data-processing-example/data-processing.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Musterbeispiel Data Processing\n",
    "\n",
    "Notwendige Packages befinden sich in `requirements.txt` - installieren mit `pip install -r requirements.txt`.\n",
    "\n",
    "Die Verwendung eines Virtual Environments (venv) wird empfohlen (`py -m venv .venv`, dann `.venv/scripts/activate` bzw. `.venv/bin/activate`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Einlesen der Wetterdaten. Diese müssen sich im selben ordner wie dieses Notebook befinden\n",
    "datafile = pathlib.Path() / \"wetterdaten_freiburg_2022_2024.csv\"\n",
    "data = pd.read_csv(datafile, sep=\";\")\n",
    "\n",
    "# Struktur des pandas dataframes ausgeben\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datumswerte konvertieren\n",
    "data[\"MESS_DATUM\"] = pd.to_datetime(data[\"MESS_DATUM\"], format=\"%Y%m%d%H\")\n",
    "\n",
    "# Nicht benötigte Spalten entfernen\n",
    "data.drop([\"STATIONS_ID\", \"QN_4\", \"eor\"], axis=1, inplace=True)\n",
    "\n",
    "# Struktur des pandas dataframes ausgeben\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Daten plotten (Temperatur und Luftfeuchtigkeit)\n",
    "fig = plt.figure(figsize=(10, 4))\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "ax.plot(data[\"MESS_DATUM\"], data[\"TT_TER\"])\n",
    "ax.set_title(\"Temperatur\")\n",
    "monthyearFmt = mdates.DateFormatter('%m-%Y')\n",
    "ax.xaxis.set_major_formatter(monthyearFmt)\n",
    "_ = plt.xticks(rotation=90)\n",
    "\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "ax.plot(data[\"MESS_DATUM\"], data[\"RF_TER\"])\n",
    "ax.set_title(\"Relative Luftfeuchtigkeit\")\n",
    "monthyearFmt = mdates.DateFormatter('%m-%Y')\n",
    "ax.xaxis.set_major_formatter(monthyearFmt)\n",
    "_ = plt.xticks(rotation=90)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fehlerwerte vereinheitlichen\n",
    "data.replace(-999, np.nan, inplace=True)\n",
    "\n",
    "# Fehlende Werte durch Interpolation ersetzen\n",
    "data[\"TT_TER\"] = data[\"TT_TER\"].interpolate()\n",
    "data[\"RF_TER\"] = data[\"RF_TER\"].interpolate()\n",
    "\n",
    "# Struktur des pandas dataframes ausgeben\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Überarbeitete Daten plotten (Temperatur und Luftfeuchtigkeit)\n",
    "fig = plt.figure(figsize=(10, 4))\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "ax.plot(data[\"MESS_DATUM\"], data[\"TT_TER\"])\n",
    "ax.set_title(\"Temperatur\")\n",
    "monthyearFmt = mdates.DateFormatter('%m-%Y')\n",
    "ax.xaxis.set_major_formatter(monthyearFmt)\n",
    "_ = plt.xticks(rotation=90)\n",
    "\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "ax.plot(data[\"MESS_DATUM\"], data[\"RF_TER\"])\n",
    "ax.set_title(\"Relative Luftfeuchtigkeit\")\n",
    "monthyearFmt = mdates.DateFormatter('%m-%Y')\n",
    "ax.xaxis.set_major_formatter(monthyearFmt)\n",
    "_ = plt.xticks(rotation=90)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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