diff --git a/module2/exo3/exercice_fr.html b/module2/exo3/exercice_fr.html
index 1f41eaba4198f8c279047d58bc4a48ac47365c7c..63139fda2fdd66e155d3a736dc7eab196b8b6f5b 100644
--- a/module2/exo3/exercice_fr.html
+++ b/module2/exo3/exercice_fr.html
@@ -13356,6 +13356,957 @@ minimum = 2.8
 </div>
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+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Partie-2:-repr&#233;sentation-graphique">Partie 2: repr&#233;sentation graphique<a class="anchor-link" href="#Partie-2:-repr&#233;sentation-graphique">&#182;</a></h2><h3 id="Sequence-plot"><em>Sequence plot</em><a class="anchor-link" href="#Sequence-plot">&#182;</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
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+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
+</div><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="Histogramme">Histogramme<a class="anchor-link" href="#Histogramme">&#182;</a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">edgecolor</span> <span class="o">=</span> <span class="s1">&#39;black&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+    </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
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+
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+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span> 
+</pre></div>
+
+    </div>
+</div>
+</div>
+
 </div>
     </div>
   </div>
diff --git a/module2/exo3/exercice_fr.ipynb b/module2/exo3/exercice_fr.ipynb
index 9a98778cd1ada4cf0d2d96b5c072b902354ca969..60e358ddc5727fc011c49c87dcaf3faaff50475a 100644
--- a/module2/exo3/exercice_fr.ipynb
+++ b/module2/exo3/exercice_fr.ipynb
@@ -118,6 +118,76 @@
     "ec = stdev(data)\n",
     "print(f\"écart-type = {ec}\")"
    ]
+  },
+  {
+   "source": [
+    "## Partie 2: représentation graphique\n",
+    "\n",
+    "### _Sequence plot_"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "### Histogramme"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "plt.hist(data, edgecolor = 'black')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {