Euler-number.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "deposit = 100\n",
    "\n",
    "def calculate(n):\n",
    "    return (1 + 1/n)**n\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2.0\n",
      "2 2.25\n",
      "3 2.37037037037037\n",
      "4 2.44140625\n",
      "5 2.4883199999999994\n",
      "6 2.5216263717421135\n",
      "7 2.546499697040712\n",
      "8 2.565784513950348\n",
      "9 2.5811747917131984\n",
      "10 2.5937424601000023\n",
      "11 2.6041990118975287\n",
      "12 2.613035290224676\n",
      "13 2.6206008878857308\n",
      "14 2.6271515563008685\n",
      "15 2.6328787177279187\n",
      "16 2.6379284973666\n",
      "17 2.64241437518311\n",
      "18 2.6464258210976865\n",
      "19 2.650034326640442\n",
      "20 2.653297705144422\n",
      "21 2.656263213926108\n",
      "22 2.658969858537786\n",
      "23 2.6614501186387796\n",
      "24 2.663731258068599\n",
      "25 2.665836331487422\n",
      "26 2.6677849665337465\n",
      "27 2.6695939778125704\n",
      "28 2.6712778534408463\n",
      "29 2.6728491439808066\n",
      "30 2.6743187758703026\n",
      "31 2.6756963059146854\n",
      "32 2.676990129378183\n",
      "33 2.678207651253779\n",
      "34 2.6793554280957674\n",
      "35 2.6804392861534603\n",
      "36 2.6814644203008586\n",
      "37 2.6824354773085255\n",
      "38 2.6833566262745787\n",
      "39 2.6842316184670922\n",
      "40 2.685063838389963\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "y = []\n",
    "\n",
    "for i in range(1, 41, 1):\n",
    "    e = calculate(i)\n",
    "    print(i, e)\n",
    "    x.append(i)\n",
    "    y.append(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 4500x3000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(15, 10), dpi=300)\n",
    "plt.scatter(x, y, color='g')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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