EXP 9 -CSV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43a30f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FILE WRITE COMPLETED\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "data = [['Year', 'Sales'], ['2018', 100], ['2019', 120], ['2020', 150], ['2021', 180]]\n",
    "with open (\"data.csv\", mode='w') as file:\n",
    "    writer = csv.writer(file)\n",
    "    writer.writerows (data)\n",
    "print(\"FILE WRITE COMPLETED\")\n",
    "file.close ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96b3dfb4",
   "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 pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.read_csv('data.csv')\n",
    "x = data['Year']\n",
    "y = data['Sales']\n",
    "plt.scatter(x, y)\n",
    "plt.title('Sales Trend')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10537230",
   "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": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}