Jupyter notebook examples

Julia_notebook.ipynb

Python_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0d914c9e80>]"
      ]
     },
     "execution_count": 4,
     "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(data)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

R_notebook.ipynb