{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min value element :  2.8\n",
      "Max value element :  23.4\n",
      "median= 14.5\n",
      "standard deviation= 4.31\n",
      "standard deviation= 4.33\n",
      "Average of the list = 14.11\n"
     ]
    }
   ],
   "source": [
    "import statistics\n",
    "import numpy as np\n",
    "\n",
    "def Average(lst):\n",
    "    return sum(lst) / len(lst)\n",
    "\n",
    "mylist = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
    "print (\"min value element : \", min(mylist))\n",
    "print (\"Max value element : \", max(mylist))\n",
    "print (\"median=\", statistics.median(mylist))\n",
    "print (\"standard deviation=\", round(np.std(mylist),2))\n",
    "print (\"standard deviation=\", round(statistics.stdev(mylist),2))\n",
    "\n",
    "average = Average(mylist)\n",
    "print(\"Average of the list =\", round(average,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "mylist = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
    "an_list = [20,40,60,80,100]\n",
    "plt.plot([20,40,60,80,100],[14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n",
    "#plt.plot([0.1, 0.2, 0.3, 0.4], [1, 2, 3, 4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}