no commit message

module3/exo3/exercice.ipynb

@@ -590,16 +590,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PolyCollection at 0x7fa2ee212ba8>"
+       "<matplotlib.collections.PolyCollection at 0x7fa2ee159128>"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -627,10 +627,72 @@
     "ax.bar(x,y_wheat,width=5, color=\"black\",label=\"Wheat Price\")\n",
     "ax.plot(x,y_wages,color=\"red\",label=\"Wages\")\n",
     "ax.fill_between(x,y_wages,0,color=\"blue\")\n",
-    "       \n",
-    "#sorted_data['Wheat'].plot(kind='bar',ax=ax,legend=True,color='black')\n",
-    "#sorted_data['Wages'].plot(title= \"Evolution des salaires et du prix du blé\",ax=ax,legend=True,linewidth=5.0)"
+    "       \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PolyCollection at 0x7fa2edf94c50>"
       ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "x=list(raw_data[\"Year\"])\n",
+    "y_wages=list(raw_data[\"Wages\"])\n",
+    "y_wheat=list(raw_data[\"Wheat\"])\n",
+    "graphique1 = plt.figure()\n",
+    "ax = graphique1.add_subplot(111)\n",
+    "ax.set(xbound=[1550,1850],ybound=[0,100],ylabel=\"Pounds\",xlabel=\"Year\",Title=\"Evolution of wheat prices and wages\")\n",
+    "ax.xaxis.set_major_locator(plt.MultipleLocator(50))\n",
+    "ax.bar(x,y_wheat,width=5, edgecolor=\"black\",color=\"white\",label=\"Wheat Price\")\n",
+    "ax.plot(x,y_wages,color=\"red\",label=\"Wages\")\n",
+    "ax.fill_between(x,y_wages,0,color=\"blue\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le bleu du remplissage entre la ligne rouge des salaires et l'axe est masqué par le noir de l'histogramme !!\n",
+    "J'ai trouvé une méthode dans la doc en ligne de matplotlib pour faire un diagramme en barre avec un gradient de couleur (ce qui pourrait permettre de se rapprocher de la version originale) mais ca me parait bien compliqué à implanter\n",
+    "[doc matplotlib](https://matplotlib.org/gallery/lines_bars_and_markers/gradient_bar.html#sphx-glr-gallery-lines-bars-and-markers-gradient-bar-py)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {