Data Visualisation-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "### Draw a line in a diagram from position (0,10) to position (5,150):\n",
    "xpoints = np.array([1,2])\n",
    "ypoints = np.array([5,10])\n",
    "\n",
    "plt.plot(xpoints, ypoints)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "\n",
    "xpoints = np.array([1,20])\n",
    "ypoints = np.array([10,200])\n",
    "plt.plot(xpoints, ypoints)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAANbUlEQVR4nO3db4yl9VmH8evrbhssZcXIlCDLOppANTFC8UhrNlZahJZdQjXxBVFKJCabTUizYgx/TNQY39QXyrZpyrpZtG1obUwBrYBYUlzRNGhmCuVPl1BCKWwWs4OtVegLA9y+mAMZZufPmeXMHufe65NM9sx5fvM893mx1z77zDl5UlVIkja+H5r0AJKk8TDoktSEQZekJgy6JDVh0CWpic2TOvAZZ5xR09PTkzq8JG1Is7OzL1bV1FLbJhb06elpZmZmJnV4SdqQknxnuW1ecpGkJgy6JDVh0CWpCYMuSU0YdElqYqSgJ3k2yWNJHklyzFtTMu+TSZ5O8miSC8c/qiT1cMv9T63Lftdyhv6BqrqgqgZLbLscOHf4tQu4dRzDSVJHn/jqt9Zlv+O65PIR4HM17yHg9CRnjWnfkqQRjPrBogK+kqSAv6iq/Yu2nw08v+D7w8PnXli4KMku5s/g2bZt23ENLEkb0S33P/WmM/Ppm+4BYM8l53L9peeN5RijBn17VR1J8i7g/iRPVtWDC7ZniZ855s4Zw38I9gMMBgPvrCHppHH9pee9Ee7pm+7h2Y/vHPsxRrrkUlVHhn8eBe4CLlq05DBwzoLvtwJHxjGgJGk0qwY9yalJTnv9MXAZ8PiiZV8Grhm+2+V9wPer6gUkScfYc8m567LfUS65nAncleT19V+oqvuS7Aaoqn3AvcAO4GngB8C16zKtJDUwrmvmi60a9Kp6Bjh/ief3LXhcwHXjHU2StBZ+UlSSmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqYuSgJ9mU5OEkdy+x7UeS/H2SbyR5Ism14x1TkrSatZyh7wEOLbPtOuCbVXU+cDHwZ0ne/hZnkyStwUhBT7IV2AkcWGZJAaclCfBO4LvAK2OZUJI0klHP0PcCNwCvLbP9U8DPAEeAx4A9VXXM2iS7kswkmZmbmzuOcSVJy1k16EmuAI5W1ewKyz4EPAL8OHAB8KkkWxYvqqr9VTWoqsHU1NTxTSxJWtIoZ+jbgSuTPAt8EfhgktsXrbkWuLPmPQ18G/jpsU4qSVrRqkGvqpuramtVTQNXAQ9U1dWLlj0HXAKQ5Ezg3cAzY55VkrSCzcf7g0l2A1TVPuBPgM8keQwIcGNVvTieESVJo1hT0KvqIHBw+HjfguePAJeNczBJ0tr4SVFJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDUxctCTbErycJK7l9l+cZJHkjyR5J/HN6IkaRSb17B2D3AI2LJ4Q5LTgU8DH66q55K8azzjSZJGNdIZepKtwE7gwDJLfgO4s6qeA6iqo+MZT5I0qlEvuewFbgBeW2b7ecCPJjmYZDbJNUstSrIryUySmbm5ubVPK0la1qpBT3IFcLSqZldYthn4eebP4j8E/EGS8xYvqqr9VTWoqsHU1NTxzixJWsIo19C3A1cm2QGcAmxJcntVXb1gzWHgxap6GXg5yYPA+cBTY59YkrSkVc/Qq+rmqtpaVdPAVcADi2IO8HfALyXZnOQdwHuZ/wWqJOkEWcu7XN4kyW6AqtpXVYeS3Ac8yvx19gNV9fiYZpQkjSBVNZEDDwaDmpmZmcixJWmjSjJbVYOltvlJUUlqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJamJkYOeZFOSh5PcvcKaX0jyapJfH894kqRRreUMfQ9waLmNSTYBfwr841sdSpK0diMFPclWYCdwYIVlHwPuAI6OYS5J0hqNeoa+F7gBeG2pjUnOBn4N2LfSTpLsSjKTZGZubm4tc0qSVrFq0JNcARytqtkVlu0FbqyqV1faV1Xtr6pBVQ2mpqbWNqkkaUWbR1izHbgyyQ7gFGBLktur6uoFawbAF5MAnAHsSPJKVf3tuAeWJC1t1aBX1c3AzQBJLgZ+b1HMqaqffP1xks8AdxtzSTqxjvt96El2J9k9zmEkScdvlEsub6iqg8DB4eMlfwFaVb/1VoeSJK2dnxSVpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITIwc9yaYkDye5e4ltv5nk0eHX15KcP94xJUmr2byGtXuAQ8CWJbZ9G/jlqvpeksuB/cB7xzCfJGlEI52hJ9kK7AQOLLW9qr5WVd8bfvsQsHU840mSRjXqJZe9wA3AayOs/W3gH5bakGRXkpkkM3NzcyMeWpI0ilWDnuQK4GhVzY6w9gPMB/3GpbZX1f6qGlTVYGpqas3DSpKWN8o19O3AlUl2AKcAW5LcXlVXL1yU5OeYvyRzeVX95/hHlSStZNUz9Kq6uaq2VtU0cBXwwBIx3wbcCXy0qp5al0klSStay7tc3iTJboCq2gf8IfBjwKeTALxSVYOxTChJGkmqaiIHHgwGNTMzM5FjS9JGlWR2uRNmPykqSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNTFy0JNsSvJwkruX2JYkn0zydJJHk1w43jHf7Jb7n1rP3UvShrSWM/Q9wKFltl0OnDv82gXc+hbnWtEnvvqt9dy9JG1IIwU9yVZgJ3BgmSUfAT5X8x4CTk9y1phmlCSNYPOI6/YCNwCnLbP9bOD5Bd8fHj73wsJFSXYxfwbPtm3b1jInt9z/1JvOzKdvugeAPZecy/WXnremfUlSR6sGPckVwNGqmk1y8XLLlniujnmiaj+wH2AwGByzfSXXX3reG+Gevukenv34zrX8uCS1N8oll+3AlUmeBb4IfDDJ7YvWHAbOWfD9VuDIWCaUJI1k1aBX1c1VtbWqpoGrgAeq6upFy74MXDN8t8v7gO9X1QuL9zUuey45d712LUkb1qjX0I+RZDdAVe0D7gV2AE8DPwCuHct0y/CauSQda01Br6qDwMHh430Lni/gunEOJklaGz8pKklNGHRJasKgS1ITBl2Smsj87zMncOBkDvjOcf74GcCLYxxnI/A1nxx8zSeHt/Kaf6KqppbaMLGgvxVJZqpqMOk5TiRf88nB13xyWK/X7CUXSWrCoEtSExs16PsnPcAE+JpPDr7mk8O6vOYNeQ1dknSsjXqGLklaxKBLUhMbKuhJ/jLJ0SSPT3qWEyXJOUn+KcmhJE8k2TPpmdZbklOS/HuSbwxf8x9PeqYTYaUbsXeV5NkkjyV5JMnMpOdZb0lOT/KlJE8O/07/4lj3v5GuoSd5P/AS8/cv/dlJz3MiDO/NelZVfT3JacAs8KtV9c0Jj7ZukgQ4tapeSvI24F+BPcP71baV5HeBAbClqq6Y9DwnwvDGOYOqOik+WJTks8C/VNWBJG8H3lFV/zWu/W+oM/SqehD47qTnOJGq6oWq+vrw8f8Ah5i/X2tbw5uNvzT89m3Dr41z5nEcRrgRuza4JFuA9wO3AVTV/44z5rDBgn6ySzINvAf4twmPsu6Glx8eAY4C91dV99e8l/kbsb824TlOtAK+kmR2eBP5zn4KmAP+anhp7UCSU8d5AIO+QSR5J3AH8DtV9d+Tnme9VdWrVXUB8/envShJ20tsC2/EPulZJmB7VV0IXA5cN7ys2tVm4ELg1qp6D/AycNM4D2DQN4DhdeQ7gM9X1Z2TnudEGv6X9CDw4clOsq5GuRF7S1V1ZPjnUeAu4KLJTrSuDgOHF/xv80vMB35sDPr/c8NfEN4GHKqqP5/0PCdCkqkkpw8f/zDwK8CTEx1qHY14I/Z2kpw6/EU/w0sPlwFt38FWVf8BPJ/k3cOnLgHG+uaG475J9CQk+WvgYuCMJIeBP6qq2yY71brbDnwUeGx4TRng96vq3smNtO7OAj6bZBPzJx1/U1UnzVv5TiJnAnfNn7OwGfhCVd032ZHW3ceAzw/f4fIMcO04d76h3rYoSVqel1wkqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJv4PIddhgnbUzPMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpoints = np.array([1, 6])\n",
    "ypoints = np.array([4,5])\n",
    "plt.plot(xpoints,ypoints,'+')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypoints = np.array([3, 8, 1,12,7,9])\n",
    "\n",
    "plt.plot(ypoints,marker='*')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(ypoints, marker = 'X')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAe6UlEQVR4nO3deZRU5Z3/8fe3sYVGNlmUVQmOiqgoiEZjDA6IC1lMZnQ0amI0yrgQxyTq0TGJgyZzfi4xiKCGiTFmMCgRgpFBDVHRmHGhRQ0YMCgYZG+Ufaf7+f3x7R6waZrqruW5t+rzOqdOVXVX1/1cbvPtW899FgshICIi6VMWO4CIiDSPCriISEqpgIuIpJQKuIhISqmAi4ik1H6F3Fjnzp1D7969C7lJEZHUe/PNN1eHELrU/3pBC3jv3r2prKws5CZFRFLPzP7e0NfVhCIiklIq4CIiKaUCLiKSUirgIiIppQIuIpJS+yzgZvZLM1tlZnN3+1pHM5thZgtq7w/Mb0wRkRRbvhwGD4YVK3L6tpmcgf8KOLve124Gng8hHA48X/tcREQacscd8MorcPvtOX3bfRbwEMLLwCf1vnwu8Gjt40eBr+Y0lYhIMaioADN48EGoqfF7M/96DjS3DfzgEMJygNr7g/b2QjMbYWaVZlZZVVXVzM2JiKTQwoVw0UXQurU/b90aLr4YFi3Kydvn/SJmCGF8CGFQCGFQly57jAQVESlebdvC9u2wdSu0auX37dpB1645efvmFvCVZtYNoPZ+VU7SiIgUkwcegCefhAsvhNdeg6uuyumFzObOhfJ74FLg/9XeP5WzRCIixWLECD/b/uY3/fm4cTl9+0y6EU4EXgWONLMlZvZtvHAPM7MFwLDa5yIisrsOHXYV7zzY5xl4COHre/nW0BxnEREpDh9/DJddBv/5n3DMMXnbjEZiiojk2vz5MGtW3jdT0PnARURKwqmnwuLFUF6e183oDFxEJJfmzYMQ8l68QQVcRCR3li2DgQN96HwBqAlFRCRXunSB+++HoYXp46ECLiKSK+XlcMUVBducmlBERHLhxz/2UZcFpAIuIpKtHTtg8mR4+eWCblZNKCIi2Sovh8pKn6yqgHQGLiKSjdWrYds2aNECDjigoJtWARcRycbIkXDCCVBdXfBNqwlFRCQbl1/uoy5btCj4plXARUSyceaZ0TatJhQRkeaYPRvuugu2bIkWQQVcRKQ5nn7aC/iOHdEiqICLiDTHbbfBu+/6GpeRqICLiDTVunV+f/DBUWOogIuINMWLL0KvXvDqq7GTqICLiDRJjx5wwQUwYEDsJOpGKCLSJEccAf/1X7FTADoDFxHJTAhw552+aENCqICLiGRizhz4wQ/gmWdiJ/k/akIREclE//7w3nt+ATMhdAYuIrIv27b5fZ8+BVmsOFMq4CIijamuhhNPhFGjYifZgwq4iEhjtm2DYcO8CSVh1AYuItKY1q3hpz+NnaJBOgMXEdmbP/4R3nkndoq90hm4iEhDQoAbb4T994fXXgOz2In2oAIuItIQM5/3ZMWKRBZvUAEXEdlTTQ2UlUGHDn5LKLWBi4jUd//9MGQIbNwYO0mjVMBFROpr2xa6doU2bWInaZQKuIhIfZdfDr/5TewU+6QCLiJSZ/16mDbNe6CkQFYF3My+a2bvmtlcM5toZq1yFUxSYPlyGDzYr9KLFIOHH4Yvfxnmzo2dJCPNLuBm1gO4DhgUQjgGaAFcmKtgkgJ33AGvvAK33x47iUhujBwJzz4Lxx4bO0lGsm1C2Q+oMLP9gNZAcmY6l/ypqPB+sQ8+6N2tHnzQn1dUxE4mkp3ycjjrrNgpMtbsAh5CWArcAywGlgPrQgh/qP86MxthZpVmVllVVdX8pJIcf/ubz85WV7Bbt4aLL4ZFi+LmEmmuqir/nf7zn2MnaZJsmlAOBM4FPgN0Bw4ws0vqvy6EMD6EMCiEMKhLly7NTyrJ8frrMGsWbN0KLVrAli3Qrp13uxJJo+XLYft26NgxdpImyWYk5hnAohBCFYCZTQE+B0zIRTBJsPPO84uX/frB7Nn+i68LmZJm/fvD228ndsj83mRTwBcDJ5tZa2ALMBSozEkqSaaHHoIzz/RVSWbO9K/t2JGoFUpEmuyVV7z5pGXL2EmaLJs28NeBJ4HZwJza9xqfo1ySNKtXw623wpgxn/56XfGePx+mTy98LpFsrFgBQ4f6YsUpZKGAHdYHDRoUKit1kp5aCxf6gq4NnXGfdRa8/74v+rqf5kiTlAgBZsyAvn3hkENip9krM3szhDCo/tf1P00a99xz8NFHcMUV3nSyN+PH+0dQFW9JEzNvFkwpDaWXxv3iFzBunF+obMyhh3ovlBC84Isk3fe/v2eTYMqogEvjHnvMP2Luv39mr7/tNjjuOFi5Mr+5RLJRXe3jGT78MHaSrOjzruzpww/hRz+CBx7w6TQ7d878Zy+6yKfi7NQpb/FEstaiBTz9tBfyFFMBlz3NmuXzQSxZ4hd3mqJv310/E0Lq+tVKCfjoIx893KmTF/IUUxOK7On8871HSVOL9+7eeANOPdWHKIskyXe/C8cf72MYUk4FXNzmzXDuubvmgmjXLrv3a90a1q6FZZrfTBJm1Ci4996iGICmJhRxa9Z4H+4lS3Lzfscc43Mql+kcQRLm6KP9VgT0v6vUheC3Hj3gL3+BCy7I3XuXlflFojFj4OOPc/e+Is3x+us+3/eaNbGT5IwKeCkLAa6/Hm680R9n2lWwKd57z/vbTtAcZxLZrFnwu98VRdNJHTWhlLK6s++amvxto18/n+WtX7/8bUMkEyNH+mLFrVvHTpIzKuClqm4Wwfvu8+f57O5X1964cqUPt+/QIX/bEqkvBFi82EcLF1HxBjWhlKbHH4eBA30mNrPC9NXesMHnXL7xxvxvS2R3zz/v8/jMmBE7Sc7pDLwUde0KvXtD+/aF22bbtvCTn3jfcJFC6t/fp4v9whdiJ8k5TSdbSjZu9KHxSaBRmiIZ29t0smpCKRV//at/jJw6NW6OEHwk3LXXxs0hxS8E+N73YM6c2EnyRgW8VPTsCWecAQMGxM1h5hdPy8v9P5hIvixYAI884uu2Fik1oRS7jz/2tu4kLbSg5hMplLVrvdkwSb//zaAmlFK0bRsMGQKXXho7yafVFe/58+H+++NmkeL0ySd+36FD6ot3Y1TAi1nLljBiBHzrW7GTNGz8eLj99l3/2URyYedOOOUUuOaa2EnyTk0oxWjHDli61LsKJtmmTd4z5uCDYyeRYrJ9uy9GcuSRcM45sdPkhJpQSsn3vgef/Wzyz2wPOMCLdwjw1lux00ix2H9/n+OnSIp3Y1TAi9F3vuNrU3bsGDtJZsaPhxNOgDffjJ1E0m7qVJg2rWR6OBVv634pmjULTjwRjjjCb2lx0UXebnn88bGTSNrdd59fvP/iF2MnKQidgReLadPgpJPgqadiJ2m6tm19YE+LFqlfZFYi+8Mf4Le/LZluqirgxeLss2Hs2HSfebz7rk87+8YbsZNI2mzf7n/8y8t9cZISoQKedq+8AuvWeV/Xa69Nd5/Xnj2he3edhUvTjRnj0xYX0Wo7mVABT7M1a/yM+/rrYyfJjfbt4cUXvQ+vSFP06+dTRRx4YOwkBZXi0zXhwANhyhQ49tjYSXJr50742c9g6FCft1xkX4YP91uJ0Rl4Gn3wAbz0kj8eOhQOOihunlzbuNEL+BNPxE4iSbd2LTz0kPc8KUE6A0+j66/3gS/vvw+tWsVOk3sdOkBlJXTrFjuJJN2kSXD11d7sdtxxsdMUnAp4Gv3qV7BkSXEW7zrdu/v9ypWwfj0cfnjcPJJMV17pYx9KsHiDmlDSY9Mmb1aoqYFOnUrjF7amxmdT/Na3SmZknTRBdbX39449x31EKuBp8cQTcMMNpdVHuqzMp5t9+OGSGZghGVq50leY+p//iZ0kKjWhpMXll/t8IaVw5r27IUN2Pd6+3ScqEtm4EY45puSb1rI6AzezDmb2pJnNN7N5ZqYOvLkUAvz4x/Dhh/681Ir37n70Ixg8WIN8xB12mJ99p2nOnzzItgnlPuDZEEJf4DhgXvaR5P8sXgw//Sk89ljsJPH16+dzvezYETuJxDZ1qi8VKM1f0MHM2gHvAH1Chm+iBR2aYfFi6NVLbcAi4HPcd+/uK02NGRM7TcHkY0GHPkAV8IiZvWVmvzCzAxrY8AgzqzSzyqqqqiw2V0ImTPDVtAEOOUTFe3d//SuMHKmmlFLVsaPPG3/LLbGTJEI2BXw/YCDwYAhhALAJuLn+i0II40MIg0IIg7p06ZLF5kpECPD4495sUlMTO03yVFbCxImwYEHsJFJodR/0jz5ag7xqZdMLZQmwJITweu3zJ2mggEsTmcHkyd7joky9PPfwjW/Al76UntWGJHeuvNLHQNx5Z+wkidHsChFCWAF8ZGZH1n5pKPDXnKQqRW+9BRdc4AN2Wrb0RQ5kT2ZevEOA557Tp5RSEYJ3IS0vj50kUbLtB/4d4DEz2x9YCFyWfaQSNXeuD9JZs8YX+5XG/eEPvojFxIlw4YWx00i+mflK8/Ipze6F0hzqhdKAEHZdpNyyBSoq4uZJixB8IqPzzvOl2KR4LVjgx7uE+3znoxeKZOuTT+D00+G11/y5infmzLzJqUUL2LxZc6UUs1tvhc99DrZujZ0kcVTAY9qwwQckbNkSO0l6LV3qC1o8/HDsJJIvY8d6U1kxz77ZTJoLJYbqaj9zPPRQeOcdNQFko1s3/xRz5JH7fKmk1EEHwbBhsVMkks7AC626Gr7+dbi5tselind2ysr87Pu002InkVz785/hK1+BZctiJ0ksFfAYOnWCzp1jpygu1dVw112aN6aYLF7sFzA7dIidJLHUC6WQtm71drzde55IbtTU+GyFRxyh9vBiUlOjAW2oF0p8o0f7bHoff6zinQ9lZfDMM/CLX8ROItkKAWbN8scq3o3Sv06hHHusL8igj4P506aN/3FcsQJeeil2GmmuGTP8ZGfq1NhJEk+9UPLtk0986PfQoX6T/Pv2t713z8KFWsEnjb7wBXjoIRg+PHaSxNMZeD69+ir07g3PPhs7SWkZPRr++EcV77Rq1Qr+9V91/DKgAp5PRx0F558PJ54YO0lpOfxw6NvXH69dGzWKNEFNDVxyCbzwQuwkqaECng9Ll3q3tg4dvEdEp06xE5Wm0aN9gI8WEkmHJUv8U+uKFbGTpIbawHNt/Xo49VQ46yz4+c9jpyltZ5zhfYlbt46dRDJxyCEwf756njSBCniutWsHN9zgk+9IXMccA/feGzuFZGLhQi/gmu+7SfSnLlc2bPBfQvA1GwcOjJtHdnnvPTj3XO8RJMmzYweceSZcdFHsJKmjAp4rV17p3Z82bYqdROrbvNkXy5g/P3YSach++8Hdd8M118ROkjpqQsmV//gP73us1XSSZ8AAWLRI05EmlRl87WuxU6SSzsCzEcKuEX99+/oCA5JMdXPQTJqkroVJMmECjBuntU2bSQU8G7/+tc9FPXNm7CSSiQULfCrfceNiJ5E6Tz8NTzyh+YGaSU0o2ai76DJ4cNwckpkjjvA/tuohlByPP+5db1XAm0Vn4M0xfbr3Oikvh0sv1S9fmpx2mi+isWkTbNwYO03p2rx5V+Fu3z52mtRSAW+qZcvgn/7JL1pKOm3ZAscfDzfeGDtJ6RozBvr0gVWrYidJNTWhNFX37j7vtOY3Sa+KCrjqKp+yVOIYNgy2bfP1LqXZtCJPpt580z/2ae1FESkwrciTjRDgu9/1KS6rq2OnkVy6+2646abYKUrHJ5/ArbdqVGyOqIBnwgwmT/YuT1pFvrh89JEP8tEf5sKYMQPuvNNn7JSsqQmlMatX+xqLN92kGdKK1c6dPpRbCmfpUujRI3aKVFETSnM89hiMGuWTIUlxqiveK1b4wCzJn7pumyreOaMC3pjrrvP5TY46KnYSybe77oKrr1a3tnxZtgx69vSh85IzKuD17dwJ3/++LwRg5qP3pPiNGgWzZ6tbW760aOFzBZ1ySuwkRUUFvL4FC7zd+7nnYieRQmrb1pdfA/j73+NmKUYHH+wrVB12WOwkRUUFvL6jjvI27yuvjJ1EYpg4Ef7hH7zfv+TGL3+p60h5ogJe55574L//2x937Ro3i8QzfDjccsuuVe0lOxs2eJPk2LGxkxQl9Z8C7wP83HPe/nnJJZqcqpS1bw+33+6PQ9DvQrbatvWzb3XDzQv9q4JfYJk2DR55RP9hxS1a5NMmvP127CTptWOH3x90EHTuHDdLkcq6gJtZCzN7y8ym5SJQQT3/PJx3ns9O17Il7L9/7ESSFO3bw7p13j9cmueb39RCxXmWiyaUfwPmAe1y8F6FtWgRvP8+bN3qM9SJ1OnY0ccA6KN/84QA/fvrE22eZTWU3sx6Ao8CPwG+F0L4UmOvT8xQ+t3bNrdv15m37F0I8JvfeDE69tjYaaRE5Wso/WjgJmCvK5Ka2QgzqzSzyqqqqiw3lwOLF8MJJ0DdHxIVb2nM+vU+E6V6UWRu7lx48cXYKUpCswu4mX0JWBVCaLTDbAhhfAhhUAhhUJcuXZq7udypu7Ciwi2ZaN8e/vQneOCB2EnS4+67/drSpk2xkxS9bNrATwW+YmbDgVZAOzObEEK4JDfRcqyuqeSww3yQhtrmJFN1IzQ3boQ1a6BXr7h5ku7nP/ez8AMOiJ2k6DX7DDyEcEsIoWcIoTdwIfBCYov31q1w1lm71rFU8ZamCgGGDIELL/TH0rAQoFUrGLRHc63kQWlcYi8vh8MP95tIc5jBD3/oixHoBKBhM2fCwIHwwQexk5SMnIzEDCHMBGbm4r1yKgRvh2vTBsaPj51G0u7LX971WKM097R9u4+87N49dpKSUdxn4LfcAp//vM/HIJIrY8fCmWdCzV47X5WmM8+El1/WmIoCKu4CPmSI/1K1aRM7iRSTtm29d4p6WbgQ4KmntK5oBMW5JuaKFZpRUPJHzSef9vzzcMYZvgShhs7nRemsiTl9OvTp4x/lRPKhrngvX+49m0q9KWXIEPj97+H882MnKTnFV8BPOQWuuELdmCT/nnnGe6XMmRM7SVxmfoG3vDx2kpJTPAX8gw+8De7AA2HMGGjdOnYiKXaXXQbz58Nxx8VOEkd1tV9jmjQpdpKSVRwFfNUqOOkkuOmm2EmklJjBoYf649mzS2+Az+rVPhWzRFMcK/IcdJCvonLOObGTSCl66SU4/XSYMAEuvjh2msI5+GBda4os3Wfgq1b5nN4A117rFy9FCu200+D+++FrX4udpHDefttnajRTj5yI0l3AL74Yhg3bNcOgSAxlZTBypF93qa4u/qaU6mr/Y6VeJ9Gluwll9GhYulRXvyUZqqrgi1+E73wHvvGN2Gnyp0ULv3BZ6t0nEyB9BXzHDh84cPbZcPTRfhNJgk6doEeP0hj5e+KJsRMIaWxCGTfOL1a+9VbsJCKfVlYGv/tdcbeFjx8PN98MO3fGTiKkpYAvXw6DB/sQ+WuugSlTYMCA2KlEGhaC90iZMiV2ktybN88XRNkvfR/ei1E6Cvgdd/iyVj/4ga+qU8xnOJJ+NTXeK+XRR2Mnyb2f/cynq5BESPZkVhUVvppOfa1aaQCBJFtVFXTs6Bf8isGGDb6c3CGHxE5SktI5mdXChT67Wd2w+IoK7zpY1/dbJKm6dPHivXGjj9JMu/vu8xWtliyJnUR2k+yGrG7doF07Pwtv1Qq2bfPnmipW0uLSS+F//9dPRtK80MGll/onip49YyeR3SS7gAOsXAlXXQUjRvgV8OXLYycSydyoUbB2bbqLN0CvXt6BQBIl2W3gIsVk2zZo2TJ2iqZZvRpuvBFuuw16946dpmSlsw1cpFj86lfQt69fCEyTWbNg8mTYvDl2EmlA8ptQRIrBccfBySenb/j5OefAsmWlMbo0hXQGLlIIAwbAxIk+3D4tli71exXvxFIBFymk5ct9yb9162InadxHH8Fhh8HYsbGTSCNUwEUKackSeOIJeP312Ekad+CB8MMf+lqXklhqAxcppBNPhMWLvUAmWZs2cOutsVPIPugMXKTQ6or3yy/7EPWkueceXyZOEk8FXCSGRYvgH/8R7r47dpJP27LFJ+J66qnYSSQDakIRieEzn/HpZocNi53k0yoqYP58LVOYEjoDF4nl3HN9oradOxuedbPQ1q3zfuoVFT7nkCSeCrhITNu2wamn+io3sX372zBkSPEvylxE1IQiElPLlnDWWXDssbGTwFe/CuvXg1nsJJIhTWYlIpJwmsxKJOkmTPDlAwvtzTd9+bfq6sJvW7KiAi6SFH/6E8yYUfgV3x95BG64QTMOplCzm1DMrBfwa6ArUAOMDyHc19jPqAlFpBFbtvii3YVeRzMEXzHosMMKu13JWD6aUHYC3w8hHAWcDFxrZv2yeD+R0lZRsWsdzaefzv/2QvBeMGYq3inV7AIeQlgeQphd+3gDMA/okatgIiVr1Cj453/eNZ1rvrzwAvTpA3Pm5Hc7kjc5aQM3s97AAGCPKdbMbISZVZpZZVVVVS42J1Lcbr0VZs6EHnk+H2rXDj73OTjiiPxuR/Im626EZtYGeAn4SQhhSmOvVRu4SBOtWZP8mQsl7/LSjdDMyoHJwGP7Kt4i0kRPP+2rwb/9dm7fNwR46CEftCOp1uwCbmYGPAzMCyHcm7tIIgL4EPuLL4bu3XP7vq++CldfDVOn5vZ9peCy6Ub4eeBPwBy8GyHAv4cQpu/tZ9SEIpIQs2dD//6wn2bTSIOcN6GEEF4JIVgIoX8I4fja216Lt4g008qVcN55MHdu9u9VU3uuNXCgincR0EhMkaQrK4NZs7Iv4Dt3+pJu48blJpdEpz/BIknXpQv87W8+c2E2Nm6Eo47Kf/dEKRgVcJE0qCveL7zgBfjII5v+Hh06+IRZUjTUhCKSFhs3wr/8C9x+e9N/9uWXYfHi3GeSqHQGLpIWbdrAs89CvyZOOVRT46vtdO+u1eaLjAq4SJoMqu1JtnOnj9Ls0mXfP1NW5k0va9fmNZoUngq4SNqEAMOH+0LIM2d6gd6XXr38JkVFBVwkbcy8SaSsbN/Fe/RoeOMNX7Qh214skjgq4CJpdMEFmb1u+3Y/U1fxLkrqhSKSZo8/7qM09zYlxk03weTJhc0kBaMCLpJm69fDihV7XqBct86bTsCbXKQoqYCLpNmVV3rXwPpzho8dC5/9LHzwQZxcUhAq4CJpZubraG7Y4EW7rinluutg0iStdVnkVMBFisGkSV60p0+HwYNh0yY4//zYqSTPVMBFisFll/kc308+6cPmr7sudiIpAHUjFCkGBxzg3QXr/Pa33rzSqhVs2RIvl+SVzsBFisHChXDRRdC6tT9v3dqXY1u0KG4uySsVcJFi0K0btGvnZ+GtWvl9u3bQtWvsZJJHKuAixWLlSrjqKnjtNb9fsSJ2IskztYGLFIspU3Y91rJpJUFn4CIiKaUCLiKSUirgIiIppQIuIpJSKuAiIimlAi4iklIW9jYRfD42ZlYF/L2ZP94ZWJ3DODFpX5KnWPYDtC9Jlc2+HBpC2GMF64IW8GyYWWUIYVDsHLmgfUmeYtkP0L4kVT72RU0oIiIppQIuIpJSaSrg42MHyCHtS/IUy36A9iWpcr4vqWkDFxGRT0vTGbiIiOxGBVxEJKUSV8DN7Gwze8/M3jezmxv4vpnZmNrv/8XMBsbImYkM9uV0M1tnZm/X3n4UI+e+mNkvzWyVmc3dy/dTcUwy2I9UHA8AM+tlZi+a2Twze9fM/q2B16TluGSyL4k/NmbWyszeMLN3avdjVAOvye0xCSEk5ga0AD4A+gD7A+8A/eq9ZjjwDGDAycDrsXNnsS+nA9NiZ81gX74ADATm7uX7aTkm+9qPVByP2qzdgIG1j9sCf0vx/5VM9iXxx6b237lN7eNy4HXg5Hwek6SdgZ8EvB9CWBhC2A48Dpxb7zXnAr8O7jWgg5l1K3TQDGSyL6kQQngZ+KSRl6TimGSwH6kRQlgeQphd+3gDMA/oUe9laTkumexL4tX+O2+sfVpee6vfSySnxyRpBbwH8NFuz5ew54HM5DVJkGnOU2o/cj1jZkcXJlrOpeWYZCJ1x8PMegMD8DO+3aXuuDSyL5CCY2NmLczsbWAVMCOEkNdjkrQl1ayBr9X/C5bJa5Igk5yz8TkONprZcGAqcHi+g+VBWo7JvqTueJhZG2AycH0IYX39bzfwI4k9LvvYl1QcmxBCNXC8mXUAfmdmx4QQdr/mktNjkrQz8CVAr92e9wSWNeM1SbDPnCGE9XUfuUII04FyM+tcuIg5k5Zj0qi0HQ8zK8cL3mMhhCkNvCQ1x2Vf+5K2YxNCWAvMBM6u962cHpOkFfBZwOFm9hkz2x+4EPh9vdf8Hvhm7dXck4F1IYTlhQ6agX3ui5l1NTOrfXwSfjw+LnjS7KXlmDQqTcejNufDwLwQwr17eVkqjksm+5KGY2NmXWrPvDGzCuAMYH69l+X0mCSqCSWEsNPMRgLP4b04fhlCeNfMrqr9/kPAdPxK7vvAZuCyWHkbk+G+nAdcbWY7gS3AhaH2UnWSmNlEvBdAZzNbAtyGX6BJ1THJYD9ScTxqnQp8A5hT2+YK8O/AIZCu40Jm+5KGY9MNeNTMWuB/YCaFEKbls35pKL2ISEolrQlFREQypAIuIpJSKuAiIimlAi4iklIq4CIiKaUCLiKSUirgIiIp9f8BJgQ2zuXXFzUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ypoints = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.plot(ypoints, '*'':r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "ypoints = np.array([3, 8, 1, 10])\n",
    "xpoints=np.array([5,6,7,9])\n",
    "plt.plot(xpoints,ypoints, marker = '*',ms = 205,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "### You can use the keyword argument markeredgecolor or the shorter mec to set the color of the edge of the markers\n",
    "ypoints = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.plot(ypoints, marker = '*', ms = 200, mec = 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "ypoints = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.plot(ypoints, marker = 'o', ms = 200,mfc='r',mec='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Use both the mec and mfc arguments to color of the entire marker\n",
    "ypoints = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.plot(ypoints, marker = 'o', ms = 20, mec = 'y', mfc = 'k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "ypoints = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.plot(ypoints, linestyle = 'dashed',marker='o', mec = 'hotpink',ms=20,mfc='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2aaef312550>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(ypoints, linestyle = 'dotted',marker=\"o\",ms=25,\n",
    "         mfc=\"green\",mec=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xpoints=np.array([10,20,30])\n",
    "ypoints=np.array([1,2,3])\n",
    "plt.plot(xpoints,ypoints,marker=\"*\",color=\"red\",mfc=\"g\",mec=\"r\",ms=20,linestyle=\"dashed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "### You can use the keyword argument linewidth or the shorter lw to change the width of the line.\n",
    "\n",
    "### The value is a floating number, in points\n",
    "ypoints = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.plot(ypoints, linewidth = 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = np.array([3, 8, 1, 10])\n",
    "y2 = np.array([6, 2, 5, 11])\n",
    "\n",
    "plt.plot(y1)\n",
    "plt.plot(y2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAjZElEQVR4nO3deZgU5dX38e8BFTVoVESCgIxGMAoC6ogafF0IUYlGXKLB+BgNCi5oBHfUx07H6JPFfUniGtxxV1BRQXFBQTbZEUQxqIwwalgE2WbO+8ddTBqYGRqYmurl97muuaa7uqr7dF3KmVN33ec2d0dERASgQdIBiIhI7lBSEBGRKkoKIiJSRUlBRESqKCmIiEiVLZIOYHPsvPPOXlJSknQYIiJ5Zfz48V+7e9PqXsvrpFBSUsK4ceOSDkNEJK+Y2b9rek2Xj0REpIqSgoiIVFFSEBGRKkoKIiJSRUlBRESqKCmIiEgVJQUREamipCAikm8+/RTmz4/lrZUURETyRUUF3HILdO4MY8bE8hF5PaNZRKRoTJ0KZ58N22wDo0fDnnvG8jGqFEREctnKlZBOw5FHhqTw5puxJQRQpSAikrvGjoVevaB1a/jwQ2jZMvaPVKUgIpJrli2Dyy6DX/4Srr4ahgypl4QASgoiIrnlrbegQwcoK4MpU+C008Cs3j5el49ERHLBokVwxRXwyivw97+HKiEBqhRERJI2ZAi0bx8qgqlTE0sIoEpBRCQ55eVw8cVhzsHDD4c7jBKmSkFEpL65w+OPw777QosWMHlyTiQEUKUgIlK/vvgCzj8f/v3vcNnowAOTjmgtqhREROpDZSXccw/st19IBOPG5VxCAFUKIiLxmz0beveG778Pt5y2a5d0RDVSpSAiEpfVq+Gmm+Dgg+H44+G993I6IYAqBRGReEyeHHoVbb99uLtojz2SjigrqhREROrSihWQSkG3bnDeeTB8eN4kBFClICJSd0aPDtVBmzYwcSLsumvSEW00JQURkc21dCn87//CE0/A7bfDKafUa7+iuqTLRyIim+ONN8IktPLy0KLi1FPzNiFAjEnBzLY2szFmNsnMpplZOtr+NzP7yMwmm9nzZrZDxjEDzGy2mc00s6Pjik1EZLMtXBhuM+3VC+66Cx55BJo0STqqzRZnpbAC6OruHYFOwDFmdjAwDGjv7h2AWcAAADPbB+gJtAOOAf5uZg1jjE9EZNO8+GJoYLfVVqG99S9+kXREdSa2MQV3d+C76OmW0Y+7++sZu40GfhU97gEMcvcVwBwzmw10BkbFFaOIyEZZsAB+/3uYMCH0LjrssKQjqnOxjimYWUMzmwgsAIa5+wfr7NILGBo9bgF8nvHaF9G2dd+zj5mNM7Nx5eXlMUQtIrIOd3j00TB20Lo1TJpUkAkBYr77yN0rgE7RuMHzZtbe3acCmNk1wGrgsWj36kZmvJr3vBe4F6C0tHS910VE6tTcuWG+wbx5YQGcAw5IOqJY1cvdR+6+EHiLMFaAmZ0JHAecHl1mglAZtMo4rCUwrz7iExFZT2Ul/OMfIQl06QJjxxZ8QoAYKwUzawqscveFZrYN0A34i5kdA1wJHO7uyzIOGQw8bma3ALsCbYAxccUnIlKjWbPgnHNC76J33oG99046onoTZ6XQHBhhZpOBsYQxhZeAu4DtgGFmNtHM/gng7tOAp4DpwKtA3+jyk4hI/Vi9Gv7611AZnHIKvPtuUSUEiPfuo8nAftVs37OWY24AbogrJhGRGk2aFOYcNGkSLhWVlCQdUSI0o1lEitvy5XDttfDzn8NFF8FrrxVtQgD1PhKRYvb++6GB3d57h0qhefOkI0qckoKIFJ/vvoNrroGnn4Y774STT046opyhy0ciUlxefz1MQlu0KDSwU0JYiyoFESkO//kPXHIJjBgB99wDR6vnZnVUKYhI4XvuudDArnHj0MBOCaFGqhREpHB99RVceGG4TPTkk3DooUlHlPNUKYhI4XGHhx6Cjh2hbduwNKYSQlZUKYhIYfnsMzj33NDm+tVXYb/15tBKLVQpiEhhqKwMt5eWlsIRR8CYMUoIm0CVgojkv48+Cg3sAEaOhJ/8JNl48pgqBRHJX6tWwY03hvGC004LHU2VEDaLKgURyU8ffhga2DVrBuPHhxXRZLOpUhCR/PL99zBgABxzDPTvD0OHKiHUIVUKIpI/Ro4MDew6dIDJk0OVIHVKSUFEct+SJaE6eP75cIfRSSclHVHB0uUjEcltr74aWlQsWxZmJishxEqVgojkpm++CQ3s3nkH7r8/LIIjsVOlICK5xT2sc7DvvrDjjqGBnRJCvVGlICK5o6wMLrgAZs6EZ5+FQw5JOqKio0pBRJLnDg8+GBrYtW8f5iAoISRClYKIJGvOHOjTJyyCM2xYSAySGFUKIpKMigq4/XY48MAwZjB6tBJCDlClICL1b/r00MBuiy3g/ffDmgeSE1QpiEj9WbkS/vQnOPxwOOMMeOstJYQco0pBROrHuHGhRUWLFjBhArRqlXREUg1VCiISr++/hyuugGOPDb9fflkJIYcpKYhIfN5+OzSvmzs3TEI7/XQwSzoqqYUuH4lI3Vu8GK68EoYMgbvvhh49ko5IsqRKQUTq1iuvhAloq1eHBnZKCHlFlYKI1I2vv4Z+/WDUKPjXv+BnP0s6ItkEqhREZPO4w6BBoTpo1iwsfqOEkLdUKYjIpvvyy9DAbvZsePFFOOigpCOSzbTBSsHS1szS9oClbWj0fB9L29nxhyYiOcsd7rsPOnWC/fYL8w6UEApCNpXCQOBfwDXR81nAk8ADMcUkIrnsk0+gd++wROabb4Z1D6RgZDOmsLOn/CmgEsBTvhqo2NBBZra1mY0xs0lmNs3M0tH2ncxsmJl9HP3eMeOYAWY228xmmtnRm/idRCQOFRVwyy2hIjj22DCgrIRQcLKpFJZa2poADmBpOxhYlMVxK4Cu7v6dmW0JjDSzocBJwBvu/mczuwq4CrjSzPYBegLtgF2B4WbW1t03mIBEJGZTp4YWFdtuG7qZ7rln0hFJTLKpFC4BBgM/trS9BzwMXLShgzz4Lnq6ZfTjQA/goWj7Q8AJ0eMewCB3X+Huc4DZQOcsv4eIxGHlSkin4cgjQ1J44w0lhAK3wUrBUz7B0nY4sBdgwExP+aps3tzMGgLjgT2Bu939AzNr5u5lAO5eZma7RLu3AEZnHP5FtG3d9+wD9AHYbbfdsglDRDbFmDEhEZSUhJXQWrZMOiKpBxtMCpa2k9bZ1NbStgiY4ilfUNux0aWfTma2A/C8mbWv7aOqe4tq3vNe4F6A0tLS9V4Xkc20bBlcdx08+ijceiv07Kl+RUUkm8tHZwP3A6dHP/cRLim9Z2k7I5sPcfeFwFvAMcB8M2sOEP1ek1i+ADJbJ7YE5mXz/iJSR0aMCA3syspCA7vTTlNCKDLZJIVKYG9P+cme8pOBfQiDyAcBV9Z0kJk1jSoEzGwboBvwEWF84sxotzOBF6PHg4GeZtbIzHYH2gBjNvobicjGW7QIzj0XfvtbuO02eOwxaNo06agkAdkkhRJP+fyM5wuAtp7yb4HaxhaaAyPMbDIwFhjm7i8BfwZ+bmYfAz+PnuPu04CngOnAq0Bf3XkkUg+GDAktKszCXUbHHZd0RJKgbG5JfdfS9hLwdPT8ZOAdS9sPgIU1HeTuk4H9qtn+DVBtYxR3vwG4IYuYRGRzlZfD738PY8fCI4/AEUckHZHkgGwqhb6EWc2dCP/IPwz09ZQv9ZQfGV9oIhILd3j88TDxrGXL0MBOCUEi2dyS6sAz0Y+I5LPPP4fzzw8roQ0ZAgcemHREkmOyuSX1YOBOYG9gK6AhsNRTvn3MsYlIXamsDA3srr02XDJ67jnYaquko5IclM2Ywl2E9hNPA6XAbwmT0UQkH3z8cWhgt3w5vPUWtGuXdESSw7JaZMdTPhto6Cmv8JT/C9BYgkiuW70abroJDjkETjgB3ntPCUE2KJtKYZmlbStgoqXtr0AZ8IN4wxKRzTJ5cmhR8cMfhnYVe+yRdESSJ7KpFM6I9rsQWEqYdXxynEGJyCZasSK0qOjWDc47D4YNU0KQjZLN3Uf/jh4uB9LxhiMim2z06FAdtGkDEyfCrrsmHZHkoWzuPuoC/AFonbm/p1x/fojkgqVLw11FgwbBHXfAr36lfkWyybIZU3gA6E9oga22EyK55I03wp1Fhx4aWlQ0aZJ0RJLnskkKizzlQ2OPRESyt3AhXHZZGDP45z+he/ekI5ICkU1SGGFp+xvwHKE7KhAW34ktKhGp2YsvQt++0KNHaG+9veaRSt3JJikcFP0uzdjmQNe6D0dEajR/fpiNPHFi6F102GFJRyQFqNakYGlrCAz2lN9aT/GIyLrcw/oGl10Gv/sdDBwI22yTdFRSoGpNCp7yCkvb8YCSgkgS5s4N8w3mzYOXX4YDDkg6Iilw2Vw+et/SdhfwJGHyGqAxBZFYVVaGAeRUCvr3h8svhy23TDoqKQLZJIWfRr//mLFNYwoicZk1C845J/Queucd2HvvpCOSIpLNjGY1vxOpD6tXw803hyZ2110HF1wADRsmHZUUmWxmNF9X3XZP+R+r2y4im2DixNCiokmTsDxmSUnSEUmRyqYh3tKMnwqgO1ASY0wixWP5crjmGjjqKLjoInjtNSUESVQ2l49uznxuabsJGBxbRCLF4v33Q3Wwzz6h1fWPfpR0RCJZDTSva1tAzfBENtV338HVV8Mzz8Cdd8LJ6kQvuSObMYUphLuNIKzP3JS170QSkWy9/jqcey4cfnhoYLfTTklHJLKWbCqF4zIerwbme8pXxxSPSGH69lu49FIYMQLuuQeOPjrpiESqtcGBZk/5v6OFdsqB5sCOsUclUkiefRbat4fGjUMDOyUEyWE1VgpRe4s7gG+Ba4G7gflAiaXtSk/5Q/UTokie+uoruPDCcJno6aehS5ekIxLZoNoqheuBo4BzgaeAn3nKDwY6AJfVQ2wi+ck9NK3r0AH22ivMQVBCkDxR25hCpad8FoClbY6n/FMAT/kCS5vGFESq89lnYSC5vDwMKnfqlHREIhultqTQwNK2I6GaqIwer1n4NZtJbyLFo7IS7r4b0unQ4vrSS9XATvJSbUnhh4R1mdckgsyuqL7+7iJFasaM0MCuQQN4771wyUgkT9WYFDzlJfUYh0j+WbUK/vY3uOWWUCGcf35IDCJ5bFNmNIvIhAnQq1doTTF+PLRunXREInVCf9aIbIzvv4erroJjjoFLLoGhQ5UQpKCoUhDJ1rvvhrGDjh3DJLRmzZKOSKTOZZUULG2HAm085f+ytDUFGnvK58QbmkiOWLIkVAcvvAB33QUnnph0RCKx2eDlI0tbCrgSGBBt2hJ4dIPHmbUysxFmNsPMppnZxdH2TmY22swmmtk4M+ucccwAM5ttZjPNTL0AJHlDh4YWFcuXh5nJSghS4LKpFE4E9iO6JdVTPs/Stl0Wx60GLnX3CWa2HTDezIYBfwXS7j7UzH4RPT/CzPYBegLtgF2B4WbW1t0rNv5riWymb76B/v3DJaMHHoBu3ZKOSKReZDPQvNJT7kRzEyxtP8jmjd29zN0nRI+XADOAFtH7bB/t9kNgXvS4BzDI3Ve4+xxgNtAZkfrkDk89FaqDnXYKYwdKCFJEsqkUnrK03QPsYGnrDfQC7tuYDzGzEkK18QHQD3jNzG4iJKWfRru1AEZnHPZFtG3d9+oD9AHYbbfdNiYMkdrNmwd9+8LMmaGz6U9/uuFjRApMNq2zbwKeAZ4F9gKu85Tfme0HmFnj6Nh+7r4YOB/o7+6tgP7AA2t2re7j19vgfq+7l7p7adOmTbMNQ6Rm7uESUceOoUL48EMlBClaWd195CkfBgzb2Dc3sy0JCeExd38u2nwmcHH0+Gng/ujxF0CrjMNb8t9LSyLx+PRT6NMHFi6E4cNDYhApYjVWCpa2kdHvJZa2xRk/Syxtizf0xmZmhCpghrvfkvHSPODw6HFX4OPo8WCgp5k1MrPdgTbAmI3/SiJZqKiA226Dzp3DojejRyshiFB776NDo9/Z3GlUnS7AGcAUM5sYbbsa6A3cbmZbAMuJxgfcfZqZPQVMJ9y51Fd3Hkkspk2Ds8+GRo3g/fehbdukIxLJGbVePrK0NQAme8rbb+wbu/tIqh8nADighmNuAG7Y2M8SycrKlfCXv8Add8D114fLRmpgJ7KWWv+P8JRXApMsbbrNR/Lb2LFQWgqjRoVmduedp4QgUo1sBpqbA9MsbWOApWs2esqPjy0qkbqybBn84Q/w0EOhxfVvfgNWUwErItkkhXTsUYjE4e23QwO70tIwCW2XXZKOSCTnZTNP4W3gI2C76GdGtE0kNy1eHBa8Of10uPlmeOIJJQSRLGXTEO9Uwq2hpwCnAh9Y2n4Vd2Aim+Tll8MEtIqK0MDueF3lFNkY2Vw+ugY40FO+ACBqnT2cMMtZJDeUl0O/fmG+wcCB0LVr0hGJ5KVsbr9osCYhRL7J8jiR+LnDoEGw775haczJk5UQRDZDNpXCq5a214Anoue/Bl6JLySRLH35ZRg7+PRTePFFOOigpCMSyXvZDDRfDtwLdAA6Avd6yq+MOzCRGrnDffdBp06w//5h3oESgkidyLYh3rOExnYiyfrkE+jdG777Dt58M1w2EpE6U2NSsLQtoZrW1YTWFe4p376a10TiUVEBt98ON94IAwaEQeWGDZOOSqTg1NYQb1Mb4YnUralTQwO7bbcNdxftuWfSEYkUrKwuHwFY2nYBtl7z3FM+N5aIRNZYuTJUBnffDTfcEGYnq1+RSKw2mBQsbccDNwO7AguA1oT1ltvFG5oUtTFjoFcv2H33sBJay5ZJRyRSFLL5s+t64GBglqd8d+BnwHuxRiXFa9kyuPTSMBP52mth8GAlBJF6lE1SWOUp/wZoYGlr4CkfAXSKNywpSiNGhLuJvvoqjCP07KmOpiL1LJsxhYWWtsbAO8BjlrYFhJXRROrGokVw+eUwdCj84x9w3HFJRyRStLKpFHoAy4D+wKvAJ8Av4wxKisiQIaGBXYMGoTpQQhBJVG3zFPYEmnnK14wfVAIPWdoOA3Yg9EAS2TQLFsDFF4cV0R55BI44IumIRITaK4XbgCXVbF8WvSay8dzhscfC2EGrVqGBnRKCSM6obUyhxFM+ed2NnvJxlraS+EKSgvX556GB3dy5Yd2D0tKkIxKRddRWKWxdy2vb1HUgUsAqK+Gf/wzN6w46CMaNU0IQyVG1VQpjLW29PeX3ZW60tJ0NjI83LCkYH38cGtgtXw5vvQXtNOdRJJfVlhT6Ac9b2k7nv0mgFNgKODHmuCTfrV4Nt94Kf/lLmIR20UVqYCeSB2priDcf+Kml7UigfbT5ZU/5m/USmeSvSZNCA7sddgjtKvbYI+mIRCRLG5y8Fs1gHlEPsUi+W7EC/vSnMH7w5z+H3kWakSySV7LukipSq1GjQnXQtm2oFHbdNemIRGQTKCnI5lm6FK65Bp58MiyCc8opqg5E8pia08umGz48TEL79tvQouLUU5UQRPKcKgXZeAsXhvbWw4eH8YPu3ZOOSETqiCoF2TgvvBDmGmy9NUyZooQgUmBUKUh25s8Pcw0mToQnnoDDDks6IhGJgSoFqZ07PPwwdOgQ5htMmqSEIFLAVClIzebOhXPPhbIyeOUVOOCApCMSkZipUpD1VVbC3XeHBnaHHhrWPFBCECkKsSUFM2tlZiPMbIaZTTOzizNeu8jMZkbb/5qxfYCZzY5eOzqu2KQWM2fC4YeHNQ/efTfMQdhyy6SjEpF6Euflo9XApe4+wcy2A8ab2TCgGWGJzw7uvsLMdgEws32AnkA7YFdguJm1dfeKGGOUNVatgptvhptuglQKLrhADexEilBsScHdy4Cy6PESM5sBtAB6A3929xXRawuiQ3oAg6Ltc8xsNtAZGBVXjBKZODH0Kdp557DWQUlJ0hGJSELqZUzBzEqA/YAPgLbA/zOzD8zsbTM7MNqtBfB5xmFfRNvWfa8+ZjbOzMaVl5fHHHmBW748XB466qhwu+lrrykhiBS52JOCmTUGngX6uftiQnWyI3AwcDnwlJkZUF1/BF9vg/u97l7q7qVNmzaNMfIC99570KkTzJgRbjP93e/UokJE4r0l1cy2JCSEx9z9uWjzF8Bz7u7AGDOrBHaOtrfKOLwlMC/O+IrSd9/B1VfDM8/AnXfCyScnHZGI5JA47z4y4AFghrvfkvHSC0DXaJ+2hJXcvgYGAz3NrJGZ7Q60AcbEFV9Rev11aN8eFi8ODeyUEERkHXFWCl2AM4ApZjYx2nY18CDwoJlNBVYCZ0ZVwzQzewqYTrhzqa/uPKoj334bGtiNGAH33ANH625fEalenHcfjaT6cQKA/6nhmBuAG+KKqSg9+2wYRD755NDAbrvtko5IRHKY2lwUqrIyuPBCmDYNnn4aunRJOiIRyQNqc1Fo3GHgQOjYEX7ykzAHQQlBRLKkSqGQfPYZ9OkDX38dBpU7dUo6IhHJM6oUCkFlZbi9tLQUunaFDz5QQhCRTaJKId/NmAHnnAMNGoQJaXvtlXREIpLHVCnkq1Wr4MYbw4I3p58Ob7+thCAim02VQj6aMCE0sGvePDSwa9066YhEpECoUsgn338PV10F3buHyWivvKKEICJ1SpVCvnj33TB20LEjTJ4MzZolHZGIFCAlhVy3ZEmoDl54Ae66C048MemIRKSA6fJRLhs6NDSwW7EiNLBTQhCRmKlSyEXffAP9+8PIkfDAA9CtW9IRiUiRUKWQS9xDn6J994UmTUIDOyUEEalHqhRyRVkZXHABzJoVOpseckjSEYlIEVKlkDR3ePDBcFfRvvuGOQhKCCKSEFUKSfr0Uzj3XPjPf2D4cOjQIemIRKTIqVJIQkUF3HYbdO4MRx0Fo0crIYhITlClUN+mT4ezz4ZGjWDUKGjTJumIRESqqFKoLytXwvXXw+GHw1lnwZtvKiGISM5RpVAfxo0L1UGrVvDhh9CyZdIRiYhUS5VCnJYtgyuugOOOgyuvhCFDlBBEJKcpKcTl7bfDbaaffx4mof3mN2CWdFQiIrXS5aO6tnhxqApeegnuvhuOPz7piEREsqZKoS69/HJoYFdRERrYKSGISJ5RpVAXysuhX78w32DgQOjaNemIREQ2iSqFzeEOgwaF9hTNm4exAyUEEcljqhQ21Zdfwvnnw5w5MHhwmJ0sIpLnVClsLHe47z7o1AkOOADGj1dCEJGCoUphY3zyCfTuDUuXwogRYVBZRKSAqFLIRkUF3HwzHHRQmIj2/vtKCCJSkFQpbMjUqdCrFzRuDB98AD/+cdIRiYjERpVCTVauhD/8AY48MlwyeuMNJQQRKXiqFKozZkyoDvbYAyZOhBYtko5IRKReKClkWroUrrsOHn88LIJz6qnqVyQiRUWXj9Z4882w+tn8+WES2q9/rYQgIkUntqRgZq3MbISZzTCzaWZ28TqvX2ZmbmY7Z2wbYGazzWymmR0dV2xrWbgwjBmcdRbccQc8+ijsvPOGjhIRKUhxVgqrgUvdfW/gYKCvme0DIWEAPwfmrtk5eq0n0A44Bvi7mTWMMb4wE7l9e9hii3CX0bHHxvpxIiK5LrYxBXcvA8qix0vMbAbQApgO3ApcAbyYcUgPYJC7rwDmmNlsoDMwqs6DW7ECzjwzzEZ+7LGwRKaIiNTPmIKZlQD7AR+Y2fHAl+4+aZ3dWgCfZzz/Itq27nv1MbNxZjauvLx80wJq1Ai6d4dJk5QQREQyxJ4UzKwx8CzQj3BJ6Rrguup2rWabr7fB/V53L3X30qZNm256YGeeCdtuu+nHi4gUoFiTgpltSUgIj7n7c8CPgd2BSWb2GdASmGBmPyJUBq0yDm8JzIszPhERWVucdx8Z8AAww91vAXD3Ke6+i7uXuHsJIRHs7+5fAYOBnmbWyMx2B9oAY+KKT0RE1hfn5LUuwBnAFDObGG272t1fqW5nd59mZk8RBqJXA33dvSLG+EREZB1x3n00kurHCTL3KVnn+Q3ADXHFJCIitdOMZhERqaKkICIiVZQURESkipKCiIhUMff15oflDTMrB/69GW+xM/B1HYWT73Qu1qbz8V86F2srhPPR2t2rnf2b10lhc5nZOHcvTTqOXKBzsTadj//SuVhboZ8PXT4SEZEqSgoiIlKl2JPCvUkHkEN0Ltam8/FfOhdrK+jzUdRjCiIisrZirxRERCSDkoKIiFQpmqRgZv3NbJqZTTWzJ8xsazPbycyGmdnH0e8dk46zPpjZxdF5mGZm/aJtRXMuzOxBM1tgZlMzttX4/c1sgJnNNrOZZnZ0MlHHp4bzcUr030elmZWus38xno+/mdlHZjbZzJ43sx0yXiuo81EUScHMWgC/B0rdvT3QEOgJXAW84e5tgDei5wXNzNoDvQnrX3cEjjOzNhTXuRgIHLPOtmq/v5ntQ/hvpV10zN/NrGH9hVovBrL++ZgKnAS8k7mxiM/HMKC9u3cAZgEDoDDPR1EkhcgWwDZmtgWwLWFVtx7AQ9HrDwEnJBNavdobGO3uy9x9NfA2cCJFdC7c/R3g23U21/T9ewCD3H2Fu88BZhMSasGo7ny4+wx3n1nN7sV6Pl6P/n8BGE1YGRIK8HwURVJw9y+Bm4C5QBmwyN1fB5q5e1m0TxmwS3JR1pupwGFm1sTMtgV+QVgGtRjPRaaavn8L4POM/b6IthUrnQ/oBQyNHhfc+SiKpBBdH+5BWB96V+AHZvY/yUaVDHefAfyFUA6/CkwirHQn1atuoahivo+7qM+HmV1D+P/lsTWbqtktr89HUSQFoBswx93L3X0V8BzwU2C+mTUHiH4vSDDGeuPuD7j7/u5+GKFM/pgiPRcZavr+XxAqqTVaEi49FquiPR9mdiZwHHC6/3eCV8Gdj2JJCnOBg81sWzMz4GfADGAwcGa0z5nAiwnFV6/MbJfo926EwcQnKNJzkaGm7z8Y6Glmjcxsd6ANMCaB+HJFUZ4PMzsGuBI43t2XZbxUeOfD3YviB0gDHxGuqT8CNAKaEO40+Tj6vVPScdbTuXgXmE64dPSzaFvRnAtCEiwDVhH+0ju7tu8PXAN8AswEuicdfz2djxOjxyuA+cBrRX4+ZhPGDiZGP/8s1POhNhciIlKlWC4fiYhIFpQURESkipKCiIhUUVIQEZEqSgoiIlJFSUEKj9mJmDlmP0k6lA0y+wyzKZhNwux1zH60gf0HYvareopOipCSghSi04CRhO6Vmy/+rpdH4t4RGAdcHfNnidRKSUEKi1ljoAthwlHPaFt3zJ7K2OcIzIZEj4/CbBRmEzB7Ojp+zV/w12E2EjgFs96YjY3+on+W0EwQzH6M2ejotT9i9l3G51webZ+MWTqL6N8B9sSshIxe/phdhtkfqvmuf8ZsevT+N0XbmkbxjY1+umR55kQAJQUpPCcAr+I+C/gWs/0Jzf8OxuwH0T6/Bp7EbGfgWqAb7vsT/lK/JOO9luN+KO6DgOdwPzD6i34GIekA3A7cjvuBZPa8MTuK0PKgM9AJOACzwzYQ+3HAlKy+pdlOhFnH7Qg9/v+UEc+tUTwnA/dn9X4iESUFKTSnAYOix4OA0wh98F8FfklYT+NYQm+jg4F9gPcwm0joedQ6472ezHjcHrN3MZsCnE5YVAXgEODp6PHjGfsfFf18CEwAfkJIEtUZEX3+9sD/Zfk9FwPLgfsxOwlY04+nG3BX9H6Dge0x2y7L9xRhi6QDEKkzZk2AroR/wJ2wwp5jdgXhH/i+hK6wY3FfQmiOOAz302p4x6UZjwcCJ+A+CbOzgCM2FA3wf7jfk0XkR+L+dcb3aMzaf7Btvd4R7qsx60xo7tgTuJDw3RsAh+D+fRafK7IeVQpSSH4FPIx7a9xLcG8FzAEOBd4C9icsRbqmAhgNdMFsTwDMtsWsbQ3vvR1QhtmWhEphjdGEyzSw9sD2a0CvjDGKFkTdabMwH9gFsyaYNSJcVlpbeN8f4v4K0I9wiQrgdUKCWLNfJ0Q2gpKCFJLTgOfX2fYs8BvcK4CXgO7Rb3AvB84CnsBsMuEf+JpuY/1f4APC+MRHGdv7AZdgNgZoDiyK3vt1wuWkUdElp2cIiWXDwpoff4w+76V1Pm+N7YCXorjfBvpH238PlEaDz9OB87L6TJGIuqSKbI5wF9L3oeew9SSMYfRIOiyRTaUxBZHNcwBhYNeAhYT1e0XylioFERGpojEFERGpoqQgIiJVlBRERKSKkoKIiFRRUhARkSr/H9MvcwbdJ4x9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])\n",
    "y = np.array([240,250, 260, 270, 280, 290, 300, 310, 320, 330])\n",
    "\n",
    "plt.plot(x, y,color=\"r\",linewidth=1)\n",
    "\n",
    "plt.xlabel(\"Average Pulse\",fontsize=10,color=\"red\")\n",
    "plt.ylabel(\"Calorie Burnage\",fontsize=10,color=\"g\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With Pyplot, you can use the title() function to set a title for the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])\n",
    "y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])\n",
    "\n",
    "plt.title(\"Sports Watch Data\",loc=\"left\",fontsize=20,color=\"green\")\n",
    "plt.xlabel(\"Average Pulse\",loc=\"left\")\n",
    "plt.ylabel(\"Calorie Burnage\")\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "x = np.array([80, 85, 90, 95, 100, 105,115, 120, 125,154])\n",
    "y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])\n",
    "\n",
    "plt.title(\"Sports Watch Data\")\n",
    "plt.xlabel(\"Average Pulse\")\n",
    "plt.ylabel(\"Calorie Burnage\")\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.grid(color=\"green\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### You can use the axis parameter in the grid() function to specify which grid lines to display.\n",
    "\n",
    "### Legal values are: 'x', 'y', and 'both'. Default value is 'both'\n",
    "x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])\n",
    "y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])\n",
    "\n",
    "plt.title(\"Sports Watch Data\")\n",
    "plt.xlabel(\"Average Pulse\")\n",
    "plt.ylabel(\"Calorie Burnage\")\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.grid(axis = 'x')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Display only grid lines for the y-axis\n",
    "x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])\n",
    "y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])\n",
    "\n",
    "plt.title(\"Sports Watch Data\")\n",
    "plt.xlabel(\"Average Pulse\")\n",
    "plt.ylabel(\"Calorie Burnage\")\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.grid(axis = 'y')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA4f0lEQVR4nO3dd3hUddbA8e+hhppI7wTp1aiAWBMVFbuI7uq6alZX133ddxd3V4plxYptXdzi6+Kq0V07RREVFJVgQVHckNBr6KEkJpSQft4/7k0yMySTCWQymZnzeZ48zL1zy/mdDDlz7505V1QVY4wxBqBRqAMwxhjTcFhRMMYYU8GKgjHGmApWFIwxxlSwomCMMaaCFQVjjDEVrCgY00CJSIqIPBLqOEx0saJgQkZEzhKRr0UkT0RyROQrERkVpH1NE5H/HMN6XUVERaSzx7x7q5m3IIDtZYrI2NrGEcB2492YDrk/e0RkvohcUIttJIvIl3UdmwkvVhRMSIhIW2A+8DegHdAdeBAoDMK+mhzruqq6G9gInOMx+xxgbRXzlhzrfupQnKq2Bk4CPgHmikhyaEMy4cSKggmVAQCq+oaqlqrqEVX9WFXToeJd61ci8jf3SGKtiJxfvrKIdBORee4RxkYRuc3juWkiMktE/iMiB4A7gHuAn7rvold47GOziBwUkS0ickM1sS7BLQAi0hg4GXjWZ97pwBIR6Ssin4lItojsF5HXRCTOXe7fQC/gfTeOSe788iOmXBHZ7vNH/AQR+cCN8VsR6RtIclU1S1WfBaYBT4hII3dfU0Rkk7u91SIy3p0/GHgeON2NLdedf6mI/FdEDrixTQtk/yaMqar92E+9/wBtgWzgFeBi4ASf55OBEuAuoCnwUyAPaOc+nwo8B8QACcA+4Hz3uWlAMXAVzhufFu68/3hsvxVwABjoTncFhlYT683ACvfxSJwi0d9n3hGgGdAPuABoDnR0l53hsa1MYKzHdC/gIHC9O872QIL7XAqQA4wGmgCvAW9WE2M8oEATn/knuvMHu9PXAt3cvPwUOAx09cj5lz7rJwHD3eVHAHuAq0L9+rGf4P3YkYIJCVU9AJyF8wfrBWCf+86/s8die3H+oBar6lvAOuBSEenprjtZVQtUNQ34F3Cjx7pLVfVdVS1T1SPVhFEGDBORFqq6W1VXVbNcqrvcCcDZwBequgHo4DHvG1UtUtWNqvqJqhaq6j7gGSDRTypuABapc8RUrKrZ7njKzVHVZapaglMUEvxsqyq73H/bAajqO6q6y83LW8AGnKJTJVVdrKoZ7vLpwBs1jMeEOSsKJmRUdY2qJqtqD2AYzjvYGR6L7FRVz46NW91lugE5qnrQ57nuHtPba9j3YZx3yncAu91TNIOqWTYT2IFTiM4BvnCfWuoxbwmAiHQSkTdFZKd76uo/QAc/ofQENvl5PsvjcT7Q2t+4qlCekxw3vptEJM09VZWLk/dq4xOR00TkcxHZJyJ5OPnyNx4T5qwomAZBVdfinC4Z5jG7u4iIx3QvnHe+u4B2ItLG57mdnpv03UUV+1yoqhfgnDpai3PEUp0vcP74nw587TPvLCovMk939zVCVdsCPwc8x+Abx3YgoOsEx2g8zhHXOhHpjTPG3wDtVTUOWOkRX1Utk18H5gE9VTUW57qDVLGciRBWFExIiMggEfmDiPRwp3vinFf/xmOxTsBvRaSpiFwLDAY+VNXtOH+Yp4tIjIiMAG7FOb1SnT1AvMcF184icoWItML5xNMhoNTP+kuAm4Bd7qkvgC/debE4Rw0Abdxt5YpId+DuKuI40WP6NWCsiPxERJqISHsRSfATR0Dc8f0GeACYqqplONdRFOf6CyLyC7yL8B6gh4g085jXBueorEBERgM/O97YTMNmRcGEykHgNOBbETmMUwxWAn/wWOZbnAu6+4FHgWtUNdt97nqci6u7gLnAA6r6iZ/9veP+my0iP+C89v/grp+Dc578f/ysn4pTpDw/x5+GcxF7uarmu/MeBE7BuSj+ATDHZzvTgfvc0zd/VNVtwCVuLDnuNk/yE0dNct18ZrjbvVZVXwJQ1dXAn3EK2B6cC8hfeaz7GbAKyBKR/e68/wEeEpGDwJ+At48jNhMGxPuUrTENg/uxzF+q6lmhjsWYaGJHCsYYYypYUTDGGFPBTh8ZY4ypYEcKxhhjKhxzo7CGoEOHDhofH39M62bnZ1NYWkjzxs1p37J93QYWZiwX3iwflSwX3iIlH8uXL9+vqh2rei6si0J8fDzff//9Ma2blJJE6tZUEnsnsjh5cd0GFmYsF94sH5UsF94iJR8isrW65+z0kTHGmApWFIwxxlSwomCMMaaCFQVjjDEVwvpC8/FITkgmKT6J+Lj4UIcScpYLb5aPSpYLb9GQj7D+8trIkSP1WD99ZIwx0UpElqvqyKqes9NHxhhjKlhRMMaYMKKqvPXdNhat3hOU7UftNYWUtBQyczOJj4snOSE51OGElOXCm+WjkuXCW6jzsS07nylz0vl6UzaXjejK2CGda16plqK6KJR/MzHaX+yWC2+Wj0qWC2+hykdpmfLyV1v488fradxIeHT8MK4f1Sso+4raomCMMeFg/Z6DTJqVTtr2XM4b1IlHxw+ja2yLoO3PioIxxjRARSVl/N/iTfz98w20iWnKs9clcMVJ3RCRoO7XioIxxjQwK7bnMnl2OmuzDnLFSd144PIhtG/dvF72bUXBGGMaiCNFpfxl0Xr+9cVmOrWJ4V83jQzKxWR/rCgYY0wDsHRTNlPmpLM1O5+fndaLKRcPom1M03qPw4qCMcaE0IGCYqZ/uJY3lm2jd/uWvH7baZzRt0PI4rGiYIwxIfLpmj3cO3clew8WcPs5J3LX2AG0aNY4pDFFbVFI6JLg9W80s1x4s3xUslx4q6t8ZB8q5MH3VzNvxS4Gdm7D8zeeSkLPuOOOry5YQzxjjKknqsq8Fbt48P3VHCwo5jfn9ufXSX1p1qR+Ow75a4gXtUcKxhhTn3bnHeG+uSv5dO1eEnrG8eQ1IxjQuU2owzqKFQVjjAmisjLlje+2Mf3DtZSUlXHfpYP5xZl9aNwouF9CO1ZRWxTSstLILcglLiYu6s+XWi68WT4qWS681TYfmfsPM2VOOt9szuGMvu15/OoR9GrfMviBHoeoLQoTF0ysaGy1OHlxqMMJKcuFN8tHJcuFt0DzUVJaxktuA7tmjRvx+NXD+emonkFvUVEXorYoGGNMMKzNOsDkWems2JHH2MGdeeSqYXSJjQl1WAGzomCMMXWgsKSUf3y+iec+30hsi6b8/Wcnc+nwrmFxdODJioIxxhynH7b9yORZ6WzYe4jxJ3fnT5cN4YRWzUId1jGxomCMMccov6iEP3+8npe+2kKXtjG8nDyKcwd1CnVYxyVoRUFEYoAlQHN3P7NU9QEReQq4HCgCNgG/UNVcd52pwK1AKfBbVV0YrPiMMeZ4fLVxP1PmpLM95wg3junNpHEDaROCBnZ1LZhfoysEzlPVk4AEYJyIjAE+AYap6ghgPTAVQESGANcBQ4FxwHMiEtomIMYY40O0Fft3X8gN//qWJo0a8dbtY3j4qmERURAgiEcK6vTPOORONnV/VFU/9ljsG+Aa9/GVwJuqWghsEZGNwGhgaXX7yM7PJiklyW8cCV0SmDFuRsV0WlYaExdMJC0rrWK6qm34ftyspv0AzBg3w+uzy577qU5yQrLXvV5T0lJISUvxu051Y6pJdWPyl4twHZM/NY2pqnyE+5iqEsiYfHMRCWPyVZsxrdzemG4Fz3GwII5fJ/Zl4tj+xDR13ruG65h8BfWagvtOfznQD/iHqn7rs8gtwFvu4+44RaLcDnee7zZvB24HiO0cS+bWzFrFlFuQS+rW1IrpvMI8r+nqBLJMbkGu13RaVlqN6yXFJ3lNZ+ZmBrQv3/3Wdh04ekxV5SLcx1Tdtj1VNybPfETKmDzVZkzV/T8J5zH526/vOo00jnbFt9O69ByKZAv7mj3ElIs3ei0TbmOqTlCLgqqWAgkiEgfMFZFhqroSQETuBUqA19zFq/rc1lHd+lR1JjAToM+QPprQO8FvDL7fOoyLiSOxd2JthgEQ0DpxMXF+912V+Lj4o6Zr2peNqXo2pqr3XRUbU9Xb9RyTKhw+MIScPedRpk2J6/AFse2XMUB6HLWdcBkTQCrVF4h665IqIg8Ah1X1aRG5GbgDOF9V893npwKo6nR3eiEwTVWrPX1kXVKNMcGyM/cI987NYPG6fZzSy2lg169Tw2tgdyxC0iVVRDoCxaqaKyItgLHAEyIyDpgMJJYXBNc84HUReQboBvQHlgUrPmOMqUpZmfLasm08/uEayhQeuHwIN50e32Ab2NW1YJ4+6gq84l5XaAS8rarz3QvIzYFP3G/6faOqd6jqKhF5G1iNc1rpTvf0kzHG1IvN+w4xZXYGyzJzOLt/Bx4bP5ye7Rp2A7u6FrSPpKpquqqerKojVHWYqj7kzu+nqj1VNcH9ucNjnUdVta+qDlTVj4IVGzifFJAHJaBPDEQ6y4U3y0elaMlFSWkZz6du4uJnv2Bt1gGeumYEr94y+qiCEA35sG80G2Oi2updB5g0ewUrdx7goqGdefjKYXRqGz4N7OqaFQVjTFQqKC7l759t5PnUTcS1bMb/3XAKFw/vGuqwQs6KgjEm6izfmsOkWels2neYCaf04P7LBhPXMjwb2NU1KwrGmKhxuLCEpxau45WlmXSLbcErt4wmcUDHUIfVoFhRMMZEhS827GPqnAx2/HiEm0/vzd3jBtG6uf0J9GUZMcZEtLz8Yh75YDXvLN/BiR1b8c4dpzMqvl2ow2qwrCgYYyLWgpVZ3P/eSnIOF/E/SX357fmVDexM1awoGGMizt6DBUybt4oPM7IY0rUtLyePYlj32FCHFRaitijMGDeD3ILcoxpURSPLhTfLR6Vwy4WqMvuHnTw8fzVHiku5+6KB3H7OiTRtXDff0w23fByLemuIFwzWEM8YU27Hj/ncM3clS9bvY2TvE3h8wgj6dWod6rAapJA0xDPGmPpQVqb8+5utPLFgLQAPXjGUG8f0plGUNLCra1YUjDFha9O+Q0yelc73W3/knAEdeWz8MHqcEF0N7Opa1BaF8tvg+d62LhpZLrxZPio11FwUl5Yxc8lmnv10Ay2aNubpa09iwindcTsvB01DzUdditqiEMht8KKF5cKb5aNSQ8zFyp15TJ6dzqpdB7hkeBemXTGUTm3qp4FdQ8xHXYvaomCMCS8FxaX89dMN/HPJZtq1asbzPz+FccOsgV1ds6JgjGnwvs/MYdLsdDbvO8y1p/bgvkuHENuyaajDikhWFIwxDdahwhKeXLCWV5dupXtcC/5962jO7m8N7ILJioIxpkFKXb+Pe+ZksCvvCMlnxHP3RQNpZQ3sgs4ybIxpUHLzi3ho/mrm/LCTvh1bMeuO0zm1tzWwqy9WFIwxDYKq8tHKLP703kpy84v53/P68Zvz+tG8iTWwq09WFIwxIbf3QAH3v7eShav2MLx7LK/echpDurUNdVhRKWqLQnJCMknxScTHxYc6lJCzXHizfFQKdi5UlXeW7+CR+aspLCljysWD+OVZfWhSRw3s6lo0vDasIZ4xJiS25+QzdU4GX27cz+g+7Xj86uGc2NEa2NUHa4hnjGkwSsuUV77O5KmF62jcSHjkqmH8bHQva2DXQFhRMMbUmw17DjJ5djo/bMslaWBHHhs/nG5xLUIdlvEQtUUhJS2FzNxM4uPiSU5IDnU4IWW58Gb5qFRXuSguLeP5xZv422cbadW8MTN+msCVCd2C3sCurkXDayOqi0Lq1lQSeydG7C83UJYLb5aPSnWRi4wdedw9awVrsw5y2YiuTLtiKB1aN6/bQOtJNLw2orYoGGOCq6C4lL8sWs8LSzbTsU1zZt54KhcO7RLqsEwNrCgYY+rcN5uzmTI7nczsfK4f3ZMpFw8mtoU1sAsHVhSMMXXmYEExj3+0lte+3Uavdi15/ZencUa/DqEOy9SCFQVjTJ34fO1e7pmbwZ4DBfzyrD78/sIBtGxmf2LCjf3GjDHHJedwEQ+9v4p303YxoHNrnrvhDE7udUKowzLHyIqCMeaYqCrvp+9m2rxVHCwo5nfn9+fOc/vRrEnDbFFhAlNjURCRzsBjQDdVvVhEhgCnq+qLQY/OGNMgZeUVcN+7K1m0Zg8n9YjliWtOY1AXa2AXCQI5UkgBXgbudafXA28BYV0UErokeP0bzSwX3iwflXxzoaq8+d12HvtgDcVlZdx7yWBuOasPjaOkRUU0vDZqbIgnIt+p6igR+a+qnuzOS1PVhBrWiwGWAM1xis8sVX1ARNrhFJV4IBP4iar+6K4zFbgVKAV+q6oL/e3DGuIZU3+2Zh9myuwMlm7OZsyJ7Xj86hHEd2gV6rDMMTjehniHRaQ9oO7GxgB5AaxXCJynqodEpCnwpYh8BFwNfKqqj4vIFGAKMNk9LXUdMBToBiwSkQGqWhrAvowxQVJaprz81Rae/ngdTRs1YvrVw7luVM+wa1FhAhNIUfg9MA/oKyJfAR2Ba2paSZ1DkEPuZFP3R4ErgSR3/ivAYmCyO/9NVS0EtojIRmA0sLS6fWTnZ5OUklTd04BzmDdj3IyK6bSsNCYumFhT+CxOXuw1XdN+AGaMm+F1WDlxwUTSstL8rpOckOz1dfmUtBRS0lL8rmNjqmRjcgRrTEWFHdi/+yKKCrrRovVGzhi+i+tHX1TxfDiOyVck/J58HeuYIICioKo/iEgiMBAQYJ2qFgeycRFpDCwH+gH/UNVvRaSzqu52t71bRDq5i3cHvvFYfYc7z3ebtwO3A8R2jiVza2YgoVTILcgldWtqrdYBAlontyDXazotK63G9ZLik7ymM3Mzax2fjamSjclx3GPSJsSWXEtsyU8oI5+cpk+SX7KEzj8mHrXfsBlTgKJ9TIF8+uhqn1kDRCQPyFDVvf7WdU/9JIhIHDBXRIb521VVm6himzOBmQB9hvTRhN4J/kI46oJQXEwcib0TSctKI68wj9jmsQFdNErsnVjjMnExcX73XRXfOzjFx8XXuK/qxlRb5ev4y0W4jsmfmsZUVT7CfUxVqW5MhUe6sD9rHMUlHSlq+iV7Gj9HmxghsUti2I7Jn9qMyfO14SucxpSKnwKhqn5/gA+AHGC2+5PtztsA3FjT+h7beQD4I7AO6OrO64pz5AEwFZjqsfxCnI++VrvNU089VY9V4suJyjQ08eXEY95GpLBceIvWfOQXlugj81dpnynzdcxji/TTNVlRm4vqREo+gO+1mr+rgXzLpAwYrKoTVHUCMATnIvJpONcCqiQiHd0jBESkBTAWWItzfeJmd7Gbgffcx/OA60SkuYj0AfoDywKIzxhznL7etJ+LZizhhS+2cP3oXnx81zmcN6hzqMMyIRDIheZ4Vd3jMb0XGKCqOSLi79pCV+AV97pCI+BtVZ0vIkuBt0XkVmAbcC2Aqq4SkbeB1UAJcKfaJ4+MCaoDBcVM/3ANbyzbTnz7lrx5+xjGnNg+1GGZEAqkKHwhIvOBd9zpCcASEWkF5Fa3kqqmAydXMT8bOL+adR4FHg0gJmPMcVq0eg/3vpvBvoOF/OqcE5k4dgAtmjUOdVgmxAIpCnfiFIIzcS4GvwrMds9LnRvE2IwxQZB9qJBp76/m/RW7GNSlDS/cNJIRPeJCHZZpIAL5SKoCs9wfY0yYUlXmrdjFtHmrOFRYwu8vGMAdiX2tgZ3xEshHUscAfwMGA82AxsBhVbXuV8aEiV25R7jv3ZV8tnYvCT3jePKaEQzo3CbUYZkGKJDTR3/HaT/xDjASuAnny2jGmAaurEx547ttTP9wLaVlyv2XDSH5jPioaWBnai+Qhnjfq+pIEUlX1RHuvK9V9Yx6idAPa4hnTPW27D/MlNnpfLslhzP7tWf6+BH0at8y1GGZBuB4G+Lli0gzIE1EngR2A9Ya0ZgGqqS0jBe/3MIzn6ynWZNGPDlhBNeO7GEN7ExAAikKN+J8z+A3wF1AT5xPIxljGpg1uw8weXY66TvyuGBIZx65ahid28aEOiwTRgL59NFW92EB8GBwwzHGHIvCklL+8dlGnlu8ibiWTfnHz07hkuFd7OjA1FqNn0UTkTNF5BMRWS8im8t/6iO4YEpKSUIelIDa3UY6y4W3cMvHD9t+5LK/fslfP9vIFSd145O7Erl0RNc6KQjhlotgi4Z8BHL66EWc00bLce6IZoxpAPKLSnh64Xpe/noLXdvG8PIvRnHuwE41r2iMH4EUhTxV/SjokRhjAvbVxv1MmZPO9pwj3DimN5PGDaRNTNNQh2UiQCBF4XMReQqYg9MdFXBuvhO0qIwxVco7UsxjH6zhre+306dDK966fQynWQM7U4cCKQqnuf96fqZVgfPqPhxjTHU+XpXFfe+uJPtwEb9O6svvzu9PTFNrYGfqlt+i4La9nqeqf6mneIwxPvYdLGTa+6v4IH03g7u25cWbRzG8x9F3/jKmLvgtCqpaKiJXAFYUjKlnqsrc/+7kofmryS8s5e6LBnL7OSfStLE1sDPBE8jpo69F5O/AW8Dh8pl2TcGY4NmZe4R752aweN0+TunlNLDr18ka2JngC6QolPc4eshjnl1TMCYIysqU177dyuMfraVM4YHLh3DT6dbAztSfQL7RHJE30pkxbga5BbnExcSFOpSQs1x4C1U+Nu87xJTZGSzLzOHs/h14bPxwerYLbQM7e214i4Z8BNIl9U9VzVfVh6qaX5+sS6qJBCWlZbzwxRb+smg9MU0acf9lQ7jmVGtgZ4LneLukHvZ4HANcBqypi8CMiXarduUxeXY6K3ceYNzQLjx01VA6tbEGdiZ0Ajl99GfPaRF5GpgXtIiMiQIFxaX87bMNPJ+6mRNaNuP/bjiFi4d3DXVYxgR0pOCrJXBiXQdS3yYumEhaVhoJXRKYMW5GqMMJKcuFt2DnY/nWHCbNSmfTvsNMOKUH9182mLiWzep8P3XBXhveoiEfgdyjOQPn00bg3J+5I96fRApLaVlppG5NDXUYDYLlwluw8nG4sISnFq7jlaWZdIttwSu3jCZxQMc6309dsteGt2jIRyBHCpd5PC4B9qhqSZDiMSYiLVm/j6lzMtiVd4SbT4/njxcNpHXzYzlQNya4Ar7Jjoi0BIYARcC+IMdlTETIyy/m4Q9WM2v5Dk7s2Ip3fnU6I+PbhTosY6pVbVFw21v8FcgB7gP+AewB4kVksqq+Uj8hGhOeFqzczf3vrSLncBF3ntuX/z3PGtiZhs/fkcLDwIVALPA5MEJVN4tIJ+BTwIqCMVXYe7CAB95bxUcrsxjarS0pvxjF0G7WwM6EB39FoUxV1wOIyBZV3QygqntFxK4pGONDVZm1fAePfLCGI8WlTBo3kNvOtgZ2Jrz4KwqNROQEnPs4l7mPy79iaa9yYzxsz8nnnrkZfLFhP6PiT+DxCSPo27F1qMMyptb8FYVYnPsylxcCz66o/ntjGBMlysqUV5dm8uTCdQjw8JVDueG03jSyBnYmTFVbFFQ1vh7jqHfJCckkxScRHxcf6lBCznLhLdB8bNx7iCmz0/l+648kDujIo+OH0eOE0Dawq2v22vAWDfmosSFeQ2YN8UwoFJeWMXPJZp5dtIGWzRvzp8uGMP7k7tbAzoSN422IZ4xxrdyZx6RZ6azefYBLh3dl2hVD6dimeajDMqbOWFEwJgAFxaU8++kGZi7ZTLtWzXj+56cybliXUIdlTJ0LqCiIyFlAf1V9WUQ6Aq1VdUtwQwuulLQUMnMziY+LJzkhOdThhJTlwptvPr7LzGHyrHQ27z/MT0b24N5LhhDbsmmow6wX9trwFg35CKQh3gPASGAg8DLQFPgPcGYN6/UEXgW6AGXATFV9VkQSgOdx7s1QAvyPqi5z15kK3AqUAr9V1YXHNqyapaSlkLo1lcTeiRH7yw2U5cJbeT7O7jGWzVtH8urSrfQ4oQX/ufU0zurfIdTh1St7bXiLhnwEcqQwHjgZ9yOpqrpLRAK5g3gJ8AdV/cFdfrmIfAI8CTyoqh+JyCXudJKIDAGuA4YC3YBFIjJAVUtrPyxjjk9M6ans3HIL/964lVvO7MMfLxpAy2Z2ttVEvkBe5UWqqiKiACLSKpANq+puYLf7+KCIrAG643zHoa27WCywy318JfCmqhYCW0RkIzAaWFrdPrLzs0lKSfIbh2/f87SstIqe6OXTVW1jcfJir+ma9gPO/VsTuiRUTHvupzrJCcle7zhS0lJISUvxu051Y6pJdWPyl4twHZM//sZUWhrDpswxdC4+m6JGO+ne630+y95Fr9XhO6bqBPJ78n1tRMKYfNVmTJ758BWuY/IVSFF4W0T+CcSJyG3ALcALAW3dJSLxOEcb3wITgYXuHdwaAWe4i3UHvvFYbYc7z3dbtwO3A8R2jiVza2ZtQiG3INerH3peYV5A/dEDWSa3INdrOpDe60nxSV7TmbmZte7X7jumQPmuU1Uuwn1M1W3bU1pWGqmZqbQsO5N2RT+nKW3IbfImeU3eZMdep6NLWI6pDn9P1f0/Cecx+dtvTevkFeYdNS/cx1QukNbZT4vIBcABnOsKf1LVTwINRkRaA7OBiap6QEQeAe5S1dki8hPgRWAsld+c9tp9FfHMBGYC9BnSRxN6J/jdv2dVBoiLiSOxdyJpWWnkFeYR2zz2qGWqktg7scZl4mLi/O67Kr5fgomPi69xX9WNqbbK1/GXi3Adkz++Yxp0wmj27riK/EP9aRaTxf6mT5JXkuGVj3AbU139nnxfG5EwJl+1GZNnPnyF05hSqb5ABPXLayLSFJgPLFTVZ9x5eUCce0pKgDxVbeteZEZVp7vLLQSmqWq1p4+O58trSSlJFReMfA9to0205kJVeef7HTz8wWqKSsr4/QUDuPWsPoz993lRmY+qROtrozqRko9j+vKaiHypqmeJyEG837ELoKratppVy9cXnKOANeUFwbULSAQWA+cBG9z584DXReQZnAvN/YFl/vZhzLHalu00sPty435G92nHExNG0KdDQJfLjIlo/nofneX+G8gnjapyJnAjkCEiae68e4DbgGdFpAlQgHt9QFVXicjbwGqcTy7daZ88MnWttExJ+TqTpxeuo3Ej4ZGrhvGz0b2sgZ0xLr/XFESkEZCuqsNqu2FV/ZKqrxMAnFrNOo8Cj9Z2X8ei/JxbIOf0Il205GLDnoNMmp3Of7flcu7Ajjw6fjjd4loctVy05CMQlgtv0ZCPGq8piMhrwFRV3VY/IQXOGuKZQBSVlPF86ib+/tlGWjVvzAOXD+XKhG7WwM5EreNtiNcVWCUiy4DD5TNV9Yo6is+YoEnfkcukWemszTrI5Sd144HLh9ChtTWwM6Y6gRSFB4MehTF17EhRKTMWreeFLzbTsU1zXrhpJBcM6RzqsIxp8AL5nkKqiHQGRrmzlqnq3uCGFXxpWWnkFuQSFxMX0ecHAxFpufhmczZTZqeTmZ3P9aN7MuXiwcS2CLyBXaTl43hYLrxFQz5qvNey+wWzZcC1wE+Ab0XkmmAHFmwTF0zk3FfODfir35EsUnJxsKCYe+dmcN3MbyhTeP2XpzH96hG1KggQOfmoC5YLb9GQj0BOH90LjCo/OnBbZy8CZgUzMGNq47O1e7h37kr2HCjgl2f14fcXWgM7Y45FIP9rGvmcLsomgCMMY+pDzuEiHnp/Fe+m7WJA59Y8d8MZnNzrhFCHZUzYCqQoLHBbTrzhTv8U+DB4IRlTM1Xl/fTdTJu3ioMFxfzu/P7ceW4/mjWx9yvGHI9ALjTfLSITcL6hLDg3y5kb9MiMqUZWXgH3vZvBojV7OalHLE9ccxqDuvjtumKMCVBAJ11VdTZOp1NjQkZVefO77Tz2wRqKy8q495LB3HJWHxpbiwpj6oy/hni+jfAqniKAhnjG1KWt2YeZMjuDpZuzGXNiOx6/egTx1sDOmDrnryHesTbCM6bOlJYpL3+1hac/XkfTRo14bPxwrhvV0xrYGRMkAX9mT0Q6ATHl0w2xF5KJLOuynAZ2K7bncv6gTjwyfhhdY49uYGeMqTuBNMS7Avgzzj0O9gK9ce6RMDT44flnDfEiU1FJGf/4fCPPLd5Im5imTLtiKJeP6GoN7IypI8fbEO9hYAywSFVPFpFzgevrMkBjyqVtz2XSrBWs33OIKxO68cDlQ2nXqlmowzImagRSFIpVNVtEGolII1X9XESeCHpkJqocKSrlzx+v46WvttCpTQwv3jyS8wdbAztj6lsgRSFXRFoDS4DXRGQvzp3RjKkTX2/az5TZGWzLyef60b2Yeskg2sbUrl+RMaZuBPL1zyuBfOAuYAGwCbg8mEHVh6SUJORBISklKdShhFyocnGgoJipc9L52QvfIgJv3DaG6VcPD3lBsNdGJcuFt2jIh7/vKfQDOqvqV+6sMuAVETkHiMPpgWTMMVm0eg/3vpvBvoOF3H7Oidw1dgAtmjUOdVjGRD1/p49mAPdUMT/ffS7sjxZM/cs+VMi091fz/opdDOrShpk3juSknnGhDssY4/JXFOJVNd13pqp+LyLxwQvJRCJV5b20XTz4/ioOFZbw+wsGcEdiX2tgZ0wD468oxPh5zr5BZAK2K/cI9727ks/W7iWhZxxPXjOCAZ3tC/PGNET+isJ3InKbqr7gOVNEbgWWBzcsEwnKypTXl23j8Y/WUlqm3H/ZEJLPiLcGdsY0YP6KwkRgrojcQGURGAk0A8YHOS4T5rbsP8yU2el8uyWHM/u1Z/r4EfRq3zLUYRljauCvId4e4Az3G8zD3NkfqOpn9RKZCUslpWW8+OUWnvlkPc2aNOLJCSO4dmQPa1FhTJgI5CY7nwOf10MsJsyt2X2AybPTSd+RxwVDOvPIVcPo3NbfpSljTEMTtXc2nzFuBrkFucTFxIU6lJA73lwUlpTyj8828tziTcS1bMo/fnYKlwzvErZHB/baqGS58BYN+aixS2pDZl1SQ++HbT8yeVY6G/Ye4uqTu3P/ZUM4wRrYGdOgHW+XVGOOkl9UwtML1/Py11vo2jaGl38xinMHdgp1WMaY42RFwdTaVxv3M2VOOttzjnDT6b2ZNG4QrZvbS8mYSBC1/5MnLphIWlYaCV0SmDFuRqjDCalAc5F3pJjHPljDW99vp0+HVrz9q9MZ3add/QVaT+y1Ucly4S0a8hG1RSEtK43UramhDqNBCCQXC1dlcf+7K8k+XMSvk/ryu/P7E9M0MhvY2WujkuXCWzTkI2qLggnMvoOFTJu3ig8ydjOka1teSh7FsO6xoQ7LGBMkVhRMlVSVuf/dyUPzV5NfWMrdFw3k9nNOpGlja2BnTCSzomCOsjP3CPfOzWDxun2c2vsEnpgwgn6dWoc6LGNMPQja2z4R6Skin4vIGhFZJSK/83juf0VknTv/SY/5U0Vko/vcRcGKzVRNFf69NJMLn0ll2ZYcpl0+hHd+dboVBGOiSDCPFEqAP6jqDyLSBlguIp8AnXFu8TlCVQtFpBOAiAwBrgOGAt2ARSIyQFVLgxijcTUp60bWtuu4f90qzu7fgcfGD6dnO2tgZ0y0qbdvNIvIe8DfgduAmaq6yOf5qQCqOt2dXghMU9Wl1W2zz5A+2ntSb7/79f3oWFpWWsXHyvIK84htHktCl4Sj1lucvNhrOpB7ss4YN8NrW+X78Sc5IZnkhOSK6ZS0FFLSUvyuU92YalLVmFSFtVu7E1MwAZUiOnVZQqu2qyjvUBGOY6pJTWOq6rUR7mOqSiBj8s1FJIzJV23G5JmP3Cm5Xs+F05hSf5Ea2m80u3dqOxn4FngKOFtEHgUKgD+q6ndAd+Abj9V2uPN8t3U7cDtAbOdYMrdm1iqW3IJcr4+U5RXmBfQRs0CWyS3I9ZoO5ONrSfFJXtOZuZm1/sib75gC9fWWbbQv+h0ttB+HG31FTrP/Y3tuLuR6b9tTQx9TXf6ePF8bkTImT7UZU3X/T8J5TP72W9M6eYV5R80L9zGVC3pREJHWwGxgoqoeEJEmwAnAGGAU8LaInAhU1T3tqMMYVZ0JzATnSCGhd4Lf/fseBcTFxJHYO5GsQ1kUlBQQ0ySGLq271DiOxN6JNS7j2ySrqiMQX/Fx8UdN17Sv6sYUqILiUv722Qa6FT5Lo8ZHaBybQqtm39G+SWe6tD7pqG3723dVQjGmcnXxe6rqtRHuY6pKIGPyzUUkjMlXbcbkmQ9f4TSmVKovEEE9fSQiTYH5wEJVfcadtwB4XFUXu9ObcArEL6F2p4+sIV7tLd+aw6RZ6Wzad5hrTu3BfZcOJq6lNbAzJpqEpCGeOH2TXwTWlBcE17vAecBiERmAcye3/cA84HUReQbnQnN/YFmw4os2hwtLeGrhOl5Zmkm32Ba8estozhnQMdRhGWMamGCePjoTuBHIEJE0d949wEvASyKyEigCblbncGWViLwNrMb55NKd9smjurFk/T6mzslgV94Rbj49nrsvGkgra2BnjKlC0P4yqOqXVH2dAODn1azzKPBosGLylJKWQmZuJvFx8V5X9iNJbn4Rj3ywhlnLd9C3Yyve+dXpjIw/uoFdNOSiNiwflSwX3qIhH1H7djElLYXUrakk9k6MyF/uRxm7uf+9VfyYX8Rvzu3Hb87rV20Du0jPRW1ZPipZLrxFQz6itihEqr0HC3jgvVV8tDKLod3a8sotoxjazRrYGWMCY0UhQqgqs5bv4JEP1nCkuJTJ4wZx29l9aGIN7IwxtWBFIQJsz8nnnrkZfLFhP6Pj2zF9wnD6drR+RcaY2rOiEMbKypRXl2by5MJ1CPDwlUO54bTeNGpU3fV9Y4zxz4pCmNq49yCTZ2ewfOuPJA7oyGNXD6d7XItQh2WMCXNWFMJMcWkZM5ds5tlFG2jZvDHP/OQkxp/cHRE7OjDGHD8rCmFk5c48Js1KZ/XuA1w6oivTLh9KxzbNQx2WMSaCRG1RKG8YFUhDqlArKC7l2U83MHPJZtq3asY/bzyVi4bW3MQvUOGUi/pg+ahkufAWDfmot/spBEM0NMRbtiWHKbPT2bz/MD8d2ZN7Lh1MbIumoQ7LGBPGQtIQzxyfQ4UlPPHRWv79zVZ6tmvBa788jTP7dQh1WMaYCGdFoQH6fN1e7p2Twe4DBdxyZh/+eNEAWjazX5UxJvii9i9NWlYauQW5xMXENZjzgz8eLuLh+auZ89+d9O/Umtm/PoNTep0Q9P02xFyEkuWjkuXCWzTkI2qLwsQFEysaW/neP7a+qSofZOzmgfdWkXekmN+e1487z+tH8yZVN7Craw0pFw2B5aOS5cJbNOQjaotCQ7HnQAH3v7uSj1fvYUSPWP7zy9MY3LVtqMMyxkQpKwohoqq8/f12HvlgDUUlZdxzySBuOdMa2BljQsuKQghsy85n6tx0vtqYzWl92vHEhBHEd2gV6rCMMcaKQn0qLVNSvs7k6YXraNxIeHT8MK4f1csa2BljGgwrCvVk/Z6DTJqVTtr2XM4b1IlHxw+ja6w1sDPGNCxWFIKsqKSM51M38bfPNtC6eROevS6BK07qZg3sjDENkhWFIFqxPZfJs9NZm3WQK07qxgOXD6F9a2tgZ4xpuKwoBMGRolJmLFrPC19splObGP5100jGDukc6rCMMaZG1hCvji3dlM3UOelkZudz/eieTL1kMG1jrIGdMabhsIZ49eBAQTGPf7SW17/dRu/2LXn9ttM4o681sDPGhBcrCnXgs7V7uGfOSvYeLOC2s/vw+wsG0qJZ/bSoMMaYumRF4ThkHyrkofmreS9tFwM7t+H5G08loWdcqMMyxphjFrU9FZJSkpAHhaSUpFqvq6rMW7GLC/6yhA8zdjNxbH/e/9+zwrYgHE8uIpHlo5Llwls05MOOFGppd94R7n93JYvW7OWknnE8OWEEA7u0CXVYxhhTJ6woBKisTHnzu+1M/3ANxWVl3HfpYH5xZh8aW4sKY0wEsaIQgMz9h5kyJ51vNudw+onteXzCcHq3twZ2xpjIY0XBj9Iy5aUvt/DnT9bRtFEjHr96OD8d1dNaVBhjIpYVhWqsyzrIpFkrWLEjj7GDO/HIVcPpEhsT6rCMMSaorCj4KCwp5bnPN/Hc4o20jWnK364/mctGdLWjA2NMVLCi4OG/235k8ux01u85xFUJ3fjT5UNp16pZqMMyxph6Y0UByC8q4c8fr+elr7bQpW0MLyWP5LxB1sDOGBN9gtYQT0R6Aq8CXYAyYKaqPuvx/B+Bp4COqrrfnTcVuBUoBX6rqgv97eN4GuKlZaWRW5DLtn0t+Nfn+WzLyefnY3oxedwg2kRZA7vyXMTFxJHQJSHU4YSc5aOS5cJbpOTDX0O8YBaFrkBXVf1BRNoAy4GrVHW1WzD+BQwCTlXV/SIyBHgDGA10AxYBA1S1tLp9HE9RyDtSzPQP1/Dmd9uJb9+SxyeMYMyJ7Y9pW8YYE05C0iVVVXcDu93HB0VkDdAdWA38BZgEvOexypXAm6paCGwRkY04BWJpdfvIzs+u8evmCV0SmDFuRsV0WlYav547nb07x1Na0oq27b6jrP3XTFlSAksq11ucvNhrO4F8rX3GuBle7x4mLphIWlaa33WSE5JJTkiumE5JSyElLcXvOlWNaeKCiTXGZ2Ny2JgcNiaHjclbvVxTEJF44GTgWxG5Atipqit8PtHTHfjGY3qHO893W7cDtwPEdo4lc2tmrWLJLchl2Z4P6VA2jNzmr7L1yEbYXvN6qVtTA9q2p7SstBrXS4pP8prOzM0MaF+++63tOmBjKmdjctiYKvcbzWMKelEQkdbAbGAiUALcC1xY1aJVzDvq3JaqzgRmAvQZ0kcTeif43b/veb+4mDjOjj+VjTn/pEXRITo2606/dv1qHEdi78Qal4mLifO776rEx8UfNV3TvqoaUyDx+SpfZ2PORg4VHaJ1s9ZH5SJcx+RPTWOqKh/hPqaqBDIm31xEwph81WZMnvnwFU5jSqX6AhHUO6+JSFNgPrBQVZ8RkeHAp0C+u0gPYBfOaaJfAKjqdHfdhcA0Va329NHxXFNISkkidWsqib0TjzoMjDaWC2+Wj0qWC2+Rkg9/1xSC1jpbnHNDLwJrVPUZAFXNUNVOqhqvqvE4p4hOUdUsYB5wnYg0F5E+QH9gWbDiM8YYc7Rgnj46E7gRyBCRNHfePar6YVULq+oqEXkb50J0CXCnv08eGWOMqXvB/PTRl1R9ncBzmXif6UeBR4MVkzHGGP+i9s5rxhhjjmZFwRhjTAUrCsYYYypYUTDGGFMhqN9TCDYR2QdsPY5NdAD211E44c5y4c3yUcly4S0S8tFbVTtW9URYF4XjJSLfV/cFjmhjufBm+ahkufAW6fmw00fGGGMqWFEwxhhTIdqLwsxQB9CAWC68WT4qWS68RXQ+ovqagjHGGG/RfqRgjDHGgxUFY4wxFaKmKIjIXSKySkRWisgbIhIjIu1E5BMR2eD+e0Ko46wPIvI7Nw+rRGSiOy9qciEiL4nIXhFZ6TGv2vGLyFQR2Sgi60TkotBEHTzV5ONa9/VRJiIjfZaPxnw8JSJrRSRdROaKSJzHcxGVj6goCiLSHfgtMFJVhwGNgeuAKcCnqtof5+Y/U0IXZf0QkWHAbTg3NjoJuExE+hNduUgBxvnMq3L8IjIE57Uy1F3nORFpXH+h1osUjs7HSuBqvO5cHtX5+AQYpqojgPXAVIjMfERFUXA1AVqISBOgJc4d364EXnGffwW4KjSh1avBwDeqmq+qJUAqMJ4oyoWqLgFyfGZXN/4rgTdVtVBVtwAbcQpqxKgqH6q6RlXXVbF4tObjY/f/Czj3ku/hPo64fERFUVDVncDTwDZgN5Cnqh8DnVV1t7vMbqBT6KKsNyuBc0SkvYi0BC4BehKdufBU3fi7A9s9ltvhzotWlg+4BfjIfRxx+YiKouCeH74S6AN0A1qJyM9DG1VoqOoa4Amcw+EFwAqcO92ZqlV1o6ho/hx3VOdDRO7F+f/yWvmsKhYL63xERVEAxgJbVHWfqhYDc4AzgD0i0hXA/XdvCGOsN6r6oqqeoqrn4BwmbyBKc+GhuvHvwDmSKtcD59RjtIrafIjIzcBlwA1a+QWviMtHtBSFbcAYEWkpIgKcD6wB5gE3u8vcDLwXovjqlYh0cv/thXMx8Q2iNBceqhv/POA6EWkuIn2A/sCyEMTXUERlPkRkHDAZuEJV8z2eirx8qGpU/AAPAmtxzqn/G2gOtMf5pMkG9992oY6znnLxBbAa59TR+e68qMkFThHcDRTjvNO71d/4gXuBTcA64OJQx19P+RjvPi4E9gALozwfG3GuHaS5P89Haj6szYUxxpgK0XL6yBhjTACsKBhjjKlgRcEYY0wFKwrGGGMqWFEwxhhTwYqCiTgiMl5EVEQGhTqWmohIpohkiMgKEflYRLrUsHyKiFxTX/GZ6GNFwUSi64EvcbpXHrd66Hp5rqqeBHwP3BPkfRnjlxUFE1FEpDVwJs4Xjq5z510sIm97LJMkIu+7jy8UkaUi8oOIvOOuX/4O/k8i8iVwrYjcJiLfue/oZ7vNBBGRviLyjfvcQyJyyGM/d7vz00XkwQDCXwL0E5F4n17+fxSRaVWM9XERWe1u/2l3Xkc3vu/cnzNrnUQT1awomEhzFbBAVdcDOSJyCk7zvzEi0spd5qfAWyLSAbgPGKuqp+C8U/+9x7YKVPUsVX0TmKOqo9x39Gtwig7As8CzqjoKj543InIhTsuD0UACcKqInFND7JcBGYEMUkTa4XzreKg6Pf4f8YjnL248E4B/BbI9Y8pZUTCR5nrgTffxm8D16vTBXwBc7t5P41Kc3kZjgCHAVyKShtPzqLfHtt7yeDxMRL4QkQzgBpybqgCcDrzjPn7dY/kL3Z//Aj8Ag3CKRFU+d/ffFpge4DgPAAXAv0TkaqC8H89Y4O/u9uYBbUWkTYDbNIYmoQ7AmLoiIu2B83D+gCvOHfZURCbh/IG/E6cr7HeqetBtjviJql5fzSYPezxOAa5S1RUikgwk1RQOMF1V/xlA6Oeq6n6PcbTG+w1bjO8KqloiIqNxmjteB/wGZ+yNgNNV9UgA+zXmKHakYCLJNcCrqtpbVeNVtSewBTgLWAycgnMr0vIjgG+AM0WkH4DbRXdANdtuA+wWkaY4RwrlvsE5TQPeF7YXArd4XKPoXt6dNgB7gE7ujZCa45xW8uJuN1ZVPwQm4pyiAvgYp0CUL5fgu64x/lhRMJHkemCuz7zZwM9UtRSYD1zs/ouq7gOSgTdEJB3nD3x1H2O9H/gW5/rEWo/5E4Hfi8gyoCuQ5277Y5zTSUvdU06zcApLjdS558dD7v7m++yvXBtgvht3KnCXO/+3wEj34vNq4I5A9mlMOeuSasxxcD+FdERVVUSuw7mGcWWo4zLmWNk1BWOOz6k4F3YFyMW5f68xYcuOFIwxxlSwawrGGGMqWFEwxhhTwYqCMcaYClYUjDHGVLCiYIwxpsL/A2IGxKMuXDdtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Set Line Properties for the Grid\n",
    "### You can also set the line properties of the grid, like this: grid\n",
    "                 ### (color = 'color', linestyle = 'linestyle', linewidth = number).\n",
    "x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])\n",
    "y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])\n",
    "\n",
    "plt.title(\"Sports Watch Data\")\n",
    "plt.xlabel(\"Average Pulse\")\n",
    "plt.ylabel(\"Calorie Burnage\")\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.grid(color = 'green', linestyle = '--', linewidth = 2.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot 1:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(2,1, 1)\n",
    "plt.plot(x,y)\n",
    "\n",
    "#plot 2:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(x,y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The subplots() function takes three arguments that describes the layout of the figure.\n",
    "\n",
    "### The layout is organized in rows and columns, which are represented by the first and second argument.\n",
    "\n",
    "### The third argument represents the index of the current plot.\n",
    "plt.subplot(1, 2, 1)\n",
    "#the figure has 1 row, 2 columns, and this plot is the first plot.\n",
    "plt.subplot(1, 2, 2)\n",
    "#the figure has 1 row, 2 columns, and this plot is the second plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot 1:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(2, 1,1)\n",
    "plt.plot(x,y)\n",
    "\n",
    "#plot 2:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2, 1,2)\n",
    "plt.plot(x,y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(2, 3, 1)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2, 3, 2)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(2, 3, 3)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2, 3, 4)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(2, 3, 5)\n",
    "plt.plot(x,y)\n",
    "\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(2, 3, 6)\n",
    "plt.plot(x,y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### You can add a title to each plot with the title() function\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x,y)\n",
    "plt.title(\"SALES\")\n",
    "\n",
    "#plot 2:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x,y)\n",
    "plt.title(\"INCOME\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### You can add a title to the entire figure with the suptitle() function:\n",
    "#plot 1:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x,y)\n",
    "plt.title(\"SALES\")\n",
    "#plot 2:\n",
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([10, 20, 30, 40])\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x,y)\n",
    "plt.title(\"INCOME\")\n",
    "\n",
    "plt.suptitle(\"MY SHOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([1,2,3,4,5])\n",
    "y=np.array([5,10,15,20,25])\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2aaeefdabe0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x=np.array([1,2,3,4,5])\n",
    "y=np.array([25,20,15,10,5])\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### With Pyplot, you can use the scatter() function to draw a scatter plot.\n",
    "\n",
    "## The scatter() function plots one dot for each observation. It needs two arrays of the same length, \n",
    "## one for the values of the x-axis, and one for values on the y-axis.\n",
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([23,12,45,23,86,103,87,94,78,77,85,86,12])\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#day one, the age and speed of 13 cars:\n",
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "plt.scatter(x, y)\n",
    "\n",
    "#day two, the age and speed of 15 cars:\n",
    "x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])\n",
    "y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])\n",
    "plt.scatter(x, y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### By comparing the two plots, I think it is safe to say that they both gives us the same conclusion\n",
    "###  the newer the car, the faster it drives\n",
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "plt.scatter(x, y, color = 'hotpink')\n",
    "\n",
    "x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])\n",
    "y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])\n",
    "plt.scatter(x, y, color = 'green')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([1,2,3,4,5])\n",
    "y=np.array([6,7,8,9,10])\n",
    "colors=([\"red\",\"yellow\",\"black\",\"blue\",\"green\"])\n",
    "size=([10,200,30,40,50])\n",
    "plt.scatter(x,y,c=colors,s=size)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### You can even set a specific color for each dot by using an array of colors as value for the c argument.\n",
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "colors = np.array([\"red\",\"green\",\"blue\",\"yellow\",\"pink\",\"black\",\"orange\",\"purple\",\"beige\",\"brown\",\"gray\",\"cyan\",\"magenta\"])\n",
    "\n",
    "plt.scatter(x, y, c=colors)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ColorMap\n",
    "## The Matplotlib module has a number of available colormaps.\n",
    "\n",
    "### A colormap is like a list of colors, where each color has a value that ranges from 0 to 100.\n",
    "\n",
    "#### Here is an example of a colormap:\n",
    "\n",
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,87,94,78,77,85,86,13])\n",
    "colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])\n",
    "\n",
    "plt.scatter(x, y, c=colors, cmap='viridis')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### You can include the colormap in the drawing by including the plt.colorbar() statement\n",
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "colors = np.array([0, 10, 20,30, 40, 45, 50, 55, 60, 70, 80, 90, 100])\n",
    "\n",
    "plt.scatter(x, y, c=colors, cmap='viridis')\n",
    "\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "sizes = np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])\n",
    "colors=np.array([1,2,3,4,5,6,7,8,9,10,11,12,13])\n",
    "plt.scatter(x, y, s=sizes,c=colors)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Alpha :You can adjust the transparency of the dots with the alpha argument.\n",
    "\n",
    "### Just like colors, make sure the array for sizes has the same length as the arrays for the x- and y-axis.\n",
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "sizes = np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])\n",
    "plt.scatter(x, y, s=sizes, alpha=1.0,color=\"r\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(100, size=(100))\n",
    "y = np.random.randint(100, size=(100))\n",
    "colors = np.random.randint(100, size=(100))\n",
    "sizes = 10 * np.random.randint(100, size=(100))\n",
    "\n",
    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap='nipy_spectral')\n",
    "\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
    "y = np.array([3, 8,1,10])\n",
    "\n",
    "plt.bar(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### If you want the bars to be displayed horizontally instead of vertically, use the barh() function.\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.barh(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "### The bar() and barh() takes the keyword argument color to set the color of the bars\n",
    "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.bar(x, y, color = \"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.bar(x, y, width = 0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "## The barh() takes the keyword argument height to set the height of the bars\n",
    "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
    "y = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.barh(x, y, height = 0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(100,5,250)\n",
    "plt.hist(x)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([10, 135, 25, 15,19])\n",
    "\n",
    "plt.pie(y)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Add labels to the pie chart with the label parameter.\n",
    "\n",
    "### The label parameter must be an array with one label for each wedge\n",
    "y = np.array([18, 25,15,10])\n",
    "mylabels = [\"Cherries\",\"Apples\", \"Bananas\",\"Dates\"]\n",
    "\n",
    "plt.pie(y, labels = mylabels)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### As mentioned the default start angle is at the x-axis, but you can change the start angle \n",
    "### by specifying a startangle parameter.\n",
    "\n",
    "### The startangle parameter is defined with an angle in degrees, default angle is 0:\n",
    "y = np.array([35, 25, 25, 15])\n",
    "mylabels = [\"Apples\", \"Bananas\", \"Cherries\", \"Dates\"]\n",
    "\n",
    "plt.pie(y, labels = mylabels, startangle = 90)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.array([35, 25, 25, 15])\n",
    "mylabels = [\"Apples\", \"Bananas\", \"Cherries\", \"Dates\"]\n",
    "myexplode = [0,0.95, 0.1, 3.2]\n",
    "\n",
    "plt.pie(y, labels = mylabels, explode = myexplode)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([35, 25, 25, 15])\n",
    "mylabels = [\"Apples\", \"Bananas\", \"Cherries\", \"Dates\"]\n",
    "mycolors = [\"black\", \"hotpink\", \"b\", \"yellow\"]\n",
    "\n",
    "plt.pie(y, labels = mylabels, colors = mycolors)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([35, 25, 25, 15])\n",
    "#mylabels = [\"Apples\", \"Bananas\", \"Cherries\", \"Dates\"]\n",
    "\n",
    "plt.pie(y)\n",
    "plt.legend(labels=mylabels)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}