control_NaN_values_in_DB.ipynb

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   "source": [
    "import os\n",
    "from pymongo.mongo_client import MongoClient\n",
    "from dotenv import load_dotenv\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Laden der Umgebungsvariablen aus der .env-Datei\n",
    "load_dotenv()\n",
    "uri = os.environ['DB_URI']\n",
    "\n",
    "# Herstellen der Verbindung zur MongoDB-Datenbank\n",
    "client = MongoClient(uri)\n",
    "db = client[\"rosen\"]\n",
    "collection = db[\"big_data_test\"]"
   ],
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [_id, instrument, configuration, defect_channel, distance, magnetization, timestamp, velocity, wall_thickness]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# MongoDB-Abfrage ausführen und Daten in DataFrame laden\n",
    "cursor = collection.find({})\n",
    "df = pd.DataFrame(list(cursor))\n",
    "\n",
    "# Funktion zum Überprüfen, ob NaN-Werte in einem Dokument vorhanden sind\n",
    "def has_nan_values(row): \n",
    "    return any(pd.isna(row))\n",
    "\n",
    "# Filtern der Datensätze mit NaN-Werten\n",
    "nan_documents = df[df.apply(has_nan_values, axis=1)]\n",
    "\n",
    "# Ausgabe der Datensätze mit NaN-Werten\n",
    "print(nan_documents)"
   ]
  },
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   "id": "f5b35dd5e9e9c86b"
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