diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb
index ccf0f7bb00e09250d1deef9cd9709d6fd6668e86..18a05efa3bc4a36013a4d805dff9027796ed4395 100644
--- a/module3/exo3/exercice_fr.ipynb
+++ b/module3/exo3/exercice_fr.ipynb
@@ -675,14 +675,6 @@
    "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exception scene 7 of act 4\n",
-      "persosList : ['Harpagon']\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1278,15 +1270,11 @@
     }
    ],
    "source": [
-    "# On ouvre le fichier local en lecture 'r' pour en faire l'analyse\n",
-    "# en utilisant l'instruction with qui se chargera de fermer\n",
-    "# le fichier une fois sortie de l'instruction.\n",
-    "# (pas d'erreur possible par oubli d'appel à l'instruction close)\n",
-    "# Un rapide coup d'oeil au fichier texte nous montre une organisation\n",
-    "# , des symboles en début de ligne etc, que l'on va utiliser pour \"parser\"\n",
-    "# le fichier, à savoir le lire de manière à ranger les données\n",
-    "# de manière intelligente dans une structure de données facilitant\n",
-    "# la manipulation et l'analyse.\n",
+    "# On ouvre le fichier local en lecture 'r' pour en faire l'analyse en utilisant l'instruction with qui se chargera de fermer\n",
+    "# le fichier une fois sortie de l'instruction (pas d'erreur possible par oubli d'appel à l'instruction close).\n",
+    "# Un rapide coup d'oeil au fichier texte nous montre une organisation, des symboles en début de ligne etc, que l'on va utiliser\n",
+    "# pour \"parser\" le fichier, à savoir le lire de manière à ranger les données de manière intelligente dans une structure de\n",
+    "# données facilitant la manipulation et l'analyse.\n",
     "with open(local_filename,'r') as avareFile:\n",
     "  # On va commencer par parser le fichier afin de récupérer la liste des personnages\n",
     "  lineNum = fill_perso_dict(avareFile, avarePersoDict)\n",
@@ -1367,7 +1355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1409,7 +1397,7 @@
        "Index: []"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1661,9 +1649,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.5 0.  1.  1. ]\n",
+      "[0.35882353 0.21994636 0.99385914 1.        ]\n",
+      "[0.21764706 0.42912061 0.97551197 1.        ]\n",
+      "[0.07647059 0.61727822 0.94518383 1.        ]\n",
+      "[0.07254902 0.78292761 0.9005867  1.        ]\n",
+      "[0.21372549 0.9005867  0.84695821 1.        ]\n",
+      "[0.35490196 0.9741386  0.78292761 1.        ]\n",
+      "[0.50392157 0.99998103 0.70492555 1.        ]\n",
+      "[0.64509804 0.9741386  0.62211282 1.        ]\n",
+      "[0.78627451 0.9005867  0.53165947 1.        ]\n",
+      "[0.92745098 0.78292761 0.43467642 1.        ]\n",
+      "[1.         0.61727822 0.32653871 1.        ]\n",
+      "[1.         0.42912061 0.21994636 1.        ]\n",
+      "[1.         0.21994636 0.11065268 1.        ]\n",
+      "[1.0000000e+00 1.2246468e-16 6.1232340e-17 1.0000000e+00]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -1699,7 +1708,7 @@
        "  'color': array([0.35882353, 0.21994636, 0.99385914, 1.        ])}}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1708,6 +1717,7 @@
     "color = iter(cm.rainbow(np.linspace(0, 1, nbPersos)))\n",
     "for i in range(nbPersos):\n",
     "  c = next(color)\n",
+    "  print(c)\n",
     "  perso = sortedData[\"perso\"].iloc[i]\n",
     "  #print(perso)\n",
     "  avarePersoDict[perso][\"color\"] = c\n",
@@ -1724,93 +1734,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "act 1 persos ['Valère' 'Élise' 'Cléante' 'Harpagon' 'La Flèche']\n",
-      "   Cléante  Harpagon  La Flèche  Valère  scene  Élise\n",
-      "0        0         0          0     630      1    491\n",
-      "1      761         0          0       0      2    154\n",
-      "2        0       465        257       0      3      0\n",
-      "3      216      1150          0       0      4    162\n",
-      "4        0       273          0     707      5     36\n",
-      "act 2 persos ['Cléante' 'La Flèche' 'Maître Simon' 'Harpagon' 'Frosine']\n",
-      "   Cléante  Frosine  Harpagon  La Flèche  Maître Simon  scene\n",
-      "0      379        0         0        903             0      1\n",
-      "1      127        0       171         12           197      2\n",
-      "2        0        1        21          0             0      3\n",
-      "3        0      130         0        301             0      4\n",
-      "4        0     1496       555          0             0      5\n",
-      "act 3 persos ['Harpagon' 'Maître Jacques' 'La Merluche' 'Brindavoine' 'Élise' 'Cléante'\n",
-      " 'Valère' 'Frosine' 'Mariane']\n",
-      "   Brindavoine  Cléante  Frosine  Harpagon  La Merluche  Mariane  \\\n",
-      "0           23       76        0       757           26        0   \n",
-      "1            0        0        0         0            0        0   \n",
-      "2            0        0       19         0            0        0   \n",
-      "3            0        0      191         0            0      185   \n",
-      "4            0        0       26       105            0        0   \n",
-      "5            0        0       26        70            0       18   \n",
-      "6            0      575       41       183            0      243   \n",
-      "7           20        0        0        23            0        0   \n",
-      "8            0       40        0        80           21        0   \n",
+      "    Cléante  Harpagon  La Flèche    Valère  scene     Élise\n",
+      "0  0.000000  0.000000   0.000000  0.561998      1  0.438002\n",
+      "1  0.831694  0.000000   0.000000  0.000000      2  0.168306\n",
+      "2  0.000000  0.644044   0.355956  0.000000      3  0.000000\n",
+      "3  0.141361  0.752618   0.000000  0.000000      4  0.106021\n",
+      "4  0.000000  0.268701   0.000000  0.695866      5  0.035433\n",
+      "[array([1.        , 0.21994636, 0.11065268, 1.        ]), array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00]), array([0.78627451, 0.9005867 , 0.53165947, 1.        ]), array([1.        , 0.42912061, 0.21994636, 1.        ]), array([0.64509804, 0.9741386 , 0.62211282, 1.        ])]\n",
+      "    Cléante   Frosine  Harpagon  La Flèche  Maître Simon  scene\n",
+      "0  0.295632  0.000000  0.000000   0.704368       0.00000      1\n",
+      "1  0.250493  0.000000  0.337278   0.023669       0.38856      2\n",
+      "2  0.000000  0.045455  0.954545   0.000000       0.00000      3\n",
+      "3  0.000000  0.301624  0.000000   0.698376       0.00000      4\n",
+      "4  0.000000  0.729400  0.270600   0.000000       0.00000      5\n",
+      "[array([1.        , 0.21994636, 0.11065268, 1.        ]), array([1.        , 0.61727822, 0.32653871, 1.        ]), array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00]), array([0.78627451, 0.9005867 , 0.53165947, 1.        ]), array([0.07254902, 0.78292761, 0.9005867 , 1.        ])]\n",
+      "   Brindavoine   Cléante   Frosine  Harpagon  La Merluche   Mariane  \\\n",
+      "0     0.011880  0.039256  0.000000  0.391012     0.013430  0.000000   \n",
+      "1     0.000000  0.000000  0.000000  0.000000     0.000000  0.000000   \n",
+      "2     0.000000  0.000000  0.633333  0.000000     0.000000  0.000000   \n",
+      "3     0.000000  0.000000  0.507979  0.000000     0.000000  0.492021   \n",
+      "4     0.000000  0.000000  0.198473  0.801527     0.000000  0.000000   \n",
+      "5     0.000000  0.000000  0.198473  0.534351     0.000000  0.137405   \n",
+      "6     0.000000  0.551823  0.039347  0.175624     0.000000  0.233205   \n",
+      "7     0.465116  0.000000  0.000000  0.534884     0.000000  0.000000   \n",
+      "8     0.000000  0.283688  0.000000  0.567376     0.148936  0.000000   \n",
       "\n",
-      "   Maître Jacques  Valère  scene  Élise  \n",
-      "0             779     272      1      3  \n",
-      "1             186      92      2      0  \n",
-      "2              11       0      3      0  \n",
-      "3               0       0      4      0  \n",
-      "4               0       0      5      0  \n",
-      "5               0       0      6     17  \n",
-      "6               0       0      7      0  \n",
-      "7               0       0      8      0  \n",
-      "8               0       0      9      0  \n",
-      "act 4 persos ['Cléante' 'Élise' 'Mariane' 'Frosine' 'Harpagon' 'Maître Jacques'\n",
-      " 'La Flèche']\n",
-      "   Cléante  Frosine  Harpagon  La Flèche  Mariane  Maître Jacques  scene  \\\n",
-      "0      245      436         0          0      236               0      1   \n",
-      "1       14        0        54          0        0               0      2   \n",
-      "2      418        0       393          0        0               0      3   \n",
-      "3      170        0       141          0        0             300      4   \n",
-      "4      163        0       129          0        0               0      5   \n",
-      "5       17        0         0         47        0               0      6   \n",
-      "6        0        0       407          0        0               0      7   \n",
+      "   Maître Jacques    Valère  scene     Élise  \n",
+      "0        0.402376  0.140496      1  0.001550  \n",
+      "1        0.669065  0.330935      2  0.000000  \n",
+      "2        0.366667  0.000000      3  0.000000  \n",
+      "3        0.000000  0.000000      4  0.000000  \n",
+      "4        0.000000  0.000000      5  0.000000  \n",
+      "5        0.000000  0.000000      6  0.129771  \n",
+      "6        0.000000  0.000000      7  0.000000  \n",
+      "7        0.000000  0.000000      8  0.000000  \n",
+      "8        0.000000  0.000000      9  0.000000  \n",
+      "[array([0.21764706, 0.42912061, 0.97551197, 1.        ]), array([1.        , 0.21994636, 0.11065268, 1.        ]), array([1.        , 0.61727822, 0.32653871, 1.        ]), array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00]), array([0.07647059, 0.61727822, 0.94518383, 1.        ]), array([0.50392157, 0.99998103, 0.70492555, 1.        ]), array([0.92745098, 0.78292761, 0.43467642, 1.        ]), array([1.        , 0.42912061, 0.21994636, 1.        ]), array([0.64509804, 0.9741386 , 0.62211282, 1.        ])]\n",
+      "    Cléante   Frosine  Harpagon  La Flèche   Mariane  Maître Jacques  scene  \\\n",
+      "0  0.251282  0.447179  0.000000   0.000000  0.242051        0.000000      1   \n",
+      "1  0.197183  0.000000  0.760563   0.000000  0.000000        0.000000      2   \n",
+      "2  0.515413  0.000000  0.484587   0.000000  0.000000        0.000000      3   \n",
+      "3  0.278232  0.000000  0.230769   0.000000  0.000000        0.490998      4   \n",
+      "4  0.558219  0.000000  0.441781   0.000000  0.000000        0.000000      5   \n",
+      "5  0.265625  0.000000  0.000000   0.734375  0.000000        0.000000      6   \n",
+      "6  0.000000  0.000000  1.000000   0.000000  0.000000        0.000000      7   \n",
       "\n",
-      "   Élise  \n",
-      "0     58  \n",
-      "1      3  \n",
-      "2      0  \n",
-      "3      0  \n",
-      "4      0  \n",
-      "5      0  \n",
-      "6      0  \n",
-      "act 5 persos ['Le Commissaire' 'Harpagon' 'Maître Jacques' 'Valère' 'Élise' 'Frosine'\n",
-      " 'Anselme' 'Mariane' 'Cléante' 'le Commissaire']\n",
-      "   Anselme  Cléante  Frosine  Harpagon  Le Commissaire  Mariane  \\\n",
-      "0        0        0        0        89             109        0   \n",
-      "1        0        0        0       182             159        0   \n",
-      "2        0        0        0       441               0        0   \n",
-      "3        0        0        4       124               0        0   \n",
-      "4      403        0        0       258               0      192   \n",
-      "5      114      130        0        89               0       36   \n",
+      "      Élise  \n",
+      "0  0.059487  \n",
+      "1  0.042254  \n",
+      "2  0.000000  \n",
+      "3  0.000000  \n",
+      "4  0.000000  \n",
+      "5  0.000000  \n",
+      "6  0.000000  \n",
+      "[array([1.        , 0.21994636, 0.11065268, 1.        ]), array([1.        , 0.61727822, 0.32653871, 1.        ]), array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00]), array([0.78627451, 0.9005867 , 0.53165947, 1.        ]), array([0.50392157, 0.99998103, 0.70492555, 1.        ]), array([0.92745098, 0.78292761, 0.43467642, 1.        ]), array([0.64509804, 0.9741386 , 0.62211282, 1.        ])]\n",
+      "    Anselme   Cléante   Frosine  Harpagon  Le Commissaire   Mariane  \\\n",
+      "0  0.000000  0.000000  0.000000  0.449495        0.550505  0.000000   \n",
+      "1  0.000000  0.000000  0.000000  0.265693        0.232117  0.000000   \n",
+      "2  0.000000  0.000000  0.000000  0.403477        0.000000  0.000000   \n",
+      "3  0.000000  0.000000  0.013333  0.413333        0.000000  0.000000   \n",
+      "4  0.326052  0.000000  0.000000  0.208738        0.000000  0.155340   \n",
+      "5  0.272727  0.311005  0.000000  0.212919        0.000000  0.086124   \n",
       "\n",
-      "   Maître Jacques  Valère  le Commissaire  scene  Élise  \n",
-      "0               0       0               0      1      0  \n",
-      "1             344       0               0      2      0  \n",
-      "2              11     641               0      3      0  \n",
-      "3               7      22               0      4    143  \n",
-      "4               7     376               0      5      0  \n",
-      "5              23       0              26      6      0  \n"
+      "   Maître Jacques    Valère  le Commissaire  scene     Élise  \n",
+      "0        0.000000  0.000000        0.000000      1  0.000000  \n",
+      "1        0.502190  0.000000        0.000000      2  0.000000  \n",
+      "2        0.010064  0.586459        0.000000      3  0.000000  \n",
+      "3        0.023333  0.073333        0.000000      4  0.476667  \n",
+      "4        0.005663  0.304207        0.000000      5  0.000000  \n",
+      "5        0.055024  0.000000        0.062201      6  0.000000  \n",
+      "[array([0.35490196, 0.9741386 , 0.78292761, 1.        ]), array([1.        , 0.21994636, 0.11065268, 1.        ]), array([1.        , 0.61727822, 0.32653871, 1.        ]), array([1.0000000e+00, 1.2246468e-16, 6.1232340e-17, 1.0000000e+00]), array([0.21372549, 0.9005867 , 0.84695821, 1.        ]), array([0.50392157, 0.99998103, 0.70492555, 1.        ]), array([0.92745098, 0.78292761, 0.43467642, 1.        ]), array([1.        , 0.42912061, 0.21994636, 1.        ]), array([0.21372549, 0.9005867 , 0.84695821, 1.        ]), array([0.64509804, 0.9741386 , 0.62211282, 1.        ])]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 432x288 with 5 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -1820,6 +1875,7 @@
     }
    ],
    "source": [
+    "from matplotlib import colors\n",
     "# Il va nous falloir compter le nombre d'actes de la pièce\n",
     "# et faire une boucle sur ces derniers pour construire \n",
     "# un dataframe par acte à partir du dataframe global\n",
@@ -1830,14 +1886,14 @@
     "actNums = pd.unique(textDataSynthesisTableDf[\"act\"])\n",
     "nbActs = actNums.size\n",
     "\n",
-    "fig = plt.subplots(nbActs, 1)\n",
+    "#fig, axs = plt.subplots(nbActs, 1)\n",
     "\n",
     "for actNum in actNums:\n",
     "  actDf = textDataSynthesisTableDf[textDataSynthesisTableDf[\"act\"]==float(actNum)].copy()\n",
     "  #actDf\n",
     "  sceneNums = pd.unique(actDf[\"scene\"])\n",
     "  actPersos = pd.unique(actDf[\"author\"])\n",
-    "  print(\"act {0:} persos {1:}\".format(actNum,actPersos))\n",
+    "  #print(\"act {0:} persos {1:}\".format(actNum,actPersos))\n",
     "  actDict = {\"scene\":[]}\n",
     "  for perso in actPersos:\n",
     "    # Création d'une colonne par personnage de l'acte\n",
@@ -1862,9 +1918,34 @@
     "  # sur les graphiques.\n",
     "  #print(actDict)\n",
     "  actSceneSynthesisGraphDf = pd.DataFrame.from_dict(actDict)\n",
-    "  print(actSceneSynthesisGraphDf)\n",
-    "  subFig = plt.subplot(nbActs, 1, actNum)\n",
-    "  \n",
+    "  # On va créer une version en pourcentage du dataframe\n",
+    "  # de synthèse par scène d'un acte.\n",
+    "  percentDf = actSceneSynthesisGraphDf.copy()\n",
+    "  #df.div(df.sum(axis=1), axis=0)\n",
+    "  percentDf[actPersos] = percentDf[actPersos].div(percentDf[actPersos].sum(axis=1), axis=0)\n",
+    "  percentDf = percentDf.sort_index(axis=1)\n",
+    "  print(percentDf)\n",
+    "  #colors = {perso:colors.rgb2hex(avarePersoDict[perso]['color']) for perso in actPersos}\n",
+    "  sortedPersos = np.sort(actPersos)\n",
+    "  colors = []\n",
+    "  for perso in sortedPersos:\n",
+    "    if not re.search('î',perso) and not re.search('le co',perso, re.IGNORECASE):\n",
+    "      colors.append(avarePersoDict[perso]['color'])\n",
+    "    else:\n",
+    "      if re.search('î',perso):\n",
+    "        colors.append(avarePersoDict[perso.replace('î','i')]['color'])\n",
+    "      else:\n",
+    "        colors.append(avarePersoDict['Le commissaire']['color'])\n",
+    "  print(colors)\n",
+    "  #plt.sca.ax(axs[actNum])\n",
+    "  #df.plot.barh(color={\"speed\": \"red\", \"lifespan\": \"green\"})\n",
+    "  percentDf.plot(\\\n",
+    "          x = 'scene', \\\n",
+    "          kind = 'barh', \\\n",
+    "          color=colors, \\\n",
+    "          stacked = True, \\\n",
+    "          title = \"Acte {}\".format(actNum), \\\n",
+    "          mark_right = True)\n",
     "  pass"
    ]
   },