380 lines
142 KiB
Text
380 lines
142 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 207,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pathlib\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.dates as mdates\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 208,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>STATIONS_ID</th>\n",
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" <th>MESS_DATUM</th>\n",
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" <th>QN_4</th>\n",
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" <th>TT_TER</th>\n",
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" <th>RF_TER</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>1939.0</td>\n",
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" <td>1.939000e+03</td>\n",
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" <td>1939.000000</td>\n",
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" <td>1939.000000</td>\n",
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" <td>1939.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>1443.0</td>\n",
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" <td>2.023268e+09</td>\n",
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" <td>5.121712</td>\n",
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" <td>-1.439144</td>\n",
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" <td>58.563177</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>0.0</td>\n",
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" <td>5.373317e+05</td>\n",
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" <td>3.262451</td>\n",
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" <td>114.311663</td>\n",
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" <td>122.156135</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>1443.0</td>\n",
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" <td>2.022111e+09</td>\n",
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" <td>1.000000</td>\n",
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" <td>-999.000000</td>\n",
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" <td>-999.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>1443.0</td>\n",
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" <td>2.023042e+09</td>\n",
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" <td>3.000000</td>\n",
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" <td>6.200000</td>\n",
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" <td>59.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>1443.0</td>\n",
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" <td>2.023092e+09</td>\n",
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" <td>3.000000</td>\n",
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" <td>10.900000</td>\n",
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" <td>74.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
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" <td>1443.0</td>\n",
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" <td>2.024012e+09</td>\n",
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" <td>9.000000</td>\n",
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" <td>16.600000</td>\n",
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" <td>87.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
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" <td>1443.0</td>\n",
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" <td>2.024052e+09</td>\n",
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" <td>9.000000</td>\n",
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" <td>34.300000</td>\n",
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" <td>100.000000</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" STATIONS_ID MESS_DATUM QN_4 TT_TER RF_TER\n",
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"count 1939.0 1.939000e+03 1939.000000 1939.000000 1939.000000\n",
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"mean 1443.0 2.023268e+09 5.121712 -1.439144 58.563177\n",
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"std 0.0 5.373317e+05 3.262451 114.311663 122.156135\n",
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"min 1443.0 2.022111e+09 1.000000 -999.000000 -999.000000\n",
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"25% 1443.0 2.023042e+09 3.000000 6.200000 59.000000\n",
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"50% 1443.0 2.023092e+09 3.000000 10.900000 74.000000\n",
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"75% 1443.0 2.024012e+09 9.000000 16.600000 87.000000\n",
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"max 1443.0 2.024052e+09 9.000000 34.300000 100.000000"
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]
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},
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"execution_count": 208,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"datafile = pathlib.Path() / \"wetterdaten_freiburg_2022_2024.csv\"\n",
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"\n",
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"data = pd.read_csv(datafile, sep=\";\")\n",
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"data.describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 209,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>STATIONS_ID</th>\n",
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" <th>MESS_DATUM</th>\n",
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" <th>QN_4</th>\n",
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" <th>TT_TER</th>\n",
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" <th>RF_TER</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>1939.0</td>\n",
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" <td>1939</td>\n",
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" <td>1939.000000</td>\n",
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" <td>1939.000000</td>\n",
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" <td>1939.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>1443.0</td>\n",
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" <td>2023-09-03 09:01:23.548220672</td>\n",
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" <td>5.121712</td>\n",
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" <td>-1.439144</td>\n",
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" <td>58.563177</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>1443.0</td>\n",
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" <td>2022-11-13 06:00:00</td>\n",
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" <td>1.000000</td>\n",
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" <td>-999.000000</td>\n",
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" <td>-999.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>1443.0</td>\n",
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" <td>2023-04-23 15:00:00</td>\n",
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" <td>3.000000</td>\n",
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" <td>6.200000</td>\n",
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" <td>59.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>1443.0</td>\n",
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" <td>2023-09-16 12:00:00</td>\n",
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" <td>3.000000</td>\n",
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" <td>10.900000</td>\n",
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" <td>74.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
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" <td>1443.0</td>\n",
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" <td>2024-01-15 15:00:00</td>\n",
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" <td>9.000000</td>\n",
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" <td>16.600000</td>\n",
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" <td>87.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
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" <td>1443.0</td>\n",
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" <td>2024-05-15 18:00:00</td>\n",
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" <td>9.000000</td>\n",
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" <td>34.300000</td>\n",
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" <td>100.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>0.0</td>\n",
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" <td>NaN</td>\n",
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" <td>3.262451</td>\n",
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" <td>114.311663</td>\n",
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" <td>122.156135</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" STATIONS_ID MESS_DATUM QN_4 TT_TER \\\n",
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"count 1939.0 1939 1939.000000 1939.000000 \n",
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"mean 1443.0 2023-09-03 09:01:23.548220672 5.121712 -1.439144 \n",
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"min 1443.0 2022-11-13 06:00:00 1.000000 -999.000000 \n",
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"25% 1443.0 2023-04-23 15:00:00 3.000000 6.200000 \n",
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"50% 1443.0 2023-09-16 12:00:00 3.000000 10.900000 \n",
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"75% 1443.0 2024-01-15 15:00:00 9.000000 16.600000 \n",
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"max 1443.0 2024-05-15 18:00:00 9.000000 34.300000 \n",
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"std 0.0 NaN 3.262451 114.311663 \n",
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"\n",
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" RF_TER \n",
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"count 1939.000000 \n",
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"mean 58.563177 \n",
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"min -999.000000 \n",
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"25% 59.000000 \n",
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"50% 74.000000 \n",
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"75% 87.000000 \n",
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"max 100.000000 \n",
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"std 122.156135 "
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]
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},
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"execution_count": 209,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data[\"MESS_DATUM\"] = pd.to_datetime(data[\"MESS_DATUM\"], format=\"%Y%m%d%H\")\n",
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"data.describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 210,
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"metadata": {},
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"outputs": [
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{
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"data": {
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x400 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data.drop([\"STATIONS_ID\", \"QN_4\", \"eor\"], axis=1, inplace=True)\n",
|
||
|
|
"\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": 211,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1000x400 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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",
|
||
|
|
"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",
|
||
|
|
"version": "3.11.1"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|