{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titre du document "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "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 completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "nu, sigma = 100, 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = np.random.normal(loc=nu,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": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD8CAYAAACRkhiPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAD+JJREFUeJzt3X3InfV9x/H3Z9o5+yCNy61Lk5RkJXtQWdMaMrfC6HCbWR2N/aOQstXAhBSxrB0dW2xh7f4IONYH5pgOO51xWEPowwyotVkolILV3nXWPDUza4K5TWayla1uA9uk3/1xLulpPMl97seT+/69X3A41/lev+tcvy+0fnI9nOtOVSFJatNPjXoCkqTRMQQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYISBJDbt41BOYzNKlS2vVqlWjnoYkLRhLly7l8ccff7yqNkw29oIPgVWrVjE+Pj7qaUjSgpJk6TDjPB0kSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNu+B/MSxdqFZtfWRk+z56x40j27cWF48EJKlhHglowRvlv8ilhc4jAUlqmCEgSQ2bNASSrEzy1SQHk+xP8qGu/okkLyR5pnu9q2+b25McTnIoyQ199WuT7O3W3Zkkc9OWJGkYw1wTOA18pKqeTvIG4FtJdnfrPlNVn+wfnOQqYBNwNfAm4J+T/EJVnQHuBrYA3wAeBTYAj81OK5KkqZr0SKCqTlTV093yS8BBYPl5NtkI7Kiql6vqCHAYWJ9kGXBZVT1RVQU8ANw04w4kSdM2pWsCSVYBbwOe7EofTPJskvuSLOlqy4FjfZtNdLXl3fLZ9UH72ZJkPMn4qVOnpjJFSdIUDB0CSV4PfAH4cFV9n96pnbcAa4ETwKdeGTpg8zpP/dXFqnuqal1VrRsbGxt2ipKkKRoqBJK8hl4APFhVXwSoqher6kxV/Qj4LLC+Gz4BrOzbfAVwvKuvGFCXJI3IMHcHBbgXOFhVn+6rL+sb9h5gX7e8C9iU5JIkq4E1wFNVdQJ4Kcl13XfeDDw8S31IkqZhmLuD3gG8H9ib5Jmu9lHgfUnW0julcxT4AEBV7U+yEzhA786i27o7gwBuBe4HLqV3V5B3BknSCE0aAlX1dQafz3/0PNtsA7YNqI8D10xlgpKkueMvhiWpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYISBJDTMEJKlhhoAkNcwQkKSGGQKS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDJg2BJCuTfDXJwST7k3yoq1+eZHeS57r3JX3b3J7kcJJDSW7oq1+bZG+37s4kmZu2JEnDGOZI4DTwkar6ZeA64LYkVwFbgT1VtQbY032mW7cJuBrYANyV5KLuu+4GtgBruteGWexFkjRFk4ZAVZ2oqqe75ZeAg8ByYCOwvRu2HbipW94I7Kiql6vqCHAYWJ9kGXBZVT1RVQU80LeNJGkEpnRNIMkq4G3Ak8CVVXUCekEBXNENWw4c69tsoqst75bPrkuSRmToEEjyeuALwIer6vvnGzqgVuepD9rXliTjScZPnTo17BQlSVM0VAgkeQ29AHiwqr7YlV/sTvHQvZ/s6hPAyr7NVwDHu/qKAfVXqap7qmpdVa0bGxsbthdJ0hQNc3dQgHuBg1X16b5Vu4DN3fJm4OG++qYklyRZTe8C8FPdKaOXklzXfefNfdtIkkbg4iHGvAN4P7A3yTNd7aPAHcDOJLcAzwPvBaiq/Ul2Agfo3Vl0W1Wd6ba7FbgfuBR4rHtJkkZk0hCoqq8z+Hw+wPXn2GYbsG1AfRy4ZioTlCTNHX8xLEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYISBJDTMEJKlhhoAkNcwQkKSGGQKS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYcP8jWFJF5hVWx8ZyX6P3nHjSParueORgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYISBJDTMEJKlhhoAkNcwQkKSGGQKS1DBDQJIaNmkIJLkvyckk+/pqn0jyQpJnute7+tbdnuRwkkNJbuirX5tkb7fuziSZ/XYkSVMxzJHA/cCGAfXPVNXa7vUoQJKrgE3A1d02dyW5qBt/N7AFWNO9Bn2nJGkeTRoCVfU14HtDft9GYEdVvVxVR4DDwPoky4DLquqJqirgAeCm6U5akjQ7ZnJN4INJnu1OFy3pasuBY31jJrra8m757LokaYSmGwJ3A28B1gIngE919UHn+es89YGSbEkynmT81KlT05yiJGky0wqBqnqxqs5U1Y+AzwLru1UTwMq+oSuA4119xYD6ub7/nqpaV1XrxsbGpjNFSdIQpvXnJZMsq6oT3cf3AK/cObQL+FySTwNvoncB+KmqOpPkpSTXAU8CNwN/M7Op60Iyqj93KGlmJg2BJA8B7wSWJpkAPg68M8laeqd0jgIfAKiq/Ul2AgeA08BtVXWm+6pb6d1pdCnwWPeSJI3QpCFQVe8bUL73POO3AdsG1MeBa6Y0O0nSnPIXw5LUMENAkhpmCEhSwwwBSWqYISBJDTMEJKlhhoAkNcwQkKSGGQKS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWqYISBJDTMEJKlhhoAkNWzSEEhyX5KTSfb11S5PsjvJc937kr51tyc5nORQkhv66tcm2dutuzNJZr8dSdJUDHMkcD+w4azaVmBPVa0B9nSfSXIVsAm4utvmriQXddvcDWwB1nSvs79TkjTPJg2Bqvoa8L2zyhuB7d3yduCmvvqOqnq5qo4Ah4H1SZYBl1XVE1VVwAN920iSRmS61wSurKoTAN37FV19OXCsb9xEV1veLZ9dHyjJliTjScZPnTo1zSlKkiYz2xeGB53nr/PUB6qqe6pqXVWtGxsbm7XJSZJ+0nRD4MXuFA/d+8muPgGs7Bu3Ajje1VcMqEuSRmi6IbAL2NwtbwYe7qtvSnJJktX0LgA/1Z0yeinJdd1dQTf3bSNJGpGLJxuQ5CHgncDSJBPAx4E7gJ1JbgGeB94LUFX7k+wEDgCngduq6kz3VbfSu9PoUuCx7iVJGqFJQ6Cq3neOVdefY/w2YNuA+jhwzZRmJ0maU/5iWJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkho26V8Wk6RXrNr6yEj2e/SOG0ey3xZ4JCBJDTMEJKlhhoAkNcwQkKSGGQKS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMZwctMqN6toukhWlGRwJJjibZm+SZJONd7fIku5M8170v6Rt/e5LDSQ4luWGmk5ckzcxsnA76zapaW1Xrus9bgT1VtQbY030myVXAJuBqYANwV5KLZmH/kqRpmotrAhuB7d3yduCmvvqOqnq5qo4Ah4H1c7B/SdKQZhoCBXwlybeSbOlqV1bVCYDu/Yquvhw41rftRFeTJI3ITC8Mv6Oqjie5Atid5DvnGZsBtRo4sBcoWwDe/OY3z3CKkqRzmdGRQFUd795PAl+id3rnxSTLALr3k93wCWBl3+YrgOPn+N57qmpdVa0bGxubyRQlSecx7RBI8rokb3hlGfgdYB+wC9jcDdsMPNwt7wI2JbkkyWpgDfDUdPcvSZq5mZwOuhL4UpJXvudzVfXlJN8Edia5BXgeeC9AVe1PshM4AJwGbquqMzOavSRpRqYdAlX1XeCtA+r/CVx/jm22Adumu09J0uzysRGS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhhkCktQwQ0CSGmYISFLDDAFJapghIEkNMwQkqWGGgCQ1zBCQpIYZApLUMENAkhpmCEhSwwwBSWrYtP/QvCTNl1VbHxnZvo/ecePI9j0fPBKQpIYZApLUMENAkhpmCEhSwwwBSWqYdwfNgVHeySBJU+GRgCQ1zBCQpIYZApLUMENAkho27yGQZEOSQ0kOJ9k63/uXJP3YvN4dlOQi4G+B3wYmgG8m2VVVB+ZzHpI0rFHd7Tdfzyya7yOB9cDhqvpuVf0A2AFsnOc5SJI68/07geXAsb7PE8CvztXOvF9fks5vvkMgA2r1qkHJFmBL9/F/khya01mNxlLgP0Y9iXlgn4uLfc6T/OWMNh967vMdAhPAyr7PK4DjZw+qqnuAe+ZrUqOQZLyq1o16HnPNPhcX+1x85vuawDeBNUlWJ/lpYBOwa57nIEnqzOuRQFWdTvJB4HHgIuC+qto/n3OQJP3YvD9ArqoeBR6d7/1egBb16a4+9rm42Ocik6pXXZeVJDXCx0ZIUsMMgXmQ5I1JPp/kO0kOJvm1JJcn2Z3kue59yajnOVNJ/jjJ/iT7kjyU5GcWS59J7ktyMsm+vto5e0tye/dolENJbhjNrKfuHH3+Vfe/3WeTfCnJG/vWLZo++9b9SZJKsrSvtiD7HIYhMD/+GvhyVf0S8FbgILAV2FNVa4A93ecFK8ly4I+AdVV1Db0L/5tYPH3eD2w4qzawtyRX0ev96m6bu7pHpiwE9/PqPncD11TVrwD/CtwOi7JPkqyk91ib5/tqC7nPSRkCcyzJZcBvAPcCVNUPquq/6D0uY3s3bDtw02hmOKsuBi5NcjHwWnq/AVkUfVbV14DvnVU+V28bgR1V9XJVHQEO03tkygVvUJ9V9ZWqOt19/Aa93/fAIuuz8xngT/nJH7Eu2D6HYQjMvZ8HTgH/kORfkvx9ktcBV1bVCYDu/YpRTnKmquoF4JP0/gV1AvjvqvoKi6zPs5yrt0GPR1k+z3ObK38IPNYtL6o+k7wbeKGqvn3WqkXV59kMgbl3MfB24O6qehvwvyzcUyLn1J0P3wisBt4EvC7JH4x2ViMz1ONRFpokHwNOAw++UhowbEH2meS1wMeAPx+0ekBtQfY5iCEw9yaAiap6svv8eXqh8GKSZQDd+8kRzW+2/BZwpKpOVdUPgS8Cv87i67PfuXob6vEoC0mSzcDvAb9fP76vfDH1+RZ6/4D5dpKj9Hp5OsnPsbj6fBVDYI5V1b8Dx5L8Yle6HjhA73EZm7vaZuDhEUxvNj0PXJfktUlCr8+DLL4++52rt13ApiSXJFkNrAGeGsH8ZkWSDcCfAe+uqv/rW7Vo+qyqvVV1RVWtqqpV9P7D//bu/7+Lps+BqsrXHL+AtcA48CzwT8AS4Gfp3VHyXPd++ajnOQt9/gXwHWAf8I/AJYulT+Ahetc6fkjvPxC3nK83eqcW/g04BPzuqOc/wz4P0zsn/kz3+rvF2OdZ648CSxd6n8O8/MWwJDXM00GS1DBDQJIaZghIUsMMAUlqmCEgSQ0zBCSpYYaAJDXMEJCkhv0/jTk0NjpPONIAAAAASUVORK5CYII=\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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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
}