{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titre du document\n"
   ]
  },
  {
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y =12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu, sigma = 100,15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x= np.random.normal(loc=mu, scale=sigma,size=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "## SHOW a plot \n",
    "%matplotlib inline\n",
    "plt.hist(x)\n",
    "plt.show()"
   ]
  },
  {
   "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
}