176 lines (175 with data), 4.1 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8KSBPEA9iZc1"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Initialize environment and import necessary libraries for the detailed comparison\n",
    "of top 20% vs bottom 20% diverse head and neck cancer clinical trials.\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import plotly\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U1XH-hqSieep"
   },
   "outputs": [],
   "source": [
    "# Load preprocessed datasets of top 20% and bottom 20% diverse studies\n",
    "df_top = pd.read_csv(\"t20.csv\")\n",
    "df_bottom = pd.read_csv(\"b20.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 183,
     "status": "ok",
     "timestamp": 1711119148341,
     "user": {
      "displayName": "Ojasvi Vachharajani",
      "userId": "08925121883437033531"
     },
     "user_tz": 420
    },
    "id": "i2oWqy-Fiehy",
    "outputId": "599a5439-bf8a-437b-b404-10b0393e84ee"
   },
   "outputs": [],
   "source": [
    "set(df_top.columns) == set(df_bottom.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sMugRtp8pli-"
   },
   "outputs": [],
   "source": [
    "df_top[\"success_category\"] = \"top\"\n",
    "df_bottom[\"success_category\"] = \"bottom\"\n",
    "\n",
    "df_all = pd.concat([df_top, df_bottom])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1711119288331,
     "user": {
      "displayName": "Ojasvi Vachharajani",
      "userId": "08925121883437033531"
     },
     "user_tz": 420
    },
    "id": "he-X6Ez4qCzq",
    "outputId": "b7f15feb-b680-4ca4-d754-2fd769fc0629"
   },
   "outputs": [],
   "source": [
    "df_all.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1711119330500,
     "user": {
      "displayName": "Ojasvi Vachharajani",
      "userId": "08925121883437033531"
     },
     "user_tz": 420
    },
    "id": "w4ohZwM8t3kU",
    "outputId": "033642de-9d3c-417b-f713-4ac567a3833a"
   },
   "outputs": [],
   "source": [
    "px.box(df_all, x=\"success_category\", y=\"num_participants\").update_layout(width=700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "executionInfo": {
     "elapsed": 221,
     "status": "ok",
     "timestamp": 1711119560043,
     "user": {
      "displayName": "Ojasvi Vachharajani",
      "userId": "08925121883437033531"
     },
     "user_tz": 420
    },
    "id": "fvVRp9sxt3nd",
    "outputId": "4b43d996-9082-4da0-e42b-d37264c8f0ef"
   },
   "outputs": [],
   "source": [
    "df_num_participants = pd.concat(\n",
    "    [\n",
    "        df_all[[\"success_category\", \"num_male_participants\"]].assign(sex=\"male\").rename(columns={\"num_male_participants\": \"num_participants\"}),\n",
    "        df_all[[\"success_category\", \"num_female_participants\"]].assign(sex=\"female\").rename(columns={\"num_female_participants\": \"num_participants\"}),\n",
    "    ]\n",
    ")\n",
    "\n",
    "px.box(df_num_participants, x=\"success_category\", y=\"num_participants\", color=\"sex\").update_layout(width=700)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMDSF+xer7CPEwEYBcCijkb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}