa2nm"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_chol.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GLxnKbEbWpB",
        "outputId": "57b22ef2-303d-492e-9374-2d1040b6abdb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13772, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_chol.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XsR3ZNObZ2s",
        "outputId": "9e5322ff-2585-479b-c202-5d68fcbea137"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55088"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_chol.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xpQ34bPbe66",
        "outputId": "8ce43c04-ac1b-4b66-d744-3aaed8c2c77b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13772 entries, 0 to 13771\n",
            "Data columns (total 4 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   LBDHDD    13772 non-null  float64\n",
            " 1   LBDHDDSI  13772 non-null  float64\n",
            " 2   LBXTC     13772 non-null  float64\n",
            " 3   LBDTCSI   13772 non-null  float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 430.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_chol.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "VuNuQKJEbhHg",
        "outputId": "b1b22ffc-0081-4352-d88c-d1d162a659ad"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             LBDHDD      LBDHDDSI         LBXTC       LBDTCSI\n",
              "count  13772.000000  13772.000000  13772.000000  13772.000000\n",
              "mean      52.945033      1.369516    176.509367      4.563753\n",
              "std       13.372292      0.345735     35.829756      0.926661\n",
              "min        5.000000      0.130000     71.000000      1.840000\n",
              "25%       45.000000      1.160000    155.000000      4.010000\n",
              "50%       51.000000      1.320000    173.000000      4.470000\n",
              "75%       58.000000      1.500000    192.000000      4.970000\n",
              "max      189.000000      4.890000    446.000000     11.530000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc147d4b-9e2d-429e-bbff-a6b7178ee5bc\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LBDHDD</th>\n",
              "      <th>LBDHDDSI</th>\n",
              "      <th>LBXTC</th>\n",
              "      <th>LBDTCSI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "      <td>13772.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>52.945033</td>\n",
              "      <td>1.369516</td>\n",
              "      <td>176.509367</td>\n",
              "      <td>4.563753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.372292</td>\n",
              "      <td>0.345735</td>\n",
              "      <td>35.829756</td>\n",
              "      <td>0.926661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>1.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.160000</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>4.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>1.320000</td>\n",
              "      <td>173.000000</td>\n",
              "      <td>4.470000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>192.000000</td>\n",
              "      <td>4.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>189.000000</td>\n",
              "      <td>4.890000</td>\n",
              "      <td>446.000000</td>\n",
              "      <td>11.530000</td>\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-203cf85a-78ed-407c-89c8-8df8d87764d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"lab_data_chol\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"LBDHDD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4848.538102019683,\n        \"min\": 5.0,\n        \"max\": 13772.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          52.945033401103686,\n          51.0,\n          13772.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LBDHDDSI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4868.596311321658,\n        \"min\": 0.13,\n        \"max\": 13772.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.3695164101074646,\n          1.32,\n          13772.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LBXTC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4807.585073892051,\n        \"min\": 35.829755630575896,\n        \"max\": 13772.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          176.5093668312518,\n          173.0,\n          13772.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LBDTCSI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4867.506394849889,\n        \"min\": 0.9266605448339295,\n        \"max\": 13772.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.563752541388324,\n          4.47,\n          13772.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_chol.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "R1-alD-Bbk8q",
        "outputId": "662b591f-f41c-4d7d-9242-e9b2d315307d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LBDHDD  LBDHDDSI  LBXTC  LBDTCSI\n",
              "51.0    1.32      173.0  4.47       2947\n",
              "54.0    1.40      155.0  4.01         11\n",
              "49.0    1.27      153.0  3.96         10\n",
              "38.0    0.98      164.0  4.24         10\n",
              "48.0    1.24      155.0  4.01         10\n",
              "                                    ... \n",
              "50.0    1.29      159.0  4.11          1\n",
              "                  170.0  4.40          1\n",
              "                  186.0  4.81          1\n",
              "                  194.0  5.02          1\n",
              "189.0   4.89      260.0  6.72          1\n",
              "Name: count, Length: 5682, dtype: int64"
            ],
            "text/html": [
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDHDD</th>\n",
              "      <th>LBDHDDSI</th>\n",
              "      <th>LBXTC</th>\n",
              "      <th>LBDTCSI</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>51.0</th>\n",
              "      <th>1.32</th>\n",
              "      <th>173.0</th>\n",
              "      <th>4.47</th>\n",
              "      <td>2947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54.0</th>\n",
              "      <th>1.40</th>\n",
              "      <th>155.0</th>\n",
              "      <th>4.01</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49.0</th>\n",
              "      <th>1.27</th>\n",
              "      <th>153.0</th>\n",
              "      <th>3.96</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38.0</th>\n",
              "      <th>0.98</th>\n",
              "      <th>164.0</th>\n",
              "      <th>4.24</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48.0</th>\n",
              "      <th>1.24</th>\n",
              "      <th>155.0</th>\n",
              "      <th>4.01</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">50.0</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">1.29</th>\n",
              "      <th>159.0</th>\n",
              "      <th>4.11</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170.0</th>\n",
              "      <th>4.40</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186.0</th>\n",
              "      <th>4.81</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194.0</th>\n",
              "      <th>5.02</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189.0</th>\n",
              "      <th>4.89</th>\n",
              "      <th>260.0</th>\n",
              "      <th>6.72</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5682 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_chol.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "W3z5hezobr-Z",
        "outputId": "64db0985-6638-4044-98a0-fd30d59b06cc"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            LBDHDD  LBDHDDSI     LBXTC   LBDTCSI\n",
              "LBDHDD    1.000000  0.999972  0.217682  0.217792\n",
              "LBDHDDSI  0.999972  1.000000  0.217525  0.217634\n",
              "LBXTC     0.217682  0.217525  1.000000  0.999995\n",
              "LBDTCSI   0.217792  0.217634  0.999995  1.000000"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LBDHDD</th>\n",
              "      <th>LBDHDDSI</th>\n",
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              "      <th>LBDTCSI</th>\n",
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              "      <th>LBDHDD</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.217682</td>\n",
              "      <td>0.217792</td>\n",
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              "      <th>LBDHDDSI</th>\n",
              "      <td>0.999972</td>\n",
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              "      <td>0.217525</td>\n",
              "      <td>0.217634</td>\n",
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              "      <th>LBXTC</th>\n",
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              "      <td>0.999995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LBDTCSI</th>\n",
              "      <td>0.217792</td>\n",
              "      <td>0.217634</td>\n",
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              "      <td>1.000000</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edb6ea86-361e-4030-aba2-f0997655ad39')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        document.querySelector('#df-edb6ea86-361e-4030-aba2-f0997655ad39 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-edb6ea86-361e-4030-aba2-f0997655ad39');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
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              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9fdcc23b-4422-463f-9f5e-f7b772e96199 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"lab_data_chol\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"LBDHDD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4516317347440248,\n        \"min\": 0.21768188883144,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9999722105516221,\n          0.217792108204539,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LBDHDDSI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4517227233701869,\n        \"min\": 0.21752508427529882,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.2176337186904982,\n          0.9999722105516221\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LBXTC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.45171534363106,\n        \"min\": 0.21752508427529882,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.21752508427529882,\n          0.9999948088312772,\n          0.21768188883144\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LBDTCSI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.451652166077076,\n        \"min\": 0.2176337186904982,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2176337186904982,\n          1.0,\n          0.217792108204539\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(lab_data_chol.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "7ofCqYTWb42r",
        "outputId": "72636c6c-de01-4bd6-d0de-4bda6b174443"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set Seaborn style\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "# Identify numerical columns\n",
        "numerical_columns = lab_data_chol.select_dtypes(include=[\"int64\", \"float64\"]).columns\n",
        "\n",
        "# Plot distribution of each numerical feature\n",
        "plt.figure(figsize=(14, len(numerical_columns) * 3))\n",
        "for idx, feature in enumerate(numerical_columns, 1):\n",
        "    plt.subplot(len(numerical_columns), 2, idx)\n",
        "    sns.histplot(lab_data_chol[feature], kde=True)\n",
        "    plt.title(f\"{feature} | Skewness: {round(lab_data_chol[feature].skew(), 2)}\")\n",
        "\n",
        "# Adjust layout and show plots\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "V1iQjUbDb8LH",
        "outputId": "485cc6d6-009c-4fe9-e786-18aea8c9e1f7"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1200 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(lab_data_chol)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7YS1p97RcJY_",
        "outputId": "e224b965-7fec-4d07-d2f4-c82b7c272032"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7bd0e0711930>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x='LBDHDD',y='LBDHDDSI',data=lab_data_chol)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "sakcinLwcQVf",
        "outputId": "609576fa-93d9-4d53-c61b-e44b9c0e84ac"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='LBDHDD', ylabel='LBDHDDSI'>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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1a9Z4PXv44YfRo0cP3HbbbQGHFCIiil9FpVV45+Pt2H2kFi5ZgaICdU6319koHVLNmD+hHwbmpmpYKYWLZkElOTkZffr08XqWmJiI9PT0M54TEZH+FJVWoeDz3bC5FSQbRbgUoKre+y6esb2y8PglfZDGo+/jlua7foiIiE6nqCpWFJbB6pSRnWJGWZUN9U7Z87kAoFd2Ev56RT9IYtScXUphEFVB5c0339S6BCIiigLFx+pQUmmDSRKwp9wKd5OzUSxGEdmJRtTY3dhVbuWZKHEuqoIKERERAFRYnaixuWBze994nJ1sQkaCEYIA1FudqLa5/PwEihcMKkREFFWO1Trw6vf7vUKKKAB56QlIMIoQBAF2lwyDKCKda1PiHoMKERFpxinLWFl4AAeqbeicnoA+OUmY89lur5GSRJOEjqlmmKSGtSiqqqLG7kav7CTk5yRrVTpFCIMKERFpouDzXfj4tyOQFf9tshKNUAUBiqJCEVU43Qpq7G4kmqSYPB6fgsegQkREEVfw+S6s3nrE7+eSKODhi3shN9WMd385gt1HalHjcMMgiuiVnYQpw/IwtEtGBCsmrTCoEBFRRDllGR//5j+kGCUBgIo/9MuB2SDh0iFd8P32w56TafNzkjmSoiPcfE5ERBG1svCA3+meRKMEl6zCJTe0AwBRFNCvQwpGdMtEv/YpDCk6wxEVIiKKqF3H6s54JgAwG0TYXKcOdTtQbYtgVRStOKJCREQR8+Xu4/h+X6XXM6MkQBQAx2lnpnROT4hkaRSlOKJCRERh53QreOGbvVj1yyGv54lGCfUuGY2TOY3nz0oicPOwzhGtkaITgwoREYVVWZUNj6zdgZ1NpnyMYsMoSv3JqR71tO/504AOMElSBKukaMWpHyIiCpt/7jyGm9762Suk5Ock490p52BC//aQTvuvkCQCkwZ1wMMX94lwpRStOKJCRERtoqgqio/VobrJ9mGnW8FzX+/Bh6edlXLV2bn477E9YDaIePjiPph5YU+vk2lvHtaZIynkhUGFiIharai0CisKy1BSaYNbVmCQRLRPMaHC6sLBE3ZPuySThMcu6YNx+dle32+SJNw6omuky6YYwqBCREStUlRahYLPd8PqlJFmMcCUYECF1Ymth2q92p3VIQXzJvTlLh5qFQYVIiIKmqKqWFFYBqtTRk6yCSqAIzUOnLC7vdpdf04nzBjVHSYDl0RS6zCoEBFR0IqP1aGk0oY0iwFOWcGBagecTY6blUQBmYlG/KFfDkMKtQmDChERBaxx4eymfZWwOd0AJByrdXptL06zGNAuyYhquxvVNpdWpVKcYFAhIqIWKaqKD7cexodbD+N4nRMuWUGNQ0aNQ/Zql5tmQapZgsOtwCCKSE8walQxxQsGFSIialZRaRUWfbMXu8utUNSGe3lOP6DNIArokmGB2SBBVVXU2N3olZ2E/JxkLUqmOMKgQkREPrkVBUu+L8HqXw7D6pKhqIAoAPJpKcViEAGoMIgC7C4ZNXY3Ek0SpgzL403H1GYMKkREdIa3firDss2lZ0ztnB5SUswSnG4FLkXF0VonLEYJvbKTMGVYHoZ2yYhgxRSvGFSIiMjLWz+V4aVvS+BWTp/gOUUSBJgMAmwuBblpZpywufHnczphRPdM5OckcySFQoZBhYiIPNyKgmU/lkFWVEg+pnkAwGIUoaoqFLVhrYrDrcBilDCieyb6tU+JeM0U37i5nYiIPP65sxx1DjdEAfA1oJJglGB3KVAUQFVVQFVhdynolpnAhbMUFhxRISIijyM1dp8BRRIEiAJgczWsWVFUFaoKiIKAVIuBC2cpbBhUiIgIQMMIyZ6K+jOeJxhF2FyK1zRQ4w6gXtlJuHdMdy6cpbBhUCEiIlTbXJi9oRjf7a30ep5iNsDuOnMqqHe7REw6OxcTB3XkSAqFFYMKEZHO/XrwBB5dtxNHax2eZ5LYONXjhigIENEw1SMIwDVDcnH/73syoFBEMKgQEemUoqpYWViGV78v8ZrWGdUjE2d1SMY/fj6EOocbsqpCAJBiMWDq8DzceG6eZjWT/jCoEBHpUFW9E09+WoxNJVWeZ5IoYMbo7vjzOZ0gCgKmDu+Cf+4sx5EaOzqkWnBJ32wYRG4WpchiUCEiimONtx1X21xITzAiPycZWw6cwOPrd6K8zulp1zHVjHkT+mFgbqrnmUEUcdlZ7bUom8iDQYWIKE4VlVZhRWEZSiptcMsKJFGASRJx8ITd61LB3/fKwuPj+yDVwpuOKfowqBARxaGi0ioUfL4bVqeMNIsBoknEwRMO2N2Kp41BFHDf2B64dkguBC6MpSjFoEJEFEfcioLPdhzD0k37UW1zIzfNDLtLQWmNA3KT/cWJJgkvXzUQ/TumNvPTiLTHoEJEFCfe+qkMy34sQ53D7TnzZFf5mQe4ZSUZG7YcixxFoejHoEJEFOMUVcXzX+/Bqi2HoKiA5Cd/CALQJT0BZoOAinoXqm2uyBZK1AoMKkREMayotArLNpfip7ITngWyvm48NkoC8tItMBsk2F0yDKKI9AQunqXox6BCRBSjGhfMVtW74CObeCSYJNicMmwuBSZJRI3djV7ZSbztmGICgwoRUQxSVBUrCstgdcqwGETUOeUz2ggAzAYRtpOf2VwynG4FiSaJtx1TzGBQISKKIYqiYseRWvx6qAb/Kbci1WLACR9rTYySAEVR4WiyHVlVgV45SZgyLI+3HVPMYFAhIooRRaVVeOfj7dh9pBZWpxt1Dhkn7C40ySIAgESjhHpXwyiKQWhYs5JgFPH8pP7o3yGVIykUUxhUiIhiQON6FJtbQYpJgihIqHXIXiFFAGAyiJ6QAjSEFEkUcNvIrhjYMS3yhRO1EW+XIiKKck3Xo3RItcApqzhS6/RqYzaIyEo0wiV7D6+kmCXcPbobbzymmMURFSKiKOVWFPxzZzm2HqrBjqN1SDeLOHzCjgqrd0hJOrmrJzHFhCSzhAqrC5IgYOLgDrjj/G688ZhiGoMKEVEUanrKrKoCKoAau3cbSRCQk2JqWEwrANX1biSYJPTvmMIFsxQ3GFSIiKLMWz+V4aVvSyArKiSxIaScfohbsllChxQzjJIIsySgxiHjv87rgkG5qcjPSeaCWYobDCpERFHErShY9mMZZEWFUWwIKKeHlESThI4pJhgkEaqqotYho3d2Eq4+O5cBheIOgwoRURSwu914/qu9+OVADWrsbgCAS4HXibOCABglEfVOGbUOGQlGoMbu5gFuFNcYVIiINFTvcmHi34tQaXOf8VnTkGIxiFBUBa6T+5FP2N1QIaBXNg9wo/jGoEJEpJGp7/yM3w7XtdguxSzB6VYgCiIMkgpZUTF5cC4u6ZvN9SgU9xhUiIg0MPXtn/HbkeZDiigABlGEzSHDYGhYj+KWVaRYDLh7NLcdkz4wqBARRdjGPeUthhSzQYTDrcApKxAAQFEgKw2nzE4dnseQQrrBoEJEFCFuRcGS70vw5k8Hmm2XYJRga3IMvgpAUYEUiwH3XNgLE8/Kgar6/36ieMKgQkQUAW/9VIZlm8tQ4zhz0WwjUWgYMWkaUgDgrPbJuPZ3nTC+XzY65KTh+PHacJdLFDUYVIiIwuyNH/fj1e/2o7lBkMapHuX0Q1MALLluECwGA7hmlvSIQYWIKAwa7+l5ceNelFtdzbZNNEpeNx43NapHBiwG/qua9Iv/9BMRhVjjPT2NB7f5IwoN9/U0F1KenzgwHCUSxQwGFSKiEFpZVIqXvy0549j701mMIuwuBYqPVbEZFgmf3DGcIylEYFAhIgqZzSWVePm7lkPK6bt6AEBAwzqVeZfnY0zP7PAVSRRjNA0q77zzDt59910cPHgQANC7d2/cddddGDt2rJZlEREFRVFVfLj1MF78tgSy4r+dJAgQBfgMKefmpWLqeV15FD7RaTQNKh06dMADDzyArl27QlVVfPTRR7j77rvx4Ycfonfv3lqWRkQUkKLSKiz/sQy/HDwBZzNDKY2jKL6aXPu7XNz/+548Cp/IB02DyoUXXuj19f333493330Xv/zyS1BBpaU/242f6+3fAXrsN/usH9HQ780llXhifTGsDhluWYUIwNeAiq+pnkZ3jeqK/zqva0C/Lxr6HGnsc3wKpm9Rs0ZFlmVs2LAB9fX1GDJkSFDfm5WVEtJ28UaP/Waf9UOrfr/2zX/w/z7bBbfifxRFEn1P9TQa3CkF/3P5gKB/tx7fNfusX5oHleLiYlx33XVwOBxITEzESy+9hF69egX1Myoqaps9TloQGl54S+3ijR77zT5rXU3kaNVvRVXx3Fd7sOrnQz5HTxp5pnr8fD6gYzJev35IUKfM6vFds89aVxMejX0MhOZBpXv37vjoo49QW1uLzz77DLNmzcJbb70VVFhRVQT0MgNtF2/02G/2WT8i1e/GBbOrfz2MXeXWZttajKLPUZR2SUYM7JiKpy7rg0SjsdV16/Fds8/6FVRQWblyZUDtbr755oB/pslkQteuDfOzAwYMwL///W+sXLkSTz/9dDClERGFTVFpFRZt3Ifdx+rQzEwPDGLDxLvdpaDxbmOjJCDJbMDsP+TjvG6Z4S+WKM4EFVSWL1/eYhtBEIIKKqdTFAVOp7PV309EFEpFpVUo+Hw3Dtc4oKJhK7GvrHL6glkFgEkSMLhTKm4Z3oXbjolaKaig8uWXX4b0ly9cuBBjxoxBx44dYbVasXbtWhQWFuL1118P6e8hImoNpyzj2S/34FitE4qiQhIBt4+U4muqJ8Us4e7R3TFxUEduOyZqA03XqFRUVGDWrFk4duwYUlJSkJ+fj9dffx3nn3++lmUREeGtn8qwdFMprM4moySnrZ41iAJUqLC7vD+QRGD+5f041UMUAkEFlS1btqC6uhoXXHCB59lHH32ERYsWwWazYdy4cXj88cdhMpkC+nnz588PrloioghYUViKl78raXY9ir+zUSQBuHtUN4YUohARW25yyksvvYTdu3d7vi4uLsajjz6KkSNH4vbbb8dXX32FJUuWhLxIIqJIcCsKHlmzDS9+6z+kCGiY6nG6zwwpKWYJM8Z0x01Du4S3UCIdCWpEZefOnbjvvvs8X69fvx6DBg3C3LlzATQcib948WLcc889oa2SiCjM3vqpDK9+vx8Ot//TUQyiAFVtmOqRBODkJh8kmyRMHNwRd57fDQYxqP//R0QtCCqonDhxAu3atfN8XVhYiDFjxni+HjhwIA4fPhy66oiIwswpy5j50TZsLqlutt3pUz2y2jDN0zs7CfeO7cFdPURhElRQadeuHQ4cOICOHTvC6XRi+/btuPfeez2fW61WGI3GkBdJRBQOBZ/vwsf/PuLzosBGAgCz4cxdPR1SzLhleB539RCFWVBBZcyYMVi4cCEeeOABfPHFF7BYLDjnnHM8nxcXFyMvLy/kRRIRhdrcz3bg49/Km21jlAQoigr7adNBSSYJH0w7FyZJCmeJRIQgF9Ped999kCQJN954I1atWoU5c+Z47fD54IMPMGrUqJAXSUQUSvM+29liSEk0SXDJ6hmjLaIA3DqiC0MKUYQENaKSmZmJt99+G7W1tUhMTIR02h/UF154AYmJiSEtkIgoVOpdLly3/F84XOP/9GsBgMkgot7p+zrBu0Z1w43ncuSYKFJadeBbSkoKVFVFZWUlBEFARkbDIrL09PRQ1kZEFBJuRcFVbxTh4AlHs+2MkgBZUf3u/LlndFfcPIxbj4kiKeigUl5ejmeeeQZffvklrNaGG0STk5Mxbtw4zJw502tXEBGR1t76qQyLv9kH/5uOGyQaJdT7OMANAMySgDs5kkKkiaCCSl1dHa677jrU19dj0qRJ6NGjB1RVxZ49e7B27Vr8/PPPWL16NZKSksJVLxFRQOqcTtz05hYcqG5+FEUQAJMo+AwpRgG4IL8dZv+hL89HIdJIUEFlxYoVkCQJ69atQ2am9/HQ06dPx/XXX48333wTd955Z0iLJCIKxtXLClFSaW+xnUkS4ZIVOHzsTz43LxWLrxrEgEKksaD+BH7zzTe44447zggpAJCVleU5Rp+ISCsTX/shoJCSaJTglBX4OkLlTwOy8co1ZzOkEEWBoP4UlpSUYMiQIX4/HzJkCPbt29fmooiIWmPCqz/gQK272TaCcHJXj5/1KFcOyMFj4/uFozwiaoWg16ikpqb6/Tw1NRV1dXVtLoqIKBh1TicufnEz3M2cMAs0BBSXW4HTx64eEcCVgzrg4Yv7hKdIImqVoIKKqqoQmxkKFYSGC7uIiCLlqjcCW4+SaJL8no2SnWzER7cO4yFuRFEo6KAyfvx4CH7utWBIIaJIuvCZL1sMKaIAGET/B7gN6JiMZX/+XTjKI6IQCCqoFBQUhKsOIqKA1TmduOLVzahzNd/ObBDhcCtwymdO9fRsl4A3/nw2EnmRKlFUCyqoTJw4MVx1EBEFJNCtxwkmCTYfoyjtkoxYc/tw7ughihGtOkJfVVX89ttvOHjwIARBQOfOnXHWWWf5nRIiImqr4/X1uOyVn3xuJ25KFABJFHyGlM4pBnx4+4jwFEhEYRF0UNm8eTMeffRRHDp0yLMmpTGszJ8/H0OHDg15kUSkb6Nf+Bb2lrb04NRUj+LjALecJAM+vH1kOMojojAKKqjs378fd955JwYNGoSHH37Y6wj9N998E7fffjs++eQT5OXxPgwiCo1AQ0pzd/W0TzFiLUdSiGJS0EfoDx48GCtWrPB63rNnT1x88cW45ZZbsHz5cjz++OMhLZKI9Ol4fX2LIUUUAEnwc1ePCGy4azhSzeZwlUhEYRZUUCksLMRf/vIXn58JgoApU6bgueeeC0lhRKRfJdXVuP6NrS0e4GYxirC7FCg+jkbommbG+7cOD1OFRBQpQQWVQ4cOoU8f/6c29u7dGwcPHmxzUUSkX0MXbgyoXYJRgs3PVM95XVKw+Gr/130QUewIKqjU19cjISHB7+cJCQmw21veNkhE5EsgIUUSBIgC/IaUUT0y8PzEgaEujYg0EvSun//85z8oLy/3+VlVVVWbCyIifTlmteKG5b+i2t78ZYLAqVEUH5t6kGwU8eld58FiaNWpC0QUpYL+E33LLbf4PCq/8Z4fnqVCRIEa+fxGuM48NNan5qZ6vrrnPCSbTCGsjIiiRVBB5f/+7//CVQcR6UygIUUSBQjwP9Xz0wNjwGvGiOJXUEGlU6dO4aqDiHTkmNUaUEhpbhQFALY+Ng5OuzOElRFRtAkqqOzcuTOgdn379m1VMUSkDxNe/VeLbSxGsdmQ0j7FiNRkM44zqBDFtaCCypVXXulZiwLAsx6l6ZoVQRCwY8eOEJZIRPFi4/79mPn+/mbbGMSGf6/YmxlyaZ9ixLo7eNIskR60eo2Kqqq44oor8NprryE3NzfkhRFR/Ki02TD+5aIW27U01dMvJxkvXtOfJ80S6Uib16h06NCBa1eIyK8LFn+HOmfLC1JamuopmjkmlGURUYzggQNEFBYHamow8e+/tNjOIApQofqd6jGKwA/3M6QQ6RWDChGF3LCFGxHIjuEEkwSb0/coSrrFgLdvGYycpKTQFkdEMYVBhYhCKpBj8AUAZqPoN6R8cOtgdElLC3FlRBSLWrXrp5HD4cD06dNhNBq92n344YehqY6IYkogIcUgNuwcbG5XD0MKETUKKqiMGzfO6+uLLroopMUQUewKJKQ0N9XTiItmiaipoILKjBkzwlUHEcWgL/ftw6zVZS22EwCYDf6negBO9xCRb61eo1JZWYmDBw9CEAR06tQJGRkZoayLiKJcICMoAGCUBMiKCrvb/1QPR1GIyJ+gg8ru3bvx1FNP4eeff/Z6PnToUDz11FPo0aNHyIojougUaEhJNEmob2YUZeFVXTGma9dQlUVEcSiooFJeXo4bb7wRmZmZeOihh9CjRw+oqoo9e/Zg1apVuOGGG7B27VpkZWWFq14i0ligu3pMBtFvSPnsrqHITEgIcWVEFI+CCirLly9Hp06d8O6778Lc5AjrMWPG4Prrr8ef//xnLF++HDNnzgx5oUSkra1Hj2LaW8Uttmuc6nH4merhNA8RBUMMpvEPP/yAW2+91SukNLJYLJg2bRq+++67kBVHRNFh6MKNAYWURKMEl6xC8XPaG0MKEQUrqBGVsrIy9O/f3+/nAwYMQFlZyzsAiCh2BDTVIwAmUUA97+ohohALakTFarUiOTnZ7+dJSUmor69vc1FEFB0CCSkmSQRUwCH7PzSfIYWIWivoXT9Wq9Xn1A8A1NXVQVUDueGDiKLZ6t9+Q8FnlS22SzRKzY6iAAwpRNQ2QQUVVVUxfvz4Zj9vesQ+EcWWjfv3Y+b7+1tsJwiAURKbDSkvXdcTwzp1CmV5RKRDQQWVlStXhqsOItJYoGejmAwiXG4FTu7qIaIICCqoDBs2LFx1EJFGPtm5E3PWHQuobUsHuDGkEFGotfoI/UaqqmLz5s1wOBwYMmQI0nhXB1HMCHQURRQAg+j/ALdXru+Fc3NzQ1kaERGAIINKTU0N5s2bh+3bt2Pw4MF46KGHcNttt2HLli0AgKysLLz++uvo27dvWIolotAJNKSYDSIcbgVOmVM9RBR5QW1P/utf/4pffvkFl112GXbt2oVbb70ViqLgvffew6pVq9CjRw/87W9/C1OpRBQqn+zcGVC7BJPk94RZgCGFiMIvqBGVjRs3YuHChRg2bBgmTZqEsWPHYsWKFRg8eDAA4MEHH8T06dPDUigRtd3KzZux+Htni+1EAZBEATauRyEijQUVVCoqKtCtWzcAQPv27WE2m9GxY0fP5x07dkRlZctnLxBR5AU71aP4OcBt3pWdcEnPnqEsjYjIr6CCiqIokCTJ87Uoil7npvAMFaLoFGhIaekAN46iEFGkBb3r53//93+RmJgIAJBlGatXr0ZGRgaAhlNriSi6BBJSRAGQBP939Tw8PhOTBgwIdWlERC0KKqjk5uZi1apVnq/btWuHjz/++Iw2RBQdAgkpFqMIu0uB4uP6i4VXdcWYrl3DURoRUUCCCipffvlls58fOXIEL730UpsKIqLQCCSkJBgl2PyMonCah4iiQVDbk1tSXV2N999/P+D2S5YsweTJkzFkyBCMGDECd911F/bu3RvKkoh05+2iohZDiiQIMIqC35Dy4EWp4SiNiChobT6Zti0KCwtxww03YODAgZBlGc899xymTZuGdevWedbBEFFgln77LV79seXbyxunevxs6gEAXHP22aErjIioDTQNKq+//rrX1wsWLMCIESOwbds2DB06VKOqiGJPt4fWBdSuuameRpzyIaJoomlQOV1tbS0ABH1fUEu7ohs/19vuaT32W299fn79Rry9veV2kihAAJoNKQ+OS8W1MTSSord3DbDPeqGHPgfTN0FVfSz192PGjBnNfl5TU4OioiLs2LEj8ApOUhQF06dPR01NDd59992gv59Ib0I1ilKyYEKoSiIiCrmgRlRSUlJa/LxTp06tKmT27NnYvXs33nnnnaC/t6KiFs3FLUEAsrJSWmwXb/TYb730+dxnAzvAzWIUmw0pPz0wBseP14aqrIjSy7tuin3WuprI0EOfG/sYiKCCSkFBQasKasnTTz+Nr7/+Gm+99RY6dOgQ9PerKgJ6mYG2izd67Hc89zmQbccGsWFc1e5q/kLBePh7FM/v2h/2WR/02GdfNF2joqoq5syZg88//xxvvvkm8vLytCyHKKrNf38jPtzfcjsumCWieKJpUJk9ezbWrl2Ll19+GUlJSSgvLwfQMIVksVi0LI0oqgR6V09LUz23DALuvpghhYhih6ZBpXHR7E033eT1vKCgAJMmTdKiJKKoE+hUjwq1xakeIqJYo2lQKS4u1vLXE0Wt2e9uxNpDgbVNNEmod/ofRfnvMQm4gecSEVGMiqpzVIgo8GkeAYDZKPoNKXcOEzBt9OgQVkZEFHkMKkRRJNCQYhAFqKr/qR5O8xBRvAjppYRE1HqBhpQEkwS3ovq9q4chhYjiCUdUiKJAICFFAGAyiLA1sx6FIYWI4g2DCpHGAgkpRkmArKhwuH1P9Tx8RTYm5/fj4VBEFHcYVIg0csPCjdgVQLuWdvX89MAYtGuXErNH4RMRNYdBhUgDwUz1NBdSONVDRPGOi2mJIizQqR5BgN+pHoAhhYj0gSMqRBEQ6I4eAEg0Sqhv5hj8FABfMqQQkU4wqBCFWcAHuAmASRT8hhSOoBCRHnHqhyiMAg0pJkkEVMDh53AUhhQi0isGFaIwCTSkJBolOGUF/nYWM6QQkZ5x6ocoxIKZ6jFKYrPrURhSiEjvOKJCFEIBT/UYGqZ6nH529fQGQwoREcARFaKQCXiqp4UD3BhQiIhOYVAhCoFAQoooAAaRB7gREQWDQYWoDQIdRTEbRDjcCpwyD3AjIgoG16gQtVKgISXBJDV7wmxXMKQQEfnDERWiVgh0qkcSBdj8TPV0AvARAwoRUbM4okIUpEBCitkgQlEBl58D3ACGFCKiQHBEhShAwRzg1tzZKACneoiIAsWgQtSCQAOKKACS4P+uHoDTPUREwWJQIWpGoCHFYhRhdylQVN7VQ0QUSlyjQuRHwLt6jBLsLm47JiIKB46oEPkQSEiRRAEiABvv6iEiChsGFaImghlFsblk+IsoDChERKHBqR+ik4INKf4wpBARhQ5HVIgQ+FSPAE71EBFFEkdUSPcCCSkJRgmyosKt+D/AjSGFiCj0OKJCuhX4VI/IURQiIo0wqJDuBBpQDKIAALBx6zERkWYYVEhXuGCWiCi2MKiQLgQaUICGU2YZUoiIogODCsW9YKZ6VKg8ZZaIKIowqFBc41QPEVFsY1ChuBVISBEAmDnVQ0QUtRhUKO50e2hdQO0MogBV5VQPEVE044FvFFfOfTbwqR63okL2f34bQwoRURTgiArFjYCnegyc6iEiihUMKhTzAl0wa5QEKIoKu5tTPUREsYJBhWJaoCEl0SSh3slRFCKiWMOgQjEr0Kkek0H0G1IYUIiIohsX01JMCiSkGCUBggA4/Ez1MKQQEUU/jqhQTOFUDxGRvjCoUMwIaKpHAEyiwJBCRBQnGFQo6gU6imKSRLhkBQ4/h6MwoBARxR6uUaGoFvBUj1GCU1bg7/w2hhQiotjEERWKWoFO9RglEfU8wI2IKC5xRIWiUiAhxWQQARVw8gA3IqK4xREViiqh2tXz0wNjoDZzjw8REcUGjqhQ1AgkpIhCw6LZ5kJKyYIJoSyLiIg0xKBCUSGQkGI2iFBUwCn7n+r56QFO9RARxRNO/ZBmAp3mAYAEkwRbM6MoANejEBHFIwYV0kSgIUUUAEkUmg0pDChERPGLQYUiLtCQYjaIcLgVKDzAjYhIt7hGhSIqmAPc/F0mCDCkEBHpBUdUKCKCmuoRBB7gRkREADiiQhEQaEixGBt29bgU/wegMKQQEekLR1QorAINKQlGCTaOohAR0WkYVChsAgkpkihABBhSiIjIJ02nfoqKinDnnXdi1KhRyM/PxxdffKFlORRCgYSUBKMEWVE51UNERH5pOqJSX1+P/Px8TJ48GTNmzNCyFAqBoA5wa2GqB2BIISIijYPK2LFjMXbs2Db/HEEI7POW2sWbSPb73GcDCymSKECA/6meth6Br8d3rcc+A/rsN/usD3roczB9i4s1KllZKSFtF2/C3e9uD60LqF1LoyihvExQj+9aj30G9Nlv9lkf9NhnX+IiqFRU1EL1v8wBgtDwwltqF28i0e9AR1IsRrHZkPLTA2Nw/Hhtm+vR47vWY58Bffabfda6msjQQ58b+xiIuAgqqoqAXmag7eJNOPod6HoUg9gwvmd3+T5ltnEdSqjr0+O71mOfAX32m33WBz322Rce+EZBC+ZsFLeiwu1nVw8XyxIRUUsYVCgowZwyy7NRiIiorTSd+rFarSgtLfV8feDAAezYsQNpaWnIzc3VsDLyJZCQYhAFqFD9TvUADClERBQ4TYPKb7/9hptvvtnzdUFBAQBg4sSJWLBggVZl0WkeemMj/q+q5XaJJgn1Tp6NQkREoaNpUBk+fDiKi4u1LIGa8eTbG7H+SMvtBABmo8iQQkREIRcXu34o9ILZ1aOq/qd6GE6IiKgtuJiWzhBoSEk0Nezqkf1sn2NIISKitmJQIS/nBRBSBAAWg/+pHjMYUoiIKDQ49UMAgKUbN2JJUcvtjJIARVFhd3Oqh4iIwo9BhYKa6mluwSxDChERhRqDis4FElIEAKZmpnoAhhQiIgoPrlHRsUBCilESIAiAw89UTx4YUoiIKHw4oqJDP5SV4b5V+1psl2iUUM9j8ImISEMMKjoT0FSPAJhEwW9IuSgDWPBfDClERBR+DCo6EkhIMUkiXLICh4/DUSQAmzmKQkREEcQ1KjoRSEhJNEpwygr8nN/GkEJERBHHoBLnSsqrce6zzYcUQTi5q4frUYiIKMpw6idOHaqtxZ9e29JiO5NBhMutwOlnV8/NA4B7xjOkEBGRNhhU4tDwhRvhO3Z44wFuREQU7RhU4kilzYbxL7d8Dr4oAAaRB7gREVH0Y1CJExcs/g51zpbHUcwGEQ63Aqfsu+304SL+a9SoUJdHRETUKgwqcWDUcxvh8LdVp4kEkwQbR1GIiCiGMKjEuMte+b7FkCIKgCQKDClERBRzGFRilN3txsUvbYLd3XxKaZzqUXwc4NaIIYWIiKIVg0oMuv/Df+O7vVUttmvprp6/39AHZ3foEMrSiIiIQopBJcbc+/5WbNpf3WwbUQAkwf9dPQBHUYiIKDYwqMSQe//3V2wqPdFsG4tRhN2lQFF9T/X877RB6JaeHobqiIiIQo9BJUbc/+G/WwwpCUYJNj+jKB/fPgS5KSnhKI2IiChsGFSimKKqKD5WhyO1tmbXpEiiABHwGVISJODb+8fAzwALERFRVGNQiVJFpVVYUViGkkobquudfts1jqL4GkfJTpRQ9MSlOH68NnyFEhERhRGDShQq3F+Fpz/bhXqnGylmAwTBd7vmpnrapxix7o4RYaySiIgo/BhUosyP+yvxyNqdqLW7IQhAvVPG6UegSKIAAb6nepKMIj65YyhSzebIFExERBRGDCpRpKi0CnM27EKdww1RACAIkBXvlNLcKMqIrulYdNWgCFRKREQUGQwqUUJRVawoLIP1ZAhRVEA9bQWsxSj6Dyld0hhSiIgo7ohaF0ANio/VoaTSBrMkNoSUJp8ZRAEWQ8P5KL6M6pGBRVcPjkyhREREEcQRlShRWe9Ejd0F22lhJMkkwS0rUNFw4qxZBFISjOiWmYjO6Qm4/4IesBj4GomIKD7xv3BR4LjVidd+2H9GSEk0inC6ZYiCAFlRoKpAaqIZT17aB0O7ZGhULRERUeQwqGiscH8VHl+/E5X1Ls+zBKOIzEQjqmxuqBDgVgFVBVIsBjwxniGFiIj0g0FFI25FxdJN+/HG5lKv9SiZiUaoqgqTJKJzmhl1Dhm1DjcSjRIev7QPhnVlSCEiIv1gUNHAsVoHHlu/E1sOnLq7xyQJ+MsFPZGXbsHKogMoqbTB7VBgEEWc1SEFU4blcSSFiIh0h0Elwn7YV4knPy1Gte3UVE+XjATMv7wf8nOSAQDndslA8bE6VNtcSE8wIj8nGaK/42mJiIjiGINKhLhlBa98vx8ri8q8nl/aLwcPjeuFJNOpVyEKAvq1503HREREDCoRcKTGjkfX7cTWQzWeZxaDiAcu6ok/9u8AgaMlREREPjGohJCiqmdM2Xy7pxJPf1aMGrvb0657ViLmX94PvdolaVgtERFR9GNQCZGi0iqsKCxrWAQrK5BEAaIg4Eitw6vdFQPa48ELeyHBKGlUKRERUexgUAmBH/dXYs6GXbC6ZKSaDUi0SDh0wgFHk2uPLUYRD43rjQlntdewUiIiotjCoNJGhfur8MjanahzuCEIAuqdDpx24TFSLQa8du1g9ORUDxERUVAYVNqgqLQKT3+2C7V2N0Sh4bbj00NKdrIJAgCn7PtCQSIiIvKPtye3kqKqWFFYhnqn++TX8AopggCkmCVkJBggq6rXuSlEREQUGI6otFLxsTqUVNpgEAWcNogCs0GEqiqwuRTUOWQYRBHpCUZN6iQiIoplHFFppWN1DlTbXKiyub2ep5gNgKpCQEOAqXW40S0zwXPqLBEREQWOIyqtUFJZjxe+3guH+9S6E1EATAYRDrcMQRAgKwpUFUg0SpgyLI9H4BMREbUCg0qQ1m8/igVf7IbNdSqkpJgNSLVIqLa54VZVKCdDSorFgMcv7cPLBImIiFqJQSVANpeMZ/7vP1iz7ajX8+xkE2RZgUkS0Sndgjq7G7UONxJNBjwxvg+GdWVIISIiai0GFR9OPwrfKAp4ZN1O7Kuo97RJMRvw5KV9kGiSTp1I63DDIIo4q0MKpgzL40gKERFRGzGonKbpUfgutwy3CtQ53F5bjwfmpmDehH7omGoBAJyTl37GHT9ck0JERNR2DCpNFJVWoeDz3bA6ZaSYJdQ7VdQ6ZK82Nw/tjOnnd4NBOrVhShQE9GufEulyiYiI4h6DykmNB7hZnTLSLQYcOGGHs8ldPZIooG9OEu4e3Z2jJURERBHCc1ROajzALc1iwNE6p1dISU8woFOqGcetLhQfq9OwSiIiIn3hiMpJ1TYX3LICU4IBUpMRk85pFiSbJagArFYnj8InIiKKIAaVk9ITjDBIIpxuBR1SzUhySLAYRFiMEgDA4eJR+ERERJHGqZ+T8nOS0S0zATUnb0JOTzB6Qoqqqqix8yh8IiKiSGNQOUkUBEwZlodEk4TyOifsLhmKqsLuklFe50SiiUfhExERRRqDShNDu2Tg4Yt7o1d2EupdCo5bnah3KeiVnYSHL+7NA9yIiIgiLCrWqLz99tt4/fXXUV5ejr59++Lxxx/HoEGDNKllaJcMHuBGREQUJTQfUVm/fj0KCgpw991348MPP0Tfvn0xbdo0VFRUaFZT4wFuI7plol/7FIYUIiIijWgeVJYtW4ZrrrkGkydPRq9evTB79mxYLBZ88MEHWpdGREREGtN06sfpdGLbtm244447PM9EUcTIkSOxZcuWgH9OSwMejZ/rbWBEj/1mn/VDj/1mn/VBD30Opm+aBpWqqirIsoysrCyv51lZWdi7d2/APycrK7B7dgJtF2/02G/2WT/02G/2WR/02GdfomIxbVtVVNRCVf1/LggNL7yldvFGj/1mn7WuJnL02G/2WetqIkMPfW7sYyA0DSoZGRmQJOmMhbMVFRVo165dwD9HVRHQywy0XbzRY7/ZZ/3QY7/ZZ33QY5990XQxrclkQv/+/bFp0ybPM0VRsGnTJgwZMkTDyoiIiCgaaD71M3XqVMyaNQsDBgzAoEGDsGLFCthsNkyaNEnr0oiIiEhjmgeVyy67DJWVlVi0aBHKy8vRr18/LF26NKipHyIiIopPmgcVALjxxhtx4403al0GERERRZmoCCptxXNUfNNjv9ln/dBjv9lnfdBDn4Ppm6CqXFNMRERE0UnzI/SJiIiI/GFQISIioqjFoEJERERRi0GFiIiIohaDChEREUUtBhUiIiKKWgwqREREFLUYVIiIiChqMagQERFR1GJQISIioqili6Dy9ttv48ILL8TAgQNx9dVXY+vWrVqXFDJLlizB5MmTMWTIEIwYMQJ33XUX9u7d69XmpptuQn5+vtf/nnjiCY0qbrvFixef0Z9LL73U87nD4cDs2bMxfPhwDBkyBPfccw+OHz+uYcWhceGFF57R7/z8fMyePRtAfLznoqIi3HnnnRg1ahTy8/PxxRdfeH2uqipeeOEFjBo1CoMGDcItt9yCkpISrzbV1dWYOXMmfve73+Hcc8/FI488AqvVGsFeBKe5PrtcLjzzzDO44oorcPbZZ2PUqFH4n//5Hxw9etTrZ/j6Z+O1116LdFcC1tJ7fuihh87oz7Rp07zaxNp7Blrut68/3/n5+Vi6dKmnTay961CIi0sJm7N+/XoUFBRg9uzZGDx4MFasWIFp06Zhw4YNyMrK0rq8NissLMQNN9yAgQMHQpZlPPfcc5g2bRrWrVuHxMRET7trrrkG9957r+frhIQELcoNmd69e2PZsmWeryVJ8vz1/Pnz8c033+Bvf/sbUlJSMGfOHMyYMQP/+Mc/tCg1ZN5//33Isuz5evfu3Zg6dapXSIv191xfX4/8/HxMnjwZM2bMOOPzv//973jzzTexYMECdO7cGS+88AKmTZuG9evXw2w2AwAeeOABlJeXY9myZXC5XHjkkUfwxBNPYOHChZHuTkCa67Pdbsf27dsxffp09O3bFzU1NZg3bx6mT5+O1atXe7W99957cc0113i+TkpKikj9rdHSewaA0aNHo6CgwPO1yWTy+jzW3jPQcr+/++47r683btyIRx99FOPHj/d6HkvvOiTUOHfVVVeps2fP9nwty7I6atQodcmSJRpWFT4VFRVqnz591MLCQs+zG2+8UZ07d66GVYXWokWL1D/+8Y8+P6upqVH79++vfvrpp55n//nPf9Q+ffqoW7ZsiVCFkTF37lx13LhxqqIoqqrG33vu06eP+vnnn3u+VhRFPf/889WlS5d6ntXU1KgDBgxQ165dq6rqqXe9detWT5tvvvlGzc/PV48cORK54lvp9D778uuvv6p9+vRRDx486Hl2wQUXqMuWLQtzdeHhq8+zZs1Sp0+f7vd7Yv09q2pg73r69OnqzTff7PUslt91a8X11I/T6cS2bdswcuRIzzNRFDFy5Ehs2bJFw8rCp7a2FgCQlpbm9XzNmjUYPnw4Lr/8cixcuBA2m02L8kJm//79GDVqFC666CLMnDkThw4dAgD89ttvcLlcXu+8Z8+eyM3NxS+//KJRtaHndDrxySefYPLkyRCa3Jceb++5qQMHDqC8vNzr3aakpGDw4MGeP89btmxBamoqBg4c6GkzcuRIiKIYN1O+dXV1EAQBqampXs///ve/Y/jw4bjyyiuxdOlSuN1ujSoMjcLCQowYMQLjx4/Hk08+iaqqKs9nenjPx48fxzfffIOrrrrqjM/i7V23JK6nfqqqqiDL8hlTPFlZWWes44gHiqJg/vz5+N3vfoc+ffp4nl9++eXIzc1FTk4OiouL8eyzz2Lfvn148cUXNay29QYNGoSCggJ0794d5eXleOmll3DDDTdgzZo1OH78OIxG4xn/Es/KykJ5eblGFYfeF198gdraWkycONHzLN7e8+ka35+vP8+Na5COHz+OzMxMr88NBgPS0tLi4v07HA48++yzmDBhApKTkz3Pb7rpJpx11llIS0vDli1b8Nxzz6G8vBwPP/ywhtW23ujRo3HxxRejc+fOKCsrw3PPPYfbbrsN7733HiRJivv3DAAffvghkpKScMkll3g9j7d3HYi4Dip6M3v2bOzevRvvvPOO1/Nrr73W89f5+fnIzs7GLbfcgtLSUnTp0iXSZbbZ2LFjPX/dt29fDB48GBdccAE+/fRTWCwWDSuLnA8++ABjxoxB+/btPc/i7T2TN5fLhfvuuw+qqnoWUDeaOnWq56/79u0Lo9GIJ598EjNnzjxjbUcsmDBhguevGxeMjhs3zjPKogcffPABrrjiCs/aq0bx9q4DEddTPxkZGZAkCRUVFV7PKyoq0K5dO42qCo+nn34aX3/9NVasWIEOHTo023bw4MEAGqZP4kFqaiq6deuG0tJStGvXDi6XCzU1NV5tKioqkJ2drVGFoXXw4EH88MMPPoeEm4q399z4/pr789yuXTtUVlZ6fe52u3HixImYfv8ulwv//d//jUOHDuGNN97wGk3xZfDgwXC73Thw4ECEKgyvvLw8ZGRkeP5Zjtf33Oinn37Cvn37cPXVV7fYNt7etS9xHVRMJhP69++PTZs2eZ4pioJNmzZhyJAhGlYWOqqq4umnn8bnn3+OFStWIC8vr8Xv2bFjBwDExR9oALBarSgrK0N2djYGDBgAo9Ho9c737t2LQ4cO4eyzz9auyBBavXo1srKy8Pvf/77ZdvH2njt37ozs7Gyvd1tXV4dff/3V8+d5yJAhqKmpwW+//eZps3nzZiiKgkGDBkW85lBoDCn79+/H8uXLkZGR0eL37NixA6IoxsXORgA4cuQIqqurPf8sx+N7bur9999H//790bdv3xbbxtu79iXup36mTp2KWbNmYcCAARg0aBBWrFgBm82GSZMmaV1aSMyePRtr167Fyy+/jKSkJM/8bEpKCiwWC0pLS7FmzRqMHTsW6enpKC4uRkFBAYYOHRrQH4Jo9Ne//hUXXHABcnNzcezYMSxevBiiKOLyyy9HSkoKJk+ejAULFiAtLQ3JycmYO3cuhgwZEhdBRVEUrF69GldeeSUMhlN/fOPlPVutVpSWlnq+PnDgAHbs2IG0tDTk5ubi5ptvxiuvvIKuXbt6tifn5ORg3LhxABoWTo8ePRqPP/44Zs+eDZfLhTlz5mDChAle02TRpLk+Z2dn495778X27duxZMkSyLLs+TOelpYGk8mELVu24Ndff8V5552HpKQkbNmyBQUFBfjjH/94xqL6aNFcn9PS0vDiiy9i/PjxaNeuHcrKyvDMM8+ga9euGD16NIDYfM9Ay/98Aw3he8OGDZg1a9YZ3x+L7zoUBFVVVa2LCLe33noLr7/+OsrLy9GvXz889thjnmHxWJefn+/zeUFBASZNmoTDhw/jwQcfxO7du1FfX4+OHTti3LhxuOuuu1ocPo5W999/P4qKilBdXY3MzEycc845uP/++z3rMBwOBxYsWIB169bB6XRi1KhRePLJJ+NiZOG7777znAPUvXt3z/N4ec8//vgjbr755jOeT5w4EQsWLICqqli0aBFWrVqFmpoanHPOOXjyySe9/l5UV1djzpw5+PLLLyGKIi655BI89thjUXvWRHN9njFjBi666CKf37dy5UoMHz4c27Ztw+zZs7F37144nU507twZf/rTnzB16tSoXbPQXJ+feuop3H333di+fTtqa2uRk5OD888/H/fdd5/XlH2svWeg5X++AeC9997D/Pnz8d133yElJcWrXSy+61DQRVAhIiKi2BTXa1SIiIgotjGoEBERUdRiUCEiIqKoxaBCREREUYtBhYiIiKIWgwoRERFFLQYVIiIiiloMKkRERBS1GFSIiIgoasX9XT9EFDoPPfQQampq8PLLL5/x2YUXXoiDBw8CgOeStDFjxmDWrFmee0iaHiEuCAKSkpKQl5eHkSNH4pZbbkFOTo7n5y1evBhffPEFPv74Y6/fc+DAAVx00UX46KOP0K9fP8/XjRITE5Gbm4thw4ZhypQp6Natm+ez1atX4+GHH/bUmJycjG7dumHs2LGYMmXKGUeWE5H2GFSIKGTuvfdeXHPNNVAUBfv27cMTTzyBuXPn4plnnvFqt2HDBiQnJ6Ourg7bt2/H0qVL8cEHH2DlypV+769qyfLly9GrVy/Y7XYUFxdj5cqV+NOf/oRXX30VI0aM8LRLTk7Ghg0boKoqamtr8fPPP+O1117D6tWr8e6770b1pXZEesSpHyIKmaSkJGRnZ6N9+/Y477zzcOWVV2L79u1ntMvKykJ2dja6d++OCRMm4N1330VGRgaeeuqpVv/u9PR0ZGdnIy8vD+PGjcPy5csxaNAgPProo5Bl2dNOEARkZ2cjJycHPXv2xNVXX41//OMfqK+vPyNQEZH2GFSIKCyOHj2Kr776CoMGDWqxrcViwXXXXYeff/4ZFRUVIfn9oihiypQpOHjwILZt29Zs26ysLFxxxRX48ssvvUINEWmPUz9EFDLPPvssXnjhBciyDIfDgcGDB3vWhLSkR48eAICDBw8iKysLALBr1y4MGTLEq10wF753794dQMO6lpYCU48ePWC1WlFdXe35/USkPQYVIgqZadOmYdKkSVBVFYcPH8bzzz+P22+/HW+//TYkSWr2e30FkO7du+OVV17xenb06FHcdNNNQdUlCEKLbRp/fyBtiShyGFSIKGQyMjLQtWtXAEC3bt2QkJCAa6+9Fj/++CNGjhzZ7Pfu3bsXANC5c2fPM6PR6Pl5jVoKPE3t2bPnjJ/Z3O9PTk5Genp6wD+fiMKPa1SIKGxEseFfMXa7vdl2drsd7733HoYOHYrMzMyQ/G5FUfDmm2+ic+fOOOuss5ptW1FRgTVr1mDcuHGemokoOnBEhYiCUltbix07dng9axyFsFqtKC8vh6qqOHLkCJ555hlkZmaesc6koqICDocDVqsV27Ztw9KlS1FVVYUXX3yx1XVVV1ejvLwcdrsdu3btwooVK7B161YsWbLEaxRGVVVPjbW1tdiyZQuWLFmClJQUzJw5s9W/n4jCg0GFiIJSWFiIK6+80uvZVVddBQBYtGgRFi1aBADIzMzEwIED8cYbbyAjI8Or/aWXXgpBEJCYmIi8vDycf/75mDp1KrKzs1td1y233AIASEhIQG5uLoYPH445c+acMXVUV1eHUaNGQRAEJCcno3v37rjyyisxZcoUJCcnt/r3E1F4CGowS+iJiIiIIoiTsURERBS1GFSIiIgoajGoEBERUdRiUCEiIqKoxaBCREREUYtBhYiIiKIWgwoRERFFLQYVIiIiiloMKkRERBS1GFSIiIgoajGoEBERUdT6/0TC4yfQzWi4AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x='LBXTC',y='LBDTCSI',data=lab_data_chol)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "dudGLBXhcbxw",
        "outputId": "4e1db319-4610-46e8-ac5f-cb23e9d8166c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='LBXTC', ylabel='LBDTCSI'>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,axes = plt.subplots(1, 2, figsize=(12, 6))\n",
        "lab_data_chol['LBDHDD'].plot(kind='hist', bins=20, title='LBDHDD', ax=axes[0])\n",
        "axes[0].spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "lab_data_chol['LBDHDDSI'].plot(kind='hist', bins=20, title='LBDHDDSI', ax=axes[1])\n",
        "axes[1].spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "H01AXgUCdWND",
        "outputId": "3e137bc3-e09c-4108-f0e1-e28e66e7072a"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,axes = plt.subplots(1, 2, figsize=(12, 6))\n",
        "lab_data_chol['LBXTC'].plot(kind='hist', bins=20, title='LBXTC', ax=axes[0])\n",
        "axes[0].spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "lab_data_chol['LBDTCSI'].plot(kind='hist', bins=20, title='LBDTCSI', ax=axes[1])\n",
        "axes[1].spines[['top', 'right']].set_visible(False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "lSSZ6JlFcf4C",
        "outputId": "730c05ba-8eb6-4dd1-8f83-73eb7686ae0d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_dropped_new_dup.to_csv('Merged_labpratory_data.csv')"
      ],
      "metadata": {
        "id": "RD-JOERC7iY0"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lab_data_chol.to_csv('lab_chol.csv')"
      ],
      "metadata": {
        "id": "_gy5yPD3_Xgi"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9hAG0SDegdea"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1xoNoY-MdkOf"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DTCQIXq0fsko"
      },
      "execution_count": 44,
      "outputs": []
    }
  ]
}