Day 32 of 75 DataSci.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19a3aa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "negative_corr = {'Hours_Work_Before_Training': [10,9,8,7,6,5,4,3,2,1],\n",
    "'Calorie_Burnage': [220,240,260,280,300,320,340,360,380,400]}\n",
    "negative_corr = pd.DataFrame(data=negative_corr)\n",
    "\n",
    "negative_corr.plot(x ='Hours_Work_Before_Training', y='Calorie_Burnage', kind='scatter')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d8fe3b0",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}