diff --git a/Kennlinien_und_Fitting.ipynb b/Kennlinien_und_Fitting.ipynb
index 5a683ea8fca5cf47d5c508fa4735ff9fcbc88222..f29b1006ea64e2794c7ea4aa7233a2921d020b07 100644
--- a/Kennlinien_und_Fitting.ipynb
+++ b/Kennlinien_und_Fitting.ipynb
@@ -189,27 +189,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "array() missing required argument 'object' (pos 0)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[109], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(combine_csvs(),columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mQ\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mP\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m----> 2\u001b[0m X\u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39mindex:\n\u001b[0;32m      4\u001b[0m     X\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mQ\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mget(i)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
-      "\u001b[1;31mTypeError\u001b[0m: array() missing required argument 'object' (pos 0)"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Q         n         H         P\n",
+      "0   -0.0  0.208333  0.380667  0.003501\n",
+      "1    0.5  0.208333  0.386749  0.004184\n",
+      "2    1.0  0.208333  0.373531  0.004867\n",
+      "3    1.5  0.208333  0.341249  0.005549\n",
+      "4    2.0  0.208333  0.287062  0.006061\n",
+      "..   ...       ...       ...       ...\n",
+      "97   2.5  0.902778  7.107076  0.112583\n",
+      "98   3.0  0.902778  7.034061  0.123044\n",
+      "99   3.5  0.902778  6.906989  0.132358\n",
+      "100 -0.0       1.0  8.403823  0.078036\n",
+      "101  0.5       1.0  8.494628  0.088328\n",
+      "\n",
+      "[102 rows x 4 columns]\n"
      ]
     }
    ],
    "source": [
     "df = pd.DataFrame(combine_csvs(),columns=['Q','n','H','P'])\n",
-    "for i in df.index:\n",
-    "    X=np.array(df['Q'].get(i)**2)\n",
-    "\n",
-    "print(X)"
+    "print(df)"
    ]
   },
   {