Progress_Report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9fca0d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the data for each parameter\n",
    "weeks = ['Week 1', 'Week 2', 'Week 3']\n",
    "param1_neha = [10, 0, 75]\n",
    "param1_amit = [8, 0, 100]\n",
    "\n",
    "# Create the bar chart for Parameter 1\n",
    "plt.bar(weeks, param1_neha, label='Neha')\n",
    "plt.bar(weeks, param1_amit, label='Amit')\n",
    "plt.title('Parameter 1: Efforts put into completing projects')\n",
    "plt.xlabel('Weeks')\n",
    "plt.ylabel('Score')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df0e5e4f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}