my_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is my notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. This\n",
    "2. is\n",
    "3. an\n",
    "4. ordered\n",
    "5. list\n",
    "\n",
    "* And this\n",
    "* is an\n",
    "* unordered list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some famous formula: $E=mc^2$\n",
    "\n",
    "And a fraction: $\\frac{1}{2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/5pWseG4kVXo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1497831d2fd0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo(\"5pWseG4kVXo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-369e543e56c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;31m# test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
     ]
    }
   ],
   "source": [
    "x = [1,2,3,4,5,6,7,8,9]\n",
    "y = [4,7,3,6,4,6,8,3,2]\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "# test\n",
    "plt.plot(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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