Cardio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "86d9239e-5f3d-48d0-89fe-98546abcbd2e",
   "metadata": {},
   "source": [
    "# Exploring the risk of Cardiovascular Disease(CVD) Incidence across different Body Mass Index (BMI) Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d07c19c5-c12b-4e31-8f4d-f0357729c963",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\user\\appdata\\local\\programs\\python\\python313\\lib\\site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\user\\appdata\\local\\programs\\python\\python313\\lib\\site-packages (from pandas) (2.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\user\\appdata\\local\\programs\\python\\python313\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\appdata\\local\\programs\\python\\python313\\lib\\site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\user\\appdata\\local\\programs\\python\\python313\\lib\\site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\appdata\\local\\programs\\python\\python313\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "701bfbb6-ee63-4a4e-abc2-f166a70c6d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import altair as alt\n",
    "\n",
    "df = pd.read_csv(r'C:\\Users\\User\\OneDrive\\Documents\\Cardiohealth\\bmi_pop_data.csv') \n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "766cc170-8124-4769-b202-a801bb67f757",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert cardio_condition to boolean\n",
    "df['cardio_condition'] = df['cardio_condition'].apply(lambda x: True if x == 1 else False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f97d4a99-fa79-4991-a9b7-9a51c204e23b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age_in_years  gender  body_mass_index       bmi_class  cardio_condition\n",
      "0         50.36    male            21.97  Healthy Weight             False\n",
      "1         55.38  female            34.93           Obese              True\n",
      "2         51.63  female            23.51  Healthy Weight              True\n",
      "3         48.25    male            28.71      Overweight              True\n",
      "4         47.84  female            23.01  Healthy Weight             False\n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a0ca11b3-0d28-4885-8b94-5f9049ddcea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sorted_asc = df.sort_values('age_in_years')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "44fdc526-5eea-4afb-a0ff-51df5cd190eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69962\n"
     ]
    }
   ],
   "source": [
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "746480c3-b48c-4e0b-b585-6c850588354f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the frequency of each BMI rank/category\n",
    "bmigendergroup_counts = df.groupby(['bmi_class', 'cardio_condition', 'gender']).size().unstack(['cardio_condition', 'gender'])\n",
    "bmigroup_counts =df.groupby(['bmi_class', 'cardio_condition']).size().unstack(fill_value=0) \n",
    "bmicategory_counts = df['bmi_class'].value_counts()\n",
    "bmicategory_counts.columns = ['bmi_class', 'frequency']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "3d0802ba-95fe-4945-b463-78fc77d766ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Create the bar chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "bmicategory_counts.plot(kind='bar', color='skyblue')\n",
    "plt.title('Distribution of BMI Classes')\n",
    "plt.xlabel('BMI Category')\n",
    "plt.ylabel('Number of Participants')\n",
    "plt.show()\n",
    "\n",
    "# Display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Save the plot as a PNG image\n",
    "plt.savefig('BMIcategory_distribution_bar_chart.png')\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "a6291b29-cf1f-4805-b47c-5b1591fe2f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the bar chart\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "width = 0.35\n",
    "\n",
    "x = np.arange(len(bmigroup_counts.index))  # Corrected: Use bmi_counts.index for x-axis positions\n",
    "\n",
    "rects1 = ax.bar(x - width/2, bmigroup_counts[False], width, label='No CVD', color='skyblue')\n",
    "rects2 = ax.bar(x + width/2, bmigroup_counts[True], width, label='CVD', color='navy')\n",
    "\n",
    "# Set chart title and labels\n",
    "ax.set_title('Distribution of BMI Classes by CVD Status')\n",
    "ax.set_xlabel('BMI Category')\n",
    "ax.set_ylabel('Number of Participants')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(bmigroup_counts.index)\n",
    "ax.legend()\n",
    "\n",
    "# Add data labels on top of the bars\n",
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "a73140bc-f27b-46f9-82aa-ab855d135874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the bar chart\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "width = 0.2 \n",
    "\n",
    "# Calculate x-positions for each group\n",
    "x = np.arange(len(bmigendergroup_counts.index))\n",
    "x_male_no_cvd = x - 1.5*width\n",
    "x_male_cvd = x - 0.5*width\n",
    "x_female_no_cvd = x + 0.5*width\n",
    "x_female_cvd = x + 1.5*width\n",
    "\n",
    "# Create bars for each group\n",
    "rects1 = ax.bar(x_male_no_cvd, bmigendergroup_counts[(False, 'male')], width, label='Male - No CVD', color='skyblue')\n",
    "rects2 = ax.bar(x_male_cvd, bmigendergroup_counts[(True, 'male')], width, label='Male - CVD', color='navy')\n",
    "rects3 = ax.bar(x_female_no_cvd, bmigendergroup_counts[(False, 'female')], width, label='Female - No CVD', color='lightcoral')\n",
    "rects4 = ax.bar(x_female_cvd, bmigendergroup_counts[(True, 'female')], width, label='Female - CVD', color='maroon')\n",
    "\n",
    "# Set chart title and labels\n",
    "ax.set_title('Distribution of BMI Classes by CVD Status and Gender')\n",
    "ax.set_xlabel('BMI Category')\n",
    "ax.set_ylabel('Number of Participants')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(bmigendergroup_counts.index)\n",
    "ax.legend()\n",
    "\n",
    "# Add data labels on top of the bars\n",
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "autolabel(rects3)\n",
    "autolabel(rects4)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}