pyplot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot data using `matplotlib.pyplot`\n",
    "\n",
    "\n",
    "See [here](https://matplotlib.org/tutorials/index.html#) for official `matplotlib` tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standard package for numerical calculations  \n",
    "import numpy as np                 \n",
    "\n",
    "# standard package for plotting!\n",
    "import matplotlib.pyplot as plt    \n",
    "\n",
    "# to plot figures in the notebook!\n",
    "%matplotlib inline                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate 100 samples from normal distribution\n",
    "x = np.random.randn(100)\n",
    "y = np.random.randn(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of X vs. Y')"
      ]
     },
     "execution_count": 3,
     "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": [
    "# simple\n",
    "plt.plot(x,y, '.')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Plot of X vs. Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatter plot of X vs. Y')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEWCAYAAABv+EDhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAf2UlEQVR4nO3de5AdZ3nn8e8zMxpLSEIrxyoUWZZlMOU1tkwCY7LOUqsEI8cQQ1aqdTZkK4BD4rAFARyzMbIIlw2+VEhwUoEt4/i27NqAAQsccEoWm0SEcIlk1ossywbjS9kMCnIs6zKWPJo5z/7RPcrR6NzmnO5+3+7+faqm7DnnzOnnXPQ+/T7vpc3dERGR+hkKHYCIiIShBCAiUlNKACIiNaUEICJSU0oAIiI1pQQgIlJTSgAiXZiZm9mZBRzHzOw2M9tnZv+U9/FElAAkE2b2WjP7lpntN7Nnzewfzez8AZ/z7Wb2zVm33W5mHxss2ny0ineOXgusA1a6+2taPP/vm9mDZjbadNv7zOz/mtnIAMftiZktMLMfmtlbZ93+4fTzVntSMvrAZGBm9mLgq8BfAicDpwIfBV4IGVcrRTSUAzgdeMLdJ9rc/yngOWATgJm9lOR9foe7T+UdnLsfBt4BfMLMXpLGcDbwB2kMjbxjkIy5u370M9APMAY81+UxvwvsBg4CDwGvSm//APCjptvXp7efDRwBpoFDJA3f5cBRYDK97a/Tx64AvgTsBR4H3tN03I8AXwT+N3AA+J0Wsd0O3AhsTePYBpzedL8DZ6b/vwT4THqsJ4EPkpxInRBvm/dhBXAP8CzwKPC76e3vmPX3H23z92elr+M84P8A13V4z3cDlzT9PgI8A7wKmJ++J/+SvrfbgZf0+Hl/CvgCYMA3gQ+E/g7qp7+f4AHop/w/wIvThuR/Am8Als66/1Lgx8D5aaNx5kwDm963Im1E/zMwAfxset/bgW/Oeq7bgY81/T4E3A98CBgFXgo8BvxKev9H0qTxH9PHLmgR/+1pw/8fgJOAv2g+7qwE8BngK8BiYDXwA5Kz35bxtjjWNuB/pA3wz6WJ5MJe/z593Ma0IX8EmN/hcR8C7mj6/VeBh9P//z3gr4EXAcPAq4EX9/h5LwKeAO4GdgDDob+D+unvRyUgGZi7HyCpXzvwV8BeM7tnpkwA/A7wJ+6+3ROPuvuT6d9+wd3H3b3h7p8HfgicUP/u4Hxgmbv/d3efdPfH0hh+o+kx33b3L6fHONzmeb7m7t9w9xdISiwXmNlpzQ8ws2GSJLXR3Q+6+xPAnwG/1Uug6fO9FrjK3Y+4+wPAzb3+fZN/AH4G+KK7H+nwuDuBN5vZi9LffzO9DZKk+DMkiW3a3e9PP8eu3P0Q8C5gPUnym55j/BIJJQDJhLvvdve3u/tK4FySs/o/T+8+jaTMcwIze6uZPWBmz5nZc+nfnjKHQ58OrJj5+/Q5rgZe0vSYp3p4nmOPSRu4Z9PX0OwUkl7Gk023PUky5tGLFcCz7n6wz78nHQD+NMl4y7vTcYCW3P1RkjLQm9Ik8Gb+NQH8L2AL8DkzGzezPzGzeb3GAeya9V8poZgHxKSk3P1hM7udpMwASeP6stmPM7PTSc7WLyQ5S582swdIykSQ9ChOePpZvz8FPO7uL+8UUg9hHzvbN7NFJIPZ47Me8wzJmfPpJOMVAKtIylu9HGccONnMFjclgea/78UfAT8F3gscJkkG6zo8/rPAW0hO9h5KkwLufpRkAPmjZrYauJekpHTLHGKRklMPQAZmZv/WzK40s5Xp76eRNDrfSR9yM/B+M3t1Otf9zLTxX0jSaO5N/+4ykh7AjH8GVjZPe0xvaz7r/SfggJldlU5THDazc/uYgvrGdCrrKPDHwHfd/bieQ1rquAu4xswWp6/hD0gGU9vF2/z3TwHfAq4zs/lmdh7J4O8dvQRoZq8E3kMycOwk4xur0/etnc8BFwH/lX89+8fMftnM1qRlrQMkiU2lnJpRApAsHAR+AfiumU2QNPwPAldCUucHriFpgA4CXwZOdveHSGro3yZpPNcA/9j0vH9LUmLYY2bPpLfdArwiLfd8OW2U30QyoPo4yVn6zSSzdebiTuDDJKWfVwP/pc3jfp9koPoxkhkwdwK3doh3treQDB6PA5uBD7v71m7BpQ31LcA1TWfxh0lmV328abzlOO7+E5L39xeBzzfdtZxkdtQBkjLRNtJEZmY3mtmN3WKS8rPkREKkvtJy1dPu/sHQsYgUST0AEZGaUgIQEakplYBERGpKPQARkZoq1TqAU045xVevXh06DBGRUrn//vufcfdls28vVQJYvXo1O3bsCB2GiEipmNmTrW5XCUhEpKaUAEREakoJQESkppQARERqSglARKSmlABEpCf7JibZNb6ffROToUORjJRqGqiIhLFl1x42bd7JkBkNd65dv4aLzlkeOiwZkHoAItLRvolJNm3eSaORbBvTaDhXb96pnkAFKAGISEfj+w8zZMbIcNJcjAwPMWTG+P52l1eWslACEJGOVixZQMOdqekGAFPTDRrurFiyIHBkMiglABHpaOnCUa5dv4ahoeRSzUNDxrXr17B0YcsrX0qJaBBYRLq66JzlnL/6ZMb3H2bFkgVq/CtCCUBEerJ04aga/opRCUhEpKaUAEREakoJQESkppQARERqSglARKSmlABERGpKCUBEpKaUAEREakoJQESkppQARERqSglARKSmlABERGpKCUBEpKaUAEREakoJQESkppQARERqSglARKSmlABERGoqWAIws9PM7O/MbLeZ7TKz94aKRUSkjkJeE3gKuNLdv2dmi4H7zWyruz8UMCYRkdoI1gNw95+4+/fS/z8I7AZODRWPiFTbvolJdo3vZ9/EZOhQohGyB3CMma0Gfh74bov7LgcuB1i1alWxgYlIJWzZtYdNm3cyZEbDnWvXr+Gic5aHDiu44IPAZrYI+BLwPnc/MPt+d7/J3cfcfWzZsmXFBygipbZvYpJNm3fSaDgAjYZz9ead6gkQOAGY2TySxv8Od787ZCwiUj69lHXG9x9myIyR4aS5GxkeYsiM8f2HiwozWsFKQGZmwC3Abnf/RKg4RKScei3rrFiygIY7jYYzMjzE1HSDoSFjxZIFAaKOS8gewL8Hfgt4nZk9kP68MWA8IrVUxsHRuZR1li4c5dr1axgaMgCGhoxr169h6cLRQmOOUbAegLt/E7BQxxeR8g6OzpR1hoaTJmSmvDO+/3DLhv2ic5Zz/uqTGd9/mBVLFqjxTwUfBBaRMMo8ODpT1pmabgAwNd2g4d6xrLN04SjnrFiixr+JEoBIhspUTinz4KjKOtmIYh2ASBWUrZxS9sFRlXUGpx6ASAbKWE6pwlm0yjqDUQ9AJANzHZSMhc6i600JQCQDZS6nLF04qoa/plQCEslAFcopUpxYJguoByCSEZVTpBcxTRZQDyAisZwVSP80KCmdxDZZQD2ASMR0ViAi+YhtsoB6ABGI7axARPLRzwrmPCkBRKDMKzL7oVKX1FVskwVUAopAmacQzpVKXVJ3MU0WUA8gArGdFeRFpS6RRCyTBdQDiERMZwV5iW0ATKTulAAiUvUVmXUqdYmUgUpAUpi6lLpEykI9AClUHUpdImWhBCCFq3qpS6QsVAISEakpJQARkZpSAhARqSklABGRmlICEBGpKSWACtJmayLSC00DrRhttiYivVIPoELKtNmaeiki4akHUCFl2WxNvRSROKgHUCGxXW2olTL1UqQ19d6qQwmgQsqw2Vrdrn7Wr1gb2S279rDuhm1cdtt21t2wjft27QkdkgxAJaCKiX2zNW0J3V2sJbLm3tvQsB3rvZ2/+uTovmfSG/UAKiiWqw21UoZeSkgxl8jUe6ueoD0AM7sVuAT4qbufGzIWKU5MvZR9E5NRxDEj5oH8MvXeYvtcYxW6BHQ78EngM4HjkILFsCV0jKWWmBvZmd7b1Zt3AvH23mL8XGNl7h42ALPVwFd76QGMjY35jh078g9KKm/fxCTrbth2QkO79Yq1uTdo3c5O79u1h6sja8CaYwaiPbsO+bnGzMzud/ex2beH7gGIBBGq1NLL2WlMJTIo1xl16BJa2UpP0ScAM7scuBxg1apVgaORqghRapnLLJoYSmRQvpk/IUtoZUqUM6KfBeTuN7n7mLuPLVu2LHQ4UhEhZiOVcRZN2WIONcss5tlbnUTfAxDJS9GllpgHeNspY8whSmihS0/9CtoDMLPPAt8GzjKzp83sHSHjkbBCrH4tcs1EGddAlDFmKH4tTBm2YWkl+CygudAsoOoqY/20X0UNFGZ5nH6eq2wDooOKcfbWjHazgJQAJDhN3cte6IQa+vihxJr02iWA6AeBpfrKNtCYlbxKXqEHJEMfP6SYt2FpRYPAElwZBxoHlecZcugBydDHl96pByDBlXWgsV95nyH3MyCZZW+krAOidaQegEQhttWvecr7DHmue/Zk3Rspy55BogQgEYll9Wveiih59ZpQe1np28/AZp0SepkpAYgUrKgz5F4SarfeyCC9g7ok9DJTAhAJIJYz5E69kbLtAyRzp0FgKY0iVgo/vvcQ9/y/H/P43kO5HWNGDFMGOw3A13V6bp2oByClUMTCouv/Zjef3vYYZuAO71z7Uq56w9mZHmOuOtXfZ+570bxhnj863XdPol1vpI7Tc+tGCUCi12spYpBVmI/vPcSntz2GAwY4cOO2x/j1sdM4Y9miTF9PrzolvZn7jk43OHB4isXzRxgdGeo7Mbaq12s2T/UpAUj0epk2OWgPYef4fsySxh8grYiwc3x/JglgrsmpU9ID2LR5J1MN5+CRKRw4+MIUS2wk8xp9LGMVkg8lAIlet1JEFoOVa1YswT058x8yaHiSDNasWDJw/P0kp05JD0juS5PUzH/N7FiNPsuGWrN5qkuDwBK9biuFsxisPGPZIt659qXHegBGMgYw6Nl/v6t+O62mPZYQ040cG2ni8vQ21eilV+oBSCl0KkVkNVh51RvO5tfHTmPn+H7WrFiSSemn31W/3ervM/ctnj+SjAGcNMLI8JBq9DInSgBSGu1KEVkOVp6xbFGmg76DJKdOSa/5vkFnAZVBVtssx7pdcyhKAFIJsQ5WDpqcOtXfQ9Xmi25Es5oCXNdrFHSiC8KIFKAqZ55FN6JZXSyo7hcd0gVhJBNFrMZtd4wQ1wzOSgyrfgeV9zbWrT7ffgb4s3qeOlAJSHpWxNlfu2Oo+x5Gc88lz22s232+cx1Dyep56kI9AOlJEZf5a3eMx/cemtOxy9xTiMmWXXtYd8M2LrttO+tu2MYjew7mcqGXTt+tuVwsKKvnqRP1AKQnRVzmr90xdo7v7/nY6il018t4RKvFddfeu5ur33g21967G8iuEe323ep1gD+r5+lXGcd5lACkJ0V0odsdY82KJT0dW9sXd9drgmzXmJ61fDFbr1ibaUPXy3erlxlPWT1PP8p64qESkPSkiC50u2OcsWxRT8euy0BfvyWuuZTxOq1EznpAO6vvVqgyTxHl0byoByA9K2Kufbtj9HLsXvYMiqmL3k88g5xpzqWMV/ROoFl9t0KsBymiPJqXrgnAzN4N3OHu+wqIRyJXxOKjTit++90+IbYu+pZde9h49/dxBzO4fsN5XeMZtMQ11zJe0Y1pVt+tohfIlXmGUS8loOXAdjO7y8wuNjPr+hcigVx0znK2XrGW2y47n61XrOWic5ZH10XfNzHJ++96gH0TR9l/+Cj7Jo5y5V0PdI1n0BJXPyWSKqxfyFuZZxh17QG4+wfN7I+Ai4DLgE+a2V3ALe7+o7wDFJmr2WeAeXbR+ynjPLznAIcmp4+77dDkNA/vOcAFLzul7d9lcaYZ65YZZVfW97WnMQB3dzPbA+wBpoClwBfNbKu7/2GeAYoMKq8uer9lJZvZdLp57+nm29vIqi6v/f3zkef7mtf4VS9jAO8B3gY8A9wM/Dd3P2pmQ8APASUAiVoeA5qD1OPPWr6YxSeNcPDI1LHrTy6eP8JZyxd3PW5ZzzSlf3mOX/XSAzgF2ODuTzbf6O4NM7skkyhEcpZ1wzlIWWnpwlH+9NJXsvHunbg7ZsZ1G7LZIVSqJe+1Lb2MAXyow327B45ApCBZNpyDlpV0Ji+9yHuKqRaCifQhi5kfmmEj3XRakJeFoAvBzOxi4C+AYeBmd78+ZDwSr9gWcYHO4iV/eS/IC5YAzGwY+BSwDniaZK3BPe7+UKiYJE4xLeKanYiKrMfHmAQlf3meaITsAbwGeNTdHwMws88BvwYoAcgxMW3wFjIRxZQEpXh5nWiEHAM4FXiq6fen09uOY2aXm9kOM9uxd+/ewoKTOMSywVvI1cSxrWSW6giZAFqtejnhAsXufpO7j7n72LJlywoIS2KS9yBYr4pMRLN3+4wlCUr1hCwBPQ2c1vT7SmA8UCwSqU6DYM01cSDzGmnz8xe14VerUs/5q08u7WZjeYxbaCwkO+Z+wkl3MQc2GwF+AFwI/BjYDvymu+9q9zdjY2O+Y8eOOR9LX5jym/0ZNjeUhyenMDPmzxvOrD7eqiEGuDrHOvy+iUnW3bDthIZ+6xVr2f7Es7keOw95jFuUaSwkpnbHzO5397HZtwfrAbj7VLrV9BaSaaC3dmr8+1WmL0wZhPpSNw+CHVcTH4JDLyQbq42ODGUySNxu4HnrFWszvxpWs06Lfso25TSPwfuYJgR0U5Z2J+g6AHe/F7g3r+cv0xemDGL5Ujc3lEenG8xsUN7wbFZKdmqI81y41a3MVKYtIPJYwVqWC6+Uqd2p9EpgDZ5lJ6aZKM0Dw8NDhju4w5CdOEjcz+UTQw08l3lf+dnyeA9jmRDQTZnanUongLJ8Ycogpi91c0M5ZMaik4ZZPH8kOTucdRWwdTds47LbtrPuhm3ct2vPnJ8fim2IW13QpozyeA/LkiDL1O4EGwTuRz+DwPft2lO6wbMYzK71dxqgDPUPsNMsoCzi7WW8I6aBvhjVdRZQbO1OdIPARYl98CzGL3O7Wn+RFwnvxeyaeNZXAetWc49lTATCfY+6HTePcYsyjIXE3u7MqHwCgHi/MDE1IDM6DWCV5UsN+V+oO6aBvlDfoxi/vzGJtd1pVukxgJjFNKjarFutvyxbGOddL45lTCTU9yjW72+R+plgEJta9ABiFOuUtqJWvBYhzx7Li+YNM5kO8o0GfJ9CfY9i/f620qpMNej4TlV6P0oAgcTa0Oa9/3jR2nXDB6mZz/zjbzScg0emePGCEeYNDwV5n0J9j2L9/s7WqqF26Np4d2rgYyr/DUoJIJCYG9oy1fr7McjZW/M//vnzhkkrTHzh9y7gjGWLcoy6tVDfo5i/vzNaNdQb796J4+C0bby7NfBl6v10owQQUMwNbRkGsPox6Nnb7H/8oyPDADx/dDrXuDsJ9T0q4riD9NRaNdRT0w0cmNc0djPz2Jnn79bAl6X30wslgMCq2tDGatCzt1j/8Yf6HuV53EHr7K0+KzMDkkVa7T6/XrbkiL330yvNApJaGXSVZllWo5ZdFrOMWn1W121Yw/Ubzuv4+fXyGVdlxbZ6AFIrWZy9xVy6q4qs6uztPqtun18vn3EVeu9KAFI7WTTgVfjHH7MsS22tPqtePr86fMYqAUktlWVBW12p1FYM9QBEJEoqteVPCUBEolWHMkxIKgGJdFGFPV/6UdfXXSfqAYh0MOhc9H0Tkzy85wCGcdbyxblcVyCPraCrsteNdKYEINLGoKuGt+zaw/vveoBDk8kq4cUnjfCnl75yTvvO9HKMrBvqKu11I52pBCTSxiBbPu+bmGTj3d/n0AvTkF507+CRKTbeffxipkEWPOW1JXMsW11L/pQARNoYZNXw+P7DuINZ+kPyX3c/riEdpLHNq6Eu0zVtZTBKACJtDDIXfcWSBWmDn/5AmhBa7zvTT2ObV0OtOfj1UfmLwosMqt9B1vt27eHKHsYABrmAeJ4XH5/L647x2tZlUcR71+6i8EoAIjkq6yygudCMof4V9d4pAUhLoRuPXpQhxrraNzHJuhu2nbBnz9Yr1uqz6qLI965dAtA00Borw5lbGWIMIZakWKWrYxUthvdOCaCmyjDXuwwxhhBTUoz1AjllEMN7p1lANVWGud5liLFoec3975dmDPUvhvdOPYCaiuHso5tj0xybGrt5w0NRxVi0GMoGs2nXzv6Ffu/UA6ipGM4+ulm6cJT/9OqV7H/+KM89f5QDR6Z4/oUptj/xbOjQgol1kZaur9C/kO+degA1Fvrso5t9E5PcteMpmuepHZlqsPHu+o4DVOmC5BJekARgZpcCHwHOBl7j7prbGUjM+63PbKeQdlKOmdlOIda48xZ74p4tlhlLcqJQPYAHgQ3ApwMdX0qgeTsFINlQp8V2CnUUc+JuFtOMJTlRkDEAd9/t7o+EOLaUx9KFo1y/4TwWnTScNP7A4vkjXLdBJY9OYrmQS2wzluRE0Y8BmNnlwOUAq1atChyNFO2ic5bzjT98XU/bKUhcZ9wxzliS4+WWAMzs60Crb94md/9Kr8/j7jcBN0GyFURG4UmJLF04ygUvOyV0GNGLbeFcGaYa111uCcDdX5/Xc0v5aWAwe7GdcWvGUvyiLwFJ9cRUpqiSGM+4yzZjqW6CDAKb2Xozexq4APiamW0JEYcUTwOD+Yl1cZ8WicUrSA/A3TcDm0McW8KKrUxRNTrjlrlQCUgKFWOZomrKskZAwtNeQFKoWMsUInWkHoAUTmUKkTgoAUgQKlOIhKcSkIhITSkBiIjUlBKASCSy2MQtlo3gpBw0BiASgUFXR++bmOSL33uKG//+MYaHtMJaeqMeQInpbK8aBl0dvWXXHi78xN9z7b0P8+zEJC9MNbTCWnqiHkBJaT+dcum0+d0gq6NnksfUtCfXyzE4eOQoJy8cZchMK6ylIyWAEopt21/prFuyHmR19EzyGB0Zwl8AHDA4Ou3MG0YrrKUjlYBKaOYf/cyZ4sjw0LGzPYlLL+WdQVZHNyePxScl53MOjGiFtfRAPYAS0n465dFreaff1dHNe+7PHx5iZNh41y+dyYZXrVTjL10pAZSQLrQRp1Z1/rkk635XR2trDemXEkBJ6R99XNrV+YtK1tpaQ/qhBFBi+kcfh26D8lVJ1rqMZ/UoAYgMqJc6f9mTtaYdV5NmAYkMaKbOPzXdAGBqukHDvTKD8rqMZ3UpAYgMqOoXudG04+pSCUgkA1Wp87eiacfVpR6ASEaWLhzlnBVLKtH4N+8zVfUeTp2pByAix2k34FvVHk6dKQGIyDHdprSq4a8WlYBE5BgN+NaLEoCIHFP1Ka1yPCUAETlGA771ojEAkYrIaqsGDfjWhxKASAVkvVWDBnzrQSUgkZLTVg3SLyUAkZLTzB3plxKASMlp5o70SwlApOQ0c0f6FWQQ2Mw+DrwJmAR+BFzm7s+FiEWkCjRzR/oRqgewFTjX3c8DfgBsDBSHSGVUaTM6KUaQBODu97n7VPrrd4CVIeIQEamzGMYAfhv4m3Z3mtnlZrbDzHbs3bu3wLBERKottzEAM/s60GolyiZ3/0r6mE3AFHBHu+dx95uAmwDGxsY8h1BFRGoptwTg7q/vdL+ZvQ24BLjQ3dWwi4gULNQsoIuBq4C17v58iBhEROou1BjAJ4HFwFYze8DMbgwUh0gumi+pKBKrID0Adz8zxHFFipD1xmwieYlhFpBIZWhjNikTJQCRDGljNikTJQCRDGljNikTJQCRDGljNikTXRFMJGPamE3KQglAJAe6pKKUgUpAIiI1pQQgIlJTSgAiIjWlBCAiUlNKACIiNWVl2onZzPYCT2bwVKcAz2TwPGVRt9cL9XvNdXu9UL/XPMjrPd3dl82+sVQJICtmtsPdx0LHUZS6vV6o32uu2+uF+r3mPF6vSkAiIjWlBCAiUlN1TQA3hQ6gYHV7vVC/11y31wv1e82Zv95ajgGIiEh9ewAiIrWnBCAiUlO1TQBm9sdm9v30ovT3mdmK0DHlycw+bmYPp695s5n9m9Ax5cnMLjWzXWbWMLNKTxU0s4vN7BEze9TMPhA6nryZ2a1m9lMzezB0LEUws9PM7O/MbHf6nX5vVs9d2wQAfNzdz3P3nwO+CnwocDx52wqc6+7nAT8ANgaOJ28PAhuAb4QOJE9mNgx8CngD8ArgLWb2irBR5e524OLQQRRoCrjS3c8G/h3wrqw+49omAHc/0PTrQqDSo+Hufp+7T6W/fgdYGTKevLn7bnd/JHQcBXgN8Ki7P+buk8DngF8LHFOu3P0bwLOh4yiKu//E3b+X/v9BYDdwahbPXesLwpjZNcBbgf3ALwcOp0i/DXw+dBCSiVOBp5p+fxr4hUCxSM7MbDXw88B3s3i+SicAM/s6sLzFXZvc/SvuvgnYZGYbgXcDHy40wIx1e73pYzaRdCnvKDK2PPTyemvAWtxW6d5sXZnZIuBLwPtmVTD6VukE4O6v7/GhdwJfo+QJoNvrNbO3AZcAF3oFFoDM4fOtsqeB05p+XwmMB4pFcmJm80ga/zvc/e6snre2YwBm9vKmX98MPBwqliKY2cXAVcCb3f350PFIZrYDLzezM8xsFPgN4J7AMUmGzMyAW4Dd7v6JTJ+7AieCfTGzLwFnAQ2SLabf6e4/DhtVfszsUeAk4F/Sm77j7u8MGFKuzGw98JfAMuA54AF3/5WgQeXEzN4I/DkwDNzq7teEjShfZvZZ4JdItkf+Z+DD7n5L0KByZGavBf4B2EnSXgFc7e73DvzcdU0AIiJ1V9sSkIhI3SkBiIjUlBKAiEhNKQGIiNSUEoCISE0pAYiI1JQSgIhITSkBiAzAzM5Pr7Ew38wWpvu1nxs6LpFeaCGYyIDM7GPAfGAB8LS7Xxc4JJGeKAGIDCjdg2c7cAT4RXefDhySSE9UAhIZ3MnAImAxSU9ApBTUAxAZkJndQ3IlrjOAn3X3dwcOSaQnlb4egEjezOytwJS735len/dbZvY6d//b0LGJdKMegIhITWkMQESkppQARERqSglARKSmlABERGpKCUBEpKaUAEREakoJQESkpv4/n7GYPByhwckAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the scatter plot\n",
    "plt.scatter(x,y, s=20, alpha=0.9)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Scatter plot of X vs. Y')"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}