MAJ exercice 4 (module 2)

module2/exo4/exercice.ipynb

@@ -371,19 +371,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -392,13 +391,19 @@
     "# Version 2.2.3 sur ce Jupyter !\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "plt.suptitle('All tags from the datasets CSV file', fontsize=14)\n",
+    "# Personnalisation du graphique\n",
+    "# https://www.tutorialgateway.org/python-matplotlib-bar-chart/\n",
+    "# https://python-graph-gallery.com/\n",
+    "# https://www.tutorialspoint.com/matplotlib/matplotlib_bar_plot.htm\n",
     "plt.style.use('seaborn-whitegrid')\n",
+    "plt.suptitle('All tags from the datasets CSV file', fontsize=14)\n",
+    "plt.title('Feel free to add new tags...')\n",
+    "plt.xlabel('Tag names')\n",
+    "plt.ylabel('Tag numbers')\n",
     "\n",
-    "# https://matplotlib.org/api/pyplot_api.html\n",
-    "plt.bar(range(len(sorted_all_tags)), sorted_all_tags.values(), width=0.7, align='center')\n",
-    "\n",
+    "plt.bar(range(len(sorted_all_tags)), sorted_all_tags.values(), width=0.8)\n",
     "plt.xticks(range(len(sorted_all_tags)), list(sorted_all_tags.keys()))\n",
+    "plt.xticks(rotation = 45, horizontalalignment = 'right')\n",
     "\n",
     "plt.show()"
    ]