{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titre du document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "x = x + 10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Petit exemple de complétion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu, sigma = 100, 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Utilisation d'autres langages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2FXSgeSG7FvOLcRndHQFVV35Jc9Hc668yGZrJ8zkMcasyNHar1KyYZlA6inC6DnafF3ffey4imgedlNlpIn95R+66s8D189opRuWjhGbBR1zUPnLEcwXhQ/QU6bt62eb2Ct2OTuWsNFH4xKLBoFlwtTrPStzz7LOeKwgLLu/vaBO5133JrLD2zy8xKh1FNAve+vfRNxmr472CsGDpqtCzEmvFefHFvINC+9/g4jENN4WcYOvA4yQrO7pTbe+lEGwK+JxFL0zyXgbBpgCPC31QKGTi0SJDng9DsBcHo1s0HsRd8enmCbGddRiq4gkEe1IivyT94WTeYZseZGz1q7yj+geCPflxMLs9CThHrr0gtxi6YgIODJCakiaco+a77sxDsAkobZzDnOPUzbodAK12Mfb5G7yj+oeY4DV9hqq9I3wluUGzKk9us1tE297mfl/qQnuHvZ/4SXW8oCV49Os/74n/TtUm+Q/0+GZwzarP5sWUZzCm6AMpwUXy37j8OFXbDJAHZQ9oJSQfM0BKcLarcoa6M5kEeZLYVV4vHJGBlODSsDLGcluo2masTWo6dBKTkAkgJZjNb7ViYbi6s6yKx58fE1v7hsodXTnm8Re18Ij6MpMXj4uf+4WYYJY1fa7a6o0VE1uOUHmXuLRLyx7h29yXTIrsHTFTXZAbycmvROxXt00QEBOsD+PnM1ZgK7yz4Lue0v8o7X5UFaT/OunaySb8OwzBQRBXKjUp6XcWjNgtNesnqQriOhfsle2vm1YgOAhayH+yB2bcWTBevvZO+1hVEIf85e0ofPYXCA6CJb1L2P4otxGVWXF57Jz9tKogU9+qYFuacc7MGwgOhrl2R6dc9wXbmjia7b1bb984UyMdPYXPJgzB1IFg4kAwcSCYONQFl2fs8XgIm7P9ijGpGANxwWftr71p2+22oKzDS2Mc8w3LR3+IC26XydgVm9uCqbMYq0gwxdh7fSAu2HU/sLPbHYhk+fg880uD0jEA6oLl+4HN3B4G9pTrk72t7q0eS0Nc8LRBJc753dwW7G52me2OVP/s1rLQElw0PqZplXHYznmxjnFVTqO3trD3Pue+4ES3qA5V3hHIHxHdfK37gvKP4+KmCy9wJAhagjvNLy/srW7e/Cu2AyynsduU787mK51X265265IytqR0ohmmJQwGUoKvy29IlkWr2mbhp1LzjVt59mPdpSbf/cWuSLmxm3QEvz9ICT4uu1H5VuUU+Wh88PU7C3aNlBqn486CUtd7uAkGvLTOA1KCK54vYCzjJVXbfP+K1IxOu7OgIKKEsY0D3brE/8rYGd6j0fSClGC21TZ6kP28YhfnzmVVZ6Qb22pC277uC1ZEvt0vPt9twWHbmyNth7glqS+0BLMbW/cpjxApShj0cVLVGctyv/GoqnNtc2bVP7jFu3YWc0nPAIgJ9stE+SrqBTNMA6sToSY4SZ5mf7Y55ywTQqgJ7ntQakZuNzoN/Qg1wYfsPxeviLXqbakgMLHgWVH2Xp4zBmrnp1eajsn3340M5hU87/VStq2JRe8fmQfzltWJk0eEdc/x2w8oYt6yOg75yztI/PA74pi3rM6QlYxdDzdFeTgrY96yOoXJbfradmqL4YOSZdN/ULvN2blzz3BPRCfMXFbnbFap1hBeXLdPWd5jqLpttkR+tijKq6qMRQi1sjop8pP8l35WtY39GmMFjcXkIxwBZXWWPueiTozG1ISQKJ8xzFmsZpPK58GuUjoWhNt1sNfUGOb8Br+xR2oG7vLbz50I6SKwPFxMPsLRXlbHUbeDPEy9hucKcwo+Ebb51IxW6m6fzOt4OKuLyilWTINmwbaUjEn1c0wi+FzbqIjhymdmuSO7zlR77ra1T+/NwSdlLJoF31fB2IYG580hOF66Apo+Wv/9mhjNghvIl6qLG500g+AL7eW2ykt35ft3W/TsiBOaBa+ouUxu61b3XGGA4NOd5dZd8Bn762+Gf697IiZC+1l0rusmzxWvIZlGHKLt0n/NV+6zbrc7IKVm1RNgLpj3caE3RXNHfqk8M9yxuKS4Lu6HZK/RhSGHhQQXRM7Z9W6Sn7n/rlUdV+Y1ujDksJDgVPlVuZT1qraZNrDY+Wk3//3oYiHBPeWXXVe/r2ob56dxUeMtOuiEDxYSnCqPyx+9jnNU6lhIcEHEpz9MaaV5/t2K6zySsQwWEsx+mzl0sebSJePDkhqH0GvRlhLMg8+HOVl+pPg5QE1DqAnuII89/HCl0WnoR6gJds0CnLrG6DT0I9QEr+pVxs5HXDM6Df0INcFsRnh0k1B62TrkBDMWWqNhQlBwaAHBxIFg4ugluDCr8G4dNVCcFVr3HYNAJ8EfRvaNVPccKBDSbL2bDAitcybV6CN4V2cnc76cfvfOQfGrXGJu3ELOUYmhj2BX6bf0lOCD+WTJHKk5q25iu5BDH8FTV0nNOnXFG/3ztXzQP/oq56jE0EfwiagL7GK08q7Uk2/LYTeSeR/4iaHTSdbuZo4EdSO+AuHoi/bYr7lHpQWug4kDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBxDGx4BUJjlGhPLKXD+YVnNYp37nMaw5boBLzCo6Xh/V2DqFa3WIwr2DX9BpD9mkLAswruO8Wxm6Gh/TofB7oJThzSabK7a/G9hvTWN2MHMAbnQT36j6zR3eV7z86M7cV+O8FlNFH8Oo3pSZlRfDBQLDoI3i0PGnCbqtWSbc0+gie9ZnUfPFR8MFAsOgj+Gr49pvp4V6TwgPx6FT57NzAhAFnVSUG+GDeymeACwIqn1360UWHZK25AQ4IqHy2fbiL2B7aMgNcEFf5bOknQaYEeCKu8hkEmwIBlc9uAcGmQNx1MASbAggmDgQTR5zgjX+LVeRPDzwogHvvFxG15n0iot5/r4ioD9Su8mtuoHwDUYNgf4gpdjNmq4ioi+aKiPr9myKiXmyrpjcEu4DgIIBgCA4CCIbgIIDgIIBg4oK7nhERdZyQabOWeNVW5cGeUSKiXm6vprdAwWJeiS3UXDjLFyXFIqI6RUzAq/IXK1AwMAMQTBwIJg4EEweCiQPBxIFg4kAwcUQJLq5Wo0aNNnxjlg2tLtcF3tDwwfjzvKPyzvfrZ+63Z3PPtTKqqlxFCT5fm3/MxDG/k1Rcr/192agXeUflnG/u/TsrRjl453orqqpcRQk+Wp9/zEwmq0iLlyTX4HdrsTIq53xz0xjLeJR3rreiqspVlOA9f3Y8HJvNO6qsYsJr0g91eIaWowrId1I7AbnKUVXlKkrw4V5Hbo7wGu2iFVnFyGHSD0+qnfHFX1T++W58MldArnJUVbmKPIsu/QPvEcOyion9pB8ezuEcVYZrvosb5AjI1RVVJuBcRQk+d1g6M739m+OGHHB5JGNn/uhjQLqmqLzzXdVIPnnmnWtlVFW5ihK8/vGT5W/9H++o8n9VQe3NZX268I7KOd+rdU/KH5xzvRVVVa7CDtHvPvpQ/EmuEfNq1JAuAC+wb//6YAt+s4Hcjso33/nVpUvVGnmcc70dVU2uuJNFHAgmDgQTB4KJA8HEgWDiQDBxIJg4EEwcCCYOBBMHgokDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBbmQKGG9jNBDsBgRbnZJOf3miY9Hehv0d/0hnbE2jv8Rc+tfHO3X/NgGCLc6yOGfF4J2Z1daz9U+zMw8eZKlJtz+yap2r6AjBFmfHo2tvSkfiWoyVVb80O46xG78vvfUxsxVjGyHY6qTZa75cmFlP+une7En31qtXr9a5Wx8TuzK2F4KtT17M5Mz/cLKb1S4vqqzGeOtjRpL01xiCLc60MU5ntymZ96Sxz/7KLvwpm+3tf/vjQK2zZckQbHEuNX/siTY3Mp8a/HSDnYytbfTUczv+9ZHyyDMfPGF0gvwJLcGVULzcvSsQTBwIJk4oCg4pIJg4EEwcCCYOBBMHgokDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBxIFg4vw/C6hxe08+0jwAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "plot(cars)"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}