module3/exo3/exercice.ipynb

c588a7a1 · 732afbed1f51733fba4ec4ed0e9bd727 · 5a857b9e · c588a7a1
Commit c588a7a1 authored Oct 23, 2023 by 732afbed1f51733fba4ec4ed0e9bd727
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Showing with 38 additions and 2 deletions

exercice.ipynb module3/exo3/exercice.ipynb +38 -2

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--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -2376,7 +2376,43 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Extrayez les colonnes \"dates\" et \"saison\"\n",
+    "dates = data['  Yr']\n",
+    "saison = data['seasonally']\n",
+    "\n",
+    "# Créez un graphique avec les données\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(dates, saison, label='Saison', color='blue')\n",
+    "plt.title('Évolution de la Saison au Fil du Temps')\n",
+    "plt.xlabel('Dates')\n",
+    "plt.ylabel('Saison')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Affichez le graphique\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
@@ -2386,7 +2422,7 @@
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-62-803ed48c5d4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mJr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Créez une colonne de date en combinant Year et Month\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'  Yr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' Mn'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Jr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Supprimez les colonnes Year et Month si nécessaire\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-26-803ed48c5d4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mJr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Créez une colonne de date en combinant Year et Month\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'  Yr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m' Mn'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Jr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Supprimez les colonnes Year et Month si nécessaire\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin)\u001b[0m\n\u001b[1;32m    374\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMutableMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_assemble_from_unit_mappings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCIndexClass\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_assemble_from_unit_mappings\u001b[0;34m(arg, errors)\u001b[0m\n\u001b[1;32m    453\u001b[0m         raise ValueError(\"to assemble mappings requires at least that \"\n\u001b[1;32m    454\u001b[0m                          \u001b[0;34m\"[year, month, day] be specified: [{required}] \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 455\u001b[0;31m                          \"is missing\".format(required=','.join(req)))\n\u001b[0m\u001b[1;32m    456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    457\u001b[0m     \u001b[0;31m# keys we don't recognize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: to assemble mappings requires at least that [year, month, day] be specified: [month,year] is missing"