Clinical_Trials_Data_Cleaning.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "5eDO9ARFtiZF"
      },
      "source": [
        "# Sprint 1\n",
        "# DATA Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "AU07jYVsYhio"
      },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmVLz8lJcz1f"
      },
      "source": [
        "#### Loading the patients data into jupyter Noteook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "z4kvVuP9bFVP"
      },
      "outputs": [],
      "source": [
        "# load the dataset's\n",
        "patients = pd.read_csv('/content/patients.csv')"
      ]
    },
    {
      "cell_type": "code",
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        "scrolled": true,
        "outputId": "90d43ede-30b4-4568-b7ab-40bbe679b3a6"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     patient_id assigned_sex given_name      surname  \\\n",
              "0             1       female        Zoe      Wellish   \n",
              "1             2       female     Pamela         Hill   \n",
              "2             3         male        Jae       Debord   \n",
              "3             4         male       Liêm         Phan   \n",
              "4             5         male        Tim      Neudorf   \n",
              "..          ...          ...        ...          ...   \n",
              "498         499         male    Mustafa    Lindström   \n",
              "499         500         male      Ruman      Bisliev   \n",
              "500         501       female      Jinke    de Keizer   \n",
              "501         502       female    Chidalu  Onyekaozulu   \n",
              "502         503         male        Pat      Gersten   \n",
              "\n",
              "                       address               city       state  zip_code  \\\n",
              "0         576 Brown Bear Drive  Rancho California  California   92390.0   \n",
              "1    2370 University Hill Road          Armstrong    Illinois   61812.0   \n",
              "2        1493 Poling Farm Road               York    Nebraska   68467.0   \n",
              "3          2335 Webster Street         Woodbridge          NJ    7095.0   \n",
              "4         1428 Turkey Pen Lane             Dothan          AL   36303.0   \n",
              "..                         ...                ...         ...       ...   \n",
              "498        2530 Victoria Court       Milton Mills          ME    3852.0   \n",
              "499   494 Clarksburg Park Road             Sedona          AZ   86341.0   \n",
              "500          649 Nutter Street      Overland Park          MO   64110.0   \n",
              "501   3652 Boone Crockett Lane            Seattle          WA   98109.0   \n",
              "502          2778 North Avenue               Burr    Nebraska   68324.0   \n",
              "\n",
              "           country                                          contact  \\\n",
              "0    United States             951-719-9170ZoeWellish@superrito.com   \n",
              "1    United States            PamelaSHill@cuvox.de+1 (217) 569-3204   \n",
              "2    United States                 402-363-6804JaeMDebord@gustr.com   \n",
              "3    United States       PhanBaLiem@jourrapide.com+1 (732) 636-8246   \n",
              "4    United States                  334-515-7487TimNeudorf@cuvox.de   \n",
              "..             ...                                              ...   \n",
              "498  United States      207-477-0579MustafaLindstrom@jourrapide.com   \n",
              "499  United States               928-284-4492RumanBisliev@gustr.com   \n",
              "500  United States            816-223-6007JinkedeKeizer@teleworm.us   \n",
              "501  United States  ChidaluOnyekaozulu@jourrapide.com1 360 443 2060   \n",
              "502  United States             PatrickGersten@rhyta.com402-848-4923   \n",
              "\n",
              "     birthdate  weight  height   bmi  \n",
              "0    7/10/1976   121.7      66  19.6  \n",
              "1     4/3/1967   118.8      66  19.2  \n",
              "2    2/19/1980   177.8      71  24.8  \n",
              "3    7/26/1951   220.9      70  31.7  \n",
              "4    2/18/1928   192.3      27  26.1  \n",
              "..         ...     ...     ...   ...  \n",
              "498  4/10/1959   181.1      72  24.6  \n",
              "499  3/26/1948   239.6      70  34.4  \n",
              "500  1/13/1971   171.2      67  26.8  \n",
              "501  2/13/1952   176.9      67  27.7  \n",
              "502   5/3/1954   138.2      71  19.3  \n",
              "\n",
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              "      <td>Neudorf</td>\n",
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              "      <td>2/18/1928</td>\n",
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              "      .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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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('patients')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "patients",
              "summary": "{\n  \"name\": \"patients\",\n  \"rows\": 503,\n  \"fields\": [\n    {\n      \"column\": \"patient_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145,\n        \"min\": 1,\n        \"max\": 503,\n        \"num_unique_values\": 503,\n        \"samples\": [\n          269,\n          74,\n          290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assigned_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\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 470,\n        \"samples\": [\n          \"Anco\",\n          \"Leo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 466,\n        \"samples\": [\n          \"Koldenhof\",\n          \"Ferreira\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"address\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 483,\n        \"samples\": [\n          \"1962 Cabell Avenue\",\n          \"3958 Liberty Avenue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 349,\n        \"samples\": [\n          \"Monroe\",\n          \"Lafayette\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 54,\n        \"samples\": [\n          \"IL\",\n          \"OR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30265.807441804434,\n        \"min\": 1002.0,\n        \"max\": 99701.0,\n        \"num_unique_values\": 430,\n        \"samples\": [\n          91505.0,\n          2048.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contact\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 483,\n        \"samples\": [\n          \"703-547-0551SunKo@einrot.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthdate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"10/12/1980\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.91674139657366,\n        \"min\": 48.8,\n        \"max\": 255.9,\n        \"num_unique_values\": 347,\n        \"samples\": [\n          239.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 27,\n        \"max\": 79,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.27643780798017,\n        \"min\": 17.1,\n        \"max\": 37.7,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          24.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# viewing the data set patients\n",
        "patients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVsJqyWub-zj",
        "outputId": "dbb020c1-988f-4bfc-e7e6-f74e43a9a1f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 503 entries, 0 to 502\n",
            "Data columns (total 14 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   patient_id    503 non-null    int64  \n",
            " 1   assigned_sex  503 non-null    object \n",
            " 2   given_name    503 non-null    object \n",
            " 3   surname       503 non-null    object \n",
            " 4   address       491 non-null    object \n",
            " 5   city          491 non-null    object \n",
            " 6   state         491 non-null    object \n",
            " 7   zip_code      491 non-null    float64\n",
            " 8   country       491 non-null    object \n",
            " 9   contact       491 non-null    object \n",
            " 10  birthdate     503 non-null    object \n",
            " 11  weight        503 non-null    float64\n",
            " 12  height        503 non-null    int64  \n",
            " 13  bmi           503 non-null    float64\n",
            "dtypes: float64(3), int64(2), object(9)\n",
            "memory usage: 55.1+ KB\n"
          ]
        }
      ],
      "source": [
        "patients.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "fGp1PNp6mV4p",
        "outputId": "119a57f2-687b-4a25-cc69-41a2f1982673"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "patient_id       0\n",
              "assigned_sex     0\n",
              "given_name       0\n",
              "surname          0\n",
              "address         12\n",
              "city            12\n",
              "state           12\n",
              "zip_code        12\n",
              "country         12\n",
              "contact         12\n",
              "birthdate        0\n",
              "weight           0\n",
              "height           0\n",
              "bmi              0\n",
              "dtype: int64"
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            "text/html": [
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# finding the null values in each field\n",
        "patients.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VAtHeNV0K1r",
        "outputId": "04731acd-33fe-4558-da65-f2869e05a30b"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(503, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "patients.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlTLzCjhcz1j"
      },
      "source": [
        "### The Following Errors are observed in the Patients Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQELQlpZcz1j"
      },
      "source": [
        "\n",
        "##### 1. Address, City, State, Country, Zip_code columns have null values.\n",
        "##### 2. Zip_code Datatype is in Float, it should be in Int.\n",
        "##### 3. Contact Column has mixed informatation of email and mobile numbers of customers.\n",
        "##### 4. The names in given_name and sur_name columns are containing special characters.\n",
        "##### 5.The Birthdate column datatype is mismatched, the birthdate column is object datatype,but it should be in datetime64.\n",
        "##### 6.The names in given_name and sur_name columns are containing special characters.\n",
        "##### 7.The values in weight column is mentioned in pound, but they should be in kg's.\n",
        "##### 8.The Values in Height columns are mentioned in inches, but they should be in meters's.\n",
        "##### 9.The bmi column values are incorrectly calculated.\n",
        "##### 10.The State Column has the Statecode insted the state column should contain the full name of the state\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "DBJ96L2m0QN7",
        "outputId": "ae147e5b-1657-4fb2-df83-64388d23f9ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-71e651c02889>:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  patients.address.fillna('unknown',inplace = True)\n",
            "<ipython-input-7-71e651c02889>:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  patients.city.fillna('unknown',inplace = True)\n",
            "<ipython-input-7-71e651c02889>:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  patients.state.fillna('unknown',inplace = True)\n",
            "<ipython-input-7-71e651c02889>:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  patients.country.fillna('unknown',inplace = True)\n",
            "<ipython-input-7-71e651c02889>:9: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  patients.zip_code.fillna(0,inplace = True)\n"
          ]
        }
      ],
      "source": [
        "# fill the null values\n",
        "patients.address.fillna('unknown',inplace = True)\n",
        "patients.city.fillna('unknown',inplace = True)\n",
        "patients.state.fillna('unknown',inplace = True)\n",
        "patients.country.fillna('unknown',inplace = True)\n",
        "\n",
        "# find and fill null values of zipcode\n",
        "patients[patients['zip_code'].isna()]\n",
        "patients.zip_code.fillna(0,inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZvl51lK0QFr",
        "outputId": "1f28ad82-4010-4cf0-847c-f502a4ac2abe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# verifying the null values in zip_code\n",
        "patients.zip_code.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VWy9lIDacz1k"
      },
      "source": [
        "#### Zip_code Datatype is in Float, it should be in Int"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "vfPhq4yv0QC5"
      },
      "outputs": [],
      "source": [
        "# converting the zip_code datatype from float to int\n",
        "patients.zip_code = patients.zip_code.astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpvxThZdcz1k"
      },
      "source": [
        "#### Contact Column has mixed informatation of email and mobile numbers of customers,so i am trying to separate the email and mobile numbers of customers and fill them into a new colums for each  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "bgTG8UOW0P9e",
        "outputId": "4a7bbcee-9ce4-4e80-890b-0bab6c5999c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-da4b01ed3b49>:8: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  patients['phone_number'].fillna('0', inplace=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "0    9517199170\n",
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              "      <th></th>\n",
              "      <th>phone_number</th>\n",
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              "      <td>7326368246</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# extracting the email and phone number from contact\n",
        "phone_pattern = r'(\\+?\\d{1,4}[\\s-]?\\(?\\d{1,4}\\)?[\\s-]?\\d{1,4}[\\s-]?\\d{1,9})'\n",
        "\n",
        "patients['phone_number'] = patients['contact'].str.extract(phone_pattern)\n",
        "patients['phone_number'] = patients['phone_number'].str.replace('[\\(\\)\\+\\-\\s]', '', regex=True)\n",
        "\n",
        "# filling the nan values with 0\n",
        "patients['phone_number'].fillna('0', inplace=True)\n",
        "\n",
        "#converting datatype of the phone_number to str\n",
        "patients['phone_number'] = patients['phone_number'].astype(str)\n",
        "\n",
        "#removing the countrycode and other digits from phone_number\n",
        "def phone_number(number):\n",
        "    if len(number) == 11:\n",
        "        number = number[1:]\n",
        "    return number\n",
        "\n",
        "patients['phone_number'] = patients['phone_number'].apply(phone_number)\n",
        "patients['phone_number'].head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "2bCPcTbElIBY",
        "outputId": "e9067034-c33d-41b4-dc5e-dac1a3f975fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-16d33471b42e>:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  patients['email'].fillna('unknown',inplace = True)\n"
          ]
        }
      ],
      "source": [
        "# extracting the email from contact\n",
        "email_pattern = r'([a-zA-Z._%+-]+@[a-zA-Z.-]+\\.[a-zA-Z]{2,})'\n",
        "patients['email'] = patients['contact'].str.extract(email_pattern)\n",
        "# filling null values with Unknown\n",
        "patients['email'].fillna('unknown',inplace = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlDSkyGNcz1l"
      },
      "source": [
        "#### Verifying the the email and mobile numbers whether they are separated"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "KGtZzhbu0P4e",
        "outputId": "f9a0f5ef-4a97-436e-e3c2-782e65d5b8b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          contact  \\\n",
              "0            951-719-9170ZoeWellish@superrito.com   \n",
              "1           PamelaSHill@cuvox.de+1 (217) 569-3204   \n",
              "2                402-363-6804JaeMDebord@gustr.com   \n",
              "3      PhanBaLiem@jourrapide.com+1 (732) 636-8246   \n",
              "4                 334-515-7487TimNeudorf@cuvox.de   \n",
              "5        386-334-5237RafaelCardosoCosta@gustr.com   \n",
              "6               775-533-5933MaryBAdams@einrot.com   \n",
              "7            XiuxiuChang@einrot.com1 408 778 3236   \n",
              "8         816-265-9578DavidGustafsson@armyspy.com   \n",
              "9   SophieCabreraIbarra@teleworm.us1 718 795 9124   \n",
              "10         906-478-8949SandyGunnarsson@dayrep.com   \n",
              "11     Abdul-NurMummarIsa@rhyta.com1 931 207 0839   \n",
              "12      OmeokachieIbeamaka@einrot.com434-509-2614   \n",
              "13     AnenechiChidi@armyspy.com+1 (205) 417-8095   \n",
              "14              AsiaWozniak@rhyta.com918-712-3469   \n",
              "15                276-225-1955SrenFLund@gustr.com   \n",
              "16         LieuThiThuTam@dayrep.com1 559 765 7836   \n",
              "17       RoxanneAndreyeva@armyspy.com678-829-8578   \n",
              "18          406-775-2696WilliamVOates@armyspy.com   \n",
              "19               ZakKelly@rhyta.com1 530 532 8397   \n",
              "\n",
              "                              email phone_number  \n",
              "0          ZoeWellish@superrito.com   9517199170  \n",
              "1              PamelaSHill@cuvox.de   2175693204  \n",
              "2              JaeMDebord@gustr.com   4023636804  \n",
              "3         PhanBaLiem@jourrapide.com   7326368246  \n",
              "4               TimNeudorf@cuvox.de   3345157487  \n",
              "5      RafaelCardosoCosta@gustr.com   3863345237  \n",
              "6             MaryBAdams@einrot.com   7755335933  \n",
              "7            XiuxiuChang@einrot.com   4087783236  \n",
              "8       DavidGustafsson@armyspy.com   8162659578  \n",
              "9   SophieCabreraIbarra@teleworm.us   7187959124  \n",
              "10       SandyGunnarsson@dayrep.com   9064788949  \n",
              "11     Abdul-NurMummarIsa@rhyta.com   9312070839  \n",
              "12    OmeokachieIbeamaka@einrot.com   4345092614  \n",
              "13        AnenechiChidi@armyspy.com   2054178095  \n",
              "14            AsiaWozniak@rhyta.com   9187123469  \n",
              "15              SrenFLund@gustr.com   2762251955  \n",
              "16         LieuThiThuTam@dayrep.com   5597657836  \n",
              "17     RoxanneAndreyeva@armyspy.com   6788298578  \n",
              "18        WilliamVOates@armyspy.com   4067752696  \n",
              "19               ZakKelly@rhyta.com   5305328397  "
            ],
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              "      <th>1</th>\n",
              "      <td>PamelaSHill@cuvox.de+1 (217) 569-3204</td>\n",
              "      <td>PamelaSHill@cuvox.de</td>\n",
              "      <td>2175693204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>402-363-6804JaeMDebord@gustr.com</td>\n",
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              "      <td>4023636804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PhanBaLiem@jourrapide.com+1 (732) 636-8246</td>\n",
              "      <td>PhanBaLiem@jourrapide.com</td>\n",
              "      <td>7326368246</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>334-515-7487TimNeudorf@cuvox.de</td>\n",
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              "      <td>3345157487</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>386-334-5237RafaelCardosoCosta@gustr.com</td>\n",
              "      <td>RafaelCardosoCosta@gustr.com</td>\n",
              "      <td>3863345237</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>775-533-5933MaryBAdams@einrot.com</td>\n",
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              "      <td>7755335933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>XiuxiuChang@einrot.com1 408 778 3236</td>\n",
              "      <td>XiuxiuChang@einrot.com</td>\n",
              "      <td>4087783236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>816-265-9578DavidGustafsson@armyspy.com</td>\n",
              "      <td>DavidGustafsson@armyspy.com</td>\n",
              "      <td>8162659578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SophieCabreraIbarra@teleworm.us1 718 795 9124</td>\n",
              "      <td>SophieCabreraIbarra@teleworm.us</td>\n",
              "      <td>7187959124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>906-478-8949SandyGunnarsson@dayrep.com</td>\n",
              "      <td>SandyGunnarsson@dayrep.com</td>\n",
              "      <td>9064788949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Abdul-NurMummarIsa@rhyta.com1 931 207 0839</td>\n",
              "      <td>Abdul-NurMummarIsa@rhyta.com</td>\n",
              "      <td>9312070839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>OmeokachieIbeamaka@einrot.com434-509-2614</td>\n",
              "      <td>OmeokachieIbeamaka@einrot.com</td>\n",
              "      <td>4345092614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>AnenechiChidi@armyspy.com+1 (205) 417-8095</td>\n",
              "      <td>AnenechiChidi@armyspy.com</td>\n",
              "      <td>2054178095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>AsiaWozniak@rhyta.com918-712-3469</td>\n",
              "      <td>AsiaWozniak@rhyta.com</td>\n",
              "      <td>9187123469</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>276-225-1955SrenFLund@gustr.com</td>\n",
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              "      <td>2762251955</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>LieuThiThuTam@dayrep.com1 559 765 7836</td>\n",
              "      <td>LieuThiThuTam@dayrep.com</td>\n",
              "      <td>5597657836</td>\n",
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              "    <tr>\n",
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              "      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-52a7ca77-d11a-44a5-a282-b6e0aa3bb270 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\": \"patients[['contact', 'email', 'phone_number']]\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"contact\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"951-719-9170ZoeWellish@superrito.com\",\n          \"RoxanneAndreyeva@armyspy.com678-829-8578\",\n          \"276-225-1955SrenFLund@gustr.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"ZoeWellish@superrito.com\",\n          \"RoxanneAndreyeva@armyspy.com\",\n          \"SrenFLund@gustr.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_number\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"9517199170\",\n          \"6788298578\",\n          \"2762251955\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "patients[['contact', 'email', 'phone_number']].head(20)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Cc5LoeS_g-yh"
      },
      "outputs": [],
      "source": [
        "# removing the patients contact column\n",
        "patients.drop('contact',axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "9Kjoz_athxwF",
        "outputId": "703e268b-f62f-4c03-fe56-6bef25b12e8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   patient_id assigned_sex given_name  surname                    address  \\\n",
              "0           1       female        Zoe  Wellish       576 Brown Bear Drive   \n",
              "1           2       female     Pamela     Hill  2370 University Hill Road   \n",
              "2           3         male        Jae   Debord      1493 Poling Farm Road   \n",
              "3           4         male       Liêm     Phan        2335 Webster Street   \n",
              "4           5         male        Tim  Neudorf       1428 Turkey Pen Lane   \n",
              "\n",
              "                city       state  zip_code        country  birthdate  weight  \\\n",
              "0  Rancho California  California     92390  United States  7/10/1976   121.7   \n",
              "1          Armstrong    Illinois     61812  United States   4/3/1967   118.8   \n",
              "2               York    Nebraska     68467  United States  2/19/1980   177.8   \n",
              "3         Woodbridge          NJ      7095  United States  7/26/1951   220.9   \n",
              "4             Dothan          AL     36303  United States  2/18/1928   192.3   \n",
              "\n",
              "   height   bmi phone_number                      email  \n",
              "0      66  19.6   9517199170   ZoeWellish@superrito.com  \n",
              "1      66  19.2   2175693204       PamelaSHill@cuvox.de  \n",
              "2      71  24.8   4023636804       JaeMDebord@gustr.com  \n",
              "3      70  31.7   7326368246  PhanBaLiem@jourrapide.com  \n",
              "4      27  26.1   3345157487        TimNeudorf@cuvox.de  "
            ],
            "text/html": [
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              "      <th>patient_id</th>\n",
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              "      <th>email</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>Zoe</td>\n",
              "      <td>Wellish</td>\n",
              "      <td>576 Brown Bear Drive</td>\n",
              "      <td>Rancho California</td>\n",
              "      <td>California</td>\n",
              "      <td>92390</td>\n",
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              "      <td>121.7</td>\n",
              "      <td>66</td>\n",
              "      <td>19.6</td>\n",
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              "      <td>ZoeWellish@superrito.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>female</td>\n",
              "      <td>Pamela</td>\n",
              "      <td>Hill</td>\n",
              "      <td>2370 University Hill Road</td>\n",
              "      <td>Armstrong</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>61812</td>\n",
              "      <td>United States</td>\n",
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              "      <td>66</td>\n",
              "      <td>19.2</td>\n",
              "      <td>2175693204</td>\n",
              "      <td>PamelaSHill@cuvox.de</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>Jae</td>\n",
              "      <td>Debord</td>\n",
              "      <td>1493 Poling Farm Road</td>\n",
              "      <td>York</td>\n",
              "      <td>Nebraska</td>\n",
              "      <td>68467</td>\n",
              "      <td>United States</td>\n",
              "      <td>2/19/1980</td>\n",
              "      <td>177.8</td>\n",
              "      <td>71</td>\n",
              "      <td>24.8</td>\n",
              "      <td>4023636804</td>\n",
              "      <td>JaeMDebord@gustr.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>male</td>\n",
              "      <td>Liêm</td>\n",
              "      <td>Phan</td>\n",
              "      <td>2335 Webster Street</td>\n",
              "      <td>Woodbridge</td>\n",
              "      <td>NJ</td>\n",
              "      <td>7095</td>\n",
              "      <td>United States</td>\n",
              "      <td>7/26/1951</td>\n",
              "      <td>220.9</td>\n",
              "      <td>70</td>\n",
              "      <td>31.7</td>\n",
              "      <td>7326368246</td>\n",
              "      <td>PhanBaLiem@jourrapide.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>male</td>\n",
              "      <td>Tim</td>\n",
              "      <td>Neudorf</td>\n",
              "      <td>1428 Turkey Pen Lane</td>\n",
              "      <td>Dothan</td>\n",
              "      <td>AL</td>\n",
              "      <td>36303</td>\n",
              "      <td>United States</td>\n",
              "      <td>2/18/1928</td>\n",
              "      <td>192.3</td>\n",
              "      <td>27</td>\n",
              "      <td>26.1</td>\n",
              "      <td>3345157487</td>\n",
              "      <td>TimNeudorf@cuvox.de</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9b65ccc-1574-4e66-b941-ed1adf0ea077')\"\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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\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-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        document.querySelector('#df-b9b65ccc-1574-4e66-b941-ed1adf0ea077 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b9b65ccc-1574-4e66-b941-ed1adf0ea077');\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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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
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              "    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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              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8a3b9fde-0e1a-41f2-891f-145572425460 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "patients",
              "summary": "{\n  \"name\": \"patients\",\n  \"rows\": 503,\n  \"fields\": [\n    {\n      \"column\": \"patient_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145,\n        \"min\": 1,\n        \"max\": 503,\n        \"num_unique_values\": 503,\n        \"samples\": [\n          269,\n          74,\n          290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assigned_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\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 470,\n        \"samples\": [\n          \"Anco\",\n          \"Leo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 466,\n        \"samples\": [\n          \"Koldenhof\",\n          \"Ferreira\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"address\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"1962 Cabell Avenue\",\n          \"3958 Liberty Avenue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 350,\n        \"samples\": [\n          \"Monroe\",\n          \"Ottawa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 55,\n        \"samples\": [\n          \"NC\",\n          \"Florida\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30827,\n        \"min\": 0,\n        \"max\": 99701,\n        \"num_unique_values\": 431,\n        \"samples\": [\n          91505,\n          2048\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"unknown\",\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthdate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"10/12/1980\",\n          \"1/20/1926\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.91674139657366,\n        \"min\": 48.8,\n        \"max\": 255.9,\n        \"num_unique_values\": 347,\n        \"samples\": [\n          239.1,\n          137.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 27,\n        \"max\": 79,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          66,\n          63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.27643780798017,\n        \"min\": 17.1,\n        \"max\": 37.7,\n        \"num_unique_values\": 183,\n        \"samples\": [\n          24.7,\n          19.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_number\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"7035470551\",\n          \"7144962264\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"SunKo@einrot.com\",\n          \"TarPoldi@superrito.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "patients.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apqDnvsDcz1l"
      },
      "source": [
        "#### The Birthdate column datatype is mismatched, the birthdate column is object datatype,but it should be in datetime64."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "hO4QWnE60P1w"
      },
      "outputs": [],
      "source": [
        "# changinng the birthdate field datatype from object to datetime64\n",
        "patients['birthdate'] = pd.to_datetime(patients['birthdate'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kl9g-Wr50PzH",
        "outputId": "1a3aba22-66f0-42b6-a15f-b921b68ff8b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "RangeIndex: 503 entries, 0 to 502\n",
            "Series name: birthdate\n",
            "Non-Null Count  Dtype         \n",
            "--------------  -----         \n",
            "503 non-null    datetime64[ns]\n",
            "dtypes: datetime64[ns](1)\n",
            "memory usage: 4.1 KB\n"
          ]
        }
      ],
      "source": [
        "patients.birthdate.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJW-Zpn6cz1m"
      },
      "source": [
        "### The names in given_name and sur_name columns are containing special characters, so i am trying to replace them with the unicode characters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "mGLVyLUr0PwZ",
        "outputId": "795337c0-69c4-4855-dffc-02f41304d4ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name     surname\n",
              "0        Zoe     Wellish\n",
              "1     Pamela        Hill\n",
              "2        Jae      Debord\n",
              "3       Liêm        Phan\n",
              "4        Tim     Neudorf\n",
              "5     Rafael       Costa\n",
              "6       Mary       Adams\n",
              "7     Xiuxiu       Chang\n",
              "8      Dsvid  Gustafsson\n",
              "9     Sophie     Cabrera"
            ],
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"patients[['given_name','surname']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Dsvid\",\n          \"Pamela\",\n          \"Rafael\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Gustafsson\",\n          \"Hill\",\n          \"Costa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# cleaning the errors in names\n",
        "patients[['given_name','surname']].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sE-eKxel7B6T",
        "outputId": "2f56860b-02b5-4c37-a517-0074f4fb774e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting unidecode\n",
            "  Downloading Unidecode-1.3.8-py3-none-any.whl.metadata (13 kB)\n",
            "Downloading Unidecode-1.3.8-py3-none-any.whl (235 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/235.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.5/235.5 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: unidecode\n",
            "Successfully installed unidecode-1.3.8\n"
          ]
        }
      ],
      "source": [
        "pip install unidecode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "F3dQryAa7JG2",
        "outputId": "b5b02d57-35b9-44c1-8183-e9edcb684292"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name     surname\n",
              "0        Zoe     Wellish\n",
              "1     Pamela        Hill\n",
              "2        Jae      Debord\n",
              "3       Liem        Phan\n",
              "4        Tim     Neudorf\n",
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              "7     Xiuxiu       Chang\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"patients[['given_name','surname']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Dsvid\",\n          \"Pamela\",\n          \"Rafael\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Gustafsson\",\n          \"Hill\",\n          \"Costa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "import unidecode\n",
        "\n",
        "# converting the the special character in names to regular characters\n",
        "def normalize_name(name):\n",
        "    return unidecode.unidecode(name)\n",
        "\n",
        "# Apply the function to the given_name & sur_name columns\n",
        "patients['given_name'] = patients['given_name'].apply(normalize_name)\n",
        "patients['surname'] = patients['surname'].apply(normalize_name)\n",
        "patients[['given_name','surname']].head(10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDBnf6vGcz1m"
      },
      "source": [
        "#### Creating a new column for Full_name of the customers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "mgGSar2ynQ1w",
        "outputId": "3bed4ca9-ea82-4550-ce9f-11d62544bb5c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Zoe Wellish\n",
              "1    Pamela Hill\n",
              "2     Jae Debord\n",
              "3      Liem Phan\n",
              "4    Tim Neudorf\n",
              "Name: full_name, dtype: object"
            ],
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              "</div><br><label><b>dtype:</b> object</label>"
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          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# creating Full name column\n",
        "patients['full_name'] = patients['given_name'] + ' ' + patients['surname']\n",
        "patients['full_name'].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eA72bLN0cz1n"
      },
      "source": [
        "#### The values in weight column is mentioned in pound, but they should be in kg's\n",
        "#### The Values in Height columns are mentioned in inches, but they should be in meters's"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "2cXZlE1nn67F"
      },
      "outputs": [],
      "source": [
        "#converting the weight from pounds to kg's\n",
        "patients['weight'] = patients['weight'] * 0.453592\n",
        "\n",
        "# converting the height from inches to mt's\n",
        "patients['height'] = patients['height'] * 0.0254"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jwvy6BDupW72"
      },
      "source": [
        "#### The bmi column values are incorrectly calculated,so i am trying to calculate the correct bmi with revised weight's and Height's of the customer's\n",
        "##### bmi: the Body Mass Index (BMI) of each patient. BMI is a simple calculation using a person's height and weight. The formula is BMI = kg/m2 where kg is a person's weight in kilograms and m2 is their height in metres squared."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "MaBmMbNKpW4q"
      },
      "outputs": [],
      "source": [
        "#BMI = kg/m2 calculation and creating column\n",
        "patients['bmi'] = patients['weight'] / (patients['height'] **2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "CR0jXOu_pW2l",
        "outputId": "a47d5c03-81ab-4ae2-d5d0-1d045859fa0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     patient_id assigned_sex given_name      surname  \\\n",
              "0             1       female        Zoe      Wellish   \n",
              "1             2       female     Pamela         Hill   \n",
              "2             3         male        Jae       Debord   \n",
              "3             4         male       Liem         Phan   \n",
              "4             5         male        Tim      Neudorf   \n",
              "..          ...          ...        ...          ...   \n",
              "498         499         male    Mustafa    Lindstrom   \n",
              "499         500         male      Ruman      Bisliev   \n",
              "500         501       female      Jinke    de Keizer   \n",
              "501         502       female    Chidalu  Onyekaozulu   \n",
              "502         503         male        Pat      Gersten   \n",
              "\n",
              "                       address               city       state  zip_code  \\\n",
              "0         576 Brown Bear Drive  Rancho California  California     92390   \n",
              "1    2370 University Hill Road          Armstrong    Illinois     61812   \n",
              "2        1493 Poling Farm Road               York    Nebraska     68467   \n",
              "3          2335 Webster Street         Woodbridge          NJ      7095   \n",
              "4         1428 Turkey Pen Lane             Dothan          AL     36303   \n",
              "..                         ...                ...         ...       ...   \n",
              "498        2530 Victoria Court       Milton Mills          ME      3852   \n",
              "499   494 Clarksburg Park Road             Sedona          AZ     86341   \n",
              "500          649 Nutter Street      Overland Park          MO     64110   \n",
              "501   3652 Boone Crockett Lane            Seattle          WA     98109   \n",
              "502          2778 North Avenue               Burr    Nebraska     68324   \n",
              "\n",
              "           country  birthdate      weight  height         bmi phone_number  \\\n",
              "0    United States 1976-07-10   55.202146  1.6764   19.642676   9517199170   \n",
              "1    United States 1967-04-03   53.886730  1.6764   19.174609   2175693204   \n",
              "2    United States 1980-02-19   80.648658  1.8034   24.797792   4023636804   \n",
              "3    United States 1951-07-26  100.198473  1.7780   31.695499   7326368246   \n",
              "4    United States 1928-02-18   87.225742  0.6858  185.459767   3345157487   \n",
              "..             ...        ...         ...     ...         ...          ...   \n",
              "498  United States 1959-04-10   82.145511  1.8288   24.561303   2074770579   \n",
              "499  United States 1948-03-26  108.680643  1.7780   34.378640   9282844492   \n",
              "500  United States 1971-01-13   77.654950  1.7018   26.813414   8162236007   \n",
              "501  United States 1952-02-13   80.240425  1.7018   27.706150   3604432060   \n",
              "502  United States 1954-05-03   62.686414  1.8034   19.274774   4028484923   \n",
              "\n",
              "                                 email            full_name  \n",
              "0             ZoeWellish@superrito.com          Zoe Wellish  \n",
              "1                 PamelaSHill@cuvox.de          Pamela Hill  \n",
              "2                 JaeMDebord@gustr.com           Jae Debord  \n",
              "3            PhanBaLiem@jourrapide.com            Liem Phan  \n",
              "4                  TimNeudorf@cuvox.de          Tim Neudorf  \n",
              "..                                 ...                  ...  \n",
              "498    MustafaLindstrom@jourrapide.com    Mustafa Lindstrom  \n",
              "499             RumanBisliev@gustr.com        Ruman Bisliev  \n",
              "500          JinkedeKeizer@teleworm.us      Jinke de Keizer  \n",
              "501  ChidaluOnyekaozulu@jourrapide.com  Chidalu Onyekaozulu  \n",
              "502           PatrickGersten@rhyta.com          Pat Gersten  \n",
              "\n",
              "[503 rows x 16 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patient_id</th>\n",
              "      <th>assigned_sex</th>\n",
              "      <th>given_name</th>\n",
              "      <th>surname</th>\n",
              "      <th>address</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>country</th>\n",
              "      <th>birthdate</th>\n",
              "      <th>weight</th>\n",
              "      <th>height</th>\n",
              "      <th>bmi</th>\n",
              "      <th>phone_number</th>\n",
              "      <th>email</th>\n",
              "      <th>full_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>Zoe</td>\n",
              "      <td>Wellish</td>\n",
              "      <td>576 Brown Bear Drive</td>\n",
              "      <td>Rancho California</td>\n",
              "      <td>California</td>\n",
              "      <td>92390</td>\n",
              "      <td>United States</td>\n",
              "      <td>1976-07-10</td>\n",
              "      <td>55.202146</td>\n",
              "      <td>1.6764</td>\n",
              "      <td>19.642676</td>\n",
              "      <td>9517199170</td>\n",
              "      <td>ZoeWellish@superrito.com</td>\n",
              "      <td>Zoe Wellish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>female</td>\n",
              "      <td>Pamela</td>\n",
              "      <td>Hill</td>\n",
              "      <td>2370 University Hill Road</td>\n",
              "      <td>Armstrong</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>61812</td>\n",
              "      <td>United States</td>\n",
              "      <td>1967-04-03</td>\n",
              "      <td>53.886730</td>\n",
              "      <td>1.6764</td>\n",
              "      <td>19.174609</td>\n",
              "      <td>2175693204</td>\n",
              "      <td>PamelaSHill@cuvox.de</td>\n",
              "      <td>Pamela Hill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>Jae</td>\n",
              "      <td>Debord</td>\n",
              "      <td>1493 Poling Farm Road</td>\n",
              "      <td>York</td>\n",
              "      <td>Nebraska</td>\n",
              "      <td>68467</td>\n",
              "      <td>United States</td>\n",
              "      <td>1980-02-19</td>\n",
              "      <td>80.648658</td>\n",
              "      <td>1.8034</td>\n",
              "      <td>24.797792</td>\n",
              "      <td>4023636804</td>\n",
              "      <td>JaeMDebord@gustr.com</td>\n",
              "      <td>Jae Debord</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>male</td>\n",
              "      <td>Liem</td>\n",
              "      <td>Phan</td>\n",
              "      <td>2335 Webster Street</td>\n",
              "      <td>Woodbridge</td>\n",
              "      <td>NJ</td>\n",
              "      <td>7095</td>\n",
              "      <td>United States</td>\n",
              "      <td>1951-07-26</td>\n",
              "      <td>100.198473</td>\n",
              "      <td>1.7780</td>\n",
              "      <td>31.695499</td>\n",
              "      <td>7326368246</td>\n",
              "      <td>PhanBaLiem@jourrapide.com</td>\n",
              "      <td>Liem Phan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>male</td>\n",
              "      <td>Tim</td>\n",
              "      <td>Neudorf</td>\n",
              "      <td>1428 Turkey Pen Lane</td>\n",
              "      <td>Dothan</td>\n",
              "      <td>AL</td>\n",
              "      <td>36303</td>\n",
              "      <td>United States</td>\n",
              "      <td>1928-02-18</td>\n",
              "      <td>87.225742</td>\n",
              "      <td>0.6858</td>\n",
              "      <td>185.459767</td>\n",
              "      <td>3345157487</td>\n",
              "      <td>TimNeudorf@cuvox.de</td>\n",
              "      <td>Tim Neudorf</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>499</td>\n",
              "      <td>male</td>\n",
              "      <td>Mustafa</td>\n",
              "      <td>Lindstrom</td>\n",
              "      <td>2530 Victoria Court</td>\n",
              "      <td>Milton Mills</td>\n",
              "      <td>ME</td>\n",
              "      <td>3852</td>\n",
              "      <td>United States</td>\n",
              "      <td>1959-04-10</td>\n",
              "      <td>82.145511</td>\n",
              "      <td>1.8288</td>\n",
              "      <td>24.561303</td>\n",
              "      <td>2074770579</td>\n",
              "      <td>MustafaLindstrom@jourrapide.com</td>\n",
              "      <td>Mustafa Lindstrom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>500</td>\n",
              "      <td>male</td>\n",
              "      <td>Ruman</td>\n",
              "      <td>Bisliev</td>\n",
              "      <td>494 Clarksburg Park Road</td>\n",
              "      <td>Sedona</td>\n",
              "      <td>AZ</td>\n",
              "      <td>86341</td>\n",
              "      <td>United States</td>\n",
              "      <td>1948-03-26</td>\n",
              "      <td>108.680643</td>\n",
              "      <td>1.7780</td>\n",
              "      <td>34.378640</td>\n",
              "      <td>9282844492</td>\n",
              "      <td>RumanBisliev@gustr.com</td>\n",
              "      <td>Ruman Bisliev</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>501</td>\n",
              "      <td>female</td>\n",
              "      <td>Jinke</td>\n",
              "      <td>de Keizer</td>\n",
              "      <td>649 Nutter Street</td>\n",
              "      <td>Overland Park</td>\n",
              "      <td>MO</td>\n",
              "      <td>64110</td>\n",
              "      <td>United States</td>\n",
              "      <td>1971-01-13</td>\n",
              "      <td>77.654950</td>\n",
              "      <td>1.7018</td>\n",
              "      <td>26.813414</td>\n",
              "      <td>8162236007</td>\n",
              "      <td>JinkedeKeizer@teleworm.us</td>\n",
              "      <td>Jinke de Keizer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>502</td>\n",
              "      <td>female</td>\n",
              "      <td>Chidalu</td>\n",
              "      <td>Onyekaozulu</td>\n",
              "      <td>3652 Boone Crockett Lane</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>WA</td>\n",
              "      <td>98109</td>\n",
              "      <td>United States</td>\n",
              "      <td>1952-02-13</td>\n",
              "      <td>80.240425</td>\n",
              "      <td>1.7018</td>\n",
              "      <td>27.706150</td>\n",
              "      <td>3604432060</td>\n",
              "      <td>ChidaluOnyekaozulu@jourrapide.com</td>\n",
              "      <td>Chidalu Onyekaozulu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>503</td>\n",
              "      <td>male</td>\n",
              "      <td>Pat</td>\n",
              "      <td>Gersten</td>\n",
              "      <td>2778 North Avenue</td>\n",
              "      <td>Burr</td>\n",
              "      <td>Nebraska</td>\n",
              "      <td>68324</td>\n",
              "      <td>United States</td>\n",
              "      <td>1954-05-03</td>\n",
              "      <td>62.686414</td>\n",
              "      <td>1.8034</td>\n",
              "      <td>19.274774</td>\n",
              "      <td>4028484923</td>\n",
              "      <td>PatrickGersten@rhyta.com</td>\n",
              "      <td>Pat Gersten</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>503 rows × 16 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-cb7c519c-33de-479b-bbf6-6cbc42af1537')\"\n",
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              "\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      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",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "        cursor: pointer;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\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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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\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('patients')\"\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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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('patients');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "patients",
              "summary": "{\n  \"name\": \"patients\",\n  \"rows\": 503,\n  \"fields\": [\n    {\n      \"column\": \"patient_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145,\n        \"min\": 1,\n        \"max\": 503,\n        \"num_unique_values\": 503,\n        \"samples\": [\n          269,\n          74,\n          290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assigned_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\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 467,\n        \"samples\": [\n          \"Anco\",\n          \"Yukitaka\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 466,\n        \"samples\": [\n          \"Koldenhof\",\n          \"Ferreira\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"address\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"1962 Cabell Avenue\",\n          \"3958 Liberty Avenue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 350,\n        \"samples\": [\n          \"Monroe\",\n          \"Ottawa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 55,\n        \"samples\": [\n          \"NC\",\n          \"Florida\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30827,\n        \"min\": 0,\n        \"max\": 99701,\n        \"num_unique_values\": 431,\n        \"samples\": [\n          91505,\n          2048\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"unknown\",\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthdate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1921-11-06 00:00:00\",\n        \"max\": \"1999-07-03 00:00:00\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"1980-10-12 00:00:00\",\n          \"1926-01-20 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.384362563554632,\n        \"min\": 22.1352896,\n        \"max\": 116.0741928,\n        \"num_unique_values\": 347,\n        \"samples\": [\n          108.4538472,\n          62.187463199999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11204695616098355,\n        \"min\": 0.6858,\n        \"max\": 2.0065999999999997,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          1.6764,\n          1.6001999999999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.833023886968792,\n        \"min\": 8.644436255866719,\n        \"max\": 185.45976663969793,\n        \"num_unique_values\": 473,\n        \"samples\": [\n          25.2810086055539,\n          19.49925327423888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_number\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"7035470551\",\n          \"7144962264\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"SunKo@einrot.com\",\n          \"TarPoldi@superrito.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 498,\n        \"samples\": [\n          \"Poldi Tar\",\n          \"Zarka Rap\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "patients"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZ4xVcN9cz1n"
      },
      "source": [
        "#### Calculating the bmi_status using the bmi values\n",
        "##### A BMI of 25.0 or more is overweight, while the healthy range is 18.5 to 24.9. The inclusion criteria for this clinical trial is 16 >= BMI >= 38.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "nbw9UFujpW0U"
      },
      "outputs": [],
      "source": [
        "def status(bmi):\n",
        "  if bmi < 18.5:\n",
        "      return 'Underweight'\n",
        "  elif 18.5 <= bmi < 24.9:\n",
        "      return 'Normal weight'\n",
        "  elif 24.9 <= bmi < 29.9:\n",
        "      return 'Overweight'\n",
        "  elif 29.9 <= bmi < 34.9:\n",
        "      return 'Obesity'\n",
        "  else:\n",
        "      return 'Severe Obesity'\n",
        "\n",
        "# Apply the function to each value in the 'revised_bmi' column\n",
        "Bmi_Status = patients['bmi'].apply(status)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "II0eyLLKpWyK",
        "outputId": "4030433c-ebfb-4917-b737-784402c6e9bd"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Normal weight\n",
              "1       Normal weight\n",
              "2       Normal weight\n",
              "3             Obesity\n",
              "4      Severe Obesity\n",
              "            ...      \n",
              "498     Normal weight\n",
              "499           Obesity\n",
              "500        Overweight\n",
              "501        Overweight\n",
              "502     Normal weight\n",
              "Name: bmi_status, Length: 503, dtype: object"
            ],
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              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# creating the Bmi_status column\n",
        "patients['bmi_status'] = Bmi_Status\n",
        "patients['bmi_status']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jV7PtW4dcz1o"
      },
      "source": [
        "#### The State Column has the Statecode insted the state column should contain the full name of the state"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KWH8u7XpWvl",
        "outputId": "ad9a633b-c5b4-406e-85f5-92a713e6f992"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['California', 'Illinois', 'Nebraska', 'NJ', 'AL', 'Florida', 'NV',\n",
              "       'CA', 'MO', 'New York', 'MI', 'TN', 'VA', 'OK', 'GA', 'MT', 'MA',\n",
              "       'NY', 'NM', 'IL', 'LA', 'PA', 'CO', 'ME', 'WI', 'SD', 'MN', 'FL',\n",
              "       'WY', 'OH', 'IA', 'NC', 'IN', 'CT', 'KY', 'DE', 'MD', 'AZ', 'TX',\n",
              "       'NE', 'AK', 'ND', 'KS', 'MS', 'WA', 'SC', 'WV', 'RI', 'NH', 'OR',\n",
              "       'unknown', 'VT', 'ID', 'DC', 'AR'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "patients.state.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "3KZaMXZcpWtC",
        "outputId": "497872f9-8f04-400c-87ca-c1a4064bb845"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      California\n",
              "1        Illinois\n",
              "2        Nebraska\n",
              "3      New Jersey\n",
              "4         Alabama\n",
              "          ...    \n",
              "498         Maine\n",
              "499       Arizona\n",
              "500      Missouri\n",
              "501    Washington\n",
              "502      Nebraska\n",
              "Name: state, Length: 503, dtype: object"
            ],
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              "      <td>Nebraska</td>\n",
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              "<p>503 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "def full_name(state):\n",
        "    states_full_name = {\n",
        "        'AL': 'Alabama', 'AK': 'Alaska', 'AZ': 'Arizona', 'AR': 'Arkansas', 'CA': 'California',\n",
        "        'CO': 'Colorado', 'CT': 'Connecticut', 'DE': 'Delaware', 'FL': 'Florida', 'GA': 'Georgia',\n",
        "        'HI': 'Hawaii', 'ID': 'Idaho', 'IL': 'Illinois', 'IN': 'Indiana', 'IA': 'Iowa',\n",
        "        'KS': 'Kansas', 'KY': 'Kentucky', 'LA': 'Louisiana', 'ME': 'Maine', 'MD': 'Maryland',\n",
        "        'MA': 'Massachusetts', 'MI': 'Michigan', 'MN': 'Minnesota', 'MS': 'Mississippi',\n",
        "        'MO': 'Missouri', 'MT': 'Montana', 'NE': 'Nebraska', 'NV': 'Nevada', 'NH': 'New Hampshire',\n",
        "        'NJ': 'New Jersey', 'NM': 'New Mexico', 'NY': 'New York', 'NC': 'North Carolina',\n",
        "        'ND': 'North Dakota', 'OH': 'Ohio', 'OK': 'Oklahoma', 'OR': 'Oregon', 'PA': 'Pennsylvania',\n",
        "        'RI': 'Rhode Island', 'SC': 'South Carolina', 'SD': 'South Dakota', 'TN': 'Tennessee',\n",
        "        'TX': 'Texas', 'UT': 'Utah', 'VT': 'Vermont', 'VA': 'Virginia', 'WA': 'Washington',\n",
        "        'WV': 'West Virginia', 'WI': 'Wisconsin', 'WY': 'Wyoming', 'DC': 'District of Columbia'\n",
        "    }\n",
        "    return states_full_name.get(state, state)\n",
        "\n",
        "# Applying the function to the 'state' column to convert the shortform's to full name\n",
        "patients['state'] = patients['state'].apply(full_name)\n",
        "\n",
        "# Displaying the updated 'state' column\n",
        "patients['state']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "35dpmj12ibKy",
        "outputId": "fa8f18d0-bcf7-49df-bb60-e182eb153d45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "full_name\n",
              "John Doe           6\n",
              "Zoe Wellish        1\n",
              "Jad Allah Gaber    1\n",
              "Asuna Morita       1\n",
              "Sophia Haugen      1\n",
              "Name: count, dtype: int64"
            ],
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>full_name</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>John Doe</th>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "#finding repeted / Duplicate entries\n",
        "patients['full_name'].value_counts().nlargest()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
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        },
        "id": "tg_pxAQwibHj",
        "outputId": "4d125cae-a724-4336-c6fd-4e48b737e153"
      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     patient_id assigned_sex given_name surname          address      city  \\\n",
              "215         216         male       John     Doe  123 Main Street  New York   \n",
              "229         230         male       John     Doe  123 Main Street  New York   \n",
              "237         238         male       John     Doe  123 Main Street  New York   \n",
              "244         245         male       John     Doe  123 Main Street  New York   \n",
              "251         252         male       John     Doe  123 Main Street  New York   \n",
              "277         278         male       John     Doe  123 Main Street  New York   \n",
              "\n",
              "        state  zip_code        country  birthdate    weight  height  \\\n",
              "215  New York     12345  United States 1975-01-01  81.64656  1.8288   \n",
              "229  New York     12345  United States 1975-01-01  81.64656  1.8288   \n",
              "237  New York     12345  United States 1975-01-01  81.64656  1.8288   \n",
              "244  New York     12345  United States 1975-01-01  81.64656  1.8288   \n",
              "251  New York     12345  United States 1975-01-01  81.64656  1.8288   \n",
              "277  New York     12345  United States 1975-01-01  81.64656  1.8288   \n",
              "\n",
              "           bmi phone_number              email full_name     bmi_status  \n",
              "215  24.412118   1234567890  johndoe@email.com  John Doe  Normal weight  \n",
              "229  24.412118   1234567890  johndoe@email.com  John Doe  Normal weight  \n",
              "237  24.412118   1234567890  johndoe@email.com  John Doe  Normal weight  \n",
              "244  24.412118   1234567890  johndoe@email.com  John Doe  Normal weight  \n",
              "251  24.412118   1234567890  johndoe@email.com  John Doe  Normal weight  \n",
              "277  24.412118   1234567890  johndoe@email.com  John Doe  Normal weight  "
            ],
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              "  <tbody>\n",
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              "      <th>215</th>\n",
              "      <td>216</td>\n",
              "      <td>male</td>\n",
              "      <td>John</td>\n",
              "      <td>Doe</td>\n",
              "      <td>123 Main Street</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>12345</td>\n",
              "      <td>United States</td>\n",
              "      <td>1975-01-01</td>\n",
              "      <td>81.64656</td>\n",
              "      <td>1.8288</td>\n",
              "      <td>24.412118</td>\n",
              "      <td>1234567890</td>\n",
              "      <td>johndoe@email.com</td>\n",
              "      <td>John Doe</td>\n",
              "      <td>Normal weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>230</td>\n",
              "      <td>male</td>\n",
              "      <td>John</td>\n",
              "      <td>Doe</td>\n",
              "      <td>123 Main Street</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>12345</td>\n",
              "      <td>United States</td>\n",
              "      <td>1975-01-01</td>\n",
              "      <td>81.64656</td>\n",
              "      <td>1.8288</td>\n",
              "      <td>24.412118</td>\n",
              "      <td>1234567890</td>\n",
              "      <td>johndoe@email.com</td>\n",
              "      <td>John Doe</td>\n",
              "      <td>Normal weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>238</td>\n",
              "      <td>male</td>\n",
              "      <td>John</td>\n",
              "      <td>Doe</td>\n",
              "      <td>123 Main Street</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>12345</td>\n",
              "      <td>United States</td>\n",
              "      <td>1975-01-01</td>\n",
              "      <td>81.64656</td>\n",
              "      <td>1.8288</td>\n",
              "      <td>24.412118</td>\n",
              "      <td>1234567890</td>\n",
              "      <td>johndoe@email.com</td>\n",
              "      <td>John Doe</td>\n",
              "      <td>Normal weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244</th>\n",
              "      <td>245</td>\n",
              "      <td>male</td>\n",
              "      <td>John</td>\n",
              "      <td>Doe</td>\n",
              "      <td>123 Main Street</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>12345</td>\n",
              "      <td>United States</td>\n",
              "      <td>1975-01-01</td>\n",
              "      <td>81.64656</td>\n",
              "      <td>1.8288</td>\n",
              "      <td>24.412118</td>\n",
              "      <td>1234567890</td>\n",
              "      <td>johndoe@email.com</td>\n",
              "      <td>John Doe</td>\n",
              "      <td>Normal weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>252</td>\n",
              "      <td>male</td>\n",
              "      <td>John</td>\n",
              "      <td>Doe</td>\n",
              "      <td>123 Main Street</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>12345</td>\n",
              "      <td>United States</td>\n",
              "      <td>1975-01-01</td>\n",
              "      <td>81.64656</td>\n",
              "      <td>1.8288</td>\n",
              "      <td>24.412118</td>\n",
              "      <td>1234567890</td>\n",
              "      <td>johndoe@email.com</td>\n",
              "      <td>John Doe</td>\n",
              "      <td>Normal weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>278</td>\n",
              "      <td>male</td>\n",
              "      <td>John</td>\n",
              "      <td>Doe</td>\n",
              "      <td>123 Main Street</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>12345</td>\n",
              "      <td>United States</td>\n",
              "      <td>1975-01-01</td>\n",
              "      <td>81.64656</td>\n",
              "      <td>1.8288</td>\n",
              "      <td>24.412118</td>\n",
              "      <td>1234567890</td>\n",
              "      <td>johndoe@email.com</td>\n",
              "      <td>John Doe</td>\n",
              "      <td>Normal weight</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-366fcf15-0d9d-457e-9e19-8c49d93895fa')\"\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",
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              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-366fcf15-0d9d-457e-9e19-8c49d93895fa button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          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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              "\n",
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              "    </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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      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-83cda09c-e5aa-41e1-acbc-a7309a29369c 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\": \"patients\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"patient_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 216,\n        \"max\": 278,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          216,\n          230,\n          278\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assigned_sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"John\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Doe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"address\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"123 Main Street\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 12345,\n        \"max\": 12345,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          12345\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthdate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1975-01-01 00:00:00\",\n        \"max\": \"1975-01-01 00:00:00\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1975-01-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.556721137788958e-14,\n        \"min\": 81.64656,\n        \"max\": 81.64656,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          81.64656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.8288,\n        \"max\": 1.8288,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.8288\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 24.41211826868098,\n        \"max\": 24.41211826868098,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          24.41211826868098\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_number\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1234567890\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"johndoe@email.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"John Doe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Normal weight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "patients.loc[patients['full_name'] == 'John Doe']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "Lv5Oz3OXibC5"
      },
      "outputs": [],
      "source": [
        "# removing duplicates\n",
        "pat_id = [230, 238, 245, 252, 278]\n",
        "patients = patients[~patients['patient_id'].isin(pat_id)]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "C3WkLN8bjErc",
        "outputId": "31e75f43-61ca-40d8-f0b0-52ce488d9782"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "full_name\n",
              "Zoe Wellish      1\n",
              "Abel Efrem       1\n",
              "Asuna Morita     1\n",
              "Sophia Haugen    1\n",
              "Emy Markus       1\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>full_name</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Zoe Wellish</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abel Efrem</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asuna Morita</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sophia Haugen</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Emy Markus</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "patients['full_name'].value_counts().nlargest()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "io1ikY3hGkbV"
      },
      "source": [
        "#### Creating age column\n",
        "###### Assuming the year of data collection is 2019"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXkuu_wTGjXP",
        "outputId": "cb1c20da-b844-460b-c927-ea2558c07658"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-fbcf9eba99a4>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  patients['age'] = 2019 - patients['birthdate'].dt.year\n"
          ]
        }
      ],
      "source": [
        "patients['age'] = 2019 - patients['birthdate'].dt.year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JpvYO2DjEm3",
        "outputId": "6e007ae7-5aa6-4f22-af89-379b30206547"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(498, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "patients.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "sVX7iRQLvZlp"
      },
      "outputs": [],
      "source": [
        "# re-arranging the column's order\n",
        "column_order = [\n",
        "    'patient_id', 'given_name', 'surname', 'full_name','assigned_sex', 'weight',\n",
        "    'height','birthdate', 'age', 'phone_number', 'email',  'address',\n",
        "    'city', 'state', 'zip_code', 'country', 'bmi', 'bmi_status', ]\n",
        "patients = patients[column_order]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "MUbQ3hHrv_VR",
        "outputId": "3c5717bf-ff38-4081-8dd5-cc12c3722adb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   patient_id given_name  surname    full_name assigned_sex      weight  \\\n",
              "0           1        Zoe  Wellish  Zoe Wellish       female   55.202146   \n",
              "1           2     Pamela     Hill  Pamela Hill       female   53.886730   \n",
              "2           3        Jae   Debord   Jae Debord         male   80.648658   \n",
              "3           4       Liem     Phan    Liem Phan         male  100.198473   \n",
              "4           5        Tim  Neudorf  Tim Neudorf         male   87.225742   \n",
              "\n",
              "   height  birthdate  age phone_number                      email  \\\n",
              "0  1.6764 1976-07-10   43   9517199170   ZoeWellish@superrito.com   \n",
              "1  1.6764 1967-04-03   52   2175693204       PamelaSHill@cuvox.de   \n",
              "2  1.8034 1980-02-19   39   4023636804       JaeMDebord@gustr.com   \n",
              "3  1.7780 1951-07-26   68   7326368246  PhanBaLiem@jourrapide.com   \n",
              "4  0.6858 1928-02-18   91   3345157487        TimNeudorf@cuvox.de   \n",
              "\n",
              "                     address               city       state  zip_code  \\\n",
              "0       576 Brown Bear Drive  Rancho California  California     92390   \n",
              "1  2370 University Hill Road          Armstrong    Illinois     61812   \n",
              "2      1493 Poling Farm Road               York    Nebraska     68467   \n",
              "3        2335 Webster Street         Woodbridge  New Jersey      7095   \n",
              "4       1428 Turkey Pen Lane             Dothan     Alabama     36303   \n",
              "\n",
              "         country         bmi      bmi_status  \n",
              "0  United States   19.642676   Normal weight  \n",
              "1  United States   19.174609   Normal weight  \n",
              "2  United States   24.797792   Normal weight  \n",
              "3  United States   31.695499         Obesity  \n",
              "4  United States  185.459767  Severe Obesity  "
            ],
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              "      <th>patient_id</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Zoe</td>\n",
              "      <td>Wellish</td>\n",
              "      <td>Zoe Wellish</td>\n",
              "      <td>female</td>\n",
              "      <td>55.202146</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Pamela</td>\n",
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              "      <td>Pamela Hill</td>\n",
              "      <td>female</td>\n",
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              "      <td>1.6764</td>\n",
              "      <td>1967-04-03</td>\n",
              "      <td>52</td>\n",
              "      <td>2175693204</td>\n",
              "      <td>PamelaSHill@cuvox.de</td>\n",
              "      <td>2370 University Hill Road</td>\n",
              "      <td>Armstrong</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>61812</td>\n",
              "      <td>United States</td>\n",
              "      <td>19.174609</td>\n",
              "      <td>Normal weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Jae</td>\n",
              "      <td>Debord</td>\n",
              "      <td>Jae Debord</td>\n",
              "      <td>male</td>\n",
              "      <td>80.648658</td>\n",
              "      <td>1.8034</td>\n",
              "      <td>1980-02-19</td>\n",
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              "      <td>JaeMDebord@gustr.com</td>\n",
              "      <td>1493 Poling Farm Road</td>\n",
              "      <td>York</td>\n",
              "      <td>Nebraska</td>\n",
              "      <td>68467</td>\n",
              "      <td>United States</td>\n",
              "      <td>24.797792</td>\n",
              "      <td>Normal weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Liem</td>\n",
              "      <td>Phan</td>\n",
              "      <td>Liem Phan</td>\n",
              "      <td>male</td>\n",
              "      <td>100.198473</td>\n",
              "      <td>1.7780</td>\n",
              "      <td>1951-07-26</td>\n",
              "      <td>68</td>\n",
              "      <td>7326368246</td>\n",
              "      <td>PhanBaLiem@jourrapide.com</td>\n",
              "      <td>2335 Webster Street</td>\n",
              "      <td>Woodbridge</td>\n",
              "      <td>New Jersey</td>\n",
              "      <td>7095</td>\n",
              "      <td>United States</td>\n",
              "      <td>31.695499</td>\n",
              "      <td>Obesity</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Tim</td>\n",
              "      <td>Neudorf</td>\n",
              "      <td>Tim Neudorf</td>\n",
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              "      <td>36303</td>\n",
              "      <td>United States</td>\n",
              "      <td>185.459767</td>\n",
              "      <td>Severe Obesity</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
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              "    0% {\n",
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              "\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",
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              "</div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "patients",
              "summary": "{\n  \"name\": \"patients\",\n  \"rows\": 498,\n  \"fields\": [\n    {\n      \"column\": \"patient_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146,\n        \"min\": 1,\n        \"max\": 503,\n        \"num_unique_values\": 498,\n        \"samples\": [\n          493,\n          74,\n          233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 467,\n        \"samples\": [\n          \"Anco\",\n          \"Yukitaka\",\n          \"Habte\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 466,\n        \"samples\": [\n          \"Koldenhof\",\n          \"Ferreira\",\n          \"Pecinova\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 498,\n        \"samples\": [\n          \"Poldi Tar\",\n          \"Zarka Rap\",\n          \"Kyouko Ono\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assigned_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\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.458640885188474,\n        \"min\": 22.1352896,\n        \"max\": 116.0741928,\n        \"num_unique_values\": 347,\n        \"samples\": [\n          108.4538472,\n          62.187463199999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1117679395328499,\n        \"min\": 0.6858,\n        \"max\": 2.0065999999999997,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          1.6764,\n          1.6001999999999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthdate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1921-11-06 00:00:00\",\n        \"max\": \"1999-07-03 00:00:00\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"1980-10-12 00:00:00\",\n          \"1926-01-20 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 79,\n        \"samples\": [\n          33,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_number\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"7035470551\",\n          \"7144962264\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"SunKo@einrot.com\",\n          \"TarPoldi@superrito.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"address\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"1962 Cabell Avenue\",\n          \"3958 Liberty Avenue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 350,\n        \"samples\": [\n          \"Monroe\",\n          \"Ottawa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"Georgia\",\n          \"Washington\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30774,\n        \"min\": 0,\n        \"max\": 99701,\n        \"num_unique_values\": 431,\n        \"samples\": [\n          91505,\n          2048\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"unknown\",\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.870834603855322,\n        \"min\": 8.644436255866719,\n        \"max\": 185.45976663969793,\n        \"num_unique_values\": 473,\n        \"samples\": [\n          25.2810086055539,\n          19.49925327423888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Obesity\",\n          \"Underweight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "patients.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "Zww7it0WRUQ8"
      },
      "outputs": [],
      "source": [
        "patients.to_csv('patients_cleaned.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "z6hMgrn0RUOT"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2U70hjIcz1t"
      },
      "source": [
        "# adverse reactions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "dybW0wwkcz1t"
      },
      "outputs": [],
      "source": [
        "# Load the data\n",
        "reaction = pd.read_csv('/content/adverse_reactions.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hc2zAtXGcz1t",
        "outputId": "649b7cb5-1eb2-493c-edc2-24f024d32d56"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 34 entries, 0 to 33\n",
            "Data columns (total 3 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   given_name        34 non-null     object\n",
            " 1   surname           34 non-null     object\n",
            " 2   adverse_reaction  34 non-null     object\n",
            "dtypes: object(3)\n",
            "memory usage: 944.0+ bytes\n"
          ]
        }
      ],
      "source": [
        "reaction.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "9FNVvMKTcz1t",
        "outputId": "8ef7b309-28d0-44a4-a3dc-9d43b6d19751"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "given_name          0\n",
              "surname             0\n",
              "adverse_reaction    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>given_name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>surname</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>adverse_reaction</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "reaction.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3OvSsvN3cz1u",
        "outputId": "b71358a1-12cc-4457-ad1c-0abedcfc1fd5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         berta napolitani\n",
              "1                lena baer\n",
              "2               joseph day\n",
              "3        flavia fiorentino\n",
              "4          manouck wubbels\n",
              "5            jasmine sykes\n",
              "6           louise johnson\n",
              "7          albinca komavec\n",
              "8               noe aranda\n",
              "9          sofia hermansen\n",
              "10           tegan johnson\n",
              "11            abel yonatan\n",
              "12           abdul-nur isa\n",
              "13             leon scholz\n",
              "14        gabriele saenger\n",
              "15             jia li teng\n",
              "16          jakob jakobsen\n",
              "17    christopher woodward\n",
              "18            ole petersen\n",
              "19         finley chandler\n",
              "20          anenechi chidi\n",
              "21     miłosław wiśniewski\n",
              "22             lixue hsueh\n",
              "23            merci leroux\n",
              "24                kang mai\n",
              "25       elliot richardson\n",
              "26          clinton miller\n",
              "27            idalia moore\n",
              "28            xiuxiu chang\n",
              "29           alex crawford\n",
              "30           monika lončar\n",
              "31              steven roy\n",
              "32          cecilie nilsen\n",
              "33        krisztina magyar\n",
              "Name: full_name, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>full_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>berta napolitani</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lena baer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>joseph day</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>flavia fiorentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>manouck wubbels</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>jasmine sykes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>louise johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>albinca komavec</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>noe aranda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>sofia hermansen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>tegan johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>abel yonatan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>abdul-nur isa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>leon scholz</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>gabriele saenger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>jia li teng</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>jakob jakobsen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>christopher woodward</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>ole petersen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>finley chandler</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>anenechi chidi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>miłosław wiśniewski</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>lixue hsueh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>merci leroux</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>kang mai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>elliot richardson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>clinton miller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>idalia moore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>xiuxiu chang</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>alex crawford</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>monika lončar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>steven roy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>cecilie nilsen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>krisztina magyar</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "reaction['full_name'] = reaction['given_name'] + ' ' + reaction['surname']\n",
        "reaction['full_name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ur3Q_EO7mv7l",
        "outputId": "42d95e08-69a9-4268-93a1-d9d3e701bd17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "reaction.full_name.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "ImEJ754NwLfX"
      },
      "outputs": [],
      "source": [
        "column_order = ['given_name', 'surname', 'full_name', 'adverse_reaction']\n",
        "reaction = reaction[column_order]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "DW1Op5xNcz1u"
      },
      "outputs": [],
      "source": [
        "reaction.to_csv(\"adverse_reactions_cleaned.csv\",index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "OhyP_hgwcz1u"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6lcfbVCcz1u"
      },
      "source": [
        "# Treatments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "eliRwHB9cz1u"
      },
      "outputs": [],
      "source": [
        "# Load the data\n",
        "treatments = pd.read_csv('/content/treatments.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I93b4jdxclWd",
        "outputId": "3e982021-7c12-49d1-a834-fd220b9c0471"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 280 entries, 0 to 279\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   given_name    280 non-null    object \n",
            " 1   surname       280 non-null    object \n",
            " 2   auralin       280 non-null    object \n",
            " 3   novodra       280 non-null    object \n",
            " 4   hba1c_start   280 non-null    float64\n",
            " 5   hba1c_end     280 non-null    float64\n",
            " 6   hba1c_change  171 non-null    float64\n",
            "dtypes: float64(3), object(4)\n",
            "memory usage: 15.4+ KB\n"
          ]
        }
      ],
      "source": [
        "treatments.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "CL9HHtaNcz1v",
        "outputId": "93ae8788-e24e-4af5-a4dd-64a925ca0029"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "given_name        0\n",
              "surname           0\n",
              "auralin           0\n",
              "novodra           0\n",
              "hba1c_start       0\n",
              "hba1c_end         0\n",
              "hba1c_change    109\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>given_name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>surname</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>auralin</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>novodra</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hba1c_start</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hba1c_end</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>hba1c_change</th>\n",
              "      <td>109</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "treatments.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ET-K0Hs0ot7t",
        "outputId": "9e7f53f5-5e99-49a1-e5ed-fd64e2ea5c91"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(280, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "treatments.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuk0jSqczvvb",
        "outputId": "7c3eaca3-c90d-418c-945f-654d20912f3f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "treatments[['given_name', 'surname', 'auralin', 'novodra', 'hba1c_start',\n",
        "       'hba1c_end', 'hba1c_change']].duplicated().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PL7HHKsVzREi"
      },
      "source": [
        "# Following are the errors in Treatments Dataset that should be cleaned.\n",
        "\n",
        "##### 1.The names in given_name and sur_name columns are containing special characters and '-' in characters.\n",
        "##### 2. 1 duplicate value is present in data.\n",
        "##### 3. hba1c column has 109 null values.\n",
        "##### 4. auralin and novodra column values are mised with the 'u' units,it should be separated and re calculated.\n",
        "##### 5.hba1c column has incorrect values it should be re calculated."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "b8OCvA9Qcz1v",
        "outputId": "ea09055b-2d81-4935-c193-dda6b94244c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    given_name       surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
              "0     veronika      jindrová  41u - 48u          -         7.63       7.20   \n",
              "1       elliot    richardson          -  40u - 45u         7.56       7.09   \n",
              "2     yukitaka      takenaka          -  39u - 36u         7.68       7.25   \n",
              "3         skye    gormanston  33u - 36u          -         7.97       7.62   \n",
              "4       alissa        montez          -  33u - 29u         7.78       7.46   \n",
              "..         ...           ...        ...        ...          ...        ...   \n",
              "275     albina      zetticci  45u - 51u          -         7.93       7.73   \n",
              "276       john   teichelmann          -  49u - 49u         7.90       7.58   \n",
              "277     mathea       lillebø  23u - 36u          -         9.04       8.67   \n",
              "278     vallie        prince  31u - 38u          -         7.64       7.28   \n",
              "279     samúel  guðbrandsson  53u - 56u          -         8.00       7.64   \n",
              "\n",
              "     hba1c_change  \n",
              "0             NaN  \n",
              "1            0.97  \n",
              "2             NaN  \n",
              "3            0.35  \n",
              "4            0.32  \n",
              "..            ...  \n",
              "275          0.20  \n",
              "276           NaN  \n",
              "277          0.37  \n",
              "278          0.36  \n",
              "279          0.36  \n",
              "\n",
              "[280 rows x 7 columns]"
            ],
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              "      <th>given_name</th>\n",
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              "      <th>auralin</th>\n",
              "      <th>novodra</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>veronika</td>\n",
              "      <td>jindrová</td>\n",
              "      <td>41u - 48u</td>\n",
              "      <td>-</td>\n",
              "      <td>7.63</td>\n",
              "      <td>7.20</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>elliot</td>\n",
              "      <td>richardson</td>\n",
              "      <td>-</td>\n",
              "      <td>40u - 45u</td>\n",
              "      <td>7.56</td>\n",
              "      <td>7.09</td>\n",
              "      <td>0.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yukitaka</td>\n",
              "      <td>takenaka</td>\n",
              "      <td>-</td>\n",
              "      <td>39u - 36u</td>\n",
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              "      <td>7.25</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>skye</td>\n",
              "      <td>gormanston</td>\n",
              "      <td>33u - 36u</td>\n",
              "      <td>-</td>\n",
              "      <td>7.97</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alissa</td>\n",
              "      <td>montez</td>\n",
              "      <td>-</td>\n",
              "      <td>33u - 29u</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.46</td>\n",
              "      <td>0.32</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>albina</td>\n",
              "      <td>zetticci</td>\n",
              "      <td>45u - 51u</td>\n",
              "      <td>-</td>\n",
              "      <td>7.93</td>\n",
              "      <td>7.73</td>\n",
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              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>john</td>\n",
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              "      <td>-</td>\n",
              "      <td>49u - 49u</td>\n",
              "      <td>7.90</td>\n",
              "      <td>7.58</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>mathea</td>\n",
              "      <td>lillebø</td>\n",
              "      <td>23u - 36u</td>\n",
              "      <td>-</td>\n",
              "      <td>9.04</td>\n",
              "      <td>8.67</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>vallie</td>\n",
              "      <td>prince</td>\n",
              "      <td>31u - 38u</td>\n",
              "      <td>-</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.28</td>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>samúel</td>\n",
              "      <td>guðbrandsson</td>\n",
              "      <td>53u - 56u</td>\n",
              "      <td>-</td>\n",
              "      <td>8.00</td>\n",
              "      <td>7.64</td>\n",
              "      <td>0.36</td>\n",
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              "type": "dataframe",
              "variable_name": "treatments",
              "summary": "{\n  \"name\": \"treatments\",\n  \"rows\": 280,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 273,\n        \"samples\": [\n          \"szilveszter\",\n          \"javier\",\n          \"avdei\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 272,\n        \"samples\": [\n          \"totth\",\n          \"silva\",\n          \"tikhonov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 121,\n        \"samples\": [\n          \"53u - 64u\",\n          \"39u - 43u\",\n          \"31u - 38u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 113,\n        \"samples\": [\n          \"31u - 36u\",\n          \"42u - 44u\",\n          \"34u - 32u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5686384372332096,\n        \"min\": 7.5,\n        \"max\": 9.95,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          8.19,\n          7.58,\n          9.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5696715306576077,\n        \"min\": 7.01,\n        \"max\": 9.58,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          7.45,\n          7.19,\n          7.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27955527609183284,\n        \"min\": 0.2,\n        \"max\": 0.99,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.26,\n          0.3,\n          0.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "treatments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujWHPoqdcz1v",
        "outputId": "59596d61-9a4d-443c-b4c7-f0f6c6e86167"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['veronika', 'elliot', 'yukitaka', 'skye', 'alissa', 'jasmine',\n",
              "       'sophia', 'eddie', 'saber', 'asia', 'joseph', 'kristiina',\n",
              "       'roxanne', 'gregor', 'simone', 'enco', 'camilla', 'gina',\n",
              "       'addolorata', 'khalid', 'mile', 'tekla', 'brancaleone', 'chiemela',\n",
              "       'isac', 'benoît', 'suhaim', 'mizuki', 'clinton', 'eugene',\n",
              "       'szilveszter', 'bao', 'laura', 'cannan', 'alexander', 'csaba',\n",
              "       'chỉ', 'władysław', 'kristján', 'krisztina', 'ásta', 'mahmud',\n",
              "       'marija', 'jens', 'sauli', 'zdeněk', 'mariana', 'jad allah', 'zak',\n",
              "       'jackson', 'albinca', 'kristoffer', 'mỹ', 'nasser', 'oles',\n",
              "       'triana.', 'juliusz', 'klementyna', 'gabryŝ', 'leixandre',\n",
              "       'onyekachukwu', 'mijndert', 'hanka', 'coralie', 'cumelén',\n",
              "       'elisabeth', 'louise', 'joe', 'fatimah', 'ivan', 'tosh', 'hugo',\n",
              "       'gabrielle', 'mette', 'ole', 'mackenzie', 'noriyuki', 'placido',\n",
              "       'julia', 'avdei', 'hideki', 'robert', 'maya', 'else', 'furuta',\n",
              "       'nilton', 'ananías', 'finley', 'yumena', 'stefanie', 'karen',\n",
              "       'michael', 'sousuke', 'melisa', 'kwasi', 'jaakko', 'abdul-nur',\n",
              "       'niels', 'idalia', 'abel', 'kai', 'markus', 'bội', 'søren',\n",
              "       'renzo', 'finlay', 'sofia', 'flaviano', 'mùi', 'marius', 'suoma',\n",
              "       'mevla', 'olof', 'kari', 'sabr', 'xiuxiu', 'yasmin', 'javier',\n",
              "       'burrell', 'liisa', 'zikoranaudodimma', 'habte', 'rafael',\n",
              "       'mgbankwo', 'nate', 'marcel', 'jowita', 'farizah', 'david',\n",
              "       'kaodilinakachukwu', 'kamila', 'urso', 'vasco', 'kwemtochukwu',\n",
              "       'rut', 'ivona', 'giovana', 'barbora', 'jesse', 'johana', 'stephen',\n",
              "       'nora', 'nicoline', 'ruman', 'hanna-mari', 'krisztián', 'mustafa',\n",
              "       'muhammad', 'manuela', 'nile', 'lewis', 'nicole', 'saidmohmad',\n",
              "       'russom', 'chidalu', 'asuna', 'sandy', 'yunadi', 'harley',\n",
              "       'rebecca', 'helen', 'malik', 'felicijan', 'jeramy', 'annie',\n",
              "       'lamara', 'nadwah', 'benigna', 'sun', 'justyna', 'malina',\n",
              "       'gabriele', 'marina', 'andré', 'samuel', 'kerman', 'jan', 'merci',\n",
              "       'kong', 'kajsa', 'tegan', 'chân', 'leah', 'fraser', 'león',\n",
              "       'bouke', 'mee', 'martha', 'monika', 'valur', 'svanhvít',\n",
              "       'eufrosina', 'minea', 'peter', 'angelico', 'amalie', 'novalie',\n",
              "       'nicolas', 'lena', 'martina', 'willem-jan', 'maximus', 'najla',\n",
              "       'emy', 'jin', 'katrine', 'kári', 'vanessa', 'nương', 'cecilie',\n",
              "       'erick', 'adib', 'þórunn', 'taylah', 'sirkka', 'diệt', 'sinead',\n",
              "       'torben', 'veronica', 'anenechi', 'cintia', 'liêm', 'daud',\n",
              "       'erica', \"hawra'\", 'sandra', 'eric', 'kisanet', 'pamela', 'arne',\n",
              "       'haylom', 'eemil', 'hường', 'manouck', 'anja', 'linda', 'rocco',\n",
              "       'elisabetta', 'fareeq', 'sargent', 'angel', 'wu', 'tomáš',\n",
              "       'johanna', 'brianna', 'kang', 'chen', 'aksel', 'ellen', 'albino',\n",
              "       'jose', 'jia li', 'ilija', 'mathilde', 'csilla', 'aaliyah',\n",
              "       'caroline', 'alex', 'chukwumoge', 'fearne', 'ursula', 'leon',\n",
              "       'hiromu', 'mika', 'leo', 'steven', 'kate', 'naja', 'albina',\n",
              "       'john', 'mathea', 'vallie', 'samúel'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "treatments.given_name.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tgC4JHKZcz1v",
        "scrolled": true,
        "outputId": "659575f5-8764-4937-dbf9-37b1a99cdc98"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['jindrová', 'richardson', 'takenaka', 'gormanston', 'montez',\n",
              "       'sykes', 'haugen', 'archer', 'ménard', 'woźniak', 'day', 'hyypiä',\n",
              "       'andreyeva', 'bole', 'baumgaertner', 'žibrik', 'zaitseva', 'cain',\n",
              "       'lombardi', 'johnsrud', 'stanić', 'walczak', 'russo', 'tobeolisa',\n",
              "       'berg', 'bonami', 'rahal', 'iwata', 'miller', 'mironov', 'totth',\n",
              "       'she', 'ehrlichmann', 'cabrera', 'mathiesen', 'sági', 'lâm',\n",
              "       'wieczorek', 'ingason', 'magyar', 'grímsdóttir', 'kadyrov',\n",
              "       'grubišić', 'poulsen', 'koivuniemi', 'synek', 'souza', 'gaber',\n",
              "       'kelly', 'addison', 'komavec', 'martinsen', 'quynh', 'mansour',\n",
              "       'zhdanov', 'terrazas', 'majewski', 'sokołowska', 'tomaszewski',\n",
              "       'alanis', 'obinna', 'ouwendijk', 'gegič', 'allaire', 'miramontes',\n",
              "       'dimmen', 'johnson', 'edwards', 'kinfe', 'fomin', 'jensen',\n",
              "       'collins', 'bidwill', 'sandgreen', 'petersen', 'mckay', 'sakai',\n",
              "       'udinesi', 'werner', 'tikhonov', 'haraguchi', 'wagner', 'isaksson',\n",
              "       'andersen', 'osman', 'quintanilla', 'enríquez', 'chandler',\n",
              "       'nakayama', 'herman', 'jakobsen', 'kristensen', 'nakahara', 'mata',\n",
              "       'mensa', 'eskelinen', 'isa', 'lange', 'moore', 'yonatan', 'cunha',\n",
              "       'solberg', 'tạ', 'frederiksen', 'lucchese', 'sheppard', 'karlsen',\n",
              "       'endrizzi', 'lương', 'hansen', 'koivunen', 'grabljevec', 'holm',\n",
              "       'laatikainen', 'amari', 'chang', 'silva', 'moquin', 'mathieu',\n",
              "       'seppälä', 'chinedum', 'kifle', 'costa', 'afamefuna', 'bowden',\n",
              "       'peters', 'wiśniewska', 'sleiman', 'gustafsson', 'nnonso',\n",
              "       'pecinová', 'sørensen', 'aranda', 'van de wiel', 'ogochukwu',\n",
              "       'halldórsdóttir', 'jakšić', 'rocha', 'vesecká', 'luoma', 'hrdá',\n",
              "       'mayberry', 'nyborg', 'østergaard', 'bisliev', 'filatov', 'bakos',\n",
              "       'lindström', 'holden', 'cindrić', 'mehari', 'webb', 'zimmerman',\n",
              "       'rushisvili', 'amanuel', 'onyekaozulu', 'morita', 'gunnarsson',\n",
              "       'barsukov', 'tucker', 'priest', 'luwam', 'vaneker', 'bubanj',\n",
              "       'hopkins', 'allen', 'dratchev', 'naifeh', 'ferrari', 'ko',\n",
              "       'kowalczyk', 'balagić', 'saenger', 'adonay', 'glockner', 'nordin',\n",
              "       'blix', 'dandonneau', 'baum', 'leroux', 'lei', 'eidem', 'bùi',\n",
              "       'wong', 'hunter', 'reynoso', 'glaser', 'chung', 'afanasyeva',\n",
              "       'lončar', 'bjarkason', 'guðjónsdóttir', 'schiavone', 'lindgren',\n",
              "       'pospíšil', 'marchesi', 'christensen', 'ferreira', 'baer',\n",
              "       'tománková', 'van der lubbe', 'henzen', 'touma', 'markus', 'kung',\n",
              "       'lynge', 'hervinsson', 'ferguson', 'vũ', 'hueber', 'nilsen',\n",
              "       'pinto', 'ghanem', 'tryggvadóttir', 'mobourne', 'piirainen',\n",
              "       'reilly', 'mortensen', 'bogolyubova', 'chidi', 'csorba', 'phan',\n",
              "       'batukayev', 'macdonald', 'tuma', 'taylor', 'ek', 'selassie',\n",
              "       'hill', 'jørgensen', 'nebay', 'laine', 'wubbels', 'lundy',\n",
              "       'christie', 'lorenzo', 'kalb', 'flamand', 'grant', 'sung',\n",
              "       'navrátil', 'dreher', 'lansell', 'mai', 'yao', 'vestergaard',\n",
              "       'luman', 'combs', 'teng', 'horvat', 'nørgaard', 'herczegh', 'rice',\n",
              "       'beauvais', 'shuler', 'crawford', 'jephcott', 'mcgregor', 'freud',\n",
              "       'scholz', 'araujo', 'horikawa', 'martinsson', 'vieira', 'roy',\n",
              "       'wilkinson', 'enoksen', 'zetticci', 'teichelmann', 'lillebø',\n",
              "       'prince', 'guðbrandsson'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "treatments.surname.unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZfAqaSjcz1v"
      },
      "source": [
        "#### The names in given_name and sur_name columns are containing special characters and '-' in characters, so i am trying to replace them with the unicode characters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "XH2kpEhPcz1w"
      },
      "outputs": [],
      "source": [
        "import unidecode\n",
        "\n",
        "# converting the the special character in names to regular characters\n",
        "def normalize_name(name):\n",
        "    return unidecode.unidecode(name)\n",
        "\n",
        "# Apply the function to the given_name & sur_name columns\n",
        "treatments['given_name'] = treatments['given_name'].apply(normalize_name)\n",
        "treatments['surname'] = treatments['surname'].apply(normalize_name)\n",
        "treatments['given_name'] = treatments['given_name'].str.replace('-','')\n",
        "treatments['surname'] = treatments['surname'].str.replace('-','')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GgEjAYeOcz1w",
        "outputId": "497f3690-9a03-4504-e34e-7e538e86506c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['veronika', 'elliot', 'yukitaka', 'skye', 'alissa', 'jasmine',\n",
              "       'sophia', 'eddie', 'saber', 'asia', 'joseph', 'kristiina',\n",
              "       'roxanne', 'gregor', 'simone', 'enco', 'camilla', 'gina',\n",
              "       'addolorata', 'khalid', 'mile', 'tekla', 'brancaleone', 'chiemela',\n",
              "       'isac', 'benoit', 'suhaim', 'mizuki', 'clinton', 'eugene',\n",
              "       'szilveszter', 'bao', 'laura', 'cannan', 'alexander', 'csaba',\n",
              "       'chi', 'wladyslaw', 'kristjan', 'krisztina', 'asta', 'mahmud',\n",
              "       'marija', 'jens', 'sauli', 'zdenek', 'mariana', 'jad allah', 'zak',\n",
              "       'jackson', 'albinca', 'kristoffer', 'my', 'nasser', 'oles',\n",
              "       'triana.', 'juliusz', 'klementyna', 'gabrys', 'leixandre',\n",
              "       'onyekachukwu', 'mijndert', 'hanka', 'coralie', 'cumelen',\n",
              "       'elisabeth', 'louise', 'joe', 'fatimah', 'ivan', 'tosh', 'hugo',\n",
              "       'gabrielle', 'mette', 'ole', 'mackenzie', 'noriyuki', 'placido',\n",
              "       'julia', 'avdei', 'hideki', 'robert', 'maya', 'else', 'furuta',\n",
              "       'nilton', 'ananias', 'finley', 'yumena', 'stefanie', 'karen',\n",
              "       'michael', 'sousuke', 'melisa', 'kwasi', 'jaakko', 'abdulnur',\n",
              "       'niels', 'idalia', 'abel', 'kai', 'markus', 'boi', 'soren',\n",
              "       'renzo', 'finlay', 'sofia', 'flaviano', 'mui', 'marius', 'suoma',\n",
              "       'mevla', 'olof', 'kari', 'sabr', 'xiuxiu', 'yasmin', 'javier',\n",
              "       'burrell', 'liisa', 'zikoranaudodimma', 'habte', 'rafael',\n",
              "       'mgbankwo', 'nate', 'marcel', 'jowita', 'farizah', 'david',\n",
              "       'kaodilinakachukwu', 'kamila', 'urso', 'vasco', 'kwemtochukwu',\n",
              "       'rut', 'ivona', 'giovana', 'barbora', 'jesse', 'johana', 'stephen',\n",
              "       'nora', 'nicoline', 'ruman', 'hannamari', 'krisztian', 'mustafa',\n",
              "       'muhammad', 'manuela', 'nile', 'lewis', 'nicole', 'saidmohmad',\n",
              "       'russom', 'chidalu', 'asuna', 'sandy', 'yunadi', 'harley',\n",
              "       'rebecca', 'helen', 'malik', 'felicijan', 'jeramy', 'annie',\n",
              "       'lamara', 'nadwah', 'benigna', 'sun', 'justyna', 'malina',\n",
              "       'gabriele', 'marina', 'andre', 'samuel', 'kerman', 'jan', 'merci',\n",
              "       'kong', 'kajsa', 'tegan', 'chan', 'leah', 'fraser', 'leon',\n",
              "       'bouke', 'mee', 'martha', 'monika', 'valur', 'svanhvit',\n",
              "       'eufrosina', 'minea', 'peter', 'angelico', 'amalie', 'novalie',\n",
              "       'nicolas', 'lena', 'martina', 'willemjan', 'maximus', 'najla',\n",
              "       'emy', 'jin', 'katrine', 'vanessa', 'nuong', 'cecilie', 'erick',\n",
              "       'adib', 'thorunn', 'taylah', 'sirkka', 'diet', 'sinead', 'torben',\n",
              "       'veronica', 'anenechi', 'cintia', 'liem', 'daud', 'erica',\n",
              "       \"hawra'\", 'sandra', 'eric', 'kisanet', 'pamela', 'arne', 'haylom',\n",
              "       'eemil', 'huong', 'manouck', 'anja', 'linda', 'rocco',\n",
              "       'elisabetta', 'fareeq', 'sargent', 'angel', 'wu', 'tomas',\n",
              "       'johanna', 'brianna', 'kang', 'chen', 'aksel', 'ellen', 'albino',\n",
              "       'jose', 'jia li', 'ilija', 'mathilde', 'csilla', 'aaliyah',\n",
              "       'caroline', 'alex', 'chukwumoge', 'fearne', 'ursula', 'hiromu',\n",
              "       'mika', 'leo', 'steven', 'kate', 'naja', 'albina', 'john',\n",
              "       'mathea', 'vallie'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "# verifying the corrected names\n",
        "treatments['given_name'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpWGwbIYcz1w",
        "outputId": "6feb47ef-a3d5-4a78-d3a4-a34389bdaabc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['jindrova', 'richardson', 'takenaka', 'gormanston', 'montez',\n",
              "       'sykes', 'haugen', 'archer', 'menard', 'wozniak', 'day', 'hyypia',\n",
              "       'andreyeva', 'bole', 'baumgaertner', 'zibrik', 'zaitseva', 'cain',\n",
              "       'lombardi', 'johnsrud', 'stanic', 'walczak', 'russo', 'tobeolisa',\n",
              "       'berg', 'bonami', 'rahal', 'iwata', 'miller', 'mironov', 'totth',\n",
              "       'she', 'ehrlichmann', 'cabrera', 'mathiesen', 'sagi', 'lam',\n",
              "       'wieczorek', 'ingason', 'magyar', 'grimsdottir', 'kadyrov',\n",
              "       'grubisic', 'poulsen', 'koivuniemi', 'synek', 'souza', 'gaber',\n",
              "       'kelly', 'addison', 'komavec', 'martinsen', 'quynh', 'mansour',\n",
              "       'zhdanov', 'terrazas', 'majewski', 'sokolowska', 'tomaszewski',\n",
              "       'alanis', 'obinna', 'ouwendijk', 'gegic', 'allaire', 'miramontes',\n",
              "       'dimmen', 'johnson', 'edwards', 'kinfe', 'fomin', 'jensen',\n",
              "       'collins', 'bidwill', 'sandgreen', 'petersen', 'mckay', 'sakai',\n",
              "       'udinesi', 'werner', 'tikhonov', 'haraguchi', 'wagner', 'isaksson',\n",
              "       'andersen', 'osman', 'quintanilla', 'enriquez', 'chandler',\n",
              "       'nakayama', 'herman', 'jakobsen', 'kristensen', 'nakahara', 'mata',\n",
              "       'mensa', 'eskelinen', 'isa', 'lange', 'moore', 'yonatan', 'cunha',\n",
              "       'solberg', 'ta', 'frederiksen', 'lucchese', 'sheppard', 'karlsen',\n",
              "       'endrizzi', 'luong', 'hansen', 'koivunen', 'grabljevec', 'holm',\n",
              "       'laatikainen', 'amari', 'chang', 'silva', 'moquin', 'mathieu',\n",
              "       'seppala', 'chinedum', 'kifle', 'costa', 'afamefuna', 'bowden',\n",
              "       'peters', 'wisniewska', 'sleiman', 'gustafsson', 'nnonso',\n",
              "       'pecinova', 'sorensen', 'aranda', 'van de wiel', 'ogochukwu',\n",
              "       'halldorsdottir', 'jaksic', 'rocha', 'vesecka', 'luoma', 'hrda',\n",
              "       'mayberry', 'nyborg', 'ostergaard', 'bisliev', 'filatov', 'bakos',\n",
              "       'lindstrom', 'holden', 'cindric', 'mehari', 'webb', 'zimmerman',\n",
              "       'rushisvili', 'amanuel', 'onyekaozulu', 'morita', 'gunnarsson',\n",
              "       'barsukov', 'tucker', 'priest', 'luwam', 'vaneker', 'bubanj',\n",
              "       'hopkins', 'allen', 'dratchev', 'naifeh', 'ferrari', 'ko',\n",
              "       'kowalczyk', 'balagic', 'saenger', 'adonay', 'glockner', 'nordin',\n",
              "       'blix', 'dandonneau', 'baum', 'leroux', 'lei', 'eidem', 'bui',\n",
              "       'wong', 'hunter', 'reynoso', 'glaser', 'chung', 'afanasyeva',\n",
              "       'loncar', 'bjarkason', 'gudjonsdottir', 'schiavone', 'lindgren',\n",
              "       'pospisil', 'marchesi', 'christensen', 'ferreira', 'baer',\n",
              "       'tomankova', 'van der lubbe', 'henzen', 'touma', 'markus', 'kung',\n",
              "       'lynge', 'hervinsson', 'ferguson', 'vu', 'hueber', 'nilsen',\n",
              "       'pinto', 'ghanem', 'tryggvadottir', 'mobourne', 'piirainen',\n",
              "       'reilly', 'mortensen', 'bogolyubova', 'chidi', 'csorba', 'phan',\n",
              "       'batukayev', 'macdonald', 'tuma', 'taylor', 'ek', 'selassie',\n",
              "       'hill', 'jorgensen', 'nebay', 'laine', 'wubbels', 'lundy',\n",
              "       'christie', 'lorenzo', 'kalb', 'flamand', 'grant', 'sung',\n",
              "       'navratil', 'dreher', 'lansell', 'mai', 'yao', 'vestergaard',\n",
              "       'luman', 'combs', 'teng', 'horvat', 'norgaard', 'herczegh', 'rice',\n",
              "       'beauvais', 'shuler', 'crawford', 'jephcott', 'mcgregor', 'freud',\n",
              "       'scholz', 'araujo', 'horikawa', 'martinsson', 'vieira', 'roy',\n",
              "       'wilkinson', 'enoksen', 'zetticci', 'teichelmann', 'lillebo',\n",
              "       'prince', 'gudbrandsson'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "treatments['surname'].unique()"
      ]
    },
    {
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      "execution_count": 55,
      "metadata": {
        "colab": {
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        },
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        "outputId": "7417cdbd-b929-4e22-e749-3747d80513e5"
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              "0        veronika jindrova\n",
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              "2        yukitaka takenaka\n",
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              "4            alissa montez\n",
              "              ...         \n",
              "275        albina zetticci\n",
              "276       john teichelmann\n",
              "277         mathea lillebo\n",
              "278          vallie prince\n",
              "279    samuel gudbrandsson\n",
              "Name: full_name, Length: 280, dtype: object"
            ],
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            ]
          },
          "metadata": {},
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      ],
      "source": [
        "treatments['full_name'] = treatments['given_name'] + ' ' + treatments['surname']\n",
        "treatments['full_name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IqTZdXbhkB9f",
        "outputId": "9f530ae8-e78d-4f44-a357-6d22dc6e9d35"
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          "metadata": {},
          "execution_count": 56
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        "treatments['full_name'].duplicated().sum()"
      ]
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              "full_name\n",
              "joseph day                2\n",
              "veronika jindrova         1\n",
              "svanhvit gudjonsdottir    1\n",
              "monika loncar             1\n",
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              "    given_name surname    auralin novodra  hba1c_start  hba1c_end  \\\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"treatments[treatments['full_name'] == 'joseph day']\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"joseph\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"day\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"29u - 36u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 7.7,\n        \"max\": 7.7,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 7.19,\n        \"max\": 7.19,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "execution_count": 58
        }
      ],
      "source": [
        "treatments[treatments['full_name'] == 'joseph day']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "xH-d2U0znM8T"
      },
      "outputs": [],
      "source": [
        "treatments = treatments.drop(136)\n"
      ]
    },
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              "   given_name surname    auralin novodra  hba1c_start  hba1c_end  \\\n",
              "10     joseph     day  29u - 36u       -          7.7       7.19   \n",
              "\n",
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              "summary": "{\n  \"name\": \"treatments[treatments['full_name'] == 'joseph day']\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"joseph\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"day\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"29u - 36u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 7.7,\n        \"max\": 7.7,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 7.19,\n        \"max\": 7.19,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 60
        }
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      "source": [
        "treatments[treatments['full_name'] == 'joseph day']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
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        },
        "id": "zl63yVQpomrO",
        "outputId": "67bc79c5-5396-4665-ab3c-de7d00442b3e"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(279, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 61
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      "source": [
        "treatments.shape"
      ]
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      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
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        },
        "id": "OSOgwYR3cz1w",
        "outputId": "19934a28-b8f4-49c0-a79a-92172aa3c429"
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          "data": {
            "text/plain": [
              "0      41u - 48u\n",
              "1              -\n",
              "2              -\n",
              "3      33u - 36u\n",
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              "         ...    \n",
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          "metadata": {},
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        "treatments.auralin"
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      "cell_type": "code",
      "execution_count": 63,
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        "id": "du41TZx-cz1w",
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              "0              -\n",
              "1      40u - 45u\n",
              "2      39u - 36u\n",
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          "metadata": {},
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        "treatments.novodra"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zo8bHXgcz1x"
      },
      "source": [
        "#### The Baseline and end dose of auralin and novodra are in units and the change in the dose from baseline(start) and end was not mentioned. here i'm' calculating the change for auraline and novodra dose's"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "aeDp5FWhcz1x"
      },
      "outputs": [],
      "source": [
        "# Extracting the start and end doses for Auralin\n",
        "auralin_start = treatments['auralin'].str.extract(r'(\\d+)u -')[0]\n",
        "auralin_end = treatments['auralin'].str.extract(r'- (\\d+)u')[0]\n",
        "\n",
        "# Converting the extracted values to numeric\n",
        "treatments['auralin_start'] = pd.to_numeric(auralin_start, errors='coerce')\n",
        "treatments['auralin_end'] = pd.to_numeric(auralin_end, errors='coerce')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "T5gypefscz1x"
      },
      "outputs": [],
      "source": [
        "# Extracting the start and end doses for Novodra\n",
        "novodra_start = treatments['novodra'].str.extract(r'(\\d+)u -')[0]\n",
        "novodra_end = treatments['novodra'].str.extract(r'- (\\d+)u')[0]\n",
        "\n",
        "# Converting the extracted values to numeric\n",
        "treatments['novodra_start'] = pd.to_numeric(novodra_start, errors='coerce')\n",
        "treatments['novodra_end'] = pd.to_numeric(novodra_end, errors='coerce')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "_Y0gGRiBsiRi"
      },
      "outputs": [],
      "source": [
        "treatments['start_dose'] = treatments['auralin_start'].combine_first(treatments['novodra_start'])\n",
        "treatments['end_dose'] = treatments['auralin_end'].combine_first(treatments['novodra_end'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "i4GXvjEvsjUG"
      },
      "outputs": [],
      "source": [
        "treatments['drug_type'] = treatments.apply(lambda row: 'Auralin' if pd.notna(row['auralin_start']) else 'Novodra', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
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        "id": "Jokff93Mpu0h",
        "outputId": "b7c8a20d-fc08-48f3-d059-2f2eebab0c93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name     surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
              "0   veronika    jindrova  41u - 48u          -         7.63       7.20   \n",
              "1     elliot  richardson          -  40u - 45u         7.56       7.09   \n",
              "2   yukitaka    takenaka          -  39u - 36u         7.68       7.25   \n",
              "3       skye  gormanston  33u - 36u          -         7.97       7.62   \n",
              "4     alissa      montez          -  33u - 29u         7.78       7.46   \n",
              "\n",
              "   hba1c_change          full_name  auralin_start  auralin_end  novodra_start  \\\n",
              "0           NaN  veronika jindrova           41.0         48.0            NaN   \n",
              "1          0.97  elliot richardson            NaN          NaN           40.0   \n",
              "2           NaN  yukitaka takenaka            NaN          NaN           39.0   \n",
              "3          0.35    skye gormanston           33.0         36.0            NaN   \n",
              "4          0.32      alissa montez            NaN          NaN           33.0   \n",
              "\n",
              "   novodra_end  start_dose  end_dose drug_type  \n",
              "0          NaN        41.0      48.0   Auralin  \n",
              "1         45.0        40.0      45.0   Novodra  \n",
              "2         36.0        39.0      36.0   Novodra  \n",
              "3          NaN        33.0      36.0   Auralin  \n",
              "4         29.0        33.0      29.0   Novodra  "
            ],
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              "      <th>0</th>\n",
              "      <td>veronika</td>\n",
              "      <td>jindrova</td>\n",
              "      <td>41u - 48u</td>\n",
              "      <td>-</td>\n",
              "      <td>7.63</td>\n",
              "      <td>7.20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>veronika jindrova</td>\n",
              "      <td>41.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>elliot</td>\n",
              "      <td>richardson</td>\n",
              "      <td>-</td>\n",
              "      <td>40u - 45u</td>\n",
              "      <td>7.56</td>\n",
              "      <td>7.09</td>\n",
              "      <td>0.97</td>\n",
              "      <td>elliot richardson</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yukitaka</td>\n",
              "      <td>takenaka</td>\n",
              "      <td>-</td>\n",
              "      <td>39u - 36u</td>\n",
              "      <td>7.68</td>\n",
              "      <td>7.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yukitaka takenaka</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>skye</td>\n",
              "      <td>gormanston</td>\n",
              "      <td>33u - 36u</td>\n",
              "      <td>-</td>\n",
              "      <td>7.97</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.35</td>\n",
              "      <td>skye gormanston</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alissa</td>\n",
              "      <td>montez</td>\n",
              "      <td>-</td>\n",
              "      <td>33u - 29u</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.46</td>\n",
              "      <td>0.32</td>\n",
              "      <td>alissa montez</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "treatments",
              "summary": "{\n  \"name\": \"treatments\",\n  \"rows\": 279,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 270,\n        \"samples\": [\n          \"szilveszter\",\n          \"yasmin\",\n          \"avdei\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 272,\n        \"samples\": [\n          \"totth\",\n          \"silva\",\n          \"tikhonov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 121,\n        \"samples\": [\n          \"53u - 64u\",\n          \"39u - 43u\",\n          \"31u - 38u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 113,\n        \"samples\": [\n          \"31u - 36u\",\n          \"42u - 44u\",\n          \"34u - 32u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5694011444392472,\n        \"min\": 7.5,\n        \"max\": 9.95,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          8.19,\n          7.58,\n          9.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.570190731154981,\n        \"min\": 7.01,\n        \"max\": 9.58,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          7.45,\n          7.19,\n          7.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27955527609183284,\n        \"min\": 0.2,\n        \"max\": 0.99,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.26,\n          0.3,\n          0.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 279,\n        \"samples\": [\n          \"szilveszter totth\",\n          \"jowita wisniewska\",\n          \"nicolas ferreira\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.647467975807256,\n        \"min\": 22.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          58.0,\n          52.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.746269047619398,\n        \"min\": 30.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          40.0,\n          67.0,\n          64.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.538635896010613,\n        \"min\": 19.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          41.0,\n          32.0,\n          54.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.953368585173067,\n        \"min\": 21.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          28.0,\n          30.0,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.082609675064745,\n        \"min\": 19.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          63.0,\n          38.0,\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.194579441467724,\n        \"min\": 21.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          35.0,\n          66.0,\n          67.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "treatments.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skhubiWvcz1x"
      },
      "source": [
        "#### The hba1c_change column values are incorrectly calculated, so i'm re calculating the hba1c_change values.hba1c_change = hba1c_start - hba1c_end"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "SGL1Lbpqcz1x"
      },
      "outputs": [],
      "source": [
        "treatments['hba1c_change'] = treatments['hba1c_start'] - treatments['hba1c_end']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "AwmrlWdEcz1x",
        "outputId": "74efb4f9-d08d-4388-f512-b5a24508bddb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name     surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
              "0   veronika    jindrova  41u - 48u          -         7.63       7.20   \n",
              "1     elliot  richardson          -  40u - 45u         7.56       7.09   \n",
              "2   yukitaka    takenaka          -  39u - 36u         7.68       7.25   \n",
              "3       skye  gormanston  33u - 36u          -         7.97       7.62   \n",
              "4     alissa      montez          -  33u - 29u         7.78       7.46   \n",
              "\n",
              "   hba1c_change          full_name  auralin_start  auralin_end  novodra_start  \\\n",
              "0          0.43  veronika jindrova           41.0         48.0            NaN   \n",
              "1          0.47  elliot richardson            NaN          NaN           40.0   \n",
              "2          0.43  yukitaka takenaka            NaN          NaN           39.0   \n",
              "3          0.35    skye gormanston           33.0         36.0            NaN   \n",
              "4          0.32      alissa montez            NaN          NaN           33.0   \n",
              "\n",
              "   novodra_end  start_dose  end_dose drug_type  \n",
              "0          NaN        41.0      48.0   Auralin  \n",
              "1         45.0        40.0      45.0   Novodra  \n",
              "2         36.0        39.0      36.0   Novodra  \n",
              "3          NaN        33.0      36.0   Auralin  \n",
              "4         29.0        33.0      29.0   Novodra  "
            ],
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              "<style scoped>\n",
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              "      <th>0</th>\n",
              "      <td>veronika</td>\n",
              "      <td>jindrova</td>\n",
              "      <td>41u - 48u</td>\n",
              "      <td>-</td>\n",
              "      <td>7.63</td>\n",
              "      <td>7.20</td>\n",
              "      <td>0.43</td>\n",
              "      <td>veronika jindrova</td>\n",
              "      <td>41.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>elliot</td>\n",
              "      <td>richardson</td>\n",
              "      <td>-</td>\n",
              "      <td>40u - 45u</td>\n",
              "      <td>7.56</td>\n",
              "      <td>7.09</td>\n",
              "      <td>0.47</td>\n",
              "      <td>elliot richardson</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yukitaka</td>\n",
              "      <td>takenaka</td>\n",
              "      <td>-</td>\n",
              "      <td>39u - 36u</td>\n",
              "      <td>7.68</td>\n",
              "      <td>7.25</td>\n",
              "      <td>0.43</td>\n",
              "      <td>yukitaka takenaka</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>skye</td>\n",
              "      <td>gormanston</td>\n",
              "      <td>33u - 36u</td>\n",
              "      <td>-</td>\n",
              "      <td>7.97</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.35</td>\n",
              "      <td>skye gormanston</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alissa</td>\n",
              "      <td>montez</td>\n",
              "      <td>-</td>\n",
              "      <td>33u - 29u</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.46</td>\n",
              "      <td>0.32</td>\n",
              "      <td>alissa montez</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>Novodra</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "treatments",
              "summary": "{\n  \"name\": \"treatments\",\n  \"rows\": 279,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 270,\n        \"samples\": [\n          \"szilveszter\",\n          \"yasmin\",\n          \"avdei\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 272,\n        \"samples\": [\n          \"totth\",\n          \"silva\",\n          \"tikhonov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 121,\n        \"samples\": [\n          \"53u - 64u\",\n          \"39u - 43u\",\n          \"31u - 38u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 113,\n        \"samples\": [\n          \"31u - 36u\",\n          \"42u - 44u\",\n          \"34u - 32u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5694011444392472,\n        \"min\": 7.5,\n        \"max\": 9.95,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          8.19,\n          7.58,\n          9.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.570190731154981,\n        \"min\": 7.01,\n        \"max\": 9.58,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          7.45,\n          7.19,\n          7.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05989043915172902,\n        \"min\": 0.1999999999999993,\n        \"max\": 0.54,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          0.4299999999999997,\n          0.3800000000000008,\n          0.3099999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 279,\n        \"samples\": [\n          \"szilveszter totth\",\n          \"jowita wisniewska\",\n          \"nicolas ferreira\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.647467975807256,\n        \"min\": 22.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          58.0,\n          52.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.746269047619398,\n        \"min\": 30.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          40.0,\n          67.0,\n          64.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.538635896010613,\n        \"min\": 19.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          41.0,\n          32.0,\n          54.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.953368585173067,\n        \"min\": 21.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          28.0,\n          30.0,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.082609675064745,\n        \"min\": 19.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          63.0,\n          38.0,\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.194579441467724,\n        \"min\": 21.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          35.0,\n          66.0,\n          67.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "treatments.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "uyLh01rCcz1x"
      },
      "outputs": [],
      "source": [
        "treatments.drop(['auralin','novodra'],axis = 1,inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "PFneUxhccz1y",
        "outputId": "528f10eb-ce24-400f-80d4-024f907a741c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name     surname  hba1c_start  hba1c_end  hba1c_change  \\\n",
              "0   veronika    jindrova         7.63       7.20          0.43   \n",
              "1     elliot  richardson         7.56       7.09          0.47   \n",
              "2   yukitaka    takenaka         7.68       7.25          0.43   \n",
              "3       skye  gormanston         7.97       7.62          0.35   \n",
              "4     alissa      montez         7.78       7.46          0.32   \n",
              "\n",
              "           full_name  auralin_start  auralin_end  novodra_start  novodra_end  \\\n",
              "0  veronika jindrova           41.0         48.0            NaN          NaN   \n",
              "1  elliot richardson            NaN          NaN           40.0         45.0   \n",
              "2  yukitaka takenaka            NaN          NaN           39.0         36.0   \n",
              "3    skye gormanston           33.0         36.0            NaN          NaN   \n",
              "4      alissa montez            NaN          NaN           33.0         29.0   \n",
              "\n",
              "   start_dose  end_dose drug_type  \n",
              "0        41.0      48.0   Auralin  \n",
              "1        40.0      45.0   Novodra  \n",
              "2        39.0      36.0   Novodra  \n",
              "3        33.0      36.0   Auralin  \n",
              "4        33.0      29.0   Novodra  "
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              "      <th>0</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>elliot</td>\n",
              "      <td>richardson</td>\n",
              "      <td>7.56</td>\n",
              "      <td>7.09</td>\n",
              "      <td>0.47</td>\n",
              "      <td>elliot richardson</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yukitaka</td>\n",
              "      <td>takenaka</td>\n",
              "      <td>7.68</td>\n",
              "      <td>7.25</td>\n",
              "      <td>0.43</td>\n",
              "      <td>yukitaka takenaka</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>skye</td>\n",
              "      <td>gormanston</td>\n",
              "      <td>7.97</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.35</td>\n",
              "      <td>skye gormanston</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alissa</td>\n",
              "      <td>montez</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.46</td>\n",
              "      <td>0.32</td>\n",
              "      <td>alissa montez</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    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",
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              "\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",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b0892eac-f40b-46da-968d-0330576ca023 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",
              "variable_name": "treatments",
              "summary": "{\n  \"name\": \"treatments\",\n  \"rows\": 279,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 270,\n        \"samples\": [\n          \"szilveszter\",\n          \"yasmin\",\n          \"avdei\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 272,\n        \"samples\": [\n          \"totth\",\n          \"silva\",\n          \"tikhonov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5694011444392472,\n        \"min\": 7.5,\n        \"max\": 9.95,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          8.19,\n          7.58,\n          9.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.570190731154981,\n        \"min\": 7.01,\n        \"max\": 9.58,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          7.45,\n          7.19,\n          7.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05989043915172902,\n        \"min\": 0.1999999999999993,\n        \"max\": 0.54,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          0.4299999999999997,\n          0.3800000000000008,\n          0.3099999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 279,\n        \"samples\": [\n          \"szilveszter totth\",\n          \"jowita wisniewska\",\n          \"nicolas ferreira\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.647467975807256,\n        \"min\": 22.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          58.0,\n          52.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.746269047619398,\n        \"min\": 30.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          40.0,\n          67.0,\n          64.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.538635896010613,\n        \"min\": 19.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          41.0,\n          32.0,\n          54.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.953368585173067,\n        \"min\": 21.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          28.0,\n          30.0,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.082609675064745,\n        \"min\": 19.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          63.0,\n          38.0,\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.194579441467724,\n        \"min\": 21.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          35.0,\n          66.0,\n          67.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "# univariate Analysis of auralin_change\n",
        "treatments.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "_cs0Qn1Ecz1y"
      },
      "outputs": [],
      "source": [
        "column_order = ['given_name', 'surname', 'full_name', 'auralin_start', 'auralin_end', 'novodra_start',\n",
        "       'novodra_end', 'start_dose', 'end_dose',  'hba1c_start', 'hba1c_end', 'hba1c_change','drug_type']\n",
        "treatments = treatments[column_order]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "rRNTxpJWuPyY",
        "outputId": "f0ff8edd-2eea-4cc0-9c0f-48ebc0b99e2d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name     surname          full_name  auralin_start  auralin_end  \\\n",
              "0   veronika    jindrova  veronika jindrova           41.0         48.0   \n",
              "1     elliot  richardson  elliot richardson            NaN          NaN   \n",
              "2   yukitaka    takenaka  yukitaka takenaka            NaN          NaN   \n",
              "3       skye  gormanston    skye gormanston           33.0         36.0   \n",
              "4     alissa      montez      alissa montez            NaN          NaN   \n",
              "\n",
              "   novodra_start  novodra_end  start_dose  end_dose  hba1c_start  hba1c_end  \\\n",
              "0            NaN          NaN        41.0      48.0         7.63       7.20   \n",
              "1           40.0         45.0        40.0      45.0         7.56       7.09   \n",
              "2           39.0         36.0        39.0      36.0         7.68       7.25   \n",
              "3            NaN          NaN        33.0      36.0         7.97       7.62   \n",
              "4           33.0         29.0        33.0      29.0         7.78       7.46   \n",
              "\n",
              "   hba1c_change drug_type  \n",
              "0          0.43   Auralin  \n",
              "1          0.47   Novodra  \n",
              "2          0.43   Novodra  \n",
              "3          0.35   Auralin  \n",
              "4          0.32   Novodra  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-601947fa-ff9a-4104-adcf-7bdda8942023\" 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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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>given_name</th>\n",
              "      <th>surname</th>\n",
              "      <th>full_name</th>\n",
              "      <th>auralin_start</th>\n",
              "      <th>auralin_end</th>\n",
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              "      <th>novodra_end</th>\n",
              "      <th>start_dose</th>\n",
              "      <th>end_dose</th>\n",
              "      <th>hba1c_start</th>\n",
              "      <th>hba1c_end</th>\n",
              "      <th>hba1c_change</th>\n",
              "      <th>drug_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>veronika</td>\n",
              "      <td>jindrova</td>\n",
              "      <td>veronika jindrova</td>\n",
              "      <td>41.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41.0</td>\n",
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              "      <td>7.63</td>\n",
              "      <td>7.20</td>\n",
              "      <td>0.43</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>elliot</td>\n",
              "      <td>richardson</td>\n",
              "      <td>elliot richardson</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>7.56</td>\n",
              "      <td>7.09</td>\n",
              "      <td>0.47</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yukitaka</td>\n",
              "      <td>takenaka</td>\n",
              "      <td>yukitaka takenaka</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>7.68</td>\n",
              "      <td>7.25</td>\n",
              "      <td>0.43</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>skye</td>\n",
              "      <td>gormanston</td>\n",
              "      <td>skye gormanston</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>7.97</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.35</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alissa</td>\n",
              "      <td>montez</td>\n",
              "      <td>alissa montez</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.46</td>\n",
              "      <td>0.32</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-601947fa-ff9a-4104-adcf-7bdda8942023')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-601947fa-ff9a-4104-adcf-7bdda8942023 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-601947fa-ff9a-4104-adcf-7bdda8942023');\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-9367f1e1-68bb-479c-9d47-f034a06e07f6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9367f1e1-68bb-479c-9d47-f034a06e07f6')\"\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",
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              "    </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-9367f1e1-68bb-479c-9d47-f034a06e07f6 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",
              "variable_name": "treatments",
              "summary": "{\n  \"name\": \"treatments\",\n  \"rows\": 279,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 270,\n        \"samples\": [\n          \"szilveszter\",\n          \"yasmin\",\n          \"avdei\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 272,\n        \"samples\": [\n          \"totth\",\n          \"silva\",\n          \"tikhonov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 279,\n        \"samples\": [\n          \"szilveszter totth\",\n          \"jowita wisniewska\",\n          \"nicolas ferreira\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.647467975807256,\n        \"min\": 22.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          58.0,\n          52.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.746269047619398,\n        \"min\": 30.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          40.0,\n          67.0,\n          64.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.538635896010613,\n        \"min\": 19.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          41.0,\n          32.0,\n          54.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.953368585173067,\n        \"min\": 21.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          28.0,\n          30.0,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.082609675064745,\n        \"min\": 19.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          63.0,\n          38.0,\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.194579441467724,\n        \"min\": 21.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          35.0,\n          66.0,\n          67.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5694011444392472,\n        \"min\": 7.5,\n        \"max\": 9.95,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          8.19,\n          7.58,\n          9.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.570190731154981,\n        \"min\": 7.01,\n        \"max\": 9.58,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          7.45,\n          7.19,\n          7.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05989043915172902,\n        \"min\": 0.1999999999999993,\n        \"max\": 0.54,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          0.4299999999999997,\n          0.3800000000000008,\n          0.3099999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "treatments.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "JUIIPfZ_LpU6",
        "outputId": "baca6ce4-adb5-4242-b90d-fb5ace0e149f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-7cb9339b8bea>:1: 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",
            "  treatments.drop(['auralin_start', 'auralin_end', 'novodra_start', 'novodra_end'], axis = 1, inplace = True)\n"
          ]
        }
      ],
      "source": [
        "treatments.drop(['auralin_start', 'auralin_end', 'novodra_start', 'novodra_end'], axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "dQpiBqiwcz1z"
      },
      "outputs": [],
      "source": [
        "treatments.to_csv(\"treatments_cleaned.csv\",index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "CK6hdMl7cz1z"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5qaAFKHcz1z"
      },
      "source": [
        "# Treatments_cut"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "5gqi_JK-bgOD"
      },
      "outputs": [],
      "source": [
        "# Load the data\n",
        "treatments_cut = pd.read_csv('/content/treatments_cut.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgkvRI0tdDSf",
        "outputId": "280a947b-9cf3-4549-909b-a7e69b4893b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 70 entries, 0 to 69\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   given_name    70 non-null     object \n",
            " 1   surname       70 non-null     object \n",
            " 2   auralin       70 non-null     object \n",
            " 3   novodra       70 non-null     object \n",
            " 4   hba1c_start   70 non-null     float64\n",
            " 5   hba1c_end     70 non-null     float64\n",
            " 6   hba1c_change  42 non-null     float64\n",
            "dtypes: float64(3), object(4)\n",
            "memory usage: 4.0+ KB\n"
          ]
        }
      ],
      "source": [
        "treatments_cut.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "ioKH9pMIXB9g",
        "outputId": "a8b85e57-77f9-4f0e-ee5f-1e05fc90cdce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "given_name       0\n",
              "surname          0\n",
              "auralin          0\n",
              "novodra          0\n",
              "hba1c_start      0\n",
              "hba1c_end        0\n",
              "hba1c_change    28\n",
              "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>given_name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>surname</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>auralin</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>novodra</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hba1c_start</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hba1c_end</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hba1c_change</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "treatments_cut.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpNcNqa82CtF",
        "outputId": "12d584ee-22fc-4171-b960-2765bf76fb63"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(70, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "treatments_cut.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWUiVs0T2Cp1",
        "outputId": "0d174d6a-3245-4f41-fb33-8b6502b37bef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ],
      "source": [
        "treatments_cut.duplicated().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8LShQXUr2Qpe"
      },
      "source": [
        "# The following are the Errors in Treatments_cut Dataset\n",
        "\n",
        "##### 1.The names in given_name and sur_name columns are containing special characters.\n",
        "##### 2.The Baseline and end dose of auralin and novodra are in units and the change in the dose from baseline(start) and end was not mentioned."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6tlLkCHwcz1z",
        "outputId": "cb65a553-1d16-4091-d254-4731230be482"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name    surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
              "0      jožka  resanovič  22u - 30u          -         7.56       7.22   \n",
              "1  inunnguaq   heilmann  57u - 67u          -         7.85       7.45   \n",
              "2      alwin   svensson  36u - 39u          -         7.78       7.34   \n",
              "3        thể      lương          -  61u - 64u         7.64       7.22   \n",
              "4     amanda    ribeiro  36u - 44u          -         7.85       7.47   \n",
              "\n",
              "   hba1c_change  \n",
              "0          0.34  \n",
              "1           NaN  \n",
              "2           NaN  \n",
              "3          0.92  \n",
              "4          0.38  "
            ],
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              "      <td>7.56</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>inunnguaq</td>\n",
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              "      <td>-</td>\n",
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              "      <th>2</th>\n",
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              "      <td>-</td>\n",
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              "      <td>-</td>\n",
              "      <td>61u - 64u</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.22</td>\n",
              "      <td>0.92</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>amanda</td>\n",
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              "      <td>7.85</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "treatments_cut",
              "summary": "{\n  \"name\": \"treatments_cut\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze\",\n          \"jo\\u017eka\",\n          \"satsita\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"wo\\u017aniak\",\n          \"resanovi\\u010d\",\n          \"batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 36,\n        \"samples\": [\n          \"36u - 46u\",\n          \"39u - 46u\",\n          \"51u - 59u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"55u - 51u\",\n          \"43u - 40u\",\n          \"26u - 25u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4230071777922546,\n        \"min\": 7.51,\n        \"max\": 9.91,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          7.55,\n          7.91,\n          7.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4187058836190694,\n        \"min\": 7.02,\n        \"max\": 9.46,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          8.57,\n          7.18,\n          7.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.270719027964801,\n        \"min\": 0.28,\n        \"max\": 0.97,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.34,\n          0.92,\n          0.37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "treatments_cut.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BZjC8jWcXB6D",
        "outputId": "855e560e-5b90-4e78-fb21-a2dea389758e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['jožka', 'inunnguaq', 'alwin', 'thể', 'amanda', 'meya', 'sara',\n",
              "       'frydryk', 'jane', 'angela', 'edelma', 'gabriela', 'annika',\n",
              "       'bernarda', 'chiho', 'dani', 'mijael', 'emma', 'flavia', 'noe',\n",
              "       'seweryna', 'david', 'beatrycze', 'miłosław', 'lóa', 'siebrigje',\n",
              "       'firenze', 'zoe', 'una', 'luboš', 'trifon', 'galit', 'kifle',\n",
              "       'meaza', 'adlan', 'alvin', 'chibuzo', 'sofia', 'guðni', 'maryam',\n",
              "       'eufemio', 'dalmacia', 'daimy', 'hans peter', 'jeremy',\n",
              "       'štefanija', 'anja', 'regolo', 'nebechi', 'satsita', 'timothy',\n",
              "       'bjørnar', 'vivien', 'genet', 'borna', 'mary', 'blanka', 'joseph',\n",
              "       'christopher', 'robert', 'maret', 'ella', 'fakhri', 'žarka',\n",
              "       'lixue', 'rovzan', 'jakob', 'bernd', 'berta', 'armina'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "treatments_cut['given_name'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lh71N6gXXB3w",
        "outputId": "2a8489b8-c96b-4f16-d642-b2d421f3c1f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['resanovič', 'heilmann', 'svensson', 'lương', 'ribeiro', 'sjöberg',\n",
              "       'miles', 'adamski', 'citizen', 'lavrentyev', 'villalpando',\n",
              "       'michalčíková', 'vaara', 'cindrić', 'higa', 'antoun', 'guerra',\n",
              "       'barlow', 'fiorentino', 'aranda', 'czerwinska', 'villadsen',\n",
              "       'woźniak', 'wiśniewski', 'hrafnsdóttir', 'koldenhof', 'fodor',\n",
              "       'wellish', 'traustadóttir', 'pecha', 'izmailov', 'casárez',\n",
              "       'mustafa', 'brhane', 'shishani', 'jackson', 'okoli', 'hermansen',\n",
              "       'heimisson', 'dratchev', 'rosario', 'madrid', 'tromp', 'radislav',\n",
              "       'montagu', 'novosel', 'mueller', 'nucci', 'ekechukwu', 'batukayev',\n",
              "       'cotton', 'nilsen', 'zsinkó', 'fesahaye', 'šlezinger', 'adams',\n",
              "       'jurković', 'tucker', 'woodward', 'maslov', 'sultygov', 'lund',\n",
              "       'fakhoury', 'rap', 'hsueh', 'kishiev', 'jakobsen', 'schneider',\n",
              "       'napolitani', 'sauvé'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "treatments_cut['surname'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUk5nVbpcz10"
      },
      "source": [
        "#### The names in given_name and sur_name columns are containing special characters, so i am trying to replace them with the unicode characters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxdIWG4DkF4c",
        "outputId": "11c25f67-3872-4238-a7b9-4a70e0fe22fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['jozka', 'inunnguaq', 'alwin', 'the', 'amanda', 'meya', 'sara',\n",
              "       'frydryk', 'jane', 'angela', 'edelma', 'gabriela', 'annika',\n",
              "       'bernarda', 'chiho', 'dani', 'mijael', 'emma', 'flavia', 'noe',\n",
              "       'seweryna', 'david', 'beatrycze', 'miloslaw', 'loa', 'siebrigje',\n",
              "       'firenze', 'zoe', 'una', 'lubos', 'trifon', 'galit', 'kifle',\n",
              "       'meaza', 'adlan', 'alvin', 'chibuzo', 'sofia', 'gudni', 'maryam',\n",
              "       'eufemio', 'dalmacia', 'daimy', 'hans peter', 'jeremy',\n",
              "       'stefanija', 'anja', 'regolo', 'nebechi', 'satsita', 'timothy',\n",
              "       'bjornar', 'vivien', 'genet', 'borna', 'mary', 'blanka', 'joseph',\n",
              "       'christopher', 'robert', 'maret', 'ella', 'fakhri', 'zarka',\n",
              "       'lixue', 'rovzan', 'jakob', 'bernd', 'berta', 'armina'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "import unidecode\n",
        "\n",
        "# converting the the special character in names to regular characters\n",
        "def normalize_name(name):\n",
        "    return unidecode.unidecode(name)\n",
        "\n",
        "# Apply the function to the given_name & sur_name columns\n",
        "treatments_cut['given_name'] = treatments_cut['given_name'].apply(normalize_name)\n",
        "treatments_cut['surname'] = treatments_cut['surname'].apply(normalize_name)\n",
        "\n",
        "treatments_cut['given_name'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "coyWfjwsoMOG",
        "outputId": "d811cd0d-120b-4b99-abfc-897e12ed9b93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['resanovic', 'heilmann', 'svensson', 'luong', 'ribeiro', 'sjoberg',\n",
              "       'miles', 'adamski', 'citizen', 'lavrentyev', 'villalpando',\n",
              "       'michalcikova', 'vaara', 'cindric', 'higa', 'antoun', 'guerra',\n",
              "       'barlow', 'fiorentino', 'aranda', 'czerwinska', 'villadsen',\n",
              "       'wozniak', 'wisniewski', 'hrafnsdottir', 'koldenhof', 'fodor',\n",
              "       'wellish', 'traustadottir', 'pecha', 'izmailov', 'casarez',\n",
              "       'mustafa', 'brhane', 'shishani', 'jackson', 'okoli', 'hermansen',\n",
              "       'heimisson', 'dratchev', 'rosario', 'madrid', 'tromp', 'radislav',\n",
              "       'montagu', 'novosel', 'mueller', 'nucci', 'ekechukwu', 'batukayev',\n",
              "       'cotton', 'nilsen', 'zsinko', 'fesahaye', 'slezinger', 'adams',\n",
              "       'jurkovic', 'tucker', 'woodward', 'maslov', 'sultygov', 'lund',\n",
              "       'fakhoury', 'rap', 'hsueh', 'kishiev', 'jakobsen', 'schneider',\n",
              "       'napolitani', 'sauve'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "treatments_cut['surname'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "7-ekkWE_zY4h",
        "outputId": "698d0313-9678-4f59-aed2-5c62de89a8d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        jozka resanovic\n",
              "1     inunnguaq heilmann\n",
              "2         alwin svensson\n",
              "3              the luong\n",
              "4         amanda ribeiro\n",
              "             ...        \n",
              "65        rovzan kishiev\n",
              "66        jakob jakobsen\n",
              "67       bernd schneider\n",
              "68      berta napolitani\n",
              "69          armina sauve\n",
              "Name: full_name, Length: 70, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>full_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>jozka resanovic</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>inunnguaq heilmann</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alwin svensson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>the luong</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>amanda ribeiro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>rovzan kishiev</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>jakob jakobsen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>bernd schneider</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>berta napolitani</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>armina sauve</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "treatments_cut['full_name'] = treatments_cut['given_name'] + ' ' + treatments_cut['surname']\n",
        "treatments_cut['full_name']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAXFCuWJoMKs"
      },
      "source": [
        "#### The Baseline and end dose of auralin and novodra are in units and the change in the dose from baseline(start) and end was not mentioned. here i'm' calculating the change for auraline and novodra dose's"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "R_ayMRoUoMIb"
      },
      "outputs": [],
      "source": [
        "# Extracting the start and end doses for Auralin\n",
        "auralin_start = treatments_cut['auralin'].str.extract(r'(\\d+)u -')[0]\n",
        "auralin_end = treatments_cut['auralin'].str.extract(r'- (\\d+)u')[0]\n",
        "\n",
        "# Converting the extracted values to numeric\n",
        "treatments_cut['auralin_start'] = pd.to_numeric(auralin_start, errors='coerce')\n",
        "treatments_cut['auralin_end'] = pd.to_numeric(auralin_end, errors='coerce')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "hmFKTd4tcz11"
      },
      "outputs": [],
      "source": [
        "# Extracting the start and end doses for Novodra\n",
        "novodra_start = treatments_cut['novodra'].str.extract(r'(\\d+)u -')[0]\n",
        "novodra_end = treatments_cut['novodra'].str.extract(r'- (\\d+)u')[0]\n",
        "\n",
        "# Converting the extracted values to numeric\n",
        "treatments_cut['novodra_start'] = pd.to_numeric(novodra_start, errors='coerce')\n",
        "treatments_cut['novodra_end'] = pd.to_numeric(novodra_end, errors='coerce')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "weSFVbxkcz11"
      },
      "outputs": [],
      "source": [
        "treatments_cut['start_dose'] = treatments_cut['auralin_start'].combine_first(treatments_cut['novodra_start'])\n",
        "treatments_cut['end_dose'] = treatments_cut['auralin_end'].combine_first(treatments_cut['novodra_end'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "yMaOxNQ6cz11"
      },
      "outputs": [],
      "source": [
        "treatments_cut['drug_type'] = treatments_cut.apply(lambda row: 'Auralin' if pd.notna(row['auralin_start']) else 'Novodra', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "7mjA1JmIyKAH",
        "outputId": "416ce41b-5112-4a57-ba33-95f1f5db65c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name    surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
              "0      jozka  resanovic  22u - 30u          -         7.56       7.22   \n",
              "1  inunnguaq   heilmann  57u - 67u          -         7.85       7.45   \n",
              "2      alwin   svensson  36u - 39u          -         7.78       7.34   \n",
              "3        the      luong          -  61u - 64u         7.64       7.22   \n",
              "4     amanda    ribeiro  36u - 44u          -         7.85       7.47   \n",
              "\n",
              "   hba1c_change           full_name  auralin_start  auralin_end  \\\n",
              "0          0.34     jozka resanovic           22.0         30.0   \n",
              "1           NaN  inunnguaq heilmann           57.0         67.0   \n",
              "2           NaN      alwin svensson           36.0         39.0   \n",
              "3          0.92           the luong            NaN          NaN   \n",
              "4          0.38      amanda ribeiro           36.0         44.0   \n",
              "\n",
              "   novodra_start  novodra_end  start_dose  end_dose drug_type  \n",
              "0            NaN          NaN        22.0      30.0   Auralin  \n",
              "1            NaN          NaN        57.0      67.0   Auralin  \n",
              "2            NaN          NaN        36.0      39.0   Auralin  \n",
              "3           61.0         64.0        61.0      64.0   Novodra  \n",
              "4            NaN          NaN        36.0      44.0   Auralin  "
            ],
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              "      <td>7.64</td>\n",
              "      <td>7.22</td>\n",
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              "      <td>the luong</td>\n",
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              "      <td>61.0</td>\n",
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              "  .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",
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              "      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-b2eaad52-86b1-47b3-b874-2ddaf74e8e54 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",
              "variable_name": "treatments_cut",
              "summary": "{\n  \"name\": \"treatments_cut\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze\",\n          \"jozka\",\n          \"satsita\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"wozniak\",\n          \"resanovic\",\n          \"batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 36,\n        \"samples\": [\n          \"36u - 46u\",\n          \"39u - 46u\",\n          \"51u - 59u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"55u - 51u\",\n          \"43u - 40u\",\n          \"26u - 25u\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4230071777922546,\n        \"min\": 7.51,\n        \"max\": 9.91,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          7.55,\n          7.91,\n          7.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4187058836190694,\n        \"min\": 7.02,\n        \"max\": 9.46,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          8.57,\n          7.18,\n          7.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.270719027964801,\n        \"min\": 0.28,\n        \"max\": 0.97,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.34,\n          0.92,\n          0.37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze wozniak\",\n          \"jozka resanovic\",\n          \"satsita batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.279313978803362,\n        \"min\": 22.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          28.0,\n          46.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.851381908837082,\n        \"min\": 30.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          47.0,\n          50.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.501220210260941,\n        \"min\": 20.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          61.0,\n          20.0,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.963188293299586,\n        \"min\": 21.0,\n        \"max\": 64.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          64.0,\n          39.0,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.92018875463246,\n        \"min\": 20.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          31.0,\n          34.0,\n          40.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.75016849357188,\n        \"min\": 21.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          34.0,\n          49.0,\n          44.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "treatments_cut.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "MuqE6NhryPiU"
      },
      "outputs": [],
      "source": [
        "treatments_cut.drop(['auralin','novodra'], axis = 1 ,inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "qiCqLsimyq4G"
      },
      "outputs": [],
      "source": [
        "treatments_cut['hba1c_change'] = treatments_cut['hba1c_start'] - treatments_cut['hba1c_end']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Al_-w-licz11",
        "outputId": "92240b5c-3a5a-4f89-af60-27413ec86e64"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name    surname  hba1c_start  hba1c_end  hba1c_change  \\\n",
              "0      jozka  resanovic         7.56       7.22          0.34   \n",
              "1  inunnguaq   heilmann         7.85       7.45          0.40   \n",
              "2      alwin   svensson         7.78       7.34          0.44   \n",
              "3        the      luong         7.64       7.22          0.42   \n",
              "4     amanda    ribeiro         7.85       7.47          0.38   \n",
              "\n",
              "            full_name  auralin_start  auralin_end  novodra_start  novodra_end  \\\n",
              "0     jozka resanovic           22.0         30.0            NaN          NaN   \n",
              "1  inunnguaq heilmann           57.0         67.0            NaN          NaN   \n",
              "2      alwin svensson           36.0         39.0            NaN          NaN   \n",
              "3           the luong            NaN          NaN           61.0         64.0   \n",
              "4      amanda ribeiro           36.0         44.0            NaN          NaN   \n",
              "\n",
              "   start_dose  end_dose drug_type  \n",
              "0        22.0      30.0   Auralin  \n",
              "1        57.0      67.0   Auralin  \n",
              "2        36.0      39.0   Auralin  \n",
              "3        61.0      64.0   Novodra  \n",
              "4        36.0      44.0   Auralin  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6459f254-c188-4536-ac3d-c53a28dbc29e\" 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",
              "        vertical-align: top;\n",
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              "\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>given_name</th>\n",
              "      <th>surname</th>\n",
              "      <th>hba1c_start</th>\n",
              "      <th>hba1c_end</th>\n",
              "      <th>hba1c_change</th>\n",
              "      <th>full_name</th>\n",
              "      <th>auralin_start</th>\n",
              "      <th>auralin_end</th>\n",
              "      <th>novodra_start</th>\n",
              "      <th>novodra_end</th>\n",
              "      <th>start_dose</th>\n",
              "      <th>end_dose</th>\n",
              "      <th>drug_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>jozka</td>\n",
              "      <td>resanovic</td>\n",
              "      <td>7.56</td>\n",
              "      <td>7.22</td>\n",
              "      <td>0.34</td>\n",
              "      <td>jozka resanovic</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>inunnguaq</td>\n",
              "      <td>heilmann</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.45</td>\n",
              "      <td>0.40</td>\n",
              "      <td>inunnguaq heilmann</td>\n",
              "      <td>57.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alwin</td>\n",
              "      <td>svensson</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.34</td>\n",
              "      <td>0.44</td>\n",
              "      <td>alwin svensson</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>the</td>\n",
              "      <td>luong</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.22</td>\n",
              "      <td>0.42</td>\n",
              "      <td>the luong</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>amanda</td>\n",
              "      <td>ribeiro</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.47</td>\n",
              "      <td>0.38</td>\n",
              "      <td>amanda ribeiro</td>\n",
              "      <td>36.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Auralin</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\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",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6459f254-c188-4536-ac3d-c53a28dbc29e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  @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-557ff530-b698-408e-91b3-792039b9a4e5 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",
              "variable_name": "treatments_cut",
              "summary": "{\n  \"name\": \"treatments_cut\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze\",\n          \"jozka\",\n          \"satsita\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"wozniak\",\n          \"resanovic\",\n          \"batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4230071777922546,\n        \"min\": 7.51,\n        \"max\": 9.91,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          7.55,\n          7.91,\n          7.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4187058836190694,\n        \"min\": 7.02,\n        \"max\": 9.46,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          8.57,\n          7.18,\n          7.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05599541536985921,\n        \"min\": 0.28000000000000025,\n        \"max\": 0.5100000000000007,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          0.3000000000000007,\n          0.3600000000000003,\n          0.29000000000000004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze wozniak\",\n          \"jozka resanovic\",\n          \"satsita batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.279313978803362,\n        \"min\": 22.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          28.0,\n          46.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.851381908837082,\n        \"min\": 30.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          47.0,\n          50.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.501220210260941,\n        \"min\": 20.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          61.0,\n          20.0,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.963188293299586,\n        \"min\": 21.0,\n        \"max\": 64.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          64.0,\n          39.0,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.92018875463246,\n        \"min\": 20.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          31.0,\n          34.0,\n          40.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.75016849357188,\n        \"min\": 21.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          34.0,\n          49.0,\n          44.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "treatments_cut.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "ymg3naXGcz12"
      },
      "outputs": [],
      "source": [
        "column_order = ['given_name', 'surname', 'full_name', 'auralin_start', 'auralin_end', 'novodra_start', 'novodra_end',\n",
        "       'start_dose', 'end_dose',  'hba1c_start', 'hba1c_end', 'hba1c_change','drug_type']\n",
        "treatments_cut = treatments_cut[column_order]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "XvmuU7Mmcz12",
        "outputId": "e5380e8a-cc7f-4e70-a0a4-ffba79690e38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name    surname           full_name  auralin_start  auralin_end  \\\n",
              "0      jozka  resanovic     jozka resanovic           22.0         30.0   \n",
              "1  inunnguaq   heilmann  inunnguaq heilmann           57.0         67.0   \n",
              "2      alwin   svensson      alwin svensson           36.0         39.0   \n",
              "3        the      luong           the luong            NaN          NaN   \n",
              "4     amanda    ribeiro      amanda ribeiro           36.0         44.0   \n",
              "\n",
              "   novodra_start  novodra_end  start_dose  end_dose  hba1c_start  hba1c_end  \\\n",
              "0            NaN          NaN        22.0      30.0         7.56       7.22   \n",
              "1            NaN          NaN        57.0      67.0         7.85       7.45   \n",
              "2            NaN          NaN        36.0      39.0         7.78       7.34   \n",
              "3           61.0         64.0        61.0      64.0         7.64       7.22   \n",
              "4            NaN          NaN        36.0      44.0         7.85       7.47   \n",
              "\n",
              "   hba1c_change drug_type  \n",
              "0          0.34   Auralin  \n",
              "1          0.40   Auralin  \n",
              "2          0.44   Auralin  \n",
              "3          0.42   Novodra  \n",
              "4          0.38   Auralin  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fdbf6d76-f71e-4714-81cd-6e030dc5f3ab\" 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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>given_name</th>\n",
              "      <th>surname</th>\n",
              "      <th>full_name</th>\n",
              "      <th>auralin_start</th>\n",
              "      <th>auralin_end</th>\n",
              "      <th>novodra_start</th>\n",
              "      <th>novodra_end</th>\n",
              "      <th>start_dose</th>\n",
              "      <th>end_dose</th>\n",
              "      <th>hba1c_start</th>\n",
              "      <th>hba1c_end</th>\n",
              "      <th>hba1c_change</th>\n",
              "      <th>drug_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>jozka</td>\n",
              "      <td>resanovic</td>\n",
              "      <td>jozka resanovic</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>7.56</td>\n",
              "      <td>7.22</td>\n",
              "      <td>0.34</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>inunnguaq</td>\n",
              "      <td>heilmann</td>\n",
              "      <td>inunnguaq heilmann</td>\n",
              "      <td>57.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.45</td>\n",
              "      <td>0.40</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alwin</td>\n",
              "      <td>svensson</td>\n",
              "      <td>alwin svensson</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.34</td>\n",
              "      <td>0.44</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>the</td>\n",
              "      <td>luong</td>\n",
              "      <td>the luong</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.22</td>\n",
              "      <td>0.42</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>amanda</td>\n",
              "      <td>ribeiro</td>\n",
              "      <td>amanda ribeiro</td>\n",
              "      <td>36.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.47</td>\n",
              "      <td>0.38</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdbf6d76-f71e-4714-81cd-6e030dc5f3ab')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </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",
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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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fdbf6d76-f71e-4714-81cd-6e030dc5f3ab');\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",
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              "            title=\"Suggest charts\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "\n",
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              "  }\n",
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              "    animation:\n",
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              "    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",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\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-cc6c4235-6b60-4c13-bb1c-b0566c31344c 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",
              "variable_name": "treatments_cut",
              "summary": "{\n  \"name\": \"treatments_cut\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze\",\n          \"jozka\",\n          \"satsita\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"wozniak\",\n          \"resanovic\",\n          \"batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze wozniak\",\n          \"jozka resanovic\",\n          \"satsita batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.279313978803362,\n        \"min\": 22.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          28.0,\n          46.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.851381908837082,\n        \"min\": 30.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          47.0,\n          50.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.501220210260941,\n        \"min\": 20.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          61.0,\n          20.0,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.963188293299586,\n        \"min\": 21.0,\n        \"max\": 64.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          64.0,\n          39.0,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.92018875463246,\n        \"min\": 20.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          31.0,\n          34.0,\n          40.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.75016849357188,\n        \"min\": 21.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          34.0,\n          49.0,\n          44.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4230071777922546,\n        \"min\": 7.51,\n        \"max\": 9.91,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          7.55,\n          7.91,\n          7.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4187058836190694,\n        \"min\": 7.02,\n        \"max\": 9.46,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          8.57,\n          7.18,\n          7.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05599541536985921,\n        \"min\": 0.28000000000000025,\n        \"max\": 0.5100000000000007,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          0.3000000000000007,\n          0.3600000000000003,\n          0.29000000000000004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "treatments_cut.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KsiOp4pacz12",
        "outputId": "e1fbab98-9916-4dce-e0ce-8e7174bfe19e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name    surname           full_name  auralin_start  auralin_end  \\\n",
              "0      jozka  resanovic     jozka resanovic           22.0         30.0   \n",
              "1  inunnguaq   heilmann  inunnguaq heilmann           57.0         67.0   \n",
              "2      alwin   svensson      alwin svensson           36.0         39.0   \n",
              "3        the      luong           the luong            NaN          NaN   \n",
              "4     amanda    ribeiro      amanda ribeiro           36.0         44.0   \n",
              "\n",
              "   novodra_start  novodra_end  start_dose  end_dose  hba1c_start  hba1c_end  \\\n",
              "0            NaN          NaN        22.0      30.0         7.56       7.22   \n",
              "1            NaN          NaN        57.0      67.0         7.85       7.45   \n",
              "2            NaN          NaN        36.0      39.0         7.78       7.34   \n",
              "3           61.0         64.0        61.0      64.0         7.64       7.22   \n",
              "4            NaN          NaN        36.0      44.0         7.85       7.47   \n",
              "\n",
              "   hba1c_change drug_type  \n",
              "0          0.34   Auralin  \n",
              "1          0.40   Auralin  \n",
              "2          0.44   Auralin  \n",
              "3          0.42   Novodra  \n",
              "4          0.38   Auralin  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9780e408-3304-414a-9233-a19cd6419eed\" 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",
              "        vertical-align: top;\n",
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              "\n",
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              "        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>given_name</th>\n",
              "      <th>surname</th>\n",
              "      <th>full_name</th>\n",
              "      <th>auralin_start</th>\n",
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              "      <th>novodra_start</th>\n",
              "      <th>novodra_end</th>\n",
              "      <th>start_dose</th>\n",
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              "      <th>hba1c_start</th>\n",
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              "      <th>drug_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>jozka</td>\n",
              "      <td>resanovic</td>\n",
              "      <td>jozka resanovic</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
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              "      <td>0.34</td>\n",
              "      <td>Auralin</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>inunnguaq</td>\n",
              "      <td>heilmann</td>\n",
              "      <td>inunnguaq heilmann</td>\n",
              "      <td>57.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alwin</td>\n",
              "      <td>svensson</td>\n",
              "      <td>alwin svensson</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.34</td>\n",
              "      <td>0.44</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>the</td>\n",
              "      <td>luong</td>\n",
              "      <td>the luong</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>61.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>7.64</td>\n",
              "      <td>7.22</td>\n",
              "      <td>0.42</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>amanda</td>\n",
              "      <td>ribeiro</td>\n",
              "      <td>amanda ribeiro</td>\n",
              "      <td>36.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>7.85</td>\n",
              "      <td>7.47</td>\n",
              "      <td>0.38</td>\n",
              "      <td>Auralin</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
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              "\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "treatments_cut",
              "summary": "{\n  \"name\": \"treatments_cut\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze\",\n          \"jozka\",\n          \"satsita\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"wozniak\",\n          \"resanovic\",\n          \"batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze wozniak\",\n          \"jozka resanovic\",\n          \"satsita batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.279313978803362,\n        \"min\": 22.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          28.0,\n          46.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"auralin_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.851381908837082,\n        \"min\": 30.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          47.0,\n          50.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.501220210260941,\n        \"min\": 20.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          61.0,\n          20.0,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"novodra_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.963188293299586,\n        \"min\": 21.0,\n        \"max\": 64.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          64.0,\n          39.0,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.92018875463246,\n        \"min\": 20.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          31.0,\n          34.0,\n          40.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.75016849357188,\n        \"min\": 21.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          34.0,\n          49.0,\n          44.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4230071777922546,\n        \"min\": 7.51,\n        \"max\": 9.91,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          7.55,\n          7.91,\n          7.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4187058836190694,\n        \"min\": 7.02,\n        \"max\": 9.46,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          8.57,\n          7.18,\n          7.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05599541536985921,\n        \"min\": 0.28000000000000025,\n        \"max\": 0.5100000000000007,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          0.3000000000000007,\n          0.3600000000000003,\n          0.29000000000000004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "treatments_cut.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "End-FiHtMkwI"
      },
      "outputs": [],
      "source": [
        "treatments_cut.drop(['auralin_start', 'auralin_end', 'novodra_start', 'novodra_end'], axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "NAG5TJ0Bcz12"
      },
      "outputs": [],
      "source": [
        "treatments_cut.to_csv(\"treatments_cut_cleaned.csv\",index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "u9oICwRHcz12"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "w8PuY-_ecz12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "31166a88-0b76-4540-b2a7-fa85d2177e48"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name    surname           full_name  start_dose  end_dose  \\\n",
              "0      jozka  resanovic     jozka resanovic        22.0      30.0   \n",
              "1  inunnguaq   heilmann  inunnguaq heilmann        57.0      67.0   \n",
              "2      alwin   svensson      alwin svensson        36.0      39.0   \n",
              "3        the      luong           the luong        61.0      64.0   \n",
              "4     amanda    ribeiro      amanda ribeiro        36.0      44.0   \n",
              "\n",
              "   hba1c_start  hba1c_end  hba1c_change drug_type  \n",
              "0         7.56       7.22          0.34   Auralin  \n",
              "1         7.85       7.45          0.40   Auralin  \n",
              "2         7.78       7.34          0.44   Auralin  \n",
              "3         7.64       7.22          0.42   Novodra  \n",
              "4         7.85       7.47          0.38   Auralin  "
            ],
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze\",\n          \"jozka\",\n          \"satsita\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"wozniak\",\n          \"resanovic\",\n          \"batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 70,\n        \"samples\": [\n          \"beatrycze wozniak\",\n          \"jozka resanovic\",\n          \"satsita batukayev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.92018875463246,\n        \"min\": 20.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          31.0,\n          34.0,\n          40.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.75016849357188,\n        \"min\": 21.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          34.0,\n          49.0,\n          44.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4230071777922546,\n        \"min\": 7.51,\n        \"max\": 9.91,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          7.55,\n          7.91,\n          7.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4187058836190694,\n        \"min\": 7.02,\n        \"max\": 9.46,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          8.57,\n          7.18,\n          7.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05599541536985922,\n        \"min\": 0.2800000000000002,\n        \"max\": 0.5100000000000007,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          0.3000000000000007,\n          0.3600000000000003,\n          0.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/treatments_cut_cleaned.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "MtOasMyWcz12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "587fc0e5-9c6e-41e1-ff26-cfc75edc91ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name     surname          full_name  start_dose  end_dose  \\\n",
              "0   veronika    jindrova  veronika jindrova        41.0      48.0   \n",
              "1     elliot  richardson  elliot richardson        40.0      45.0   \n",
              "2   yukitaka    takenaka  yukitaka takenaka        39.0      36.0   \n",
              "3       skye  gormanston    skye gormanston        33.0      36.0   \n",
              "4     alissa      montez      alissa montez        33.0      29.0   \n",
              "\n",
              "   hba1c_start  hba1c_end  hba1c_change drug_type  \n",
              "0         7.63       7.20          0.43   Auralin  \n",
              "1         7.56       7.09          0.47   Novodra  \n",
              "2         7.68       7.25          0.43   Novodra  \n",
              "3         7.97       7.62          0.35   Auralin  \n",
              "4         7.78       7.46          0.32   Novodra  "
            ],
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              "      <th>0</th>\n",
              "      <td>veronika</td>\n",
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              "      <td>veronika jindrova</td>\n",
              "      <td>41.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>7.63</td>\n",
              "      <td>7.20</td>\n",
              "      <td>0.43</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>elliot</td>\n",
              "      <td>richardson</td>\n",
              "      <td>elliot richardson</td>\n",
              "      <td>40.0</td>\n",
              "      <td>45.0</td>\n",
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              "      <td>7.09</td>\n",
              "      <td>0.47</td>\n",
              "      <td>Novodra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yukitaka</td>\n",
              "      <td>takenaka</td>\n",
              "      <td>yukitaka takenaka</td>\n",
              "      <td>39.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>7.68</td>\n",
              "      <td>7.25</td>\n",
              "      <td>0.43</td>\n",
              "      <td>Novodra</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>skye</td>\n",
              "      <td>gormanston</td>\n",
              "      <td>skye gormanston</td>\n",
              "      <td>33.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>7.97</td>\n",
              "      <td>7.62</td>\n",
              "      <td>0.35</td>\n",
              "      <td>Auralin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alissa</td>\n",
              "      <td>montez</td>\n",
              "      <td>alissa montez</td>\n",
              "      <td>33.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>7.78</td>\n",
              "      <td>7.46</td>\n",
              "      <td>0.32</td>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\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",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 279,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 270,\n        \"samples\": [\n          \"szilveszter\",\n          \"yasmin\",\n          \"avdei\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 272,\n        \"samples\": [\n          \"totth\",\n          \"silva\",\n          \"tikhonov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 279,\n        \"samples\": [\n          \"szilveszter totth\",\n          \"jowita wisniewska\",\n          \"nicolas ferreira\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.082609675064745,\n        \"min\": 19.0,\n        \"max\": 63.0,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          63.0,\n          38.0,\n          57.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_dose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.194579441467724,\n        \"min\": 21.0,\n        \"max\": 75.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          35.0,\n          66.0,\n          67.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_start\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5694011444392472,\n        \"min\": 7.5,\n        \"max\": 9.95,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          8.19,\n          7.58,\n          9.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.570190731154981,\n        \"min\": 7.01,\n        \"max\": 9.58,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          7.45,\n          7.19,\n          7.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hba1c_change\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05989043915172901,\n        \"min\": 0.1999999999999993,\n        \"max\": 0.54,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          0.4299999999999997,\n          0.3800000000000008,\n          0.3099999999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Novodra\",\n          \"Auralin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "df1 = pd.read_csv(\"/content/treatments_cleaned.csv\")\n",
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "1m9ylgIocz13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "772d1db2-6983-4107-808a-0b6a88dc0bbf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   patient_id given_name  surname    full_name assigned_sex      weight  \\\n",
              "0           1        Zoe  Wellish  Zoe Wellish       female   55.202146   \n",
              "1           2     Pamela     Hill  Pamela Hill       female   53.886730   \n",
              "2           3        Jae   Debord   Jae Debord         male   80.648658   \n",
              "3           4       Liem     Phan    Liem Phan         male  100.198473   \n",
              "4           5        Tim  Neudorf  Tim Neudorf         male   87.225742   \n",
              "\n",
              "   height   birthdate  age  phone_number                      email  \\\n",
              "0  1.6764  1976-07-10   43    9517199170   ZoeWellish@superrito.com   \n",
              "1  1.6764  1967-04-03   52    2175693204       PamelaSHill@cuvox.de   \n",
              "2  1.8034  1980-02-19   39    4023636804       JaeMDebord@gustr.com   \n",
              "3  1.7780  1951-07-26   68    7326368246  PhanBaLiem@jourrapide.com   \n",
              "4  0.6858  1928-02-18   91    3345157487        TimNeudorf@cuvox.de   \n",
              "\n",
              "                     address               city       state  zip_code  \\\n",
              "0       576 Brown Bear Drive  Rancho California  California     92390   \n",
              "1  2370 University Hill Road          Armstrong    Illinois     61812   \n",
              "2      1493 Poling Farm Road               York    Nebraska     68467   \n",
              "3        2335 Webster Street         Woodbridge  New Jersey      7095   \n",
              "4       1428 Turkey Pen Lane             Dothan     Alabama     36303   \n",
              "\n",
              "         country         bmi      bmi_status  \n",
              "0  United States   19.642676   Normal weight  \n",
              "1  United States   19.174609   Normal weight  \n",
              "2  United States   24.797792   Normal weight  \n",
              "3  United States   31.695499         Obesity  \n",
              "4  United States  185.459767  Severe Obesity  "
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              "      <td>185.459767</td>\n",
              "      <td>Severe Obesity</td>\n",
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              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2",
              "summary": "{\n  \"name\": \"df2\",\n  \"rows\": 498,\n  \"fields\": [\n    {\n      \"column\": \"patient_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146,\n        \"min\": 1,\n        \"max\": 503,\n        \"num_unique_values\": 498,\n        \"samples\": [\n          493,\n          74,\n          233\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 467,\n        \"samples\": [\n          \"Anco\",\n          \"Yukitaka\",\n          \"Habte\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 466,\n        \"samples\": [\n          \"Koldenhof\",\n          \"Ferreira\",\n          \"Pecinova\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 498,\n        \"samples\": [\n          \"Poldi Tar\",\n          \"Zarka Rap\",\n          \"Kyouko Ono\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assigned_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\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.458640885188474,\n        \"min\": 22.1352896,\n        \"max\": 116.0741928,\n        \"num_unique_values\": 347,\n        \"samples\": [\n          108.4538472,\n          62.1874632\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11176793953284989,\n        \"min\": 0.6858,\n        \"max\": 2.0066,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          1.6764,\n          1.6001999999999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birthdate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 493,\n        \"samples\": [\n          \"1980-10-12\",\n          \"1926-01-20\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 20,\n        \"max\": 98,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          33,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phone_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2530896794,\n        \"min\": 0,\n        \"max\": 9899364563,\n        \"num_unique_values\": 484,\n        \"samples\": [\n          7035470551,\n          7144962264\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"SunKo@einrot.com\",\n          \"TarPoldi@superrito.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"address\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 484,\n        \"samples\": [\n          \"1962 Cabell Avenue\",\n          \"3958 Liberty Avenue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 350,\n        \"samples\": [\n          \"Monroe\",\n          \"Ottawa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"Georgia\",\n          \"Washington\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30774,\n        \"min\": 0,\n        \"max\": 99701,\n        \"num_unique_values\": 431,\n        \"samples\": [\n          91505,\n          2048\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"unknown\",\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.870834603855323,\n        \"min\": 8.644436255866719,\n        \"max\": 185.45976663969796,\n        \"num_unique_values\": 473,\n        \"samples\": [\n          25.2810086055539,\n          19.49925327423888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi_status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Obesity\",\n          \"Underweight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "df2 = pd.read_csv(\"/content/patients_cleaned.csv\")\n",
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "GKmedGy2cz13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "49f48c7a-2d06-459e-da8a-2e651fdc4e01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  given_name     surname          full_name           adverse_reaction\n",
              "0      berta  napolitani   berta napolitani  injection site discomfort\n",
              "1       lena        baer          lena baer               hypoglycemia\n",
              "2     joseph         day         joseph day               hypoglycemia\n",
              "3     flavia  fiorentino  flavia fiorentino                      cough\n",
              "4    manouck     wubbels    manouck wubbels          throat irritation"
            ],
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              "      <th>given_name</th>\n",
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              "      <th>adverse_reaction</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>berta</td>\n",
              "      <td>napolitani</td>\n",
              "      <td>berta napolitani</td>\n",
              "      <td>injection site discomfort</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>lena</td>\n",
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              "      <td>lena baer</td>\n",
              "      <td>hypoglycemia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>joseph</td>\n",
              "      <td>day</td>\n",
              "      <td>joseph day</td>\n",
              "      <td>hypoglycemia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>flavia</td>\n",
              "      <td>fiorentino</td>\n",
              "      <td>flavia fiorentino</td>\n",
              "      <td>cough</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>manouck</td>\n",
              "      <td>wubbels</td>\n",
              "      <td>manouck wubbels</td>\n",
              "      <td>throat irritation</td>\n",
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              "        document.querySelector('#df-5f57f53d-e77d-4e20-a01d-6709ca8ddea9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\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",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\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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              "    }\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-926ee2e4-120c-4d72-b95d-7c0ddac3877b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df3",
              "summary": "{\n  \"name\": \"df3\",\n  \"rows\": 34,\n  \"fields\": [\n    {\n      \"column\": \"given_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          \"jia li\",\n          \"finley\",\n          \"idalia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"surname\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"nilsen\",\n          \"jakobsen\",\n          \"moore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          \"jia li teng\",\n          \"finley chandler\",\n          \"idalia moore\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adverse_reaction\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"injection site discomfort\",\n          \"hypoglycemia\",\n          \"nausea\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "df3 = pd.read_csv(\"/content/adverse_reactions_cleaned.csv\")\n",
        "df3.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "aTT6m0BScz13"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "gDqYYI3Hcz13"
      },
      "outputs": [],
      "source": []
    }
  ],
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    "kernelspec": {
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