FAQ/Probas-Stat/Generation_lois_classiques.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rc(\"figure\", figsize=(10,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**On rappellera l'utilisation de la combinaison** `shift`+`tab` **après une fonction pour voir rapidement les options disponibles**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loi Binomiale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 15, 14, 13, 20, 15,  9, 20, 18, 14, 16, 13, 11, 17, 14, 11, 16,\n",
       "       15, 14, 19, 17, 19, 18, 12, 15, 15, 16, 15, 16, 14, 17, 20, 18, 17,\n",
       "       12, 15, 15,  8, 18, 21, 14, 13, 17, 17, 20, 13, 14, 15, 16, 17])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valeurs = np.random.binomial(30, 0.5, size=(50))\n",
    "valeurs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attention pour obtenir les fréquences on utilisera la syntaxe suivante**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 13 artists>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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naLolyVOq6tzVd3DPys51TU7tXdk5QGX18a82OMsiVdXF2XmdznO6+782Pc8SdffHuvt7\nunu7u7ezEwdP6u4vbni0pfnLJM9Ikqp6TJL7xy9cPZUvJPmp1e1nJvnUBmfZuNW/ZVcmubm733DC\nU47fJ9htnU7nGL6v3tyyql6T5Bezcxruw0le+K0XXu5nVfXWJE/Pzne2X0ry6uwcxN+e5MIkn0vy\nC919qhf47gu7rNGrknxnkn9fbfah7v71jQy4EKdap+6+8oTnj2XnJ1j3bRDs8rX0p0nenOQJSe5I\n8lvd/f5NzbgEu6zTJ5O8MTuXM/87yW909/WbmnHTqurHk/xjko9l5+xksnM5/Lo4fv+fu1mnP8w9\nPIbvq2gCADhd++nyHADAaRNNAAADogkAYEA0AQAMiCYAgAHRBAAwIJoAAAZEEwDAwP8Ckg8b+1ME\nyCYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3b0992b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "abcisses, hauteur = np.unique(valeurs, return_counts=True)\n",
    "plt.bar(abcisses, hauteur)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loi Uniforme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Réels entre 0 et 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.93850128,  0.18796392,  0.37865753,  0.64244049,  0.44601222,\n",
       "         0.10272039],\n",
       "       [ 0.47396528,  0.87546649,  0.25431498,  0.56618553,  0.5551907 ,\n",
       "         0.95649703],\n",
       "       [ 0.06446791,  0.83413468,  0.66846618,  0.07198018,  0.7942323 ,\n",
       "         0.03978156],\n",
       "       [ 0.65865506,  0.81875555,  0.57030029,  0.95289563,  0.94730548,\n",
       "         0.43713166],\n",
       "       [ 0.52279956,  0.93615673,  0.97364778,  0.45149953,  0.21552192,\n",
       "         0.70556249]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valeurs = np.random.rand(5,6)\n",
    "valeurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.29220573,  0.31079192,  0.33056074,  0.80402969,  0.95871391,\n",
       "         0.06779476,  0.72618233,  0.60256657,  0.36620845,  0.78124706],\n",
       "       [ 0.51109507,  0.3656647 ,  0.26048209,  0.36322291,  0.44623542,\n",
       "         0.85275595,  0.70288951,  0.30791714,  0.42389332,  0.29471026],\n",
       "       [ 0.67833804,  0.45984061,  0.63228031,  0.09483863,  0.73457366,\n",
       "         0.34676863,  0.49151663,  0.41049183,  0.94920027,  0.49208565],\n",
       "       [ 0.52450493,  0.89564584,  0.752529  ,  0.48117346,  0.12238883,\n",
       "         0.26602871,  0.41601298,  0.2806451 ,  0.46037733,  0.71840041],\n",
       "       [ 0.78521371,  0.4763424 ,  0.60864779,  0.38421241,  0.37108375,\n",
       "         0.41245364,  0.07457419,  0.41192982,  0.18390829,  0.76739349],\n",
       "       [ 0.29251458,  0.79456066,  0.53845913,  0.38195163,  0.48843095,\n",
       "         0.03257068,  0.16813476,  0.80288264,  0.70925839,  0.63567889],\n",
       "       [ 0.99637249,  0.92991527,  0.74616779,  0.52096469,  0.33833945,\n",
       "         0.25375972,  0.12494955,  0.23886472,  0.43596301,  0.6388692 ],\n",
       "       [ 0.53432153,  0.7166131 ,  0.91914358,  0.14828653,  0.29420762,\n",
       "         0.7996343 ,  0.96542959,  0.25318054,  0.11806101,  0.46362725],\n",
       "       [ 0.67315851,  0.37590313,  0.23212098,  0.69326345,  0.08171893,\n",
       "         0.05141434,  0.10053517,  0.95722003,  0.51047609,  0.04611967],\n",
       "       [ 0.26371469,  0.57514721,  0.72296217,  0.03897916,  0.28078845,\n",
       "         0.16123482,  0.03110692,  0.57663138,  0.4377996 ,  0.67042687]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valeurs = np.random.random(size=(10, 10))\n",
    "valeurs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Pour les histogrammes on fera attention à l'option** `normed` **pour normaliser le résultat.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.34677963,  0.41439373,  1.55397649,  1.45037806,  1.45037806,\n",
       "         0.82878746,  0.82878746,  1.45037806,  0.3107953 ,  0.72518903]),\n",
       " array([ 0.03110692,  0.12763348,  0.22416003,  0.32068659,  0.41721315,\n",
       "         0.5137397 ,  0.61026626,  0.70679282,  0.80331937,  0.89984593,\n",
       "         0.99637249]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dT1fVx7eOH0vy6SRvTvKVqkqSs929cfHGBgBYP0tdY9XdJ5Kc2HHfsW3ffyzJx2ZHAwDY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3b0992208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(valeurs.flatten(), normed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  13.,   17.,   32.,   46.,   60.,   68.,   76.,   90.,   93.,  100.]),\n",
       " array([ 0.03110692,  0.12763348,  0.22416003,  0.32068659,  0.41721315,\n",
       "         0.5137397 ,  0.61026626,  0.70679282,  0.80331937,  0.89984593,\n",
       "         0.99637249]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y3CT3rHvuw/S15Bp9RZLLF9dfaI02b43OO+73svO7lN+y7rkP09eS/46ekeQ9Sa5ZbH/a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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3b09922e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(valeurs.flatten(), cumulative=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Réels  entre 2 valeurs à spécifiées"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valeurs = np.random.uniform(-1, 5, size=(200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.1972013 ,  0.18005336,  0.14575748,  0.22292321,  0.20577527,\n",
       "         0.13718351,  0.10288764,  0.1972013 ,  0.1972013 ,  0.12860955]),\n",
       " array([-0.95344737, -0.37028692,  0.21287353,  0.79603398,  1.37919442,\n",
       "         1.96235487,  2.54551532,  3.12867577,  3.71183622,  4.29499666,\n",
       "         4.87815711]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3L36bnZ3z3iSPrXuIDfWG7n71lfCRERujux/r7p3+0NW9yi2eVqi7P5XkG+ueY9N091e6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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3ad56b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(valeurs, normed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.115,  0.22 ,  0.305,  0.435,  0.555,  0.635,  0.695,  0.81 ,\n",
       "         0.925,  1.   ]),\n",
       " array([-0.95344737, -0.37028692,  0.21287353,  0.79603398,  1.37919442,\n",
       "         1.96235487,  2.54551532,  3.12867577,  3.71183622,  4.29499666,\n",
       "         4.87815711]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qDy3+PnjzumfaL6rquVX111X1/sW1/bl1z7TfVNWBqvrbqnrXumfZT6rqI1X1gaq6v6rO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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3ad450048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(valeurs, cumulative=True, normed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entiers entre 2 valeurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3, -4,  4,  0, -2],\n",
       "       [ 5,  4,  5,  4,  0],\n",
       "       [ 5, -1, -3, -3,  2],\n",
       "       [ 3, -2,  5, -4,  3],\n",
       "       [-3,  0, -3,  1, -5]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valeurs = np.random.randint(-5, 6, size=(5,5))\n",
    "valeurs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attention ** la plus haute valeur indiquée est en fait exclue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Echantillon d'un tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valeurs = np.sin(np.linspace(-np.pi, np.pi, 50))\n",
    "choix = np.random.choice(valeurs, size=(30), replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 22 artists>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAFpCAYAAABeYWb6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nm6gX+7nveAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3ad17e5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "abc, haut = np.unique(choix, return_counts=True)\n",
    "plt.bar(abc, haut, width=np.pi/100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mélange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nouveau tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valeurs = np.arange(10)\n",
    "valeurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 4, 8, 3, 1, 6, 2, 9, 0, 7])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.permutation(valeurs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valeurs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sur le tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  2, -2,  0, -1,  2, -1,  1, -1,  0, -1,  2, -2,  2])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valeurs = np.random.randint(-2, 3, size=(15))\n",
    "valeurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-2, -1,  0,  1,  2]), array([2, 4, 2, 2, 5]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(valeurs, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2, -2,  1, -1,  2, -1,  0,  2, -2, -1, -1,  1,  2,  2,  0])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.shuffle(valeurs)\n",
    "valeurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-2, -1,  0,  1,  2]), array([2, 4, 2, 2, 5]))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(valeurs, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussienne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valeurs = np.random.normal(-5, 12, size=(200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ADBBTAAADxBQAwAAxBQAwYPdODwAAbI/9Rx7a6RHm5uzRu3Z6hP/LmSkAgAFiCgBggJgC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3ad402f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(valeurs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** On va plutôt utiliser la libraire** `seaborn` **pour un affichage plus représentatif via une estimation par noyau.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd39d49c470>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3bl3P5x555BFuu+02li9fzvLly9m6dWv/bIWEtL0l1QC61EsuKDbKxp03ZNHa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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39d95cba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(valeurs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 11 artists>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B7Nixo9qTat5TTz2Va6+9ttozatrIyEiefPLJ9Pb2Zv/+/XnllVeqPalmvfDCC1m6dGme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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39d4d7240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valeurs = np.random.poisson(4.0, size=(400))\n",
    "abc, haut = np.unique(valeurs, return_counts=True)\n",
    "plt.bar(abc, haut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd39d3ccf28>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KWoZM16tlLNw8IwmA9w5rKlzkMpW1DNnl69XZmgKXURQXHUxORjRnKlqoqG03\nOo6IW1BZy5CdLG0EYKLKWkbZLTMvnV0fqTQ4iYh7UFnLkBUUNWACctKjjY4iXm7a+BiiwgLYU1hN\nV0+/0XFEDKeyliFp7+rjXGULmUkRhAX7Gx1HvJzFbObmGYn09DrYW1htdBwRw6msZUiOnW/A5YLp\n42OMjiI+Yun0RCxmE+8dqcTlchkdR8RQKmsZkvxz9QBMz4w1OIn4iojQAGZMiKWiroOS6jaj44gY\nSmUtg+p3ODl+vpGY8ACSbCFGxxEfsmRaIgAf5l8wOImIsVTWMqiiyhY6e/qZNj4Wk8lkdBzxIVMy\nookKC2DfyRp6+hxGxxExjMpaBpV/rgGAGeM1BS5jy2w2kTs1ga4eBwdP1RodR8QwKmsZVH5RPf5+\nZm3eIYZYPC0BgA8LqgxOImIclbV8opqmTqoaOslJj8bPajE6jvgge2QQ2WlRnClvpqax0+g4IoZQ\nWcsnKrg0BT5dU+BioCU6uxYfp7KWT3T4zMWdj6aO0/PVYpxZE20EB1jZfbwKh1NbZ4rvUVnLNTW2\ndnO6vJmJKZHaZUsM5e9nYUFOHC3tvRwrajQ6jsiYU1nLNe07WQPAgpw4g5OI/NUz1wV65lp8j8pa\nrmlvYQ0Ws4k5k+xGRxEhLT6M1LhQ8s810NLeY3QckTGlsparqqxrp7y2nanjYggN8jM6jghw8eza\n6XKx57g29xDforKWq9p7QlPg4n4W5MRhtZj5oKBKm3uIT1FZy8e4XC72naghwN+iR7bErYQE+jFn\nko2axk7OVrQYHUdkzKis5WPOVbZQ39LN7Ik2Avy0EIq4l788c60bzcR3qKzlYwamwCdrClzcz6S0\nKGIjAjlwqpaunn6j44iMCZW1XKG3z8H+EzWEB/uRnR5ldByRjzGbTCyZnkhvn1M3monPUFnLFQ6e\nrqWju5/caQlYzPr2EPe0dHoiVouJdw9X4NSNZuID9NtYrvDekUpMwM0zkoyOInJNESH+zM2Ko6qh\nkxMlWtFMvJ/KWgaU1bRRVNnKlHEx2CKDjI4j8omWz0kGYOfBCoOTiIw+lbUM2HX04t21N89MNDiJ\nyOAyEsIZlxhOQVEDtc1dRscRGVUqawGgq6efvMJqosMDmJ6pZ6vFM9w6OxkX8O4hnV2Ld1NZC3Dx\nca2eXgc3TU/EbDYZHUdkSOZm2QkP8eejgip6eh1GxxEZNSprweVy8d7hSizmi4/EiHgKq8XMzTMS\n6ezp56P4EZOXAAAbo0lEQVRjVUbHERk1KmvhTHkzFXXtzJwQS2So9q0Wz7JsVjL+VjNv7i2lr99p\ndByRUaGyFrbvLwfgU3NTDU4iMnwRIf7cPDOJprYenV2L11JZ+7jqxk7yz9UzLjGczKRwo+OIXJdV\n81Pxt5p5I69EZ9filVTWPu7tg+W4gJXzUjGZdGOZeKaI0ABunplEY2sPu3V2LV5IZe3D2rv62F1Q\nRUx4ILMm6nEt8Wyr5qfid+nsut+hs2vxLiprH7brSCW9/U5WzEnWOuDi8SJCA1g2M4mG1h4+KtDZ\ntXgX/Yb2UX39TnYeqiAowKLHtcRrrJqfSoCfhT9+eJ6O7j6j44iMGJW1j9pbWE1LRy9LpiUSFGA1\nOo7IiIgIDeCu3HTaOvvY9v55o+OIjBiVtQ9yOl28ta8Mi9nEp+amGB1HZEStmJtCQkwwu45UUlzV\nanQckRGhsvZBh8/UUd3YycIp8USHBxodR2REWS1m1n9qEi7gt9tP43Rqv2vxfCprH+NyuXhjbykm\nLl7fE/FGWWlRLMiJo6S6jV1HK42OI3LDVNY+5kRJE6XVbcyeZCMhJsToOCKjZu2y8QQFWNiyq4jq\nxk6j44jcEJW1j3kjrwSA2xemGxlDZNRFhAbw4Mosenod/PSV4/T1a1cu8VxDKutNmzaxdu1a1q1b\nR0FBwRXv7dmzh8997nOsXbuWH//4xwDs27ePBQsWsH79etavX8+TTz458sll2IoutHCqrJmcjGjS\n4sOMjiMy6uZPjmPp9ETKa9v5/c5zRscRuW6DPrOzf/9+SktL2bx5M0VFRWzYsIHNmzcPvP/d736X\n559/nri4OB544AFWrlwJwLx583j22WdHL7kM25t5pQCsXpBmcBKRsXP/8gmcv9DCe0cqmZQaybzs\nOKMjiQzboGfWeXl5LF++HIDMzExaWlpob28HoLy8nIiICBISEjCbzdx0003k5eWNbmK5LpV17Rw5\ne3HDjqzUSKPjiIwZfz8L/3T3FAL8LPzqrVOU1bQZHUlk2AYt6/r6eqKiogZeR0dHU1dXB0BdXR3R\n0dFXfe/cuXM8/PDD3HfffezevXukc8swvbm3DIDbF6Rpww7xOQkxIXzh9my6ex08syWfxtZuoyOJ\nDMuwl65yuQZ/ZjE9PZ1HHnmEVatWUV5ezoMPPsiOHTvw9/e/5jFRUcFYrRYAbDbfvZ46GmOvaexk\n38kaUuLCWL4wA7PZPctaX3ffNRbjX20Lo7vfxS9fL+TZbcf4/iNLCA3yG/W/dzC+/LXX2Idu0LK2\n2+3U19cPvK6trcVms131vZqaGux2O3FxcaxevRqA1NRUYmNjqampISXl2qtlNTV1Dgygrs43p6lG\na+wv7TiD0+li5dxkGhraR/zPHwn6uvvm2GFsx784x05ZVQs7D1Xwf/57D99YMx2/SycJRvDlr73G\n3jaswh50Gjw3N5ft27cDUFhYiN1uJzQ0FIDk5GTa29upqKigv7+f9957j9zcXF577TWef/554OJU\neUNDA3FxuqnDCK0dvXxQcIGY8EDdWCM+z2Qycd+tE5g10capsmZ+/Mfj2k5TPMKgZ9azZs0iJyeH\ndevWYTKZ2LhxI9u2bSMsLIwVK1bwxBNP8M1vfhOA1atXk5GRgc1m47HHHmPnzp309fXxxBNPfOIU\nuIyetw+W09fv5Lb5qVgteqxexGw28eW7JvPsy8coKGrgp68c55/unqKfD3FrJtdQLkKPgcvTIZoa\nGbmxt3f18a8/3YO/n4WnH16Iv59x032D0dfdN8cOxo2/t8/BD7cWcLK0iTmTbHz50zljvq+7L3/t\nNfYRngYXz7XjQBndvQ5Wz09166IWMYK/n4VHPzuNiSmRHDxdx3OvFmpKXNyWytpLtXf18c7BCsJD\n/LlpZpLRcUTcUoC/ha/fO42s1EgOna7jR9uO0dunZUnF/aisvdTls+pV81MJ0Fm1yDUF+lv5+r3T\nmTIumoKiBn64tYDu3n6jY4lcQWXthQbOqoP9uFln1SKD8vez8M/3TGPWRBsnS5v4j5eO0tbZa3Qs\nkQEqay80cFa9IE1n1SJD5Gc1809355A7JZ7iqlY2vXCYuuYuo2OJACprr9PS0cvbOqsWuS4Ws5kv\n3J7N6gVp1DR2sum3hyit9s07lsW9qKy9zOu7S+jpdXBnbobOqkWug8lk4nM3Z3Lf8gm0dvTy7787\nxP6TNUbHEh+nsvYiNU2d7DpaiT0qiJtmJBodR8SjrZiTwlc+MxWTycRzrxayZdc5nE63WJZCfJDK\n2ov88YPzOJwu7lk6TqsxiYyA2ZNsfOfBOdijgnhrbxn/9Yej2rFLDKHf6F6iuKqV/SdryUgIY26W\n3eg4Il4jKTaEf/v8HKZlxlBY0sT/fn4f7x+tHNIOhCIjRWXtBVwuF1t3FQHwuZvHa79qkREWHOjH\n1z43jYdWZQHw6z+f5j9eOsL5C60GJxNfMez9rMX9HDpdx8nSJqaOiyE7LcroOCJeyWQysXR6IlPH\nxfCbP58iv6iB7/7mIDkZ0dyxMI2JKZH6H2UZNSprD9fV089LO89itZi4b/kEo+OIeL2osAAe/dw0\nTpU18/qeEgqLGyksbsQeGcTsSTZmT7KTnhCGWcUtI0hl7eFe/aiYprYe7spNJz462Og4Ij7BZDKR\nnRZFdloU5ypaeOdQOflFDby1r4y39pURFGAhPT6ccYmX/4kgIkTbBMv1U1l7sPLadt45WIEtMpDV\nC9KMjiPik8YnRzA+OYLePgeFxY0cPlvHucpWTpY2cbK0aeDzYsID/6q8w0mLG/r2iCIqaw/ldLn4\nzfZTOF0uHvjUJG2BKWIwfz8LMyfamDnRBkBHdx/FVa2cv/CXfw6cquXAqVoArBYzs7PsTMuIZvr4\nWIID9etYrk3fHR7qnYMVFFW2MmeSjanjYoyOIyJ/IyTQjykZMUzJuPjz6XK5qG/pHijuE6WN7Cus\nZl9hNVaLiblZdm6dncK4xHCDk4s7Ull7oLKaNrbuOkdYsB9/t2Ki0XFEZAhMJhO2yCBskUHMnxwH\nQLcT3s4rJq+wZuCfjIRwbpufyuxJNt2kJgNU1h6mp8/Bz14rpN/h4h9uzyYiNMDoSCJynVLiwrgz\nN4M7FqVzoqSJnYcqyD9Xz09fOU5CTDB3LExn3mQ7FrOWxPB1KmsPs/ndc1Q1dLJ8djLTMmONjiMi\nI8BkMpGTEU1ORjQ1jZ28kVdKXmE1//P6CV756Dy3L0xn0ZR4LSPsw/SV9yAHTtWy60glybYQ7l2W\naXQcERkFcdHBfOH2bP79SwtYNjOJprYefvXWKb71szzePlhOV0+/0RHFADqz9hDnL7Ty89dPEOBv\n4ct35eBn1d3fIt4sNjKI9SsncceidP68r4z3j1by0jtn2fbBeRZPSWDZrCQSY0OMjiljRGXtAepb\nunj25QL6HU6+9plpJNlCjY4kImMkKiyA+5ZP4PZFaXxw9ALvHalk5+EKdh6uINkWypwsG7Mn2kiM\nDdFyp15MZe3mOrv7+eGWAlo7evm7FRN1nVrER4UH+3PHonRWLUjlyJl69hyv5nhxA6982M4rHxYT\nHuLPxOQIJqZEkpkUQbItFD+rrnR6C5W1G+vpdfB/Xy6gsr6D5bOTuXV2stGRRMRgFrOZOVl25mTZ\n6ezuJ7+onqNn6zlT0czB03UcPF136fNMJNtDyUgIJz0+jIyEcBJjg3VnuYdSWbupnj4HP9yaz+ny\nZuZMsrHuVm3SISJXCg60sjAnnoU58bhcLmqbuzhb3kJxdSslVa2U17ZTWt32l88PsDItM4YZE2KZ\nkhGjVdM8iL5Sbqinz8GzWws4VdbM7Ik2vnRXDmazrkWJyLWZTCbiooKJiwpm8bQEAPodTirq2imu\naqO4qpUTJY3sPVHD3hM1WC1mFuTEsWJOCil23Qfj7lTWbqarp58fbTvGydImZk6I5cufztGzlSJy\nXawWM+nx4aTHh7NsZhIul4vy2naOnr14zfujgio+KqgiKzWSe27KZHxShNGR5RpU1m6kpb2H/3jp\nCCXVbcycEMs/3T1FRS0iI8ZkMpEaF0ZqXBh3LEqnoKiBdw6Vc6KkiU2/PcT8yXF87qZMYiICjY4q\nf0Nl7SYaW7t55hf7qahtJ3dqPA+tytKNICIyasxmEzMmxDJjQixnK5p56Z2z7DtRw+EzddyVm85t\n81P1O8iN6CvhBmoaO/n3Fw5RUdvOynkpfGF1tn5IRGTMTEiO5Dufn8M/3J5NUICVl98/z7+/cJiq\nhg6jo8klagSDVdS18++/O0xDaw8Prs5mzbLxWthARMac2WQid2oC3/3ifOZPjuP8hVae+OUB3j5Q\njsvlMjqez1NZG6ikupWnXzwysODJvbdOVFGLiKFCg/z48l05fOXuKQT4WXhp51meubQwkxhHZW2Q\nsxXN/MdLR+jo7uPvV2dpwRMRcStzsuz8f/8wj5yMaI6db+DffrGf4+cbjI7ls1TWBjhR0sj/v/ko\nvX1OvnxXDkumJRodSUTkYyJDA/jGmumsvWU8HV19/OAP+fx+51n6+p1GR/M5KusxdvRcPc9sKcDp\ndPGVz0xhXnac0ZFERK7JbDKxcl4q33lwDnHRwew4UM5Tvz2om8/GmMp6DB04VcuPtx3DbIavfW46\nMyfYjI4kIjIkafFhbHxoDkumJVBW087/+eUBdhwox+nUzWdjQWU9Rj4qqOK5V4/jZzXzv9bMICcj\n2uhIIiLDEuhv5e9XZ/OVu6fg72fh9zvP8r3f6RGvsaCyHgPvHq7gF2+eJDjAyr/cN5OJKZFGRxIR\nuW5zsux894vzmZtl51xlCxt/cYCX3y+iq6ff6GheS2U9yt7aV8oLO84QHuLP4/fPIiMh3OhIIiI3\nLDzEn3+6ewpf/cwUwoL9eCOvlA3/vZePCqpw6rnsEaflRkeJy+Xi1Y+KeW13CVFhAfzLfTOJjw42\nOpaIyIiaPcnOlIwY3tpXyp/3lfGLN0/y1r5S7lyUzrzsOO0YOEJU1qPA5XKx+d1z7DhQji0ykH9Z\nN5PYyCCjY4mIjIoAfwt3LxnH0umJvPJhMXuOV/PffzrBqx8Vs2pBGgsmx+HvZzE6pkdTWY8wp8vF\nC9tPs+voBRJignls3UyiwgKMjiUiMuqiwwP5wu3Z3Jmbzpt7S/mooIpfvXWKrbuKuHlmIstmJuv3\n4XVSWY8gh9PJL944RV5hNan2UP7XuhmEB/sbHUtEZEzZIoP4/G1Z3JWbwbuHK3j/6AVe31PKW3vL\nmJttZ8WcFGy2MKNjehSV9Qjpdzj52WuFHDpdR2ZiON9YM53gQD+jY4mIGCYqLIDP3pTJnYvS2Xui\nhrcPlLO3sIa9hTVkp5/n5hmJzJoYq10Gh0BlPQJ6eh385JXjHDvfQFZqJP/82WkEBeg/rYgIgL+f\nhaXTE1kyLYETpU28faCcgqIGTpY0Eh0ewK2zklkyPZHQIJ3gXIsa5Qa1dvTyw635FFe1MXVcDF/9\nzBTdSCEichUmk4mc9Ghy0qPpxcSWHaf56FgVW3YV8eruYhZNSWD57GQSY0OMjup2VNY3oKapk//a\nnE9tcxe5U+L5/KosrBZN54iIDCbJFsrffWoin1mawQf5Vew8VMGuI5XsOlLJlHHRfGpOCjkZ0do2\n+BKV9XU6XdbET145TltnH3csSuczSzL0TSUiMkzBgX7cNj+VFXOTOXq2nrcPlHP8fCPHzzeSEBPM\nynmpLMyJx8/q2ydCKuthcrlc/Hl/GS/vOo/JBA+unMTNM5OMjiUi4tEsZjOzJ9mZPclOaXUbbx8s\nZ9+JGn711ile/aiY2+ansnR6IgE+eplRZT0MHd19/OrNUxw6U0dk6MWl9iYka51vEZGRlBYfxhfv\nmMxnb8pk+/4ydh2t5KV3zvL6nhI+NTeFW2Yl+9xNvL412uvkcrk4dLqO3719hpaOXialRPLw3VOI\nCNEz1CIioyUqLIB1t07g9oVpvH2wnJ2HKnj5/fO8tbeMW2Ync+usJCJCfWORFZX1IBpaunnxnTMc\nOVuP1WLmnqXjWLUgVc8FioiMkbBgf+5Zmslt89J493AFOw6U8/qeEv68r5T5k+NYMSeF1DjvXmRF\nZX0NDS3dvLG3lA/zL+BwupiYEslDq7K0GYeIiEGCA63csSidFXNS2HO8ih0HK9h9rJrdx6oZlxjO\nkmkJzMuO88opcu8b0Q1wulycLmtm97Eq9p2oweF0YY8K4q7cdBbkxGPW3d4iIoYL8LewbFYyN81M\n4lhRA+8eruR4cQPnL7Ty0s6zTMuMZfZEG9MyY7ymuIc0ik2bNpGfn4/JZGLDhg1MmzZt4L09e/bw\ngx/8AIvFwtKlS/nqV7866DHupKfPwbmKFk6UNLL/ZA0NrT0AxEUFcceidBbkxGnKW0TEDZlNJqaP\nj2X6+FgaW7vZfayKPcerOXiqloOnarFaTExIjmRSaiRZqVFkJIThZ/XMu8kHLev9+/dTWlrK5s2b\nKSoqYsOGDWzevHng/e9+97s8//zzxMXF8cADD7By5UoaGxs/8Rgj9PY5aO7opbGlmwsNHVTWdVBe\n107xhVYczosbpQf6W1g8LYHcKfFMSInUmbSIiIeIDg/kztwM7liUTmV9B4dP13H4bB0nS5s4WdoE\nFGM2mYiPCSbFHkpSbAixkYHYIoKIDg8kLNjPrRe1GrSs8/LyWL58OQCZmZm0tLTQ3t5OaGgo5eXl\nREREkJCQAMBNN91EXl4ejY2N1zxmLOw/WcN7hyvp6u2nu8dBR3cfHd39H/s8kwlS48LITosiOy2K\niSmRPvsMn4iINzCZTCTbQkm2hXLX4gzau/o4U97MqbImSqrbqKht50J9x1WPDQ6wEhrkh7+fGavF\njNVqxs9ixs968fWCyXHMybKP8YguGrSs6+vrycnJGXgdHR1NXV0doaGh1NXVER0dfcV75eXlNDU1\nXfOYa4mKCsZ6aXriRrdOq9pTyunyZoICLAQF+BEdEcSElECiIwKJiQgk2R5GWnwYyXFhblfOvrxt\nnMbuu3x5/Br7KP8dQEZqNCtzL752Ol3UNHZSUdtGbWMnNU1d1DV10trRe+mfHjrb++ntd9Lf7+DS\nxCsAYSEBrFqSOTK5hjn2YV95d7lcg3/SdRzT1NQJXBxAXV3bsP+Ov/bpRWncuTB10Gns1ubOG/p7\nRtpIjN1Taey+OXbw7fFr7MaM3Qqk20JItw2+YYjD6aSv30m/w0VIoHVEMl8e+3AKe9Cyttvt1NfX\nD7yura3FZrNd9b2amhrsdjt+fn7XPGas6HqziIjcKIvZjMXf+GvZgybIzc1l+/btABQWFmK32wem\ns5OTk2lvb6eiooL+/n7ee+89cnNzP/EYERERGZ5Bz6xnzZpFTk4O69atw2QysXHjRrZt20ZYWBgr\nVqzgiSee4Jvf/CYAq1evJiMjg4yMjI8dIyIiItfH5Lqei9Cj4PJ1AF3D0dh9jS+PHXx7/Bq7b499\nONesjZ+IFxERkU+kshYREXFzKmsRERE3p7IWERFxcyprERERN6eyFhERcXMqaxERETenshYREXFz\nbrMoioiIiFydzqxFRETcnMpaRETEzamsRURE3JzKWkRExM2prEVERNycylpERMTNuV1Z9/f38/jj\nj3PfffexZs0aDh48aHSkMbFp0ybWrl3LunXrKCgoMDrOmHr66adZu3Ytn/3sZ9mxY4fRccZcd3c3\ny5cvZ9u2bUZHGVOvvfYad911F/fccw+7du0yOs6Y6ejo4JFHHmH9+vWsW7eODz/80OhIY+LMmTMs\nX76cF154AYCqqirWr1/P/fffz9e+9jV6e3sNTjh6rjb2hx56iAceeICHHnqIurq6Qf8MtyvrV199\nlaCgIF566SWeeuopvve97xkdadTt37+f0tJSNm/ezFNPPcVTTz1ldKQxs3fvXs6ePcvmzZv5+c9/\nzqZNm4yONOZ++tOfEhERYXSMMdXU1MSPf/xjXnzxRZ577jl27txpdKQx88c//pGMjAx++9vf8sMf\n/tAnft47Ozt58sknWbhw4cDHnn32We6//35efPFF0tLS2Lp1q4EJR8/Vxv7MM8+wZs0aXnjhBVas\nWMEvf/nLQf8ctyvru+66i29/+9sAREdH09zcbHCi0ZeXl8fy5csByMzMpKWlhfb2doNTjY25c+fy\nwx/+EIDw8HC6urpwOBwGpxo7RUVFnDt3jptvvtnoKGMqLy+PhQsXEhoait1u58knnzQ60piJiooa\n+L3W2tpKVFSUwYlGn7+/P//zP/+D3W4f+Ni+ffu49dZbAVi2bBl5eXlGxRtVVxv7xo0bWblyJXDl\n98Mncbuy9vPzIyAgAIBf//rX3HHHHQYnGn319fVX/MBGR0cPaVrEG1gsFoKDgwHYunUrS5cuxWKx\nGJxq7Hz/+9/nW9/6ltExxlxFRQXd3d08/PDD3H///V77i/pqbr/9di5cuMCKFSt44IEHePzxx42O\nNOqsViuBgYFXfKyrqwt/f38AYmJivPZ33tXGHhwcjMViweFw8OKLL3LnnXcO/ueMVsCh2LJlC1u2\nbLniY//8z//MkiVL+N3vfkdhYSHPPfecQemM44srwL7zzjts3bqVX/ziF0ZHGTOvvPIKM2bMICUl\nxegohmhubuZHP/oRFy5c4MEHH+S9997DZDIZHWvUvfrqqyQmJvL8889z6tQpNmzY4HP3K/wtX/yd\n53A4+Nd//VcWLFhwxRT5tRha1vfeey/33nvvxz6+ZcsW3n33XX7yk5/g5+dnQLKxZbfbqa+vH3hd\nW1uLzWYzMNHY+vDDD3nuuef4+c9/TlhYmNFxxsyuXbsoLy9n165dVFdX4+/vT3x8PIsWLTI62qiL\niYlh5syZWK1WUlNTCQkJobGxkZiYGKOjjbrDhw+zePFiALKysqitrcXhcPjUjBJcPLvs7u4mMDCQ\nmpqaK6aJfcG3v/1t0tLSeOSRR4b0+W43DV5eXs7vf/97fvSjHw1Mh3u73Nxctm/fDkBhYSF2u53Q\n0FCDU42NtrY2nn76aX72s58RGRlpdJwx9cwzz/Dyyy/zhz/8gXvvvZevfOUrPlHUAIsXL2bv3r04\nnU6ampro7Oz0iWu3AGlpaeTn5wNQWVlJSEiIzxU1wKJFiwZ+7+3YsYMlS5YYnGjsvPbaa/j5+fHo\no48O+RhDz6yvZsuWLTQ3N/OlL31p4GPPP//8wLUNbzRr1ixycnJYt24dJpOJjRs3Gh1pzLz55ps0\nNTXx9a9/feBj3//+90lMTDQwlYy2uLg4Vq5cyZo1awD4zne+g9nsducOo2Lt2rVs2LCBBx54gP7+\nfp544gmjI42648eP8/3vf5/KykqsVivbt2/nP//zP/nWt77F5s2bSUxM5O677zY65qi42tgbGhoI\nCAhg/fr1wMUbiwf7PtAWmSIiIm7ON/5XVkRExIOprEVERNycylpERMTNqaxFRETcnMpaRETEzams\nRURE3JzKWkRExM2prEVERNzc/wNmpO/3v5XxFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39d445470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(valeurs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loi exponentielle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd39d313908>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QsGfPHqxbty703E033YScnBwolUoAwI9+9CNkZ2dHp9ok5hgaQ7ZZD0UCbk8KMhm1sGbo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39d2a4f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valeurs = np.random.exponential(1.0, size=(100))\n",
    "sns.kdeplot(valeurs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# On pourra explorer le module pour bien d'autres lois"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py3]",
   "language": "python",
   "name": "conda-env-py3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}