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     "text": [
      "C:\\Users\\David Gaming\\AppData\\Local\\Temp\\ipykernel_18604\\2711366225.py:8: DtypeWarning: Columns (10,11,14,15,16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.concat((pd.read_csv(f) for f in all_files), ignore_index=True)\n",
      "C:\\Users\\David Gaming\\AppData\\Local\\Temp\\ipykernel_18604\\2711366225.py:8: DtypeWarning: Columns (10,11,14,15,16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.concat((pd.read_csv(f) for f in all_files), ignore_index=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      operator_name     domain_name  domain_id  \\\n",
      "0  Eco Counter GmbH  Stadt Tübingen        667   \n",
      "1  Eco Counter GmbH  Stadt Tübingen        667   \n",
      "2  Eco Counter GmbH  Stadt Tübingen        667   \n",
      "3  Eco Counter GmbH  Stadt Tübingen        667   \n",
      "4  Eco Counter GmbH  Stadt Tübingen        667   \n",
      "\n",
      "                                     counter_site  counter_site_id  \\\n",
      "0  Fuß- & Radtunnel Südportal - Derendinger Allee        100003358   \n",
      "1  Fuß- & Radtunnel Südportal - Derendinger Allee        100003358   \n",
      "2  Fuß- & Radtunnel Südportal - Derendinger Allee        100003358   \n",
      "3  Fuß- & Radtunnel Südportal - Derendinger Allee        100003358   \n",
      "4  Fuß- & Radtunnel Südportal - Derendinger Allee        100003358   \n",
      "\n",
      "  counter_serial  longitude  latitude                      timezone  \\\n",
      "0    YAH24052174    9.04806    48.518  (UTC+01:00) Europe/Paris DST   \n",
      "1    YAH24052174    9.04806    48.518  (UTC+01:00) Europe/Paris DST   \n",
      "2    YAH24052174    9.04806    48.518  (UTC+01:00) Europe/Paris DST   \n",
      "3    YAH24052174    9.04806    48.518  (UTC+01:00) Europe/Paris DST   \n",
      "4    YAH24052174    9.04806    48.518  (UTC+01:00) Europe/Paris DST   \n",
      "\n",
      "               iso_timestamp channels_in channels_out channels_unknown  \\\n",
      "0  2013-06-21T00:00:00+02:00        2844         2647               na   \n",
      "1  2013-06-22T00:00:00+02:00        2412         2233               na   \n",
      "2  2013-06-23T00:00:00+02:00        1884         1808               na   \n",
      "3  2013-06-24T00:00:00+02:00        2072         1878               na   \n",
      "4  2013-06-25T00:00:00+02:00        2426         2419               na   \n",
      "\n",
      "   channels_all site_temperature site_rain_accumulation site_snow_accumulation  \n",
      "0          5491             17.5                    1.5                    0.0  \n",
      "1          4645             16.5                    0.2                    0.0  \n",
      "2          3692             14.5                    0.0                    0.0  \n",
      "3          3950             12.5                    5.4                    0.0  \n",
      "4          4845             11.5                    6.0                    0.0  \n"
     ]
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "\n",
    "path = '../data/tageswerte_pro_Jahr/'\n",
    "all_files = glob.glob(os.path.join(path , \"*.csv\")) #kein plan was das hier genau ist, hab es in Stack Overflow gefunden https://stackoverflow.com/questions/20906474/import-multiple-csv-files-into-pandas-and-concatenate-into-one-dataframe\n",
    "\n",
    "df = pd.concat((pd.read_csv(f) for f in all_files), ignore_index=True)\n",
    "\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           Hotspots  Durchschnitt Jahre\n",
      "6                                    Kurpfalzbrücke         4994.045846\n",
      "10                                       Renzstraße         3394.062305\n",
      "5                            Konrad-Adenauer-Brücke         2120.285610\n",
      "1                                    Fernmeldeturm.         1828.993555\n",
      "11  Schlosspark Lindenhof (Richtung Jugendherberge)         1333.949980\n",
      "4                                   Jungbuschbrücke         1104.775576\n",
      "9            Neckarauer Übergang -Schwetzinger Str.         1011.494404\n",
      "7                              Lindenhofüberführung          943.561290\n",
      "8                                     Luzenbergstr.          667.606890\n",
      "3                      Feudenheimstr. stadtauswärts          512.098278\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Hotspots seit Aufzeichnungsbeginn bis 2025 - gemittelt über die Jahre. \n",
    "# Vllt noch den Vergleich zu den einzelnen Jahren ziehen und zeigen wie es sich verändert hat?!\n",
    "hotspots = (\n",
    "    df[df[\"domain_name\"].str.strip() == \"Stadt Mannheim\"]\n",
    "    .groupby(\"counter_site\", as_index=False)[\"channels_all\"]\n",
    "    .mean()\n",
    "    .sort_values(\"channels_all\", ascending=False)\n",
    "    .head(10)\n",
    ").rename(columns={\n",
    "        \"counter_site\": \"Hotspots\",\n",
    "        \"channels_all\": \"Durchschnitt Jahre\"\n",
    "    })\n",
    "print(hotspots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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