{ "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": [ "
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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
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105 rows × 54 columns

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" ], "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 }