Firq/data-processing-example
Firq/data-processing-example
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions
data-processing-example/data-processing.ipynb

506 lines
147 KiB
Text
Raw Normal View History

Initial Commit
2024-05-20 10:19:55 +00:00
{
"cells": [
Musterlösung preview
2024-05-26 12:06:52 +00:00
{
"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",
Fixed formatting of help text
2024-05-27 07:08:01 +00:00
"\n",
Musterlösung preview
2024-05-26 12:06:52 +00:00
"Die Verwendung eines Virtual Environments (venv) wird empfohlen (`py -m venv .venv`, dann `.venv/scripts/activate` bzw. `.venv/bin/activate`)"
]
},
Initial Commit
2024-05-20 10:19:55 +00:00
{
"cell_type": "code",
Rendered version
2024-05-27 13:38:21 +00:00
"execution_count": 1,
Initial Commit
2024-05-20 10:19:55 +00:00
"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"
]
},
Added further description
2024-05-27 07:26:15 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Beschreibung des Datensatzes\n",
"\n",
"- Datensatz beinhaltet Daten der Wetterstation Freiburg des Deutschen Wetterdienstes\n",
"- Datensatz ist csv mit Trennzeichen `;`\n",
"- Datensatz umfasst 6 Spalten:\n",
"\n",
"\n",
"| | STATIONS_ID | MESS_DATUM | QN_4 | TT_TER | RF_TER | eor |\n",
"|--------------------|-----------------------|------------------------------|----------------------|-----------------------|----------------------------|------------|\n",
"| Beschreibung | ID der Wetterstation | Datum und Stunde der Messung | Qualität der Messung | Temperatur | Relative Luftfeutchtigkeit | End of Row |\n",
"| Datentyp | Integer | String (Format yyyymmddhh) | Integer | Float | Float | String |\n",
"| Fehlende Einträge | Nein | Nein | Nein | Ja (-999) | Ja (-999) | Nein |\n",
"| Relevant | Nein | Ja | Teilweise | Ja | Ja | Nein |\n",
"\n",
"\n",
"## Schritte der Datenverarbeitung\n",
"\n",
"1. Einlesen des Datensatzes\n",
"2. Konvertierung der Datumsspalte in datetime objekte\n",
"3. Entfernen nicht benötigter Spalten\n",
"4. Plotten der Daten für bessere Übersicht\n",
"5. Identifizieren und kompensieren von Fehlerwerten durch Interpolation\n",
"6. Plotten des Ergebnisses"
]
},
Initial Commit
2024-05-20 10:19:55 +00:00
{
"cell_type": "code",
Rendered version
2024-05-27 13:38:21 +00:00
"execution_count": 2,
Initial Commit
2024-05-20 10:19:55 +00:00
"metadata": {},
Rendered version
2024-05-27 13:38:21 +00:00
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STATIONS_ID</th>\n",
" <th>MESS_DATUM</th>\n",
" <th>QN_4</th>\n",
" <th>TT_TER</th>\n",
" <th>RF_TER</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1939.0</td>\n",
" <td>1.939000e+03</td>\n",
" <td>1939.000000</td>\n",
" <td>1939.000000</td>\n",
" <td>1939.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1443.0</td>\n",
" <td>2.023268e+09</td>\n",
" <td>5.121712</td>\n",
" <td>-1.439144</td>\n",
" <td>58.563177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>5.373317e+05</td>\n",
" <td>3.262451</td>\n",
" <td>114.311663</td>\n",
" <td>122.156135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1443.0</td>\n",
" <td>2.022111e+09</td>\n",
" <td>1.000000</td>\n",
" <td>-999.000000</td>\n",
" <td>-999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1443.0</td>\n",
" <td>2.023042e+09</td>\n",
" <td>3.000000</td>\n",
" <td>6.200000</td>\n",
" <td>59.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1443.0</td>\n",
" <td>2.023092e+09</td>\n",
" <td>3.000000</td>\n",
" <td>10.900000</td>\n",
" <td>74.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1443.0</td>\n",
" <td>2.024012e+09</td>\n",
" <td>9.000000</td>\n",
" <td>16.600000</td>\n",
" <td>87.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1443.0</td>\n",
" <td>2.024052e+09</td>\n",
" <td>9.000000</td>\n",
" <td>34.300000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATIONS_ID MESS_DATUM QN_4 TT_TER RF_TER\n",
"count 1939.0 1.939000e+03 1939.000000 1939.000000 1939.000000\n",
"mean 1443.0 2.023268e+09 5.121712 -1.439144 58.563177\n",
"std 0.0 5.373317e+05 3.262451 114.311663 122.156135\n",
"min 1443.0 2.022111e+09 1.000000 -999.000000 -999.000000\n",
"25% 1443.0 2.023042e+09 3.000000 6.200000 59.000000\n",
"50% 1443.0 2.023092e+09 3.000000 10.900000 74.000000\n",
"75% 1443.0 2.024012e+09 9.000000 16.600000 87.000000\n",
"max 1443.0 2.024052e+09 9.000000 34.300000 100.000000"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
Initial Commit
2024-05-20 10:19:55 +00:00
"source": [
Musterlösung preview
2024-05-26 12:06:52 +00:00
"# Einlesen der Wetterdaten. Diese müssen sich im selben ordner wie dieses Notebook befinden\n",
Initial Commit
2024-05-20 10:19:55 +00:00
"datafile = pathlib.Path() / \"wetterdaten_freiburg_2022_2024.csv\"\n",
"data = pd.read_csv(datafile, sep=\";\")\n",
Musterlösung preview
2024-05-26 12:06:52 +00:00
"\n",
"# Struktur des pandas dataframes ausgeben\n",
Initial Commit
2024-05-20 10:19:55 +00:00
"data.describe()"
]
},
{
"cell_type": "code",
Rendered version
2024-05-27 13:38:21 +00:00
"execution_count": 3,
Initial Commit
2024-05-20 10:19:55 +00:00
"metadata": {},
Rendered version
2024-05-27 13:38:21 +00:00
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MESS_DATUM</th>\n",
" <th>TT_TER</th>\n",
" <th>RF_TER</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1939</td>\n",
" <td>1939.000000</td>\n",
" <td>1939.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2023-09-03 09:01:23.548220672</td>\n",
" <td>-1.439144</td>\n",
" <td>58.563177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2022-11-13 06:00:00</td>\n",
" <td>-999.000000</td>\n",
" <td>-999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2023-04-23 15:00:00</td>\n",
" <td>6.200000</td>\n",
" <td>59.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2023-09-16 12:00:00</td>\n",
" <td>10.900000</td>\n",
" <td>74.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2024-01-15 15:00:00</td>\n",
" <td>16.600000</td>\n",
" <td>87.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2024-05-15 18:00:00</td>\n",
" <td>34.300000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>114.311663</td>\n",
" <td>122.156135</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MESS_DATUM TT_TER RF_TER\n",
"count 1939 1939.000000 1939.000000\n",
"mean 2023-09-03 09:01:23.548220672 -1.439144 58.563177\n",
"min 2022-11-13 06:00:00 -999.000000 -999.000000\n",
"25% 2023-04-23 15:00:00 6.200000 59.000000\n",
"50% 2023-09-16 12:00:00 10.900000 74.000000\n",
"75% 2024-01-15 15:00:00 16.600000 87.000000\n",
"max 2024-05-15 18:00:00 34.300000 100.000000\n",
"std NaN 114.311663 122.156135"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
Initial Commit
2024-05-20 10:19:55 +00:00
"source": [
Musterlösung preview
2024-05-26 12:06:52 +00:00
"# Datumswerte konvertieren\n",
Initial Commit
2024-05-20 10:19:55 +00:00
"data[\"MESS_DATUM\"] = pd.to_datetime(data[\"MESS_DATUM\"], format=\"%Y%m%d%H\")\n",
Musterlösung preview
2024-05-26 12:06:52 +00:00
"\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",
Initial Commit
2024-05-20 10:19:55 +00:00
"data.describe()"
]
},
{
"cell_type": "code",
Rendered version
2024-05-27 13:38:21 +00:00
"execution_count": 4,
Initial Commit
2024-05-20 10:19:55 +00:00
"metadata": {},
Rendered version
2024-05-27 13:38:21 +00:00
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAGGCAYAAABmGOKbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACLZElEQVR4nO3dd3gUVdsG8HvTNoUkBNIoSSD0hEAgtNDBSMCIYomFVwQERAQLIAiiFFFRaWJBxFcE/fQVwYaCQGgCEkWQIiX0JiGhJ0Ag9Xx/hB12s7vJBnYzczb377pywc7Mbp7ZzJkzz5kz5+iEEAJEREREREREZHcuagdARERERERE5KyYdBMRERERERE5CJNuIiIiIiIiIgdh0k1ERERERETkIEy6iYiIiIiIiByESTcRERERERGRgzDpJiIiIiIiInIQJt1EREREREREDsKkm4iIiIiIiMhBmHQTERERkZQ2bNgAnU6HDRs22PVzdTodJk+ebNfPVMPKlSsRGxsLT09P6HQ6XL58udTt//rrL7Rv3x4+Pj7Q6XTYuXNnhcRpSdeuXdG0adM7+owBAwagTp06t/3eKlWq3NHvNzh+/Dh0Oh0WLlxol88j+TDpJtIYnU5n04+9LzBkMnfuXFZcRESSWbhwoUk95ubmhlq1amHAgAE4ffp0hcezYsUKzSXWOp0OI0aMsMtnXbhwAY888gi8vLzw0Ucf4csvv4SPjw/eeust/Pjjj2bb5+fnIzk5GRcvXsTs2bPx5ZdfIiIiwi6xOFJ6ejomT56sagPB7dDi8UeO46Z2AERk6ssvvzR5/cUXXyAlJcVseZMmTSoyLE2ZO3cuAgMDMWDAALVDISKicnr99ddRt25d3LhxA3/88QcWLlyIzZs3Y8+ePfD09KywOFasWIGPPvrIYuJz/fp1uLnJfZn8119/4cqVK5g6dSoSEhKU5W+99RYefvhh9OnTx2T7I0eO4MSJE/j0008xePDgCo729qWnp2PKlCmoU6cOYmNjTdZ9+umnKCoqUicwIxEREbh+/Trc3d2VZaUdf+R85D6bEDmhJ554wuT1H3/8gZSUFLPlzkIIgRs3bsDLy4txEBFVAr169UKrVq0AAIMHD0ZgYCDeeecdLFu2DI888ojK0RWryOTfUc6ePQsAqFq1qkO2l4FxkqsmnU7nFMcU3T52LyeSUFFREd577z1ER0fD09MTISEhGDp0KC5dumSyXZ06dXDvvfdiw4YNaNWqFby8vBATE6N0Tf/+++8RExMDT09PxMXFYceOHSbvNzzPdPToUSQmJsLHxwc1a9bE66+/DiHEHcW0atUqJaZPPvkEAPD555+je/fuCA4Ohl6vR1RUFD7++GOz9+/duxe//fab0kWxa9euAIDJkydDp9OZfV+GLo3Hjx+3KQ4iIqo4nTp1AlB8p9VYWloaHn74YVSrVg2enp5o1aoVli1bVubnbdq0CcnJyQgPD4der0dYWBhGjhyJ69evK9sMGDAAH330EQDTx7oMjJ/pXrp0KXQ6HX777Tez3/XJJ59Ap9Nhz549dxy3Law9w17ymeGuXbuif//+AIDWrVtDp9NhwIAB0Ol0uHbtGhYtWqTs84ABAzBgwAB06dIFAJCcnGxSt9q6T+WpgwHg119/RZcuXeDr6ws/Pz+0bt0aX3/9tdn79+3bh27dusHb2xu1atXCu+++a/J9tG7dGgAwcOBAZZ8M34OlZ7ovXLiAfv36wc/PD1WrVkX//v2xa9cum5653rlzJ4KCgtC1a1dcvXoVAHD69Gk89dRTCAkJgV6vR3R0NBYsWGDyvpJ/n7KOP3I+vNNNJKGhQ4di4cKFGDhwIJ5//nkcO3YMH374IXbs2IHff//dpGX38OHD6Nu3L4YOHYonnngCM2bMQO/evTFv3jy88sorePbZZwEA06ZNwyOPPIIDBw7AxeVWe1xhYSF69uyJdu3a4d1338XKlSsxadIkFBQU4PXXX7+tmA4cOIDHH38cQ4cOxZAhQ9CoUSMAwMcff4zo6Gjcd999cHNzw88//4xnn30WRUVFGD58OADgvffew3PPPYcqVapgwoQJAICQkJDb+h6txUFERBXHkIwFBAQoy/bu3YsOHTqgVq1aGDduHHx8fPDtt9+iT58++O677/DAAw9Y/bwlS5YgJycHw4YNQ/Xq1bF161Z88MEH+Pfff7FkyRIAxXVWenq6xce3SkpKSkKVKlXw7bffKompweLFixEdHa0M+HUncdvThAkT0KhRI8yfP1/pzl+vXj0kJCRg8ODBaNOmDZ5++mkAQL169QAAtWrVwltvvYXnn38erVu3VupWR+zTwoUL8dRTTyE6Ohrjx49H1apVsWPHDqxcuRJ9+/ZVtrt06RJ69uyJBx98EI888giWLl2Kl19+GTExMejVqxeaNGmC119/HRMnTsTTTz+tNOC0b9/e4u8tKipC7969sXXrVgwbNgyNGzfGTz/9pDRQlOavv/5CYmIiWrVqhZ9++gleXl7IzMxEu3btlGfxg4KC8Ouvv2LQoEHIzs7Giy++aPGzynP8kZMQRKRpw4cPF8ZFddOmTQKA+Oqrr0y2W7lypdnyiIgIAUBs2bJFWbZq1SoBQHh5eYkTJ04oyz/55BMBQKxfv15Z1r9/fwFAPPfcc8qyoqIikZSUJDw8PMS5c+duO6aVK1ea7WtOTo7ZssTERBEZGWmyLDo6WnTp0sVs20mTJglLp7XPP/9cABDHjh2zKQ4iIrI/w7l4zZo14ty5c+LUqVNi6dKlIigoSOj1enHq1Cll27vuukvExMSIGzduKMuKiopE+/btRYMGDZRl69evN6u7LNUl06ZNEzqdzqTeK1m/GgMgJk2apLx+/PHHRXBwsCgoKFCWnTlzRri4uIjXX3+93HFbA0AMHz7c6npL+yuEEMeOHRMAxOeff64sM3zff/31l8m2Pj4+on///lY/e8mSJSbLbd0nW+vgy5cvC19fX9G2bVtx/fp1k22LioqU/3fp0kUAEF988YWyLDc3V4SGhoqHHnpIWfbXX3+Z7btB//79RUREhPL6u+++EwDEe++9pywrLCwU3bt3N/uM/v37Cx8fHyGEEJs3bxZ+fn4iKSnJ5HsYNGiQqFGjhjh//rzJ733ssceEv7+/cixa+vuUdvyR82H3ciLJLFmyBP7+/rj77rtx/vx55ScuLg5VqlTB+vXrTbaPiopCfHy88rpt27YAgO7duyM8PNxs+dGjR81+p/FIqobW3Ly8PKxZs+a2Yqpbty4SExPNfo/x89RZWVk4f/48unTpgqNHjyIrK8vm78hW1uIgIiLHSUhIQFBQEMLCwvDwww/Dx8cHy5YtQ+3atQEAFy9exLp16/DII4/gypUrSp1y4cIFJCYm4tChQ6WOdm5cl1y7dg3nz59H+/btIYQwe4zKVo8++ijOnj1r0q176dKlKCoqwqOPPmqXuLXIEfuUkpKCK1euYNy4cWbPOZfsYl2lShWTMW08PDzQpk0bi9cqtli5ciXc3d0xZMgQZZmLi4vSm86S9evXIzExEXfddRe+//576PV6AMVjwXz33Xfo3bs3hBAm1z+JiYnIysrC33//fVtxkvNh93IiyRw6dAhZWVkIDg62uN4wEIqBcWINAP7+/gCAsLAwi8tLPoPt4uKCyMhIk2UNGzYEcKtLYHljqlu3rsXtfv/9d0yaNAmpqanIyckxWZeVlaXEaC/W4iAiIsf56KOP0LBhQ2RlZWHBggXYuHGjksgAxY9FCSHw2muv4bXXXrP4GWfPnkWtWrUsrjt58iQmTpyIZcuWmdVpt9uA27NnT/j7+2Px4sW46667ABR3LY+NjVXqxDuNW4scsU+GZ/dtmYO7du3aZol4QEAAdu/ebfPvM3bixAnUqFED3t7eJsvr169vcfsbN24gKSkJcXFx+Pbbb01GtD937hwuX76M+fPnY/78+RbfX/L6hyovJt1EkikqKkJwcDC++uori+uDgoJMXru6ulrcztpyUWKANEfEZGmE8CNHjuCuu+5C48aNMWvWLISFhcHDwwMrVqzA7NmzbZryw9ogJIWFhRaXc6RyIqKK16Z
"text/plain": [
"<Figure size 1000x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
Initial Commit
2024-05-20 10:19:55 +00:00
"source": [
Musterlösung preview
2024-05-26 12:06:52 +00:00
"# Daten plotten (Temperatur und Luftfeuchtigkeit)\n",
Initial Commit
2024-05-20 10:19:55 +00:00
"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",
Rendered version
2024-05-27 13:38:21 +00:00
"execution_count": 5,
Initial Commit
2024-05-20 10:19:55 +00:00
"metadata": {},
Rendered version
2024-05-27 13:38:21 +00:00
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MESS_DATUM</th>\n",
" <th>TT_TER</th>\n",
" <th>RF_TER</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1939</td>\n",
" <td>1939.000000</td>\n",
" <td>1939.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2023-09-03 09:01:23.548220672</td>\n",
" <td>11.634683</td>\n",
" <td>72.296029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2022-11-13 06:00:00</td>\n",
" <td>-9.100000</td>\n",
" <td>20.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2023-04-23 15:00:00</td>\n",
" <td>6.500000</td>\n",
" <td>60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2023-09-16 12:00:00</td>\n",
" <td>11.100000</td>\n",
" <td>75.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2024-01-15 15:00:00</td>\n",
" <td>16.700000</td>\n",
" <td>87.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2024-05-15 18:00:00</td>\n",
" <td>34.300000</td>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>7.940810</td>\n",
" <td>17.596512</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MESS_DATUM TT_TER RF_TER\n",
"count 1939 1939.000000 1939.000000\n",
"mean 2023-09-03 09:01:23.548220672 11.634683 72.296029\n",
"min 2022-11-13 06:00:00 -9.100000 20.000000\n",
"25% 2023-04-23 15:00:00 6.500000 60.000000\n",
"50% 2023-09-16 12:00:00 11.100000 75.000000\n",
"75% 2024-01-15 15:00:00 16.700000 87.000000\n",
"max 2024-05-15 18:00:00 34.300000 100.000000\n",
"std NaN 7.940810 17.596512"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
Initial Commit
2024-05-20 10:19:55 +00:00
"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",
Musterlösung preview
2024-05-26 12:06:52 +00:00
"# Struktur des pandas dataframes ausgeben\n",
"data.describe()"
]
},
{
"cell_type": "code",
Rendered version
2024-05-27 13:38:21 +00:00
"execution_count": 6,
Musterlösung preview
2024-05-26 12:06:52 +00:00
"metadata": {},
Rendered version
2024-05-27 13:38:21 +00:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
Musterlösung preview
2024-05-26 12:06:52 +00:00
"source": [
"# Überarbeitete Daten plotten (Temperatur und Luftfeuchtigkeit)\n",
Initial Commit
2024-05-20 10:19:55 +00:00
"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()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
Musterlösung preview
2024-05-26 12:06:52 +00:00
"version": "3.11.0"
Initial Commit
2024-05-20 10:19:55 +00:00
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue Copy permalink