Box Plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7434846b-30ca-4c2d-861d-06da6133d7e1",
   "metadata": {},
   "source": [
    "# Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8437849f-5028-4ac0-bd2e-53ad8a60b5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9aa75f2-aa3b-4f9b-97a2-c1d72288796e",
   "metadata": {},
   "outputs": [],
   "source": [
    "one = [1, 2, 3,4,5 ,6 ,7 ,8, 9]\n",
    "two = [1,2,3,4,5,4,3,2,1]\n",
    "three = [6,7,8,9,8,7,6,5,4]\n",
    "\n",
    "data =list([one, two, three])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff7703c-3dd8-48ee-ad78-44d806462f7d",
   "metadata": {},
   "source": [
    "one, two, three were already lists but then they were again combined in a list named as data so that all three lists can be plotted in a single graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba350f1a-ebea-4fd5-ab3b-7c0d06437310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bLAAAJxxhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEjmuMLi5ptvjqampli3bl2icQCARlZ1WOzYsSNuvfXWWLp0acp5AIAGVlVYHDhwINasWRPf+9734tWvfnXqmQCABjWrmoO6u7vjsssui/e85z3x5S9/+SX3rVQqUalUJrbL5XI1SwIJ7N27N0ZGRmp2/lKpdNTftZDP52PRokU1Oz8ntkOHDsXg4ODL3r/aa7JQKERLS0umY/iXzGFx9913x65du2LHjh0va/++vr7o7e3NPBiQ1t69e2Px4sVTslZXV1dNz79nzx5xcZIaHByMZcuWZT4u6zU5MDAQnZ2dmdchY1g89dRT8YlPfCIeeOCBmD179ss6Zv369dHT0zOxXS6Xo729PduUwHF74U5Ff39/FIvFmqwxOjoaQ0ND0dHREc3NzcnPXyqVoqurq6Z3XTixFQqFGBgYeNn7V3tNFgqFasYjMobFwMBAPPvss0dV3JEjR2Lbtm3xrW99KyqVSsycOfOoY3K5XORyuTTTAsetWCzW9CexFStW1Ozc0NLSkvn6dU1OrUxh8e53vzsee+yxox67+uqro1AoxGc+85kXRQUAcHLJFBb5fD7OP//8ox575StfGa95zWte9DgAcPLxzpsAQDJV/brpv9uyZUuCMQCA6cAdCwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASCZTWGzYsCGWLl0ac+fOjblz58by5cvj/vvvr9VsAECDyRQWCxYsiJtvvjkGBgZi586d8a53vStWrVoVjz/+eK3mAwAayKwsO19++eVHbd90002xYcOG2L59eyxZsiTpYABA48kUFv/uyJEj8eMf/zgOHjwYy5cvn3S/SqUSlUplYrtcLle7ZMM4dOhQRETs2rWrZmuMjo7G0NBQdHR0RHNzc03WKJVKNTkv9dH0/OG44IwZ0fzcnoinG/PlVc3P7YkLzpgRTc8frvcowCQyh8Vjjz0Wy5cvj8OHD8ecOXNi06ZNcd555026f19fX/T29h7XkI1mcHAwIiKuv/76Ok+SRj6fr/cIJDD7wJOx64Y5EdtuiNhW72mqU4yIXTfMidKBJyPionqPAxxD0/j4+HiWA/7xj3/Ek08+GcPDw/GTn/wkvv/978fWrVsnjYtj3bFob2+P4eHhmDt37vFNf4L661//Gvfee28UCoVoaWmpyRqlUim6urqiv78/isViTdaI+FdULFq0qGbnZ+o88puH49pVb4s777wzioVCvcepSmlwMNasWRO33fe/ccH/ERYwlcrlcrS2tv7X79+Z71iccsopcc4550RExLJly2LHjh3xjW98I2699dZj7p/L5SKXy2VdpqGddtppcd11103JWsViMTo7O6dkLRrb+KzZ8cj+sRh91eKItjfVe5yqjO4fi0f2j8X4rNn1HgWYxHE/0To2NnbUHQkA4OSV6Y7F+vXrY+XKlbFw4cIYGRmJjRs3xpYtW2Lz5s21mg8AaCCZwuLZZ5+NK6+8Mp555plobW2NpUuXxubNm+OSSy6p1XwAQAPJFBa33XZbreYAAKaBxvxldgDghCQsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMlkCou+vr648MILI5/Px7x582L16tXxxBNP1Go2AKDBZAqLrVu3Rnd3d2zfvj0eeOCB+Oc//xmXXnppHDx4sFbzAQANZFaWnX/5y18etX3HHXfEvHnzYmBgIN7+9rcnHQwAaDyZwuI/DQ8PR0TEqaeeOuk+lUolKpXKxHa5XD6eJYEqHTp0KCIidu3aVbM1RkdHY2hoKDo6OqK5uTn5+UulUvJzAmlVHRZjY2Oxbt26WLFiRZx//vmT7tfX1xe9vb3VLgMkMjg4GBER119/fZ0nOX75fL7eIwCTqDosuru7Y/fu3fHQQw+95H7r16+Pnp6eie1yuRzt7e3VLgtUafXq1RERUSgUoqWlpSZrlEql6Orqiv7+/igWizVZI5/Px6JFi2pybuD4VRUWH/3oR+MXv/hFbNu2LRYsWPCS++ZyucjlclUNB6Rz2mmnxXXXXTclaxWLxejs7JyStYATS6awGB8fj4997GOxadOm2LJlS5x11lm1mgsAaECZwqK7uzs2btwY9913X+Tz+di/f39ERLS2ttbkhVoAQGPJ9D4WGzZsiOHh4XjnO98Z8+fPn/jzox/9qFbzAQANJPNTIQAAk/FZIQBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASEZYAADJCAsAIBlhAQAkIywAgGSEBQCQjLAAAJIRFgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkEzmsNi2bVtcfvnl0dbWFk1NTXHvvffWYCwAoBFlDouDBw/GG9/4xvj2t79di3kAgAY2K+sBK1eujJUrV9ZiFgCgwWUOi6wqlUpUKpWJ7XK5XOslG9KhQ4dicHDwZe9fKpWO+juLQqEQLS0tmY/j5JL1moyo/rp0TcL0UfOw6Ovri97e3lov0/AGBwdj2bJlmY/r6urKfMzAwEB0dnZmPo6TS7XXZET269I1CdNH0/j4+HjVBzc1xaZNm2L16tWT7nOsOxbt7e0xPDwcc+fOrXbpaSfrT4ejo6MxNDQUHR0d0dzcnGktPx3yclRzx6La69I1CSe+crkcra2t//X7d83vWORyucjlcrVepuG1tLRk/oltxYoVNZoGqrsmI1yXcLLzPhYAQDKZ71gcOHAg9u3bN7H9hz/8IR599NE49dRTY+HChUmHAwAaS+aw2LlzZ1x88cUT2z09PRERsXbt2rjjjjuSDQYANJ7MYfHOd74zjuP1ngDANOY1FgBAMsICAEhGWAAAyQgLACAZYQEAJCMsAIBkhAUAkIywAACSERYAQDI1/3TT//TCu3aWy+WpXhoAqNIL37f/27tvT3lYjIyMREREe3v7VC8NABynkZGRaG1tnfTfm8an+IM/xsbG4umnn458Ph9NTU1TufS0Ui6Xo729PZ566qmYO3duvceBiHBdcuJxTaYzPj4eIyMj0dbWFjNmTP5Kiim/YzFjxoxYsGDBVC87bc2dO9f/LJxwXJecaFyTabzUnYoXePEmAJCMsAAAkhEWDSqXy8UXv/jFyOVy9R4FJrguOdG4JqfelL94EwCYvtyxAACSERYAQDLCAgBIRlgAAMkIiwazbdu2uPzyy6OtrS2ampri3nvvrfdInOT6+vriwgsvjHw+H/PmzYvVq1fHE088Ue+xOMlt2LAhli5dOvHGWMuXL4/777+/3mOdFIRFgzl48GC88Y1vjG9/+9v1HgUiImLr1q3R3d0d27dvjwceeCD++c9/xqWXXhoHDx6s92icxBYsWBA333xzDAwMxM6dO+Nd73pXrFq1Kh5//PF6jzbt+XXTBtbU1BSbNm2K1atX13sUmPCXv/wl5s2bF1u3bo23v/3t9R4HJpx66qnxta99La699tp6jzKtTflnhQDT2/DwcET864s4nAiOHDkSP/7xj+PgwYOxfPnyeo8z7QkLIJmxsbFYt25drFixIs4///x6j8NJ7rHHHovly5fH4cOHY86cObFp06Y477zz6j3WtCcsgGS6u7tj9+7d8dBDD9V7FIhzzz03Hn300RgeHo6f/OQnsXbt2ti6dau4qDFhASTx0Y9+NH7xi1/Etm3bYsGCBfUeB+KUU06Jc845JyIili1bFjt27IhvfOMbceutt9Z5sulNWADHZXx8PD72sY/Fpk2bYsuWLXHWWWfVeyQ4prGxsahUKvUeY9oTFg3mwIEDsW/fvontP/zhD/Hoo4/GqaeeGgsXLqzjZJysuru7Y+PGjXHfffdFPp+P/fv3R0REa2trNDc313k6Tlbr16+PlStXxsKFC2NkZCQ2btwYW7Zsic2bN9d7tGnPr5s2mC1btsTFF1/8osfXrl0bd9xxx9QPxEmvqanpmI/ffvvtcdVVV03tMPD/XHvttfGrX/0qnnnmmWhtbY2lS5fGZz7zmbjkkkvqPdq0JywAgGS88yYAkIywAACSERYAQDLCAgBIRlgAAMkICwAgGWEBACQjLACAZIQFAJCMsAAAkhEWAEAywgIASOb/AoLBkXxoFyyWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "232e310c-97b9-4823-95d9-f644cd80349e",
   "metadata": {},
   "source": [
    "top represents the maximum value in data list item"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b67f395-2115-4658-becc-d8a18998a60d",
   "metadata": {},
   "source": [
    "yellow line represents median value in the data list item"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96629a83-f5b7-47c4-88ca-aec4483332d0",
   "metadata": {},
   "source": [
    "bottom shows minimum value in the data list item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50270679-b777-4c70-a2eb-8d4db2b7c0a8",
   "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
}