16352437_ICP1.ipynb

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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### **The Initial step is to import the required Libraries**"
      ],
      "metadata": {
        "id": "kE8Qi-k76Inr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "IijUMWwA7MvK"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Firstly I have Created an empty DataFrame and added data to it.**"
      ],
      "metadata": {
        "id": "6chGd09M68po"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_empty1= pd.DataFrame()\n",
        "print(df_empty1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKlY4nnQ6bLR",
        "outputId": "e473291c-0aca-4cbb-bb88-772ceead8ff4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: []\n",
            "Index: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **I have filled this empty data frame with the sample data**"
      ],
      "metadata": {
        "id": "lyq84Uc57rsc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_empty1['Course_Name'] = ['Machine Learning', 'Data Science', 'AI Fundamentals']\n",
        "df_empty1['Session_Count'] = [4, 5, 3]\n",
        "df_empty1['Student_Count'] = [25, 45, 30]\n",
        "df_empty1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "pCeN1cWw9FQi",
        "outputId": "73ae480f-51e8-4cea-bcce-f997d17b0708"
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      "execution_count": 7,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "        Course_Name  Session_Count  Student_Count\n",
              "0  Machine Learning              4             25\n",
              "1      Data Science              5             45\n",
              "2   AI Fundamentals              3             30"
            ],
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              "      <th>2</th>\n",
              "      <td>AI Fundamentals</td>\n",
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              "    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",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_empty1",
              "summary": "{\n  \"name\": \"df_empty1\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Course_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Machine Learning\",\n          \"Data Science\",\n          \"AI Fundamentals\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Session_Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Student_Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 25,\n        \"max\": 45,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          25,\n          45,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Now I have customized the index**"
      ],
      "metadata": {
        "id": "s5YpMl0Q-E8Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_empty2 = pd.DataFrame(columns=['Course_Name', 'Session_Count', 'Student_Count'],\n",
        "                           index=['a', 'b', 'c'])\n",
        "df_empty2"
      ],
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        "outputId": "47974504-5a4a-47e4-f3b8-44f5eccd46e0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Course_Name Session_Count Student_Count\n",
              "a         NaN           NaN           NaN\n",
              "b         NaN           NaN           NaN\n",
              "c         NaN           NaN           NaN"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_empty2');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_empty2",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Reading hapititis Dataset from Github.**"
      ],
      "metadata": {
        "id": "Aii-9roLBKur"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/datasets/hepatitis/master/data/hepatitis.csv')\n",
        "df.head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "hq_Bl3ESBPbs",
        "outputId": "45a5fdbd-1234-44ea-d106-c1a552293991"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
              "0    30    male   False       False   False   False    False     False   \n",
              "1    50  female   False       False    True   False    False     False   \n",
              "2    78  female    True       False    True   False    False      True   \n",
              "3    31  female     NaN        True   False   False    False      True   \n",
              "4    34  female    True       False   False   False    False      True   \n",
              "5    34  female    True       False   False   False    False      True   \n",
              "6    51  female   False       False    True   False     True      True   \n",
              "7    23  female    True       False   False   False    False      True   \n",
              "8    39  female    True       False    True   False    False      True   \n",
              "9    30  female    True       False   False   False    False      True   \n",
              "10   39  female   False        True   False   False    False     False   \n",
              "11   32  female    True        True    True   False    False      True   \n",
              "12   41  female    True        True    True   False    False      True   \n",
              "13   30  female    True       False    True   False    False      True   \n",
              "14   47  female   False        True   False   False    False      True   \n",
              "\n",
              "   liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
              "0       False           False   False   False   False        1.0   \n",
              "1       False           False   False   False   False        0.9   \n",
              "2       False           False   False   False   False        0.7   \n",
              "3       False           False   False   False   False        0.7   \n",
              "4       False           False   False   False   False        1.0   \n",
              "5       False           False   False   False   False        0.9   \n",
              "6       False            True    True   False   False        NaN   \n",
              "7       False           False   False   False   False        1.0   \n",
              "8        True           False   False   False   False        0.7   \n",
              "9       False           False   False   False   False        1.0   \n",
              "10       True           False   False   False   False        1.3   \n",
              "11       True           False    True   False   False        1.0   \n",
              "12       True           False   False   False   False        0.9   \n",
              "13       True           False   False   False   False        2.2   \n",
              "14      False           False   False   False   False        NaN   \n",
              "\n",
              "    alk_phosphate   sgot  albumin  protime  histology class  \n",
              "0            85.0   18.0      4.0      NaN      False  live  \n",
              "1           135.0   42.0      3.5      NaN      False  live  \n",
              "2            96.0   32.0      4.0      NaN      False  live  \n",
              "3            46.0   52.0      4.0     80.0      False  live  \n",
              "4             NaN  200.0      4.0      NaN      False  live  \n",
              "5            95.0   28.0      4.0     75.0      False  live  \n",
              "6             NaN    NaN      NaN      NaN      False   die  \n",
              "7             NaN    NaN      NaN      NaN      False  live  \n",
              "8             NaN   48.0      4.4      NaN      False  live  \n",
              "9             NaN  120.0      3.9      NaN      False  live  \n",
              "10           78.0   30.0      4.4     85.0      False  live  \n",
              "11           59.0  249.0      3.7     54.0      False  live  \n",
              "12           81.0   60.0      3.9     52.0      False  live  \n",
              "13           57.0  144.0      4.9     78.0      False  live  \n",
              "14            NaN   60.0      NaN      NaN      False  live  "
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>51</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>23</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>39</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>30</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>120.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>39</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1.3</td>\n",
              "      <td>78.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>85.0</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>32</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>249.0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>54.0</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>41</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.9</td>\n",
              "      <td>81.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>52.0</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>30</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2.2</td>\n",
              "      <td>57.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>78.0</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>47</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      display:flex;\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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1.2121490330948563,\n        \"min\": 0.3,\n        \"max\": 8.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          1.8,\n          2.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alk_phosphate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.50810877756307,\n        \"min\": 26.0,\n        \"max\": 295.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          191.0,\n          85.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sgot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89.65088973850219,\n        \"min\": 14.0,\n        \"max\": 648.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          278.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"albumin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6515230826162132,\n        \"min\": 2.1,\n        \"max\": 6.4,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          2.4,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.875243744811442,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          30.0,\n          66.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Datapreprocessing and Filteration steps**"
      ],
      "metadata": {
        "id": "DpjlSFWeBzy1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Finding the Size of Data**"
      ],
      "metadata": {
        "id": "dHwbHDg3Bd6c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0niA-W7BkJB",
        "outputId": "409a6e03-c1c7-4b2b-9af8-48234de98344"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(155, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Splitting the Data into Numeric and Categorial**"
      ],
      "metadata": {
        "id": "wBTlewYHB8qz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_df = df.select_dtypes(include=np.number).copy()\n",
        "num_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "EC77Nw-dCTxH",
        "outputId": "2c382b7a-7130-4e2a-92d2-00937fff74fa"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  bilirubin  alk_phosphate   sgot  albumin  protime\n",
              "0     30        1.0           85.0   18.0      4.0      NaN\n",
              "1     50        0.9          135.0   42.0      3.5      NaN\n",
              "2     78        0.7           96.0   32.0      4.0      NaN\n",
              "3     31        0.7           46.0   52.0      4.0     80.0\n",
              "4     34        1.0            NaN  200.0      4.0      NaN\n",
              "..   ...        ...            ...    ...      ...      ...\n",
              "150   46        7.6            NaN  242.0      3.3     50.0\n",
              "151   44        0.9          126.0  142.0      4.3      NaN\n",
              "152   61        0.8           75.0   20.0      4.1      NaN\n",
              "153   53        1.5           81.0   19.0      4.1     48.0\n",
              "154   43        1.2          100.0   19.0      3.1     42.0\n",
              "\n",
              "[155 rows x 6 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bilirubin</th>\n",
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              "      <th>sgot</th>\n",
              "      <th>albumin</th>\n",
              "      <th>protime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.7</td>\n",
              "      <td>96.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>0.7</td>\n",
              "      <td>46.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>80.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>200.0</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
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              "      <td>7.6</td>\n",
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              "      <td>50.0</td>\n",
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              "    <tr>\n",
              "      <th>151</th>\n",
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              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>61</td>\n",
              "      <td>0.8</td>\n",
              "      <td>75.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>53</td>\n",
              "      <td>1.5</td>\n",
              "      <td>81.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>48.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>43</td>\n",
              "      <td>1.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>155 rows × 6 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "num_df",
              "summary": "{\n  \"name\": \"num_df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bilirubin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2121490330948563,\n        \"min\": 0.3,\n        \"max\": 8.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          1.8,\n          2.8,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alk_phosphate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.50810877756307,\n        \"min\": 26.0,\n        \"max\": 295.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          191.0,\n          85.0,\n          194.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sgot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89.65088973850219,\n        \"min\": 14.0,\n        \"max\": 648.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          278.0,\n          18.0,\n          157.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"albumin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6515230826162132,\n        \"min\": 2.1,\n        \"max\": 6.4,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          2.4,\n          4.5,\n          2.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.875243744811442,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          30.0,\n          66.0,\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "k7P4rX5lDMT8",
        "outputId": "e0f52b3d-ceb7-4d89-a7a7-200d8e6f7457"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                int64\n",
              "bilirubin        float64\n",
              "alk_phosphate    float64\n",
              "sgot             float64\n",
              "albumin          float64\n",
              "protime          float64\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **I have performed Different Operations on this numeric data**"
      ],
      "metadata": {
        "id": "ePHP_in1D7GC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Mean**"
      ],
      "metadata": {
        "id": "41Sj-0ySE0SD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_df.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "PIdF4s32EFr5",
        "outputId": "cda8bd60-a9d1-49ac-8685-db9d670b1f1e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               41.200000\n",
              "bilirubin          1.427517\n",
              "alk_phosphate    105.325397\n",
              "sgot              85.894040\n",
              "albumin            3.817266\n",
              "protime           61.852273\n",
              "dtype: float64"
            ],
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              "      <td>85.894040</td>\n",
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              "      <th>albumin</th>\n",
              "      <td>3.817266</td>\n",
              "    </tr>\n",
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              "      <th>protime</th>\n",
              "      <td>61.852273</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 20
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**Median**"
      ],
      "metadata": {
        "id": "CsN0Hdt1E3Yt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_df.median()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "c-pUo3h4E5ky",
        "outputId": "d1e57806-8945-4f54-aa25-16a0174b5c95"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age              39.0\n",
              "bilirubin         1.0\n",
              "alk_phosphate    85.0\n",
              "sgot             58.0\n",
              "albumin           4.0\n",
              "protime          61.0\n",
              "dtype: float64"
            ],
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bilirubin</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alk_phosphate</th>\n",
              "      <td>85.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sgot</th>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>albumin</th>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>protime</th>\n",
              "      <td>61.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 21
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Sum**"
      ],
      "metadata": {
        "id": "Aw2H7QoVFERt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_df.sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "dm8jP4QBFH5X",
        "outputId": "302c18bf-f4de-4b0b-e6e5-050910e777d0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               6386.0\n",
              "bilirubin          212.7\n",
              "alk_phosphate    13271.0\n",
              "sgot             12970.0\n",
              "albumin            530.6\n",
              "protime           5443.0\n",
              "dtype: float64"
            ],
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              "      <th>age</th>\n",
              "      <td>6386.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bilirubin</th>\n",
              "      <td>212.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alk_phosphate</th>\n",
              "      <td>13271.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sgot</th>\n",
              "      <td>12970.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>albumin</th>\n",
              "      <td>530.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>protime</th>\n",
              "      <td>5443.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Range of values**"
      ],
      "metadata": {
        "id": "5UKt4VMjFM8y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_df.max() - num_df.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "gy2h5VEuFTwU",
        "outputId": "68c4d5dd-53de-4cc6-d25f-a616633028af"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               71.0\n",
              "bilirubin          7.7\n",
              "alk_phosphate    269.0\n",
              "sgot             634.0\n",
              "albumin            4.3\n",
              "protime          100.0\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bilirubin</th>\n",
              "      <td>7.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alk_phosphate</th>\n",
              "      <td>269.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sgot</th>\n",
              "      <td>634.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>albumin</th>\n",
              "      <td>4.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>protime</th>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **created a catagorical dataframe from the original dataframe**"
      ],
      "metadata": {
        "id": "TPSfKjfuFiSP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cat_df = df.select_dtypes(exclude=np.number).copy()\n",
        "cat_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "h5i6vJm6GTuR",
        "outputId": "e8ddf7e4-1c0b-4591-dac6-d55ac524ad61"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex steroid  antivirals fatigue malaise anorexia liver_big liver_firm  \\\n",
              "0      male   False       False   False   False    False     False      False   \n",
              "1    female   False       False    True   False    False     False      False   \n",
              "2    female    True       False    True   False    False      True      False   \n",
              "3    female     NaN        True   False   False    False      True      False   \n",
              "4    female    True       False   False   False    False      True      False   \n",
              "..      ...     ...         ...     ...     ...      ...       ...        ...   \n",
              "150  female    True       False    True    True     True      True      False   \n",
              "151  female    True       False    True   False    False      True       True   \n",
              "152  female   False       False    True    True    False     False       True   \n",
              "153    male   False       False    True   False    False      True      False   \n",
              "154  female    True       False    True   False    False      True      False   \n",
              "\n",
              "    spleen_palpable spiders ascites varices  histology class  \n",
              "0             False   False   False   False      False  live  \n",
              "1             False   False   False   False      False  live  \n",
              "2             False   False   False   False      False  live  \n",
              "3             False   False   False   False      False  live  \n",
              "4             False   False   False   False      False  live  \n",
              "..              ...     ...     ...     ...        ...   ...  \n",
              "150           False    True    True    True       True   die  \n",
              "151           False   False   False   False       True  live  \n",
              "152           False    True   False   False       True  live  \n",
              "153            True    True   False    True       True  live  \n",
              "154            True    True    True   False       True   die  \n",
              "\n",
              "[155 rows x 14 columns]"
            ],
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              "\n",
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              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cat_df",
              "summary": "{\n  \"name\": \"cat_df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"steroid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fatigue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"malaise\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anorexia\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_big\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_firm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spleen_palpable\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spiders\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ascites\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"varices\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Checking the data types of the categorial data**"
      ],
      "metadata": {
        "id": "LTZWQIiXGgPo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cat_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "igiYDFRaGqWu",
        "outputId": "a494aa03-123f-4341-d667-f930104350ca"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex                object\n",
              "steroid            object\n",
              "antivirals           bool\n",
              "fatigue            object\n",
              "malaise            object\n",
              "anorexia           object\n",
              "liver_big          object\n",
              "liver_firm         object\n",
              "spleen_palpable    object\n",
              "spiders            object\n",
              "ascites            object\n",
              "varices            object\n",
              "histology            bool\n",
              "class              object\n",
              "dtype: object"
            ],
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>steroid</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>antivirals</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fatigue</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malaise</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>anorexia</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liver_big</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liver_firm</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spleen_palpable</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spiders</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ascites</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>varices</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histology</th>\n",
              "      <td>bool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Finding the Unique values of for each category**"
      ],
      "metadata": {
        "id": "cMGMNnzFHGcD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Rowwise**"
      ],
      "metadata": {
        "id": "S0TcMqsWHh7t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cat_df.nunique(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "luEXKJCdHWb9",
        "outputId": "157c0eaa-d0d2-4438-a0e3-87ff9949b1cc"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex                2\n",
              "steroid            2\n",
              "antivirals         2\n",
              "fatigue            2\n",
              "malaise            2\n",
              "anorexia           2\n",
              "liver_big          2\n",
              "liver_firm         2\n",
              "spleen_palpable    2\n",
              "spiders            2\n",
              "ascites            2\n",
              "varices            2\n",
              "histology          2\n",
              "class              2\n",
              "dtype: int64"
            ],
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "      <th>anorexia</th>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "      <th>spleen_palpable</th>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>ascites</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>varices</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histology</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Column wise**"
      ],
      "metadata": {
        "id": "Xm0iyUZvHmmK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['antivirals'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pdL1sd7HwAZ",
        "outputId": "55f91d98-8449-43ec-9a46-64ac7bd5f598"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Finding out the Missing values**"
      ],
      "metadata": {
        "id": "A6clTi0HIkjt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First I found if a cell of the dataset if NaN (missing value) or not. Then I have counted how many missing values there are for each column(.sum() function)."
      ],
      "metadata": {
        "id": "RyQwScglI6HA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Below are the Missing values Found**"
      ],
      "metadata": {
        "id": "GWKSK0MCJRKI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "gDlRIZ0jIvHL",
        "outputId": "7160a59d-9e76-4746-c4e1-5d68a3075557"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                 0\n",
              "sex                 0\n",
              "steroid             1\n",
              "antivirals          0\n",
              "fatigue             1\n",
              "malaise             1\n",
              "anorexia            1\n",
              "liver_big          10\n",
              "liver_firm         11\n",
              "spleen_palpable     5\n",
              "spiders             5\n",
              "ascites             5\n",
              "varices             5\n",
              "bilirubin           6\n",
              "alk_phosphate      29\n",
              "sgot                4\n",
              "albumin            16\n",
              "protime            67\n",
              "histology           0\n",
              "class               0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>malaise</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>anorexia</th>\n",
              "      <td>1</td>\n",
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              "      <td>10</td>\n",
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              "      <td>11</td>\n",
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              "      <td>5</td>\n",
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              "      <td>5</td>\n",
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              "      <td>5</td>\n",
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              "      <th>varices</th>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>bilirubin</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alk_phosphate</th>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sgot</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>albumin</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>protime</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histology</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Counting the percentage of missing values**"
      ],
      "metadata": {
        "id": "kCFdj3a-Njq6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values_count = df.isnull().sum()\n",
        "total_entries = len(df)\n",
        "missing_percentage = (missing_values_count / total_entries) * 100\n",
        "missing_summary = pd.DataFrame({\n",
        "    'Missing Values': missing_values_count,\n",
        "    'Percentage Missing': missing_percentage\n",
        "})\n",
        "\n",
        "print(\"\\nPercentage of missing values in each column:\\n\", missing_summary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHLlOBEHNuyJ",
        "outputId": "852eea2f-09e1-4c96-f726-9a5a8f992ac8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Percentage of missing values in each column:\n",
            "                  Missing Values  Percentage Missing\n",
            "age                           0            0.000000\n",
            "sex                           0            0.000000\n",
            "steroid                       1            0.645161\n",
            "antivirals                    0            0.000000\n",
            "fatigue                       1            0.645161\n",
            "malaise                       1            0.645161\n",
            "anorexia                      1            0.645161\n",
            "liver_big                    10            6.451613\n",
            "liver_firm                   11            7.096774\n",
            "spleen_palpable               5            3.225806\n",
            "spiders                       5            3.225806\n",
            "ascites                       5            3.225806\n",
            "varices                       5            3.225806\n",
            "bilirubin                     6            3.870968\n",
            "alk_phosphate                29           18.709677\n",
            "sgot                          4            2.580645\n",
            "albumin                      16           10.322581\n",
            "protime                      67           43.225806\n",
            "histology                     0            0.000000\n",
            "class                         0            0.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Different tasks applied on Missing values**"
      ],
      "metadata": {
        "id": "Q1YjBKIJKMo4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Drop rows with any missing values**"
      ],
      "metadata": {
        "id": "yOjYLBLlKkye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "eiKjAJZlKVFT",
        "outputId": "2e696563-0de8-4d41-e68c-fdcdad0debd4"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
              "5     34  female    True       False   False   False    False      True   \n",
              "10    39  female   False        True   False   False    False     False   \n",
              "11    32  female    True        True    True   False    False      True   \n",
              "12    41  female    True        True    True   False    False      True   \n",
              "13    30  female    True       False    True   False    False      True   \n",
              "..   ...     ...     ...         ...     ...     ...      ...       ...   \n",
              "139   45  female    True        True   False   False    False      True   \n",
              "143   49  female   False       False    True    True    False      True   \n",
              "145   31  female   False       False    True   False    False      True   \n",
              "153   53    male   False       False    True   False    False      True   \n",
              "154   43  female    True       False    True   False    False      True   \n",
              "\n",
              "    liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
              "5        False           False   False   False   False        0.9   \n",
              "10        True           False   False   False   False        1.3   \n",
              "11        True           False    True   False   False        1.0   \n",
              "12        True           False   False   False   False        0.9   \n",
              "13        True           False   False   False   False        2.2   \n",
              "..         ...             ...     ...     ...     ...        ...   \n",
              "139      False           False   False   False   False        1.3   \n",
              "143      False            True    True   False   False        1.4   \n",
              "145      False           False   False   False   False        1.2   \n",
              "153      False            True    True   False    True        1.5   \n",
              "154      False            True    True    True   False        1.2   \n",
              "\n",
              "     alk_phosphate   sgot  albumin  protime  histology class  \n",
              "5             95.0   28.0      4.0     75.0      False  live  \n",
              "10            78.0   30.0      4.4     85.0      False  live  \n",
              "11            59.0  249.0      3.7     54.0      False  live  \n",
              "12            81.0   60.0      3.9     52.0      False  live  \n",
              "13            57.0  144.0      4.9     78.0      False  live  \n",
              "..             ...    ...      ...      ...        ...   ...  \n",
              "139           85.0   44.0      4.2     85.0       True  live  \n",
              "143           85.0   70.0      3.5     35.0       True   die  \n",
              "145           75.0  173.0      4.2     54.0       True  live  \n",
              "153           81.0   19.0      4.1     48.0       True  live  \n",
              "154          100.0   19.0      3.1     42.0       True   die  \n",
              "\n",
              "[80 rows x 20 columns]"
            ],
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              "      <td>59.0</td>\n",
              "      <td>249.0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>54.0</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>41</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.9</td>\n",
              "      <td>81.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>3.9</td>\n",
              "      <td>52.0</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>30</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2.2</td>\n",
              "      <td>57.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>78.0</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>45</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1.3</td>\n",
              "      <td>85.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>85.0</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>49</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1.4</td>\n",
              "      <td>85.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>35.0</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>31</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1.2</td>\n",
              "      <td>75.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>54.0</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>1.5</td>\n",
              "      <td>81.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>48.0</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>43</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>1.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>42.0</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 20 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d20ce887-d06a-40f3-a77f-fc059e0a5f74')\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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",
              "    }\n",
              "\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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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-d20ce887-d06a-40f3-a77f-fc059e0a5f74 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-d20ce887-d06a-40f3-a77f-fc059e0a5f74');\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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-013e821a-449f-4dce-bd26-e7d0a8bea538')\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --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",
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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",
              "    }\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-013e821a-449f-4dce-bd26-e7d0a8bea538 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\": \"df\",\n  \"rows\": 80,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 20,\n        \"max\": 72,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          52,\n          35,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"steroid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fatigue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"malaise\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anorexia\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_big\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_firm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spleen_palpable\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spiders\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ascites\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"varices\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bilirubin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8752133555433892,\n        \"min\": 0.3,\n        \"max\": 4.8,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          3.5,\n          0.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alk_phosphate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.68477938833952,\n        \"min\": 26.0,\n        \"max\": 280.0,\n        \"num_unique_values\": 59,\n        \"samples\": [\n          95.0,\n          72.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sgot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71.59997436531611,\n        \"min\": 14.0,\n        \"max\": 420.0,\n        \"num_unique_values\": 58,\n        \"samples\": [\n          28.0,\n          89.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"albumin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5762918893860575,\n        \"min\": 2.1,\n        \"max\": 5.0,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          4.7,\n          3.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.427774142368342,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          76.0,\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "3b1d615f-fee3-488b-b4b5-33b9326ec0ae"
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      "execution_count": 38,
      "outputs": [
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              "     age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
              "0     30    male   False       False   False   False    False     False   \n",
              "1     50  female   False       False    True   False    False     False   \n",
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              "3     31  female     NaN        True   False   False    False      True   \n",
              "4     34  female    True       False   False   False    False      True   \n",
              "..   ...     ...     ...         ...     ...     ...      ...       ...   \n",
              "150   46  female    True       False    True    True     True      True   \n",
              "151   44  female    True       False    True   False    False      True   \n",
              "152   61  female   False       False    True    True    False     False   \n",
              "153   53    male   False       False    True   False    False      True   \n",
              "154   43  female    True       False    True   False    False      True   \n",
              "\n",
              "    liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
              "0        False           False   False   False   False        1.0   \n",
              "1        False           False   False   False   False        0.9   \n",
              "2        False           False   False   False   False        0.7   \n",
              "3        False           False   False   False   False        0.7   \n",
              "4        False           False   False   False   False        1.0   \n",
              "..         ...             ...     ...     ...     ...        ...   \n",
              "150      False           False    True    True    True        7.6   \n",
              "151       True           False   False   False   False        0.9   \n",
              "152       True           False    True   False   False        0.8   \n",
              "153      False            True    True   False    True        1.5   \n",
              "154      False            True    True    True   False        1.2   \n",
              "\n",
              "     alk_phosphate   sgot  albumin  protime  histology class  \n",
              "0             85.0   18.0      4.0      NaN      False  live  \n",
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              "..             ...    ...      ...      ...        ...   ...  \n",
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              "152           75.0   20.0      4.1      NaN       True  live  \n",
              "153           81.0   19.0      4.1     48.0       True  live  \n",
              "154          100.0   19.0      3.1     42.0       True   die  \n",
              "\n",
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              "      const quickchartButtonEl =\n",
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              "      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",
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              "    })();\n",
              "  </script>\n",
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              "\n",
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              "    <style>\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\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",
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              "      (() => {\n",
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              "        document.querySelector('#id_53610eac-5984-49d7-af6c-c33301791a94 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"steroid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fatigue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"malaise\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anorexia\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_big\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_firm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spleen_palpable\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spiders\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ascites\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"varices\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bilirubin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2121490330948563,\n        \"min\": 0.3,\n        \"max\": 8.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          1.8,\n          2.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alk_phosphate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.50810877756307,\n        \"min\": 26.0,\n        \"max\": 295.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          191.0,\n          85.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sgot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89.65088973850219,\n        \"min\": 14.0,\n        \"max\": 648.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          278.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"albumin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6515230826162132,\n        \"min\": 2.1,\n        \"max\": 6.4,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          2.4,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.875243744811442,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          30.0,\n          66.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Drop columns with any missing values**"
      ],
      "metadata": {
        "id": "pyWcOXpzKsZj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "c1sexoq-LT3P",
        "outputId": "8d217013-6d4e-404b-c921-1f8a7a83c172"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex  antivirals  histology class\n",
              "0     30    male       False      False  live\n",
              "1     50  female       False      False  live\n",
              "2     78  female       False      False  live\n",
              "3     31  female        True      False  live\n",
              "4     34  female       False      False  live\n",
              "..   ...     ...         ...        ...   ...\n",
              "150   46  female       False       True   die\n",
              "151   44  female       False       True  live\n",
              "152   61  female       False       True  live\n",
              "153   53    male       False       True  live\n",
              "154   43  female       False       True   die\n",
              "\n",
              "[155 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-354d1d68-0beb-4b3c-b2b6-1bb11b7a6993\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>78</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
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              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>46</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>44</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
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              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>43</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>155 rows × 5 columns</p>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "e_PoWW_KM9GR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Drop rows where all values are missing**"
      ],
      "metadata": {
        "id": "0ir1vp-1K7Iw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=0, how='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "earG1tOzLb8Z",
        "outputId": "37f01761-b86f-465e-b977-3dab593aa489"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
              "0     30    male   False       False   False   False    False     False   \n",
              "1     50  female   False       False    True   False    False     False   \n",
              "2     78  female    True       False    True   False    False      True   \n",
              "3     31  female     NaN        True   False   False    False      True   \n",
              "4     34  female    True       False   False   False    False      True   \n",
              "..   ...     ...     ...         ...     ...     ...      ...       ...   \n",
              "150   46  female    True       False    True    True     True      True   \n",
              "151   44  female    True       False    True   False    False      True   \n",
              "152   61  female   False       False    True    True    False     False   \n",
              "153   53    male   False       False    True   False    False      True   \n",
              "154   43  female    True       False    True   False    False      True   \n",
              "\n",
              "    liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
              "0        False           False   False   False   False        1.0   \n",
              "1        False           False   False   False   False        0.9   \n",
              "2        False           False   False   False   False        0.7   \n",
              "3        False           False   False   False   False        0.7   \n",
              "4        False           False   False   False   False        1.0   \n",
              "..         ...             ...     ...     ...     ...        ...   \n",
              "150      False           False    True    True    True        7.6   \n",
              "151       True           False   False   False   False        0.9   \n",
              "152       True           False    True   False   False        0.8   \n",
              "153      False            True    True   False    True        1.5   \n",
              "154      False            True    True    True   False        1.2   \n",
              "\n",
              "     alk_phosphate   sgot  albumin  protime  histology class  \n",
              "0             85.0   18.0      4.0      NaN      False  live  \n",
              "1            135.0   42.0      3.5      NaN      False  live  \n",
              "2             96.0   32.0      4.0      NaN      False  live  \n",
              "3             46.0   52.0      4.0     80.0      False  live  \n",
              "4              NaN  200.0      4.0      NaN      False  live  \n",
              "..             ...    ...      ...      ...        ...   ...  \n",
              "150            NaN  242.0      3.3     50.0       True   die  \n",
              "151          126.0  142.0      4.3      NaN       True  live  \n",
              "152           75.0   20.0      4.1      NaN       True  live  \n",
              "153           81.0   19.0      4.1     48.0       True  live  \n",
              "154          100.0   19.0      3.1     42.0       True   die  \n",
              "\n",
              "[155 rows x 20 columns]"
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              "          + ' to learn more about interactive tables.';\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "            'suggestCharts', [key], {});\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"steroid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n   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2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_big\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_firm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spleen_palpable\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": 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            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "NkqjwVAmM_VW",
        "outputId": "c7f9ac51-3d1f-4caa-8baa-e787000ac64e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
              "0     30    male   False       False   False   False    False     False   \n",
              "1     50  female   False       False    True   False    False     False   \n",
              "2     78  female    True       False    True   False    False      True   \n",
              "3     31  female     NaN        True   False   False    False      True   \n",
              "4     34  female    True       False   False   False    False      True   \n",
              "..   ...     ...     ...         ...     ...     ...      ...       ...   \n",
              "150   46  female    True       False    True    True     True      True   \n",
              "151   44  female    True       False    True   False    False      True   \n",
              "152   61  female   False       False    True    True    False     False   \n",
              "153   53    male   False       False    True   False    False      True   \n",
              "154   43  female    True       False    True   False    False      True   \n",
              "\n",
              "    liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
              "0        False           False   False   False   False        1.0   \n",
              "1        False           False   False   False   False        0.9   \n",
              "2        False           False   False   False   False        0.7   \n",
              "3        False           False   False   False   False        0.7   \n",
              "4        False           False   False   False   False        1.0   \n",
              "..         ...             ...     ...     ...     ...        ...   \n",
              "150      False           False    True    True    True        7.6   \n",
              "151       True           False   False   False   False        0.9   \n",
              "152       True           False    True   False   False        0.8   \n",
              "153      False            True    True   False    True        1.5   \n",
              "154      False            True    True    True   False        1.2   \n",
              "\n",
              "     alk_phosphate   sgot  albumin  protime  histology class  \n",
              "0             85.0   18.0      4.0      NaN      False  live  \n",
              "1            135.0   42.0      3.5      NaN      False  live  \n",
              "2             96.0   32.0      4.0      NaN      False  live  \n",
              "3             46.0   52.0      4.0     80.0      False  live  \n",
              "4              NaN  200.0      4.0      NaN      False  live  \n",
              "..             ...    ...      ...      ...        ...   ...  \n",
              "150            NaN  242.0      3.3     50.0       True   die  \n",
              "151          126.0  142.0      4.3      NaN       True  live  \n",
              "152           75.0   20.0      4.1      NaN       True  live  \n",
              "153           81.0   19.0      4.1     48.0       True  live  \n",
              "154          100.0   19.0      3.1     42.0       True   die  \n",
              "\n",
              "[155 rows x 20 columns]"
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              "      <td>242.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>50.0</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>44</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.9</td>\n",
              "      <td>126.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>4.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>0.8</td>\n",
              "      <td>75.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>1.5</td>\n",
              "      <td>81.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>48.0</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>43</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>1.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>42.0</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>155 rows × 20 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        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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              "  </button>\n",
              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    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",
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              "    20% {\n",
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              "      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",
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              "      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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_0955eb2d-7269-478b-b2fb-b7447573d845\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0955eb2d-7269-478b-b2fb-b7447573d845 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "      })();\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"steroid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fatigue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"malaise\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anorexia\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_big\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_firm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spleen_palpable\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spiders\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ascites\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"varices\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bilirubin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2121490330948563,\n        \"min\": 0.3,\n        \"max\": 8.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          1.8,\n          2.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alk_phosphate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.50810877756307,\n        \"min\": 26.0,\n        \"max\": 295.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          191.0,\n          85.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sgot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89.65088973850219,\n        \"min\": 14.0,\n        \"max\": 648.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          278.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"albumin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6515230826162132,\n        \"min\": 2.1,\n        \"max\": 6.4,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          2.4,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.875243744811442,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          30.0,\n          66.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Drop columns where all values are missing**"
      ],
      "metadata": {
        "id": "LoS8u9X2LL5h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=1, how='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "RG2Td46dLiwE",
        "outputId": "6d9f2aed-e66c-4151-f4f5-c84f12d498db"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
              "0     30    male   False       False   False   False    False     False   \n",
              "1     50  female   False       False    True   False    False     False   \n",
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              "3     31  female     NaN        True   False   False    False      True   \n",
              "4     34  female    True       False   False   False    False      True   \n",
              "..   ...     ...     ...         ...     ...     ...      ...       ...   \n",
              "150   46  female    True       False    True    True     True      True   \n",
              "151   44  female    True       False    True   False    False      True   \n",
              "152   61  female   False       False    True    True    False     False   \n",
              "153   53    male   False       False    True   False    False      True   \n",
              "154   43  female    True       False    True   False    False      True   \n",
              "\n",
              "    liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
              "0        False           False   False   False   False        1.0   \n",
              "1        False           False   False   False   False        0.9   \n",
              "2        False           False   False   False   False        0.7   \n",
              "3        False           False   False   False   False        0.7   \n",
              "4        False           False   False   False   False        1.0   \n",
              "..         ...             ...     ...     ...     ...        ...   \n",
              "150      False           False    True    True    True        7.6   \n",
              "151       True           False   False   False   False        0.9   \n",
              "152       True           False    True   False   False        0.8   \n",
              "153      False            True    True   False    True        1.5   \n",
              "154      False            True    True    True   False        1.2   \n",
              "\n",
              "     alk_phosphate   sgot  albumin  protime  histology class  \n",
              "0             85.0   18.0      4.0      NaN      False  live  \n",
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              "..             ...    ...      ...      ...        ...   ...  \n",
              "150            NaN  242.0      3.3     50.0       True   die  \n",
              "151          126.0  142.0      4.3      NaN       True  live  \n",
              "152           75.0   20.0      4.1      NaN       True  live  \n",
              "153           81.0   19.0      4.1     48.0       True  live  \n",
              "154          100.0   19.0      3.1     42.0       True   die  \n",
              "\n",
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              "<p>155 rows × 20 columns</p>\n",
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              "    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",
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              "      quickchartButtonEl.style.display =\n",
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              "\n",
              "    </div>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"steroid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n   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2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_big\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_firm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spleen_palpable\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": 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{\n      \"column\": \"albumin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6515230826162132,\n        \"min\": 2.1,\n        \"max\": 6.4,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          2.4,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.875243744811442,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          30.0,\n          66.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n  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            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "EtK7UYvsNB_C",
        "outputId": "bc8947be-e16d-4ab7-88b5-265e5755c82f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
              "0     30    male   False       False   False   False    False     False   \n",
              "1     50  female   False       False    True   False    False     False   \n",
              "2     78  female    True       False    True   False    False      True   \n",
              "3     31  female     NaN        True   False   False    False      True   \n",
              "4     34  female    True       False   False   False    False      True   \n",
              "..   ...     ...     ...         ...     ...     ...      ...       ...   \n",
              "150   46  female    True       False    True    True     True      True   \n",
              "151   44  female    True       False    True   False    False      True   \n",
              "152   61  female   False       False    True    True    False     False   \n",
              "153   53    male   False       False    True   False    False      True   \n",
              "154   43  female    True       False    True   False    False      True   \n",
              "\n",
              "    liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
              "0        False           False   False   False   False        1.0   \n",
              "1        False           False   False   False   False        0.9   \n",
              "2        False           False   False   False   False        0.7   \n",
              "3        False           False   False   False   False        0.7   \n",
              "4        False           False   False   False   False        1.0   \n",
              "..         ...             ...     ...     ...     ...        ...   \n",
              "150      False           False    True    True    True        7.6   \n",
              "151       True           False   False   False   False        0.9   \n",
              "152       True           False    True   False   False        0.8   \n",
              "153      False            True    True   False    True        1.5   \n",
              "154      False            True    True    True   False        1.2   \n",
              "\n",
              "     alk_phosphate   sgot  albumin  protime  histology class  \n",
              "0             85.0   18.0      4.0      NaN      False  live  \n",
              "1            135.0   42.0      3.5      NaN      False  live  \n",
              "2             96.0   32.0      4.0      NaN      False  live  \n",
              "3             46.0   52.0      4.0     80.0      False  live  \n",
              "4              NaN  200.0      4.0      NaN      False  live  \n",
              "..             ...    ...      ...      ...        ...   ...  \n",
              "150            NaN  242.0      3.3     50.0       True   die  \n",
              "151          126.0  142.0      4.3      NaN       True  live  \n",
              "152           75.0   20.0      4.1      NaN       True  live  \n",
              "153           81.0   19.0      4.1     48.0       True  live  \n",
              "154          100.0   19.0      3.1     42.0       True   die  \n",
              "\n",
              "[155 rows x 20 columns]"
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-6f1b11ce-c5d1-4d94-aa9c-6ede79a97514 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\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",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "    <style>\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\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",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"steroid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fatigue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"malaise\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"anorexia\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_big\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"liver_firm\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spleen_palpable\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spiders\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ascites\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"varices\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bilirubin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2121490330948563,\n        \"min\": 0.3,\n        \"max\": 8.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          1.8,\n          2.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alk_phosphate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.50810877756307,\n        \"min\": 26.0,\n        \"max\": 295.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          191.0,\n          85.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sgot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 89.65088973850219,\n        \"min\": 14.0,\n        \"max\": 648.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          278.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"albumin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6515230826162132,\n        \"min\": 2.1,\n        \"max\": 6.4,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          2.4,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"protime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.875243744811442,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          30.0,\n          66.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Replace the missing value with a specific value**"
      ],
      "metadata": {
        "id": "a2xLg16lMHXd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_filled_constant = df.fillna(2)\n",
        "\n",
        "print(\"DataFrame after replacing missing values with 2:\\n\", df_filled_constant.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9-HydsCMI-a",
        "outputId": "445050fe-7248-4d20-f7ee-fb5dc17c6437"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame after replacing missing values with 2:\n",
            "    age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
            "0   30    male   False       False   False   False    False     False   \n",
            "1   50  female   False       False    True   False    False     False   \n",
            "2   78  female    True       False    True   False    False      True   \n",
            "3   31  female       2        True   False   False    False      True   \n",
            "4   34  female    True       False   False   False    False      True   \n",
            "\n",
            "  liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
            "0      False           False   False   False   False        1.0   \n",
            "1      False           False   False   False   False        0.9   \n",
            "2      False           False   False   False   False        0.7   \n",
            "3      False           False   False   False   False        0.7   \n",
            "4      False           False   False   False   False        1.0   \n",
            "\n",
            "   alk_phosphate   sgot  albumin  protime  histology class  \n",
            "0           85.0   18.0      4.0      2.0      False  live  \n",
            "1          135.0   42.0      3.5      2.0      False  live  \n",
            "2           96.0   32.0      4.0      2.0      False  live  \n",
            "3           46.0   52.0      4.0     80.0      False  live  \n",
            "4            2.0  200.0      4.0      2.0      False  live  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using Inplace Argument**"
      ],
      "metadata": {
        "id": "yfv4HRSNOEUn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "XWqtkqOhOJFT"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "dqPEybrZOm-h",
        "outputId": "d3b8c73d-9c8c-4f96-90d8-c943d5638470"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex  antivirals  histology class\n",
              "0     30    male       False      False  live\n",
              "1     50  female       False      False  live\n",
              "2     78  female       False      False  live\n",
              "3     31  female        True      False  live\n",
              "4     34  female       False      False  live\n",
              "..   ...     ...         ...        ...   ...\n",
              "150   46  female       False       True   die\n",
              "151   44  female       False       True  live\n",
              "152   61  female       False       True  live\n",
              "153   53    male       False       True  live\n",
              "154   43  female       False       True   die\n",
              "\n",
              "[155 rows x 5 columns]"
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>46</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>44</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>43</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>155 rows × 5 columns</p>\n",
              "</div>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-bb99d689-2b7b-42db-9b71-d3f36db2ea83 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6698c541-6e26-4382-8d46-442fa9857dc9\">\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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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              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_6698c541-6e26-4382-8d46-442fa9857dc9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dropping only the rows in which the specific column has missing values**"
      ],
      "metadata": {
        "id": "B9WZ3tQOOjRc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp=df.copy()\n",
        "df_temp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "yU0qhqX3PJZ2",
        "outputId": "23c8aa26-0f4e-47ba-ddff-1efd45040619"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex  antivirals  histology class\n",
              "0     30    male       False      False  live\n",
              "1     50  female       False      False  live\n",
              "2     78  female       False      False  live\n",
              "3     31  female        True      False  live\n",
              "4     34  female       False      False  live\n",
              "..   ...     ...         ...        ...   ...\n",
              "150   46  female       False       True   die\n",
              "151   44  female       False       True  live\n",
              "152   61  female       False       True  live\n",
              "153   53    male       False       True  live\n",
              "154   43  female       False       True   die\n",
              "\n",
              "[155 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aace2d86-ca4d-4400-a3c4-5f799cba04c1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>antivirals</th>\n",
              "      <th>histology</th>\n",
              "      <th>class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>78</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>46</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>44</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>43</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>155 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aace2d86-ca4d-4400-a3c4-5f799cba04c1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\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",
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              "\n",
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              "        document.querySelector('#df-aace2d86-ca4d-4400-a3c4-5f799cba04c1 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-aace2d86-ca4d-4400-a3c4-5f799cba04c1');\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",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    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",
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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-d40c35da-7d4f-4f29-8e62-e1420c009e07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3b4d6a93-4ea5-4a45-949e-7daf8e048110\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_temp')\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3b4d6a93-4ea5-4a45-949e-7daf8e048110 button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_temp');\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_temp",
              "summary": "{\n  \"name\": \"df_temp\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp.dropna(subset=['antivirals'],axis=0,inplace=True)\n",
        "df_temp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "BKfnc2ruP4U0",
        "outputId": "d4fcd67b-3bea-45d0-c479-932ab241136b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex  antivirals  histology class\n",
              "0     30    male       False      False  live\n",
              "1     50  female       False      False  live\n",
              "2     78  female       False      False  live\n",
              "3     31  female        True      False  live\n",
              "4     34  female       False      False  live\n",
              "..   ...     ...         ...        ...   ...\n",
              "150   46  female       False       True   die\n",
              "151   44  female       False       True  live\n",
              "152   61  female       False       True  live\n",
              "153   53    male       False       True  live\n",
              "154   43  female       False       True   die\n",
              "\n",
              "[155 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1da10648-7ac2-43f0-bf53-82fbbe8b273a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>antivirals</th>\n",
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              "      <th>class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>78</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>46</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>44</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>43</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>155 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1da10648-7ac2-43f0-bf53-82fbbe8b273a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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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",
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              "\n",
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              "        document.querySelector('#df-1da10648-7ac2-43f0-bf53-82fbbe8b273a button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1da10648-7ac2-43f0-bf53-82fbbe8b273a');\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",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-67f95aca-4769-48b3-8946-e47dbf11c083\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-67f95aca-4769-48b3-8946-e47dbf11c083')\"\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",
              "     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-67f95aca-4769-48b3-8946-e47dbf11c083 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_1d54135f-18a9-4f28-b0bf-348edcdcfb61\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_temp')\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1d54135f-18a9-4f28-b0bf-348edcdcfb61 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_temp');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_temp",
              "summary": "{\n  \"name\": \"df_temp\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**I have checked whether there are still missing values for the column \"anti virals\"**"
      ],
      "metadata": {
        "id": "bz4k8A3-Y5lR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp.isna().sum()/len(df_temp)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "ejRUp3fXQZJi",
        "outputId": "17706c99-3434-43a3-f37d-adb69a738507"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           0.0\n",
              "sex           0.0\n",
              "antivirals    0.0\n",
              "histology     0.0\n",
              "class         0.0\n",
              "dtype: float64"
            ],
            "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>age</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>antivirals</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histology</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**I have kept only columns where there are at least the 70% of not null values**"
      ],
      "metadata": {
        "id": "YR2cehD6ZQxU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp.dropna(thresh=0.7*len(df_temp),axis=1,inplace=True)\n",
        "df_temp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "4uIimwMcRHft",
        "outputId": "ea18caf1-5a8c-4818-e981-78160f68fd57"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex  antivirals  histology class\n",
              "0     30    male       False      False  live\n",
              "1     50  female       False      False  live\n",
              "2     78  female       False      False  live\n",
              "3     31  female        True      False  live\n",
              "4     34  female       False      False  live\n",
              "..   ...     ...         ...        ...   ...\n",
              "150   46  female       False       True   die\n",
              "151   44  female       False       True  live\n",
              "152   61  female       False       True  live\n",
              "153   53    male       False       True  live\n",
              "154   43  female       False       True   die\n",
              "\n",
              "[155 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bc6e3ba6-ec06-4e94-a44e-a87d9f65d2c0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>age</th>\n",
              "      <th>sex</th>\n",
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              "      <th>histology</th>\n",
              "      <th>class</th>\n",
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              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>78</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>female</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>46</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>44</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>61</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>live</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>43</td>\n",
              "      <td>female</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>die</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>155 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc6e3ba6-ec06-4e94-a44e-a87d9f65d2c0')\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\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",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-bc6e3ba6-ec06-4e94-a44e-a87d9f65d2c0 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-bc6e3ba6-ec06-4e94-a44e-a87d9f65d2c0');\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",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c5392854-044c-4054-a853-b85ddcbf646b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5392854-044c-4054-a853-b85ddcbf646b')\"\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",
              "     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-c5392854-044c-4054-a853-b85ddcbf646b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a7538cb8-2729-4f5e-82ee-0d59c54015b6\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_temp')\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a7538cb8-2729-4f5e-82ee-0d59c54015b6 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_temp');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_temp",
              "summary": "{\n  \"name\": \"df_temp\",\n  \"rows\": 155,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 7,\n        \"max\": 78,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          40,\n          70,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"antivirals\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"histology\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"die\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEV3k65LWVUk",
        "outputId": "7246e55d-4c6b-4475-ef18-a11c86ffdcb1"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "155"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp['class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "1H2dEbn5W7ZV",
        "outputId": "d028fc4d-71f7-458c-8ec0-4eb78109fa76"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "live    123\n",
              "die      32\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>live</th>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>die</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp['class'].value_counts()/len(df_temp)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "JRcK43e6XN64",
        "outputId": "3f1e4e71-fb0c-4dfc-8852-ad5e2569dc55"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "live    79.354839\n",
              "die     20.645161\n",
              "Name: count, 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",
              "    .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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>live</th>\n",
              "      <td>79.354839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>die</th>\n",
              "      <td>20.645161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.value_counts(df_temp['class']).plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "YqM0SWSpXdj4",
        "outputId": "d7970386-7784-44af-8646-e8fbf06fcd23"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-67-f9e029809b12>:1: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
            "  pd.value_counts(df_temp['class']).plot.bar()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='class'>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**I have checked the male and female instances in the sex column**"
      ],
      "metadata": {
        "id": "Je1YTHanaBxS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp['sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "Btxx8oEVaDiV",
        "outputId": "e2c31a25-a9ff-4229-b160-34ba424487a2"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "female    139\n",
              "male       16\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Computed percentage of this data**"
      ],
      "metadata": {
        "id": "aC2-glqIaLcB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp['sex'].value_counts()/len(df_temp)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "A_xkeEdbaQHv",
        "outputId": "921d8229-c988-4346-b884-4899f5736fea"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "female    89.677419\n",
              "male      10.322581\n",
              "Name: count, dtype: float64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>89.677419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>10.322581</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Plotting the results**"
      ],
      "metadata": {
        "id": "Zl6Mkd9radFh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.value_counts(df_temp['sex']).plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "d52LFfrRajU4",
        "outputId": "50001d1d-fe6f-45ac-d750-21162805949b"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-71-d605c86ea8eb>:1: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
            "  pd.value_counts(df_temp['sex']).plot.bar()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sex'>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Replacing missing values with the mean values of the column**"
      ],
      "metadata": {
        "id": "zUVfj3zNbJAu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/datasets/hepatitis/master/data/hepatitis.csv')\n",
        "num_col= df.select_dtypes(include='number').columns\n",
        "df[num_col] = df[num_col].apply(pd.to_numeric, errors='coerce')\n",
        "\n",
        "# mean values for numeric columns\n",
        "mean_val = df[num_col].mean()\n",
        "\n",
        "# Replaced missing values with the mean\n",
        "df[num_col].fillna(mean_val, inplace=True)\n",
        "\n",
        "# Results\n",
        "print(\"DataFrame after replacing missing values with column means:\\n\", df.head())\n",
        "print(\"\\nMissing values after replacement:\\n\", df.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCOluvDQbc12",
        "outputId": "785a87df-d982-4a49-8948-157168bf32eb"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame after replacing missing values with column means:\n",
            "    age     sex steroid  antivirals fatigue malaise anorexia liver_big  \\\n",
            "0   30    male   False       False   False   False    False     False   \n",
            "1   50  female   False       False    True   False    False     False   \n",
            "2   78  female    True       False    True   False    False      True   \n",
            "3   31  female     NaN        True   False   False    False      True   \n",
            "4   34  female    True       False   False   False    False      True   \n",
            "\n",
            "  liver_firm spleen_palpable spiders ascites varices  bilirubin  \\\n",
            "0      False           False   False   False   False        1.0   \n",
            "1      False           False   False   False   False        0.9   \n",
            "2      False           False   False   False   False        0.7   \n",
            "3      False           False   False   False   False        0.7   \n",
            "4      False           False   False   False   False        1.0   \n",
            "\n",
            "   alk_phosphate   sgot  albumin  protime  histology class  \n",
            "0           85.0   18.0      4.0      NaN      False  live  \n",
            "1          135.0   42.0      3.5      NaN      False  live  \n",
            "2           96.0   32.0      4.0      NaN      False  live  \n",
            "3           46.0   52.0      4.0     80.0      False  live  \n",
            "4            NaN  200.0      4.0      NaN      False  live  \n",
            "\n",
            "Missing values after replacement:\n",
            " age                 0\n",
            "sex                 0\n",
            "steroid             1\n",
            "antivirals          0\n",
            "fatigue             1\n",
            "malaise             1\n",
            "anorexia            1\n",
            "liver_big          10\n",
            "liver_firm         11\n",
            "spleen_palpable     5\n",
            "spiders             5\n",
            "ascites             5\n",
            "varices             5\n",
            "bilirubin           6\n",
            "alk_phosphate      29\n",
            "sgot                4\n",
            "albumin            16\n",
            "protime            67\n",
            "histology           0\n",
            "class               0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-c39c31782a71>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[num_col].fillna(mean_val, inplace=True)\n"
          ]
        }
      ]
    }
  ]
}