Test Jupyter Notecook

test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esto es un test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pylab 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": [
       "<matplotlib.figure.Figure at 0x7f28a1b38780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10)\n",
    "line, = plt.plot(x, np.sin(x), '--', linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tkinter'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-f75431acfd06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mturtle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mturtle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPen\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[0mturtle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/turtle.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;31m# print(_ver)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtkinter\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mTK\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tkinter'"
     ]
    }
   ],
   "source": [
    "import turtle\n",
    "turtle.Pen()\n",
    "turtle.forward(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}