{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Title of the document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 10\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##example of completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu, sigma = 100, 15\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[82.02224521 96.79858385 84.9599978  ... 84.84929436 97.17244428\n",
      " 86.39885076]\n"
     ]
    }
   ],
   "source": [
    "x = np.random.normal(loc = mu, scale = sigma, size = 100000)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "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": [
    "%matplotlib inline\n",
    "plt.hist(x)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use of other languages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-63677292ba96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plot' is not defined"
     ]
    }
   ],
   "source": [
    "plot(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "function (object, ...) \n",
      "UseMethod(\"sigma\")\n",
      "<bytecode: 0x243da20>\n",
      "<environment: namespace:stats>\n"
     ]
    }
   ],
   "source": [
    "print(sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}