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)" ] }, {