diff --git a/python_scripts/run_simstadt_from_python/Parse heat demand csv.ipynb b/python_scripts/run_simstadt_from_python/Parse heat demand csv.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..db4a9da1ad68f0928ea5586e86fccc6cf45bef88 --- /dev/null +++ b/python_scripts/run_simstadt_from_python/Parse heat demand csv.ipynb @@ -0,0 +1,489 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d2c68f35", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "66f2a037", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(r\"C:\\Users\\eric.duminil\\git\\simstadt2\\TestRepository\\Gruenbuehl.proj\\99_HeatDemand.flow\\04_MonthlyEnergyBalance.step\\Gruenbuehl_LOD2_ALKIS_1010_DIN18599_HEATING.csv\",\n", + " skiprows=list(range(19)) + [20],\n", + " sep=';',\n", + " decimal=','\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c94d6456", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GMLIdParentGMLIdLatitudeLongitudeX-coordinateY-coordinateLODYear of constructionYear of refurbishmentRefurbishment Variant...March Heating demandApril Heating demandMay Heating demandJune Heating demandJuly Heating demandAugust Heating demandSeptember Heating demandOctober Heating demandNovember Heating demandDecember Heating demand
0DEBW_LOD2_1004495NaN48.879509.214783515829.635415809.40LOD_21968NaNOriginal...15987456667320069084491907526508
1DEBW_LOD2_1004496NaN48.879399.214563515813.565415796.91LOD_21968NaNOriginal...39731011117000120208549336905
2DEBW_LOD2_1003853NaN48.878009.215593515888.845415642.86LOD_21968NaNOriginal...2998868125000120157835975006
3DEBW_LOD2_1003651NaN48.877789.214673515821.595415617.69LOD_21957NaNOriginal...11847278533310040164281516121212
4DEBW_LOD2_1003854NaN48.878119.215593515889.085415655.22LOD_21968NaNOriginal...36691102154000140193843105964
..................................................................
100DEBW_LOD2_2158NaN48.878619.218133516075.535415710.81LOD_21968NaNOriginal...8581215328910028644471078015176
101DEBW_LOD2_2200NaN48.878569.219083516145.225415705.71LOD_21994NaNOriginal...15099270820710029577752001328298
102DEBW_LOD2_2147NaN48.878569.217713516044.745415705.26LOD_21968NaNOriginal...8622217429810029244761082015219
103DEBW_LOD2_2146NaN48.878699.217713516044.425415719.66LOD_21968NaNOriginal...689517622261002843871858011813
104DEBW_LOD2_1249NaN48.879229.217223516008.535415779.04LOD_21968NaNOriginal...750519812540003074159930712859
\n", + "

105 rows × 54 columns

\n", + "
" + ], + "text/plain": [ + " GMLId ParentGMLId Latitude Longitude X-coordinate \\\n", + "0 DEBW_LOD2_1004495 NaN 48.87950 9.21478 3515829.63 \n", + "1 DEBW_LOD2_1004496 NaN 48.87939 9.21456 3515813.56 \n", + "2 DEBW_LOD2_1003853 NaN 48.87800 9.21559 3515888.84 \n", + "3 DEBW_LOD2_1003651 NaN 48.87778 9.21467 3515821.59 \n", + "4 DEBW_LOD2_1003854 NaN 48.87811 9.21559 3515889.08 \n", + ".. ... ... ... ... ... \n", + "100 DEBW_LOD2_2158 NaN 48.87861 9.21813 3516075.53 \n", + "101 DEBW_LOD2_2200 NaN 48.87856 9.21908 3516145.22 \n", + "102 DEBW_LOD2_2147 NaN 48.87856 9.21771 3516044.74 \n", + "103 DEBW_LOD2_2146 NaN 48.87869 9.21771 3516044.42 \n", + "104 DEBW_LOD2_1249 NaN 48.87922 9.21722 3516008.53 \n", + "\n", + " Y-coordinate LOD Year of construction Year of refurbishment \\\n", + "0 5415809.40 LOD_2 1968 NaN \n", + "1 5415796.91 LOD_2 1968 NaN \n", + "2 5415642.86 LOD_2 1968 NaN \n", + "3 5415617.69 LOD_2 1957 NaN \n", + "4 5415655.22 LOD_2 1968 NaN \n", + ".. ... ... ... ... \n", + "100 5415710.81 LOD_2 1968 NaN \n", + "101 5415705.71 LOD_2 1994 NaN \n", + "102 5415705.26 LOD_2 1968 NaN \n", + "103 5415719.66 LOD_2 1968 NaN \n", + "104 5415779.04 LOD_2 1968 NaN \n", + "\n", + " Refurbishment Variant ... March Heating demand April Heating demand \\\n", + "0 Original ... 15987 4566 \n", + "1 Original ... 3973 1011 \n", + "2 Original ... 2998 868 \n", + "3 Original ... 11847 2785 \n", + "4 Original ... 3669 1102 \n", + ".. ... ... ... ... \n", + "100 Original ... 8581 2153 \n", + "101 Original ... 15099 2708 \n", + "102 Original ... 8622 2174 \n", + "103 Original ... 6895 1762 \n", + "104 Original ... 7505 1981 \n", + "\n", + " May Heating demand June Heating demand July Heating demand \\\n", + "0 673 2 0 \n", + "1 117 0 0 \n", + "2 125 0 0 \n", + "3 333 1 0 \n", + "4 154 0 0 \n", + ".. ... ... ... \n", + "100 289 1 0 \n", + "101 207 1 0 \n", + "102 298 1 0 \n", + "103 226 1 0 \n", + "104 254 0 0 \n", + "\n", + " August Heating demand September Heating demand October Heating demand \\\n", + "0 0 690 8449 \n", + "1 0 120 2085 \n", + "2 0 120 1578 \n", + "3 0 401 6428 \n", + "4 0 140 1938 \n", + ".. ... ... ... \n", + "100 0 286 4447 \n", + "101 0 295 7775 \n", + "102 0 292 4476 \n", + "103 0 284 3871 \n", + "104 0 307 4159 \n", + "\n", + " November Heating demand December Heating demand \n", + "0 19075 26508 \n", + "1 4933 6905 \n", + "2 3597 5006 \n", + "3 15161 21212 \n", + "4 4310 5964 \n", + ".. ... ... \n", + "100 10780 15176 \n", + "101 20013 28298 \n", + "102 10820 15219 \n", + "103 8580 11813 \n", + "104 9307 12859 \n", + "\n", + "[105 rows x 54 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "adbf8c7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8001948" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Yearly Heating demand'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c62ba61", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}