Bar Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e8e0ed2d-7e19-4d5b-93d7-d0c75a04fbc9",
   "metadata": {},
   "source": [
    "## Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b35163d8-a22e-4c3a-986c-aba9a05556b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "414360e8-7bdf-4267-8837-8063b4ce20a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "students = {\"Maheera\": 72, \"Ayesha\": 87, \"Mairal\": 55}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25932e35-0205-45d3-b220-10a6034c604b",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = list(students.keys())\n",
    "values = list(students.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1761a065-773a-4e78-8538-9fb0dbf1722d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(names, values)\n",
    "plt.title(\"Vertical Bar Plot\")\n",
    "plt.xlabel(\"Names\")\n",
    "plt.ylabel(\"Marks\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "db2e6d4e-75d8-489d-bcef-feff7c1c32c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(names, values, color = 'g')\n",
    "plt.title(\"Horizontal Bar Plot\")\n",
    "plt.xlabel(\"Names\")\n",
    "plt.ylabel(\"Marks\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f1c53c-e8c8-45f0-bb9b-c6c3d82b2241",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}