entrega 1

module2/exo4/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Journal de bord du Mooc / Mooc's logbook\n",
+    "\n",
+    "FR\n",
+    "\n",
+    "Espace réservé au journal de bord du Mooc\n",
+    "\n",
+    "EN\n",
+    "\n",
+    "Reserved for the Mooc's logbook\n",
+    "\n",
+    "# Markdown language\n",
+    "\n",
+    "- Entrance time: 00h 25; 14/06/2025\n",
+    "- Exit time: 01h 21; 14/06/2025\n",
+    "\n",
+    "I didn't notice big difference between h3 and **bold** text.\n",
+    "\n",
+    "I think that the code instruction is confusing and it doesn't look like the solution.\n",
+    "\n",
+    "# Statistical description of today's temperatures\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 1,
+     "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": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "data = np.array([17, 18, 20, 22, 24, 26, 28, 30, 31, 32, 33, 32, 31, 30, 28, 26, 24, 22, 21])\n",
+    "plt.hist(data)\n",
+    "plt.grid(True)\n",
+    "plt.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "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 = np.arange(0,19)\n",
+    "plt.plot(x,data)\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +114,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-