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      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis Tutorial for OMOP Dataset Extract"
      ],
      "metadata": {
        "id": "IOXDFOyvkxQK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory Data Analysis** (EDA) is the process of analyzing and summarizing the main characteristics of a dataset, often using visual methods. It’s an essential step in data analysis that helps to understand the data's structure, detect patterns, identify anomalies, test hypotheses, and determine relationships among variables."
      ],
      "metadata": {
        "id": "1KTdvYGGWEj7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setting up the enviroment"
      ],
      "metadata": {
        "id": "yo0CnaRJYDFH"
      }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nnoY2RqRVv0R"
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      "source": [
        "# Importing all the libraries\n",
        "import pandas as pd  #Data Manipulation\n",
        "import numpy as np   # Numerical Analysis\n",
        "import matplotlib.pyplot as plt  # Data Visualization\n",
        "import seaborn as sns  # Data Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the dataset\n",
        "The dataset that we will be using in this tutorial is the list of all patients suffering from COVID-19 in the OMOP database and their demographic data.\n",
        "\n"
      ],
      "metadata": {
        "id": "UIiZkd2alXEj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace '/content/COVID-19-BMI.csv' with the actual path to your dataset\n",
        "df = pd.read_csv('/content/COVID-19-BMI.csv')\n",
        "\n",
        "# Display the first few rows of the dataset to get a glimpse of its content\n",
        "df.head()"
      ],
      "metadata": {
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          "height": 206
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        "id": "x6hTA-_eYT0E",
        "outputId": "a37ae83d-dd1e-4548-cc37-e160b6f3c59f"
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      "execution_count": null,
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              "   person_id gender   race  condition_concept_id condition_start_date  age  \\\n",
              "0          1      F  white              37311061           2020-03-11   62   \n",
              "1          2      F  white              37311061           2020-03-02   75   \n",
              "2          3      M  white              37311061           2020-03-14   52   \n",
              "3          5      F  white              37311061           2020-03-10   32   \n",
              "4          6      F  white              37311061           2020-02-12   44   \n",
              "\n",
              "   weight  height   bmi  \n",
              "0    71.3   160.8  27.6  \n",
              "1    87.7   169.5  30.5  \n",
              "2    91.9   177.2  29.4  \n",
              "3    74.4   158.1  29.8  \n",
              "4    71.0     NaN   NaN  "
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              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 88166,\n  \"fields\": [\n    {\n      \"column\": \"person_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35868,\n        \"min\": 1,\n        \"max\": 124150,\n        \"num_unique_values\": 88166,\n        \"samples\": [\n          17609,\n          35990,\n          53330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"black\",\n          \"other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition_concept_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 37311061,\n        \"max\": 37311061,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          37311061\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition_start_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"2020-02-12\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 0,\n        \"max\": 111,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.063568320792292,\n        \"min\": 1.8,\n        \"max\": 181.2,\n        \"num_unique_values\": 1374,\n        \"samples\": [\n          72.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.80676944776194,\n        \"min\": 45.1,\n        \"max\": 198.7,\n        \"num_unique_values\": 1405,\n        \"samples\": [\n          61.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.871769110883569,\n        \"min\": 9.4,\n        \"max\": 60.1,\n        \"num_unique_values\": 374,\n        \"samples\": [\n          47.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the number of rows and columns of your dataset\n",
        "df.shape"
      ],
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        },
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        {
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          "data": {
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              "(88166, 9)"
            ]
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          "metadata": {},
          "execution_count": 73
        }
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    {
      "cell_type": "code",
      "source": [
        "# Provides a concise summary of the DataFrame, including data types and non-null counts.\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X59bXEI5YtpE",
        "outputId": "a97c6b3c-9e07-4811-da4d-d2fac3977c4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 88166 entries, 0 to 88165\n",
            "Data columns (total 9 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   person_id             88166 non-null  int64  \n",
            " 1   gender                88166 non-null  object \n",
            " 2   race                  88166 non-null  object \n",
            " 3   condition_concept_id  88166 non-null  int64  \n",
            " 4   condition_start_date  88166 non-null  object \n",
            " 5   age                   88166 non-null  int64  \n",
            " 6   weight                88166 non-null  float64\n",
            " 7   height                74408 non-null  float64\n",
            " 8   bmi                   72352 non-null  float64\n",
            "dtypes: float64(3), int64(3), object(3)\n",
            "memory usage: 6.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling missing values\n",
        "Handling missing values involves identifying and addressing gaps in the dataset by either imputing values, removing affected records, or using algorithms that can handle missing data to maintain data quality and accuracy during analysis."
      ],
      "metadata": {
        "id": "R3vpADNpeyyx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find count of null values in all the columns\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "oN8kBQGuaQR6",
        "outputId": "9029b80b-2e23-4da5-8b86-1a9d0f835af7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "person_id                   0\n",
              "gender                      0\n",
              "race                        0\n",
              "condition_concept_id        0\n",
              "condition_start_date        0\n",
              "age                         0\n",
              "weight                      0\n",
              "height                  13758\n",
              "bmi                     15814\n",
              "dtype: int64"
            ],
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              "      <td>0</td>\n",
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              "      <th>race</th>\n",
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              "      <th>condition_concept_id</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>condition_start_date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>height</th>\n",
              "      <td>13758</td>\n",
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              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>15814</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop columns that have all values as null\n",
        "df.dropna(axis=1, how='all', inplace=True)"
      ],
      "metadata": {
        "id": "miwMtm3maeIq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Since we need both height and weight values to calculate the BMI value, we will remove all the records that have height missing\n",
        "df.dropna(subset=['height'], inplace=True)"
      ],
      "metadata": {
        "id": "QjFra1-zDcdQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x4Tx4CTTavcL",
        "outputId": "5b2f787c-84e4-4e00-972d-de622ff56044"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(74408, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Replace the null values in the BMI column using the BMI formula.\n",
        "df['bmi'] = df['bmi'].fillna(round(df['weight'] / ((df['height'] / 100) ** 2), 1))"
      ],
      "metadata": {
        "id": "IT9MVAbTD2Ch"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "E_ZS0nE8EH2U",
        "outputId": "951b86e0-cbb0-468e-f2ef-efed13493f5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "person_id               0\n",
              "gender                  0\n",
              "race                    0\n",
              "condition_concept_id    0\n",
              "condition_start_date    0\n",
              "age                     0\n",
              "weight                  0\n",
              "height                  0\n",
              "bmi                     0\n",
              "dtype: int64"
            ],
            "text/html": [
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              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "      <th>condition_concept_id</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>condition_start_date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>0</td>\n",
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              "      <th>height</th>\n",
              "      <td>0</td>\n",
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          },
          "metadata": {},
          "execution_count": 90
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No records are dropped since there are no null values in the dataset."
      ],
      "metadata": {
        "id": "o6rEH3MnfWXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Statistical Summary for all the numerical columns in the dataset\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1YYdfbfn_0ta",
        "outputId": "1d018f8b-cc75-41cd-e431-06dddab96976"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           person_id  condition_concept_id           age        weight  \\\n",
              "count   74408.000000               74408.0  74408.000000  74408.000000   \n",
              "mean    62182.400844            37311061.0     42.894487     71.418606   \n",
              "std     35896.702891                   0.0     25.114759     24.169582   \n",
              "min         1.000000            37311061.0      0.000000      1.800000   \n",
              "25%     31086.750000            37311061.0     20.000000     64.100000   \n",
              "50%     62211.500000            37311061.0     45.000000     77.300000   \n",
              "75%     93303.250000            37311061.0     62.000000     87.000000   \n",
              "max    124150.000000            37311061.0    111.000000    181.200000   \n",
              "\n",
              "             height           bmi  \n",
              "count  74408.000000  74408.000000  \n",
              "mean     160.535589     26.214993  \n",
              "std       24.806769      4.970352  \n",
              "min       45.100000      6.000000  \n",
              "25%      157.600000     23.600000  \n",
              "50%      166.400000     27.800000  \n",
              "75%      174.800000     29.400000  \n",
              "max      198.700000     60.100000  "
            ],
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              "      <th>mean</th>\n",
              "      <td>62182.400844</td>\n",
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              "      <td>71.418606</td>\n",
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              "      <td>24.169582</td>\n",
              "      <td>24.806769</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>1.800000</td>\n",
              "      <td>45.100000</td>\n",
              "      <td>6.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>64.100000</td>\n",
              "      <td>157.600000</td>\n",
              "      <td>23.600000</td>\n",
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              "      <td>77.300000</td>\n",
              "      <td>166.400000</td>\n",
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              "      <td>93303.250000</td>\n",
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              "      <td>87.000000</td>\n",
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              "      <td>181.200000</td>\n",
              "      <td>198.700000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"person_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38650.60729387278,\n        \"min\": 1.0,\n        \"max\": 124150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          62182.40084399527,\n          62211.5,\n          74408.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition_concept_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17254454.46320583,\n        \"min\": 0.0,\n        \"max\": 37311061.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          74408.0,\n          37311061.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26291.765760088645,\n        \"min\": 0.0,\n        \"max\": 74408.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          42.89448715191915,\n          45.0,\n          74408.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26281.64679193525,\n        \"min\": 1.8,\n        \"max\": 74408.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          71.4186055262875,\n          77.3,\n          74408.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26260.40860886891,\n        \"min\": 24.80676944776194,\n        \"max\": 74408.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          160.5355889151704,\n          166.4,\n          74408.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26298.211514988816,\n        \"min\": 4.970352212412843,\n        \"max\": 74408.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          26.21499301150414,\n          27.8,\n          74408.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Visualization\n",
        "\n",
        "Data visualization is the practice of transforming data into visual formats, such as charts, graphs, and maps, to make information more accessible and understandable. It is crucial in data analysis because it helps identify trends, patterns, and insights at a glance, facilitating clearer communication and more informed decision-making."
      ],
      "metadata": {
        "id": "h59kzOQhCMxo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Distribution of Age\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.histplot(df['age'], kde=True, bins=30, color='skyblue')\n",
        "plt.title('Distribution of Age')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "Dm-pjgzybBjQ",
        "outputId": "1f419d06-cfa9-4402-e7c4-7eb39e692a8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Distribution of Weight\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.histplot(df['weight'], kde=True, bins=30, color='green')\n",
        "plt.title('Distribution of Weight')\n",
        "plt.xlabel('Weight (kg)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "DuYpqrGm-EVO",
        "outputId": "63af610d-accc-471f-f274-76b4dbc4474e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Gender Distribution\n",
        "plt.figure(figsize=(5, 3))\n",
        "gender_count = df['gender'].value_counts()\n",
        "sns.barplot(x=gender_count.index, y=gender_count.values, palette='pastel')\n",
        "plt.title('Gender Distribution')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Number of Patients')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GwbfHO6P-HYe",
        "outputId": "775fbabb-93c5-4638-8641-f9cecb388faa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-84-536bf44b70a7>:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=gender_count.index, y=gender_count.values, palette='pastel')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Race Distribution\n",
        "plt.figure(figsize=(5, 3))\n",
        "race_count = df['race'].value_counts()\n",
        "sns.barplot(x=race_count.index, y=race_count.values)\n",
        "plt.title('Race Distribution')\n",
        "plt.xlabel('Race')\n",
        "plt.ylabel('Number of Patients')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "BLmPVSz7-J6U",
        "outputId": "f7be9a80-abb4-4c3a-9105-99fc41154650"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Boxplot of Age by Gender\n",
        "plt.figure(figsize=(5, 3))\n",
        "sns.boxplot(x='gender', y='age', data=df)\n",
        "plt.title('Age Distribution by Gender')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Age')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "CNV9azza_fuh",
        "outputId": "0455cd72-97c4-4887-b080-12e5c2633146"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8. Correlation heatmap between numerical features (age, weight)\n",
        "plt.figure(figsize=(4, 3))\n",
        "correlation_matrix = df[['age', 'weight','height','bmi']].corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='Blues')\n",
        "plt.title('Correlation Heatmap between Age, Weight, Height and BMI')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "zVNcRB8SA8Bp",
        "outputId": "d48918e2-06ff-44f7-95be-2c994efca048"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}