# voici un changement
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titre du document\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5-5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=x+9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "print(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5+9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu,sigma=100,15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "normal() got an unexpected keyword argument 'scal'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-21047d169b2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.normal\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: normal() got an unexpected keyword argument 'scal'"
     ]
    }
   ],
   "source": [
    "np.random.normal(loc=mu, scal=sigma, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.normal(loc=mu, scal=sigma, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mu,sigma=100,15\n",
    "x=np.random.normal(loc=mu, scale=sigma, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}