From b795869240f0a7bfd3a07ffe220960237e3c8b29 Mon Sep 17 00:00:00 2001 From: Viktor Koves <3187531+vkoves@users.noreply.github.com> Date: Mon, 20 Jan 2025 23:15:48 -0600 Subject: [PATCH 1/4] Started anomaly detection notebook --- src/data/analysis/anomaly_decetion.ipynb | 1641 ++++++++++++++++++++++ 1 file changed, 1641 insertions(+) create mode 100644 src/data/analysis/anomaly_decetion.ipynb diff --git a/src/data/analysis/anomaly_decetion.ipynb b/src/data/analysis/anomaly_decetion.ipynb new file mode 100644 index 0000000..3d188bd --- /dev/null +++ b/src/data/analysis/anomaly_decetion.ipynb @@ -0,0 +1,1641 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection\n", + "\n", + "**The problem:** When we started looking into grading buildings by their GHG emissions intensity,\n", + "all of the buildings that we were going to be giving an A grade appear to be outliers, missing data\n", + "or having faulty data.\n", + "\n", + "The A buildings from our [first PR](https://github.com/vkoves/electrify-chicago/pull/140/commits/14546521270ade9e47623f615af4e6868c6c9cfc) are as follows:\n", + "\n", + "- https://electrifychicago.net/building/1830-n-winchester-ave/ (ID 117024)\n", + " Multi-family housing. Reported 0 natural gas use for the past two years despite non-zero use before.\n", + "\n", + "- https://electrifychicago.net/building/830-n-michigan-ave/ (ID 124236)\n", + " Topshop and UNIQLO building, may be largely vacant, had precipitous declines\n", + "\n", + "- https://electrifychicago.net/building/u-s-cellular-plaza-8430-goby-llc/ (ID 160142)\n", + " Large decline in electricity use (3x from 2017-202), never reported gas use. Could be correct?\n", + "\n", + "- https://electrifychicago.net/building/moody-bible-institute-solheim-center/ (ID 165717)\n", + " Moody's gym, went from 2M kBTUs of natural gas to 0 in 2021 and 2022.\n", + "\n", + "- https://electrifychicago.net/building/newberry-plaza-townhouse-owners-association/ (ID 172137)\n", + " Similarly went to 0 gas from 800k KBTU,\n", + "\n", + "- https://electrifychicago.net/building/u-s-cellular-plaza-8420-goby-llc/ (ID 251770)\n", + "\n", + "- https://electrifychicago.net/building/4434-4444-n-damen-ave/ (ID 254001)\n", + " Robey Condominiums, multi-family housing. Reported 0 natural gas use for the past two years\n", + " despite non-zero use before.\n", + "\n", + "\n", + "## Dependencies\n", + "\n", + "This notebook requires:\n", + "\n", + "- pandas\n", + "- numpy\n", + "- plotly\n", + "- statsmodels\n", + "- nbformat" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import plotly.subplots as sp\n", + "import plotly.graph_objects as go\n", + "import plotly.express as px\n", + "from plotly.offline import iplot\n", + "import plotly.io as pio\n", + "from plotly.subplots import make_subplots\n", + "import statsmodels.api as sm\n", + "import os\n", + "from pathlib import Path\n", + "import json\n", + "\n", + "from IPython.display import Image\n", + "\n", + "from plotly.offline import init_notebook_mode\n", + "init_notebook_mode(connected=True)\n", + "\n", + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set pathing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success: Found 'electrify-chicago' as the base directory.\n", + "Current working directory is correctly set to '/home/viktor/Documents/Projects/electrify-chicago/src/data/analysis'.\n" + ] + } + ], + "source": [ + "# get static dir for saving images\n", + "current_dir = Path.cwd()\n", + "project_root = current_dir\n", + "\n", + "while True:\n", + " if os.path.basename(project_root) == 'electrify-chicago':\n", + " print(\"Success: Found 'electrify-chicago' as the base directory.\")\n", + " break\n", + " new_root = os.path.dirname(project_root)\n", + " if new_root == project_root: # Reached the filesystem root\n", + " raise FileNotFoundError(\"Error: 'electrify-chicago' directory not found in the path hierarchy.\")\n", + " project_root = new_root\n", + "static_blog_pth = os.path.join(project_root, 'static', 'blog', 'GHGIntensityPredictCompliance')\n", + "os.makedirs(static_blog_pth, exist_ok=True)\n", + "\n", + "expected_dir_name = \"analysis\"\n", + "fig_dir = os.path.join(current_dir, 'output', 'compliance_analysis')\n", + "\n", + "# Check if the current directory is the \"analysis\" folder\n", + "if current_dir.name != expected_dir_name:\n", + " raise AssertionError(f\"Expected working directory to be '{expected_dir_name}', but got '{current_dir.name}'.\\n\"\n", + " f\"Please ensure you are in the correct directory.\")\n", + "\n", + "print(f\"Current working directory is correctly set to '{current_dir}'.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notebook options and custom plotting function" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "reduce_memory = True # option to display plotly as static images to reduce memory, if possible\n", + "export_to_blog = False # if true, saves plots and regressions to blog static folder for website publishing\n", + "\n", + "if export_to_blog:\n", + " dirs = [static_blog_pth, fig_dir]\n", + "else:\n", + " dirs = [fig_dir]\n", + "\n", + "def show_fig(fig, reduce_memory):\n", + " if reduce_memory:\n", + " try:\n", + " png_image = pio.to_image(fig, format='png')\n", + " return (png_image, reduce_memory)\n", + "\n", + " except:\n", + " print(\"Error exporting plotly to png, displaying html graph instead\")\n", + " reduce_memory = False\n", + "\n", + " if not reduce_memory:\n", + " return (iplot(fig), reduce_memory)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 3749 unique building ids\n", + "0 2020\n", + "1 2020\n", + "2 2020\n", + "3 2020\n", + "4 2020\n", + " ... \n", + "24886 2022\n", + "24887 2022\n", + "24888 2022\n", + "24889 2022\n", + "24890 2022\n", + "Name: DataYear, Length: 24891, dtype: int64\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReported
02520642020Submitted Data64028.01160.918.11.02.0NaN2384738.9NaN7438787.05594040.1NaN240.8323.6246.0329.91
12324582020Submitted Data627680.04871.77.81.01.022.016397682.843537490.6NaNNaNNaN95.5146.0100.3150.71
22546162020Submitted Data555524.04581.48.21.02.049.028606427.72199940.1NaNNaNNaN55.5148.356.7151.81
31038122020Submitted Data130007.01092.18.41.03.061.06489281.31493523.2NaNNaNNaN61.4151.863.0154.81
42540732020Submitted Data83000.0295.83.61.04.0100.01614582.3825006.6NaNNaNNaN29.464.929.664.31
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" + ], + "text/plain": [ + " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", + "0 252064 2020 Submitted Data 64028.0 1160.9 \n", + "1 232458 2020 Submitted Data 627680.0 4871.7 \n", + "2 254616 2020 Submitted Data 555524.0 4581.4 \n", + "3 103812 2020 Submitted Data 130007.0 1092.1 \n", + "4 254073 2020 Submitted Data 83000.0 295.8 \n", + "\n", + " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", + "0 18.1 1.0 2.0 NaN \n", + "1 7.8 1.0 1.0 22.0 \n", + "2 8.2 1.0 2.0 49.0 \n", + "3 8.4 1.0 3.0 61.0 \n", + "4 3.6 1.0 4.0 100.0 \n", + "\n", + " ElectricityUse NaturalGasUse DistrictSteamUse DistrictChilledWaterUse \\\n", + "0 2384738.9 NaN 7438787.0 5594040.1 \n", + "1 16397682.8 43537490.6 NaN NaN \n", + "2 28606427.7 2199940.1 NaN NaN \n", + "3 6489281.3 1493523.2 NaN NaN \n", + "4 1614582.3 825006.6 NaN NaN \n", + "\n", + " AllOtherFuelUse SiteEUI SourceEUI WeatherNormalizedSiteEUI \\\n", + "0 NaN 240.8 323.6 246.0 \n", + "1 NaN 95.5 146.0 100.3 \n", + "2 NaN 55.5 148.3 56.7 \n", + "3 NaN 61.4 151.8 63.0 \n", + "4 NaN 29.4 64.9 29.6 \n", + "\n", + " WeatherNormalizedSourceEUI Reported \n", + "0 329.9 1 \n", + "1 150.7 1 \n", + "2 151.8 1 \n", + "3 154.8 1 \n", + "4 64.3 1 " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Construct the path to the CSV file (one level above the current directory)\n", + "data_path = os.path.join( current_dir.parent, 'dist', 'benchmarking-all-years.csv')\n", + "\n", + "df = pd.read_csv(data_path)\n", + "\n", + "# Create the \"reported\" column\n", + "df['Reported'] = df['GHGIntensity'].notna().astype(int)\n", + "\n", + "print(f\"There are {df['ID'].unique().shape[0]} unique building ids\")\n", + "\n", + "df['DataYear'] = df['DataYear'].astype(int)\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in Building Benchmark Data to get Building Names" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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", + "
IDPropertyName
0100001Presence SMEMC St Elizabeth Campus
1100002Clemente Community Academy HS -CPS
2100019Dixon Building
3100068Joffco Square
4100148The Jeffery Cyril Building
\n", + "
" + ], + "text/plain": [ + " ID PropertyName\n", + "0 100001 Presence SMEMC St Elizabeth Campus\n", + "1 100002 Clemente Community Academy HS -CPS\n", + "2 100019 Dixon Building\n", + "3 100068 Joffco Square\n", + "4 100148 The Jeffery Cyril Building" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names_path = os.path.join( current_dir.parent, 'dist', 'building-benchmarks.csv')\n", + "\n", + "building_names = pd.read_csv(names_path)[['ID', 'PropertyName' ]]\n", + "building_names.drop_duplicates(keep='first')\n", + "building_names.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merge names to data" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName_xPropertyName_yPropertyName
02520642020Submitted Data64028.01160.918.11.02.0NaN2384738.9NaN7438787.05594040.1NaN240.8323.6246.0329.91Mansueto LibraryMansueto LibraryMansueto Library
12324582020Submitted Data627680.04871.77.81.01.022.016397682.843537490.6NaNNaNNaN95.5146.0100.3150.71Harper Square CooperativeHarper Square CooperativeHarper Square Cooperative
22546162020Submitted Data555524.04581.48.21.02.049.028606427.72199940.1NaNNaNNaN55.5148.356.7151.81Former Coyne CollegeFormer Coyne CollegeFormer Coyne College
31038122020Submitted Data130007.01092.18.41.03.061.06489281.31493523.2NaNNaNNaN61.4151.863.0154.81400 W Superior St400 W Superior St400 W Superior St
42540732020Submitted Data83000.0295.83.61.04.0100.01614582.3825006.6NaNNaNNaN29.464.929.664.31Blue Moon LoftsBlue Moon LoftsBlue Moon Lofts
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" + ], + "text/plain": [ + " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", + "0 252064 2020 Submitted Data 64028.0 1160.9 \n", + "1 232458 2020 Submitted Data 627680.0 4871.7 \n", + "2 254616 2020 Submitted Data 555524.0 4581.4 \n", + "3 103812 2020 Submitted Data 130007.0 1092.1 \n", + "4 254073 2020 Submitted Data 83000.0 295.8 \n", + "\n", + " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", + "0 18.1 1.0 2.0 NaN \n", + "1 7.8 1.0 1.0 22.0 \n", + "2 8.2 1.0 2.0 49.0 \n", + "3 8.4 1.0 3.0 61.0 \n", + "4 3.6 1.0 4.0 100.0 \n", + "\n", + " ElectricityUse NaturalGasUse DistrictSteamUse DistrictChilledWaterUse \\\n", + "0 2384738.9 NaN 7438787.0 5594040.1 \n", + "1 16397682.8 43537490.6 NaN NaN \n", + "2 28606427.7 2199940.1 NaN NaN \n", + "3 6489281.3 1493523.2 NaN NaN \n", + "4 1614582.3 825006.6 NaN NaN \n", + "\n", + " AllOtherFuelUse SiteEUI SourceEUI WeatherNormalizedSiteEUI \\\n", + "0 NaN 240.8 323.6 246.0 \n", + "1 NaN 95.5 146.0 100.3 \n", + "2 NaN 55.5 148.3 56.7 \n", + "3 NaN 61.4 151.8 63.0 \n", + "4 NaN 29.4 64.9 29.6 \n", + "\n", + " WeatherNormalizedSourceEUI Reported PropertyName_x \\\n", + "0 329.9 1 Mansueto Library \n", + "1 150.7 1 Harper Square Cooperative \n", + "2 151.8 1 Former Coyne College \n", + "3 154.8 1 400 W Superior St \n", + "4 64.3 1 Blue Moon Lofts \n", + "\n", + " PropertyName_y PropertyName \n", + "0 Mansueto Library Mansueto Library \n", + "1 Harper Square Cooperative Harper Square Cooperative \n", + "2 Former Coyne College Former Coyne College \n", + "3 400 W Superior St 400 W Superior St \n", + "4 Blue Moon Lofts Blue Moon Lofts " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.merge( df, building_names, how='left', on='ID')\n", + "df['PropertyName'] = df['PropertyName'].fillna(\"[Building Name Unavailable]\").replace(\"\", \"[Building Name Unavailable]\")\n", + "df = df[df['ReportingStatus'].isin(['Submitted Data'])]\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check that every building/year combo exists only once" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "group_counts = df.groupby(['ID', 'DataYear']).size()\n", + "\n", + "# Assert that the maximum count in any group is at most 1\n", + "assert group_counts.max() <= 1, \"There are buildings with more than one row in a given year!\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the latest year we have data for " + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2022" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get buildings with zero natural gas use in past year\n", + "latestYear = df['DataYear'].max()\n", + "latestYear" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_62756/3929095217.py:1: UserWarning:\n", + "\n", + "Boolean Series key will be reindexed to match DataFrame index.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName
177531758912022Submitted Data172500.0452.43.01.04.0100.02069532.43384519.40.00.0NaN36.462.336.462.31[Building Name Unavailable]
177552512452022Submitted Data249095.01434.05.82.03.574.03345590.218702028.90.00.0NaN88.5116.494.1121.113800 N. Lake Shore Drive
212842566582022Submitted Data393938.01948.44.98.04.075.06388293.520841006.30.00.0NaN69.1101.071.9102.61Midpointe Apartments
212852500622022Submitted Data66285.0525.47.91.02.0NaN2649529.23322169.70.00.0NaN90.1164.591.9165.61RJ Quinn Academy
212861015452022Submitted Data51163.0300.35.91.02.050.0541560.14310877.60.00.0NaN94.8118.1101.2123.81[Building Name Unavailable]
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" + ], + "text/plain": [ + " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", + "17753 175891 2022 Submitted Data 172500.0 452.4 \n", + "17755 251245 2022 Submitted Data 249095.0 1434.0 \n", + "21284 256658 2022 Submitted Data 393938.0 1948.4 \n", + "21285 250062 2022 Submitted Data 66285.0 525.4 \n", + "21286 101545 2022 Submitted Data 51163.0 300.3 \n", + "\n", + " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", + "17753 3.0 1.0 4.0 100.0 \n", + "17755 5.8 2.0 3.5 74.0 \n", + "21284 4.9 8.0 4.0 75.0 \n", + "21285 7.9 1.0 2.0 NaN \n", + "21286 5.9 1.0 2.0 50.0 \n", + "\n", + " ElectricityUse NaturalGasUse DistrictSteamUse \\\n", + "17753 2069532.4 3384519.4 0.0 \n", + "17755 3345590.2 18702028.9 0.0 \n", + "21284 6388293.5 20841006.3 0.0 \n", + "21285 2649529.2 3322169.7 0.0 \n", + "21286 541560.1 4310877.6 0.0 \n", + "\n", + " DistrictChilledWaterUse AllOtherFuelUse SiteEUI SourceEUI \\\n", + "17753 0.0 NaN 36.4 62.3 \n", + "17755 0.0 NaN 88.5 116.4 \n", + "21284 0.0 NaN 69.1 101.0 \n", + "21285 0.0 NaN 90.1 164.5 \n", + "21286 0.0 NaN 94.8 118.1 \n", + "\n", + " WeatherNormalizedSiteEUI WeatherNormalizedSourceEUI Reported \\\n", + "17753 36.4 62.3 1 \n", + "17755 94.1 121.1 1 \n", + "21284 71.9 102.6 1 \n", + "21285 91.9 165.6 1 \n", + "21286 101.2 123.8 1 \n", + "\n", + " PropertyName \n", + "17753 [Building Name Unavailable] \n", + "17755 3800 N. Lake Shore Drive \n", + "21284 Midpointe Apartments \n", + "21285 RJ Quinn Academy \n", + "21286 [Building Name Unavailable] " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latestData = df[df['DataYear'] == latestYear]\n", + "latestData.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName
212871604382022Submitted Data1484327.09938.87.11.04.075.063482520.50.00.029929202.7NaN66.4145.864.4144.91155 North Wacker
212971001792022Submitted Data450612.0NaNNaN1.00.0NaN15191302.20.00.00.0NaNNaNNaNNaNNaN0Saint Anthony Hospital - Main Hospital
213221569422022Submitted Data201402.01318.26.51.03.068.02852269.00.014194821.60.0NaN84.6124.489.7130.61Burton-Judson Courts
213542501472022Submitted Data186957.71766.09.41.01.0NaN13408061.50.00.00.0NaN71.7200.871.7200.81820 W Jackson Blvd
213661018672022Submitted Data641962.04402.26.91.03.067.033422010.00.00.00.0NaN52.1145.852.9148.21125 South Wacker
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" + ], + "text/plain": [ + " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", + "21287 160438 2022 Submitted Data 1484327.0 9938.8 \n", + "21297 100179 2022 Submitted Data 450612.0 NaN \n", + "21322 156942 2022 Submitted Data 201402.0 1318.2 \n", + "21354 250147 2022 Submitted Data 186957.7 1766.0 \n", + "21366 101867 2022 Submitted Data 641962.0 4402.2 \n", + "\n", + " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", + "21287 7.1 1.0 4.0 75.0 \n", + "21297 NaN 1.0 0.0 NaN \n", + "21322 6.5 1.0 3.0 68.0 \n", + "21354 9.4 1.0 1.0 NaN \n", + "21366 6.9 1.0 3.0 67.0 \n", + "\n", + " ElectricityUse NaturalGasUse DistrictSteamUse \\\n", + "21287 63482520.5 0.0 0.0 \n", + "21297 15191302.2 0.0 0.0 \n", + "21322 2852269.0 0.0 14194821.6 \n", + "21354 13408061.5 0.0 0.0 \n", + "21366 33422010.0 0.0 0.0 \n", + "\n", + " DistrictChilledWaterUse AllOtherFuelUse SiteEUI SourceEUI \\\n", + "21287 29929202.7 NaN 66.4 145.8 \n", + "21297 0.0 NaN NaN NaN \n", + "21322 0.0 NaN 84.6 124.4 \n", + "21354 0.0 NaN 71.7 200.8 \n", + "21366 0.0 NaN 52.1 145.8 \n", + "\n", + " WeatherNormalizedSiteEUI WeatherNormalizedSourceEUI Reported \\\n", + "21287 64.4 144.9 1 \n", + "21297 NaN NaN 0 \n", + "21322 89.7 130.6 1 \n", + "21354 71.7 200.8 1 \n", + "21366 52.9 148.2 1 \n", + "\n", + " PropertyName \n", + "21287 155 North Wacker \n", + "21297 Saint Anthony Hospital - Main Hospital \n", + "21322 Burton-Judson Courts \n", + "21354 820 W Jackson Blvd \n", + "21366 125 South Wacker " + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noGasUse = latestData[latestData['NaturalGasUse'].isin([0, np.nan])]\n", + "noGasUse.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Count of \"Gas Free\" Buildings Latest Year" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "235" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noGasUse['ID'].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loop Through Gas Free Buildings And See If They Used Gas in Previous Years" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_62756/3249074751.py:2: UserWarning:\n", + "\n", + "Boolean Series key will be reindexed to match DataFrame index.\n", + "\n", + "/tmp/ipykernel_62756/3249074751.py:2: UserWarning:\n", + "\n", + "Boolean Series key will be reindexed to match DataFrame index.\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "array([254001, 165664, 165819, 159892, 260149, 175817, 242935, 256639,\n", + " 174228, 175914, 135050, 103602, 102987, 113670, 172256, 115942,\n", + " 175754, 254378, 175995, 116550, 260135, 175492, 159425, 100179,\n", + " 172145, 137144, 101757, 138730, 172565, 101448, 101396, 260116,\n", + " 157988, 105441, 252312, 172772, 115605, 165717, 116644, 255944,\n", + " 172540, 172157, 159423, 175885, 251883, 238480, 117179, 175334,\n", + " 172393, 251959, 175882, 160137, 254035, 254387, 255012, 242279,\n", + " 162325, 256614, 260101, 129344, 160414, 172137, 103962, 101827,\n", + " 101748, 158391, 173618, 101717, 101760, 160130, 125048, 101559,\n", + " 102854, 103761, 210090, 117599, 117024, 252054, 101722, 103573,\n", + " 165439, 105034, 120931, 132278, 165328, 103598, 135660, 119689,\n", + " 103739, 174289, 175484, 104879, 256803, 251379])" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noGasUseIds = noGasUse['ID']\n", + "usedGasBefore = df[df['DataYear'] < latestYear][df['NaturalGasUse'] > 0][df['ID'].isin(noGasUseIds)]\n", + "gasAnomalyIds = usedGasBefore['ID'].unique()\n", + "gasAnomalyIds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Did this catch our A buildings?" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{117024, 165717, 172137, 254001}" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AGradedBuildingIds = [117024, 124236, 160142, 165717, 172137, 251770, 254001]\n", + "AGradedBuildingIds = set(AGradedBuildingIds)\n", + "AGradedBuildingIds.intersection(gasAnomalyIds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis: Unique values for GHG Intensity\n", + "\n", + "Conclusion: strange that some values are highly represented while others are not. How are these calculated? " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Round GHG Intensity values to 1 digit\n", + "df['GHGIntensity'] = df['GHGIntensity'].round(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Distribution of GHG Intensities" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[54], line 94\u001b[0m\n\u001b[1;32m 83\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(\n\u001b[1;32m 84\u001b[0m xaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGHG Intensity\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 85\u001b[0m yaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCount\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 89\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m800\u001b[39m\n\u001b[1;32m 90\u001b[0m )\n\u001b[1;32m 92\u001b[0m \u001b[38;5;66;03m# Show the plot\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;66;03m#pio.show(fig)\u001b[39;00m\n\u001b[0;32m---> 94\u001b[0m \u001b[43miplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mdir\u001b[39m \u001b[38;5;129;01min\u001b[39;00m [static_blog_pth, fig_dir]:\n\u001b[1;32m 97\u001b[0m fig\u001b[38;5;241m.\u001b[39mwrite_html( os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mdir\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdistribution_of_GHG_intensity.html\u001b[39m\u001b[38;5;124m'\u001b[39m), include_plotlyjs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcdn\u001b[39m\u001b[38;5;124m\"\u001b[39m )\n", + "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/offline/offline.py:387\u001b[0m, in \u001b[0;36miplot\u001b[0;34m(figure_or_data, show_link, link_text, validate, image, filename, image_width, image_height, config, auto_play, animation_opts)\u001b[0m\n\u001b[1;32m 382\u001b[0m post_script \u001b[38;5;241m=\u001b[39m build_save_image_post_script(\n\u001b[1;32m 383\u001b[0m image, filename, image_height, image_width, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miplot\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 384\u001b[0m )\n\u001b[1;32m 386\u001b[0m \u001b[38;5;66;03m# Show figure\u001b[39;00m\n\u001b[0;32m--> 387\u001b[0m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mauto_play\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto_play\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_script\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_script\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m \u001b[49m\u001b[43manimation_opts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manimation_opts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 394\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 391\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 396\u001b[0m )\n\u001b[1;32m 398\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n", + "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" + ] + } + ], + "source": [ + "\n", + "fig = sp.make_subplots(\n", + " rows=1,\n", + " cols=2,\n", + " column_widths=[0.8, 0.2],\n", + " horizontal_spacing=0.2,\n", + " subplot_titles = [\n", + " '',\n", + " 'GHG Intensity Outliers
(GHG Intensity values > 50)'\n", + " ]\n", + ")\n", + "\n", + "\n", + "fig.add_trace( go.Histogram(\n", + " x=df['GHGIntensity'],\n", + " #nbinsx=int((df['GHGIntensity'].max() - df['GHGIntensity'].min()) / 0.5),\n", + " xbins = dict(start=0, end=100, size=.5),\n", + " name='Histogram of Values',\n", + " hovertemplate = \" %{y} Buildings
with GHG Intensity between %{x}\"\n", + " ),\n", + " row=1,col=1\n", + ")\n", + "\n", + "\n", + "# Add a light red box to the first plot\n", + "fig.add_shape(\n", + " type=\"rect\",\n", + " x0=50,\n", + " x1=100,\n", + " y0=0,\n", + " y1=1500,\n", + " fillcolor=\"rgba(255, 0, 0, 0.1)\", # Light red with transparency\n", + " line=dict(width=0),\n", + " row=1, col=1\n", + ")\n", + "\n", + "outlier_subset = df.dropna(subset=['GHGIntensity'])\n", + "outlier_subset = outlier_subset[outlier_subset['GHGIntensity']>50]\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=[0] * len(outlier_subset['GHGIntensity']), # Make x an array of zeros with the correct length\n", + " y=outlier_subset['GHGIntensity'],\n", + " mode='markers',\n", + " marker=dict( color='blue', opacity=0.6),\n", + " customdata=df['DataYear'],\n", + " hovertext=outlier_subset['PropertyName'], # Add PropertyName to hovertext\n", + " hovertemplate=\"%{hovertext}
GHG Intensity: %{y} in %{customdata}\",\n", + " name=''\n", + " ),\n", + " row=1, col=2\n", + ")\n", + "\n", + "# Add a light red background to the second subplot\n", + "fig.add_shape(\n", + " type=\"rect\",\n", + " x0=-1,\n", + " x1=1,\n", + " y0=50,\n", + " y1=900,\n", + " fillcolor=\"rgba(255, 0, 0, 0.1)\", # Light red with transparency\n", + " line=dict(width=0),\n", + " layer=\"below\",\n", + " row=1, col=2\n", + ")\n", + "\n", + "\n", + "fig.update_xaxes(visible=False, row=1, col=2)\n", + "fig.update_xaxes(range=[0, 100], row=1,col=1)\n", + "fig.update_xaxes( title_text='' , row=1,col=2)\n", + "fig.update_yaxes( title_text='GHG Intensity', row=1,col=2)\n", + "\n", + "## Add an outline to the bars\n", + "fig.update_traces(marker=dict(line=dict(width=.1, color='black')))\n", + "\n", + "fig.add_annotation(\n", + " x=80,\n", + " y=300,\n", + " text=\"Some buildings had
outlier GHG intensity
levels (up to 800) →\",\n", + " showarrow=False, # No arrow for this annotation\n", + " font=dict(size=10), # Customize font size\n", + ")\n", + "\n", + "# Update layout for better display\n", + "fig.update_layout(\n", + " xaxis_title='GHG Intensity',\n", + " yaxis_title='Count',\n", + " showlegend=False,\n", + " title='Distribution of GHG Intensities',\n", + " height=400,\n", + " width=800\n", + ")\n", + "\n", + "# Show the plot\n", + "#pio.show(fig)\n", + "iplot(fig)\n", + "\n", + "for dir in [static_blog_pth, fig_dir]:\n", + " fig.write_html( os.path.join(dir,'distribution_of_GHG_intensity.html'), include_plotlyjs=\"cdn\" )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compliance type counts over time" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GHGIntensity\n", + "0.0 2\n", + "0.1 1\n", + "0.2 4\n", + "0.3 8\n", + "0.4 5\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "value_counts.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[107], line 33\u001b[0m\n\u001b[1;32m 26\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCount of Buildings That Did/Didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt Report Emissions by Year\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 27\u001b[0m xaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYear of Emissions
(One year before data is reported)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 28\u001b[0m yaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCount of Buildings\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 29\u001b[0m legend_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCategory\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# Show the plot\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m#pio.show(fig)\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m \u001b[43miplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mdir\u001b[39m \u001b[38;5;129;01min\u001b[39;00m [static_blog_pth, fig_dir]:\n\u001b[1;32m 37\u001b[0m fig\u001b[38;5;241m.\u001b[39mwrite_html( os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mdir\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreporting_counts_over_time.html\u001b[39m\u001b[38;5;124m'\u001b[39m), include_plotlyjs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcdn\u001b[39m\u001b[38;5;124m\"\u001b[39m )\n", + "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/offline/offline.py:387\u001b[0m, in \u001b[0;36miplot\u001b[0;34m(figure_or_data, show_link, link_text, validate, image, filename, image_width, image_height, config, auto_play, animation_opts)\u001b[0m\n\u001b[1;32m 382\u001b[0m post_script \u001b[38;5;241m=\u001b[39m build_save_image_post_script(\n\u001b[1;32m 383\u001b[0m image, filename, image_height, image_width, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miplot\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 384\u001b[0m )\n\u001b[1;32m 386\u001b[0m \u001b[38;5;66;03m# Show figure\u001b[39;00m\n\u001b[0;32m--> 387\u001b[0m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mauto_play\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto_play\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_script\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_script\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m \u001b[49m\u001b[43manimation_opts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manimation_opts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 394\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 391\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 396\u001b[0m )\n\u001b[1;32m 398\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n", + "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" + ] + } + ], + "source": [ + "# Count each new column per year\n", + "value_counts = df.groupby('DataYear')['Reported'].value_counts()\n", + "non_reporting_counts = value_counts.xs(0, level='Reported')\n", + "reporting_counts = value_counts.xs(1, level='Reported')\n", + "\n", + "# Create the figure\n", + "fig = go.Figure()\n", + "\n", + "# Add traces for each category\n", + "fig.add_trace(go.Scatter(x=reporting_counts.index, y=reporting_counts.values,\n", + " mode='lines+markers', name='Reported',\n", + " line=dict(width=4, color='rgba(0, 0,255, 0.7)'),\n", + " marker=dict(symbol='circle', size=10)))\n", + "\n", + "fig.add_trace(go.Scatter(x=non_reporting_counts.index, y=non_reporting_counts.values,\n", + " mode='lines+markers', name=\"Didn't Report\",\n", + " line=dict(width=4, color='rgba(255, 0, 0, 0.7)'),\n", + " marker=dict(symbol='circle', size=10)))\n", + "\n", + "fig.add_trace( go.Scatter( x=[2018.5, 2019.5, 2019.5, 2018.5, 2018.5],\n", + " y=[0, 0, np.max(reporting_counts)*1.2, np.max(reporting_counts)*1.2, 0 ],\n", + " fill='toself', mode='lines', name='Covid Data Disruption' )\n", + ")\n", + "\n", + "# Update layout\n", + "fig.update_layout(title=\"Count of Buildings That Did/Didn't Report Emissions by Year\",\n", + " xaxis_title='Year of Emissions
(One year before data is reported)',\n", + " yaxis_title='Count of Buildings',\n", + " legend_title='Category')\n", + "\n", + "# Show the plot\n", + "#pio.show(fig)\n", + "iplot(fig)\n", + "\n", + "\n", + "for dir in [static_blog_pth, fig_dir]:\n", + " fig.write_html( os.path.join(dir,'reporting_counts_over_time.html'), include_plotlyjs=\"cdn\" )\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2ee4bc25fbcd9e24fd1620d3172682e980f13836 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Viktor=20K=C3=B6ves?= <3187531+vkoves@users.noreply.github.com> Date: Tue, 21 Jan 2025 21:02:21 -0600 Subject: [PATCH 2/4] Added analysis requirements.txt --- src/data/analysis/anomaly_decetion.ipynb | 73 +++++++++++------------- src/data/analysis/requirements.txt | 9 +++ 2 files changed, 42 insertions(+), 40 deletions(-) create mode 100644 src/data/analysis/requirements.txt diff --git a/src/data/analysis/anomaly_decetion.ipynb b/src/data/analysis/anomaly_decetion.ipynb index 3d188bd..782d67d 100644 --- a/src/data/analysis/anomaly_decetion.ipynb +++ b/src/data/analysis/anomaly_decetion.ipynb @@ -42,37 +42,30 @@ "- numpy\n", "- plotly\n", "- statsmodels\n", - "- nbformat" + "- nbformat\n", + "\n", + "To install, _in this `src/data/analysis` directory_, run:\n", + "\n", + "```\n", + "pip install -r requirements.txt\n", + "```" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pandas'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msubplots\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msp\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" + ] } ], "source": [ @@ -106,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -154,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -435,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -524,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -767,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -786,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1040,7 +1033,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1269,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1296,7 +1289,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1351,7 +1344,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1389,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1406,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1533,7 +1526,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1559,7 +1552,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1619,7 +1612,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -1633,7 +1626,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/src/data/analysis/requirements.txt b/src/data/analysis/requirements.txt new file mode 100644 index 0000000..01275b8 --- /dev/null +++ b/src/data/analysis/requirements.txt @@ -0,0 +1,9 @@ +python-slugify==4.0.1 +pandas==2.1.2 +numpy +pytest==7.4.4 +pandas +numpy +plotly +statsmodels +nbformat From 2e5f12c2142a3ecbf124fd8d6724ed6aea5ddc8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Viktor=20K=C3=B6ves?= <3187531+vkoves@users.noreply.github.com> Date: Tue, 21 Jan 2025 21:03:14 -0600 Subject: [PATCH 3/4] Rename anomaly detection notebook --- src/data/analysis/anomaly_decetion.ipynb | 1634 --- src/data/analysis/anomaly_detection.ipynb | 13896 ++++++++++++++++++++ 2 files changed, 13896 insertions(+), 1634 deletions(-) delete mode 100644 src/data/analysis/anomaly_decetion.ipynb create mode 100644 src/data/analysis/anomaly_detection.ipynb diff --git a/src/data/analysis/anomaly_decetion.ipynb b/src/data/analysis/anomaly_decetion.ipynb deleted file mode 100644 index 782d67d..0000000 --- a/src/data/analysis/anomaly_decetion.ipynb +++ /dev/null @@ -1,1634 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Anomaly Detection\n", - "\n", - "**The problem:** When we started looking into grading buildings by their GHG emissions intensity,\n", - "all of the buildings that we were going to be giving an A grade appear to be outliers, missing data\n", - "or having faulty data.\n", - "\n", - "The A buildings from our [first PR](https://github.com/vkoves/electrify-chicago/pull/140/commits/14546521270ade9e47623f615af4e6868c6c9cfc) are as follows:\n", - "\n", - "- https://electrifychicago.net/building/1830-n-winchester-ave/ (ID 117024)\n", - " Multi-family housing. Reported 0 natural gas use for the past two years despite non-zero use before.\n", - "\n", - "- https://electrifychicago.net/building/830-n-michigan-ave/ (ID 124236)\n", - " Topshop and UNIQLO building, may be largely vacant, had precipitous declines\n", - "\n", - "- https://electrifychicago.net/building/u-s-cellular-plaza-8430-goby-llc/ (ID 160142)\n", - " Large decline in electricity use (3x from 2017-202), never reported gas use. Could be correct?\n", - "\n", - "- https://electrifychicago.net/building/moody-bible-institute-solheim-center/ (ID 165717)\n", - " Moody's gym, went from 2M kBTUs of natural gas to 0 in 2021 and 2022.\n", - "\n", - "- https://electrifychicago.net/building/newberry-plaza-townhouse-owners-association/ (ID 172137)\n", - " Similarly went to 0 gas from 800k KBTU,\n", - "\n", - "- https://electrifychicago.net/building/u-s-cellular-plaza-8420-goby-llc/ (ID 251770)\n", - "\n", - "- https://electrifychicago.net/building/4434-4444-n-damen-ave/ (ID 254001)\n", - " Robey Condominiums, multi-family housing. Reported 0 natural gas use for the past two years\n", - " despite non-zero use before.\n", - "\n", - "\n", - "## Dependencies\n", - "\n", - "This notebook requires:\n", - "\n", - "- pandas\n", - "- numpy\n", - "- plotly\n", - "- statsmodels\n", - "- nbformat\n", - "\n", - "To install, _in this `src/data/analysis` directory_, run:\n", - "\n", - "```\n", - "pip install -r requirements.txt\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'pandas'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msubplots\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msp\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import plotly.subplots as sp\n", - "import plotly.graph_objects as go\n", - "import plotly.express as px\n", - "from plotly.offline import iplot\n", - "import plotly.io as pio\n", - "from plotly.subplots import make_subplots\n", - "import statsmodels.api as sm\n", - "import os\n", - "from pathlib import Path\n", - "import json\n", - "\n", - "from IPython.display import Image\n", - "\n", - "from plotly.offline import init_notebook_mode\n", - "init_notebook_mode(connected=True)\n", - "\n", - "pd.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set pathing" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success: Found 'electrify-chicago' as the base directory.\n", - "Current working directory is correctly set to '/home/viktor/Documents/Projects/electrify-chicago/src/data/analysis'.\n" - ] - } - ], - "source": [ - "# get static dir for saving images\n", - "current_dir = Path.cwd()\n", - "project_root = current_dir\n", - "\n", - "while True:\n", - " if os.path.basename(project_root) == 'electrify-chicago':\n", - " print(\"Success: Found 'electrify-chicago' as the base directory.\")\n", - " break\n", - " new_root = os.path.dirname(project_root)\n", - " if new_root == project_root: # Reached the filesystem root\n", - " raise FileNotFoundError(\"Error: 'electrify-chicago' directory not found in the path hierarchy.\")\n", - " project_root = new_root\n", - "static_blog_pth = os.path.join(project_root, 'static', 'blog', 'GHGIntensityPredictCompliance')\n", - "os.makedirs(static_blog_pth, exist_ok=True)\n", - "\n", - "expected_dir_name = \"analysis\"\n", - "fig_dir = os.path.join(current_dir, 'output', 'compliance_analysis')\n", - "\n", - "# Check if the current directory is the \"analysis\" folder\n", - "if current_dir.name != expected_dir_name:\n", - " raise AssertionError(f\"Expected working directory to be '{expected_dir_name}', but got '{current_dir.name}'.\\n\"\n", - " f\"Please ensure you are in the correct directory.\")\n", - "\n", - "print(f\"Current working directory is correctly set to '{current_dir}'.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Notebook options and custom plotting function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "reduce_memory = True # option to display plotly as static images to reduce memory, if possible\n", - "export_to_blog = False # if true, saves plots and regressions to blog static folder for website publishing\n", - "\n", - "if export_to_blog:\n", - " dirs = [static_blog_pth, fig_dir]\n", - "else:\n", - " dirs = [fig_dir]\n", - "\n", - "def show_fig(fig, reduce_memory):\n", - " if reduce_memory:\n", - " try:\n", - " png_image = pio.to_image(fig, format='png')\n", - " return (png_image, reduce_memory)\n", - "\n", - " except:\n", - " print(\"Error exporting plotly to png, displaying html graph instead\")\n", - " reduce_memory = False\n", - "\n", - " if not reduce_memory:\n", - " return (iplot(fig), reduce_memory)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read in data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are 3749 unique building ids\n", - "0 2020\n", - "1 2020\n", - "2 2020\n", - "3 2020\n", - "4 2020\n", - " ... \n", - "24886 2022\n", - "24887 2022\n", - "24888 2022\n", - "24889 2022\n", - "24890 2022\n", - "Name: DataYear, Length: 24891, dtype: int64\n" - ] - }, - { - "data": { - "text/html": [ - "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReported
02520642020Submitted Data64028.01160.918.11.02.0NaN2384738.9NaN7438787.05594040.1NaN240.8323.6246.0329.91
12324582020Submitted Data627680.04871.77.81.01.022.016397682.843537490.6NaNNaNNaN95.5146.0100.3150.71
22546162020Submitted Data555524.04581.48.21.02.049.028606427.72199940.1NaNNaNNaN55.5148.356.7151.81
31038122020Submitted Data130007.01092.18.41.03.061.06489281.31493523.2NaNNaNNaN61.4151.863.0154.81
42540732020Submitted Data83000.0295.83.61.04.0100.01614582.3825006.6NaNNaNNaN29.464.929.664.31
\n", - "
" - ], - "text/plain": [ - " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", - "0 252064 2020 Submitted Data 64028.0 1160.9 \n", - "1 232458 2020 Submitted Data 627680.0 4871.7 \n", - "2 254616 2020 Submitted Data 555524.0 4581.4 \n", - "3 103812 2020 Submitted Data 130007.0 1092.1 \n", - "4 254073 2020 Submitted Data 83000.0 295.8 \n", - "\n", - " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", - "0 18.1 1.0 2.0 NaN \n", - "1 7.8 1.0 1.0 22.0 \n", - "2 8.2 1.0 2.0 49.0 \n", - "3 8.4 1.0 3.0 61.0 \n", - "4 3.6 1.0 4.0 100.0 \n", - "\n", - " ElectricityUse NaturalGasUse DistrictSteamUse DistrictChilledWaterUse \\\n", - "0 2384738.9 NaN 7438787.0 5594040.1 \n", - "1 16397682.8 43537490.6 NaN NaN \n", - "2 28606427.7 2199940.1 NaN NaN \n", - "3 6489281.3 1493523.2 NaN NaN \n", - "4 1614582.3 825006.6 NaN NaN \n", - "\n", - " AllOtherFuelUse SiteEUI SourceEUI WeatherNormalizedSiteEUI \\\n", - "0 NaN 240.8 323.6 246.0 \n", - "1 NaN 95.5 146.0 100.3 \n", - "2 NaN 55.5 148.3 56.7 \n", - "3 NaN 61.4 151.8 63.0 \n", - "4 NaN 29.4 64.9 29.6 \n", - "\n", - " WeatherNormalizedSourceEUI Reported \n", - "0 329.9 1 \n", - "1 150.7 1 \n", - "2 151.8 1 \n", - "3 154.8 1 \n", - "4 64.3 1 " - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Construct the path to the CSV file (one level above the current directory)\n", - "data_path = os.path.join( current_dir.parent, 'dist', 'benchmarking-all-years.csv')\n", - "\n", - "df = pd.read_csv(data_path)\n", - "\n", - "# Create the \"reported\" column\n", - "df['Reported'] = df['GHGIntensity'].notna().astype(int)\n", - "\n", - "print(f\"There are {df['ID'].unique().shape[0]} unique building ids\")\n", - "\n", - "df['DataYear'] = df['DataYear'].astype(int)\n", - "\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read in Building Benchmark Data to get Building Names" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "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", - "
IDPropertyName
0100001Presence SMEMC St Elizabeth Campus
1100002Clemente Community Academy HS -CPS
2100019Dixon Building
3100068Joffco Square
4100148The Jeffery Cyril Building
\n", - "
" - ], - "text/plain": [ - " ID PropertyName\n", - "0 100001 Presence SMEMC St Elizabeth Campus\n", - "1 100002 Clemente Community Academy HS -CPS\n", - "2 100019 Dixon Building\n", - "3 100068 Joffco Square\n", - "4 100148 The Jeffery Cyril Building" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "names_path = os.path.join( current_dir.parent, 'dist', 'building-benchmarks.csv')\n", - "\n", - "building_names = pd.read_csv(names_path)[['ID', 'PropertyName' ]]\n", - "building_names.drop_duplicates(keep='first')\n", - "building_names.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Merge names to data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName_xPropertyName_yPropertyName
02520642020Submitted Data64028.01160.918.11.02.0NaN2384738.9NaN7438787.05594040.1NaN240.8323.6246.0329.91Mansueto LibraryMansueto LibraryMansueto Library
12324582020Submitted Data627680.04871.77.81.01.022.016397682.843537490.6NaNNaNNaN95.5146.0100.3150.71Harper Square CooperativeHarper Square CooperativeHarper Square Cooperative
22546162020Submitted Data555524.04581.48.21.02.049.028606427.72199940.1NaNNaNNaN55.5148.356.7151.81Former Coyne CollegeFormer Coyne CollegeFormer Coyne College
31038122020Submitted Data130007.01092.18.41.03.061.06489281.31493523.2NaNNaNNaN61.4151.863.0154.81400 W Superior St400 W Superior St400 W Superior St
42540732020Submitted Data83000.0295.83.61.04.0100.01614582.3825006.6NaNNaNNaN29.464.929.664.31Blue Moon LoftsBlue Moon LoftsBlue Moon Lofts
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" - ], - "text/plain": [ - " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", - "0 252064 2020 Submitted Data 64028.0 1160.9 \n", - "1 232458 2020 Submitted Data 627680.0 4871.7 \n", - "2 254616 2020 Submitted Data 555524.0 4581.4 \n", - "3 103812 2020 Submitted Data 130007.0 1092.1 \n", - "4 254073 2020 Submitted Data 83000.0 295.8 \n", - "\n", - " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", - "0 18.1 1.0 2.0 NaN \n", - "1 7.8 1.0 1.0 22.0 \n", - "2 8.2 1.0 2.0 49.0 \n", - "3 8.4 1.0 3.0 61.0 \n", - "4 3.6 1.0 4.0 100.0 \n", - "\n", - " ElectricityUse NaturalGasUse DistrictSteamUse DistrictChilledWaterUse \\\n", - "0 2384738.9 NaN 7438787.0 5594040.1 \n", - "1 16397682.8 43537490.6 NaN NaN \n", - "2 28606427.7 2199940.1 NaN NaN \n", - "3 6489281.3 1493523.2 NaN NaN \n", - "4 1614582.3 825006.6 NaN NaN \n", - "\n", - " AllOtherFuelUse SiteEUI SourceEUI WeatherNormalizedSiteEUI \\\n", - "0 NaN 240.8 323.6 246.0 \n", - "1 NaN 95.5 146.0 100.3 \n", - "2 NaN 55.5 148.3 56.7 \n", - "3 NaN 61.4 151.8 63.0 \n", - "4 NaN 29.4 64.9 29.6 \n", - "\n", - " WeatherNormalizedSourceEUI Reported PropertyName_x \\\n", - "0 329.9 1 Mansueto Library \n", - "1 150.7 1 Harper Square Cooperative \n", - "2 151.8 1 Former Coyne College \n", - "3 154.8 1 400 W Superior St \n", - "4 64.3 1 Blue Moon Lofts \n", - "\n", - " PropertyName_y PropertyName \n", - "0 Mansueto Library Mansueto Library \n", - "1 Harper Square Cooperative Harper Square Cooperative \n", - "2 Former Coyne College Former Coyne College \n", - "3 400 W Superior St 400 W Superior St \n", - "4 Blue Moon Lofts Blue Moon Lofts " - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.merge( df, building_names, how='left', on='ID')\n", - "df['PropertyName'] = df['PropertyName'].fillna(\"[Building Name Unavailable]\").replace(\"\", \"[Building Name Unavailable]\")\n", - "df = df[df['ReportingStatus'].isin(['Submitted Data'])]\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Check that every building/year combo exists only once" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "group_counts = df.groupby(['ID', 'DataYear']).size()\n", - "\n", - "# Assert that the maximum count in any group is at most 1\n", - "assert group_counts.max() <= 1, \"There are buildings with more than one row in a given year!\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get the latest year we have data for " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2022" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get buildings with zero natural gas use in past year\n", - "latestYear = df['DataYear'].max()\n", - "latestYear" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_62756/3929095217.py:1: UserWarning:\n", - "\n", - "Boolean Series key will be reindexed to match DataFrame index.\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName
177531758912022Submitted Data172500.0452.43.01.04.0100.02069532.43384519.40.00.0NaN36.462.336.462.31[Building Name Unavailable]
177552512452022Submitted Data249095.01434.05.82.03.574.03345590.218702028.90.00.0NaN88.5116.494.1121.113800 N. Lake Shore Drive
212842566582022Submitted Data393938.01948.44.98.04.075.06388293.520841006.30.00.0NaN69.1101.071.9102.61Midpointe Apartments
212852500622022Submitted Data66285.0525.47.91.02.0NaN2649529.23322169.70.00.0NaN90.1164.591.9165.61RJ Quinn Academy
212861015452022Submitted Data51163.0300.35.91.02.050.0541560.14310877.60.00.0NaN94.8118.1101.2123.81[Building Name Unavailable]
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" - ], - "text/plain": [ - " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", - "17753 175891 2022 Submitted Data 172500.0 452.4 \n", - "17755 251245 2022 Submitted Data 249095.0 1434.0 \n", - "21284 256658 2022 Submitted Data 393938.0 1948.4 \n", - "21285 250062 2022 Submitted Data 66285.0 525.4 \n", - "21286 101545 2022 Submitted Data 51163.0 300.3 \n", - "\n", - " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", - "17753 3.0 1.0 4.0 100.0 \n", - "17755 5.8 2.0 3.5 74.0 \n", - "21284 4.9 8.0 4.0 75.0 \n", - "21285 7.9 1.0 2.0 NaN \n", - "21286 5.9 1.0 2.0 50.0 \n", - "\n", - " ElectricityUse NaturalGasUse DistrictSteamUse \\\n", - "17753 2069532.4 3384519.4 0.0 \n", - "17755 3345590.2 18702028.9 0.0 \n", - "21284 6388293.5 20841006.3 0.0 \n", - "21285 2649529.2 3322169.7 0.0 \n", - "21286 541560.1 4310877.6 0.0 \n", - "\n", - " DistrictChilledWaterUse AllOtherFuelUse SiteEUI SourceEUI \\\n", - "17753 0.0 NaN 36.4 62.3 \n", - "17755 0.0 NaN 88.5 116.4 \n", - "21284 0.0 NaN 69.1 101.0 \n", - "21285 0.0 NaN 90.1 164.5 \n", - "21286 0.0 NaN 94.8 118.1 \n", - "\n", - " WeatherNormalizedSiteEUI WeatherNormalizedSourceEUI Reported \\\n", - "17753 36.4 62.3 1 \n", - "17755 94.1 121.1 1 \n", - "21284 71.9 102.6 1 \n", - "21285 91.9 165.6 1 \n", - "21286 101.2 123.8 1 \n", - "\n", - " PropertyName \n", - "17753 [Building Name Unavailable] \n", - "17755 3800 N. Lake Shore Drive \n", - "21284 Midpointe Apartments \n", - "21285 RJ Quinn Academy \n", - "21286 [Building Name Unavailable] " - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "latestData = df[df['DataYear'] == latestYear]\n", - "latestData.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName
212871604382022Submitted Data1484327.09938.87.11.04.075.063482520.50.00.029929202.7NaN66.4145.864.4144.91155 North Wacker
212971001792022Submitted Data450612.0NaNNaN1.00.0NaN15191302.20.00.00.0NaNNaNNaNNaNNaN0Saint Anthony Hospital - Main Hospital
213221569422022Submitted Data201402.01318.26.51.03.068.02852269.00.014194821.60.0NaN84.6124.489.7130.61Burton-Judson Courts
213542501472022Submitted Data186957.71766.09.41.01.0NaN13408061.50.00.00.0NaN71.7200.871.7200.81820 W Jackson Blvd
213661018672022Submitted Data641962.04402.26.91.03.067.033422010.00.00.00.0NaN52.1145.852.9148.21125 South Wacker
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" - ], - "text/plain": [ - " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", - "21287 160438 2022 Submitted Data 1484327.0 9938.8 \n", - "21297 100179 2022 Submitted Data 450612.0 NaN \n", - "21322 156942 2022 Submitted Data 201402.0 1318.2 \n", - "21354 250147 2022 Submitted Data 186957.7 1766.0 \n", - "21366 101867 2022 Submitted Data 641962.0 4402.2 \n", - "\n", - " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", - "21287 7.1 1.0 4.0 75.0 \n", - "21297 NaN 1.0 0.0 NaN \n", - "21322 6.5 1.0 3.0 68.0 \n", - "21354 9.4 1.0 1.0 NaN \n", - "21366 6.9 1.0 3.0 67.0 \n", - "\n", - " ElectricityUse NaturalGasUse DistrictSteamUse \\\n", - "21287 63482520.5 0.0 0.0 \n", - "21297 15191302.2 0.0 0.0 \n", - "21322 2852269.0 0.0 14194821.6 \n", - "21354 13408061.5 0.0 0.0 \n", - "21366 33422010.0 0.0 0.0 \n", - "\n", - " DistrictChilledWaterUse AllOtherFuelUse SiteEUI SourceEUI \\\n", - "21287 29929202.7 NaN 66.4 145.8 \n", - "21297 0.0 NaN NaN NaN \n", - "21322 0.0 NaN 84.6 124.4 \n", - "21354 0.0 NaN 71.7 200.8 \n", - "21366 0.0 NaN 52.1 145.8 \n", - "\n", - " WeatherNormalizedSiteEUI WeatherNormalizedSourceEUI Reported \\\n", - "21287 64.4 144.9 1 \n", - "21297 NaN NaN 0 \n", - "21322 89.7 130.6 1 \n", - "21354 71.7 200.8 1 \n", - "21366 52.9 148.2 1 \n", - "\n", - " PropertyName \n", - "21287 155 North Wacker \n", - "21297 Saint Anthony Hospital - Main Hospital \n", - "21322 Burton-Judson Courts \n", - "21354 820 W Jackson Blvd \n", - "21366 125 South Wacker " - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "noGasUse = latestData[latestData['NaturalGasUse'].isin([0, np.nan])]\n", - "noGasUse.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get Count of \"Gas Free\" Buildings Latest Year" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "235" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "noGasUse['ID'].count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loop Through Gas Free Buildings And See If They Used Gas in Previous Years" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_62756/3249074751.py:2: UserWarning:\n", - "\n", - "Boolean Series key will be reindexed to match DataFrame index.\n", - "\n", - "/tmp/ipykernel_62756/3249074751.py:2: UserWarning:\n", - "\n", - "Boolean Series key will be reindexed to match DataFrame index.\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "array([254001, 165664, 165819, 159892, 260149, 175817, 242935, 256639,\n", - " 174228, 175914, 135050, 103602, 102987, 113670, 172256, 115942,\n", - " 175754, 254378, 175995, 116550, 260135, 175492, 159425, 100179,\n", - " 172145, 137144, 101757, 138730, 172565, 101448, 101396, 260116,\n", - " 157988, 105441, 252312, 172772, 115605, 165717, 116644, 255944,\n", - " 172540, 172157, 159423, 175885, 251883, 238480, 117179, 175334,\n", - " 172393, 251959, 175882, 160137, 254035, 254387, 255012, 242279,\n", - " 162325, 256614, 260101, 129344, 160414, 172137, 103962, 101827,\n", - " 101748, 158391, 173618, 101717, 101760, 160130, 125048, 101559,\n", - " 102854, 103761, 210090, 117599, 117024, 252054, 101722, 103573,\n", - " 165439, 105034, 120931, 132278, 165328, 103598, 135660, 119689,\n", - " 103739, 174289, 175484, 104879, 256803, 251379])" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "noGasUseIds = noGasUse['ID']\n", - "usedGasBefore = df[df['DataYear'] < latestYear][df['NaturalGasUse'] > 0][df['ID'].isin(noGasUseIds)]\n", - "gasAnomalyIds = usedGasBefore['ID'].unique()\n", - "gasAnomalyIds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Did this catch our A buildings?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{117024, 165717, 172137, 254001}" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "AGradedBuildingIds = [117024, 124236, 160142, 165717, 172137, 251770, 254001]\n", - "AGradedBuildingIds = set(AGradedBuildingIds)\n", - "AGradedBuildingIds.intersection(gasAnomalyIds)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analysis: Unique values for GHG Intensity\n", - "\n", - "Conclusion: strange that some values are highly represented while others are not. How are these calculated? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Round GHG Intensity values to 1 digit\n", - "df['GHGIntensity'] = df['GHGIntensity'].round(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot Distribution of GHG Intensities" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[54], line 94\u001b[0m\n\u001b[1;32m 83\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(\n\u001b[1;32m 84\u001b[0m xaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGHG Intensity\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 85\u001b[0m yaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCount\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 89\u001b[0m width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m800\u001b[39m\n\u001b[1;32m 90\u001b[0m )\n\u001b[1;32m 92\u001b[0m \u001b[38;5;66;03m# Show the plot\u001b[39;00m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;66;03m#pio.show(fig)\u001b[39;00m\n\u001b[0;32m---> 94\u001b[0m \u001b[43miplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mdir\u001b[39m \u001b[38;5;129;01min\u001b[39;00m [static_blog_pth, fig_dir]:\n\u001b[1;32m 97\u001b[0m fig\u001b[38;5;241m.\u001b[39mwrite_html( os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mdir\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdistribution_of_GHG_intensity.html\u001b[39m\u001b[38;5;124m'\u001b[39m), include_plotlyjs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcdn\u001b[39m\u001b[38;5;124m\"\u001b[39m )\n", - "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/offline/offline.py:387\u001b[0m, in \u001b[0;36miplot\u001b[0;34m(figure_or_data, show_link, link_text, validate, image, filename, image_width, image_height, config, auto_play, animation_opts)\u001b[0m\n\u001b[1;32m 382\u001b[0m post_script \u001b[38;5;241m=\u001b[39m build_save_image_post_script(\n\u001b[1;32m 383\u001b[0m image, filename, image_height, image_width, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miplot\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 384\u001b[0m )\n\u001b[1;32m 386\u001b[0m \u001b[38;5;66;03m# Show figure\u001b[39;00m\n\u001b[0;32m--> 387\u001b[0m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mauto_play\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto_play\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_script\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_script\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m \u001b[49m\u001b[43manimation_opts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manimation_opts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 394\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 391\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 396\u001b[0m )\n\u001b[1;32m 398\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" - ] - } - ], - "source": [ - "\n", - "fig = sp.make_subplots(\n", - " rows=1,\n", - " cols=2,\n", - " column_widths=[0.8, 0.2],\n", - " horizontal_spacing=0.2,\n", - " subplot_titles = [\n", - " '',\n", - " 'GHG Intensity Outliers
(GHG Intensity values > 50)'\n", - " ]\n", - ")\n", - "\n", - "\n", - "fig.add_trace( go.Histogram(\n", - " x=df['GHGIntensity'],\n", - " #nbinsx=int((df['GHGIntensity'].max() - df['GHGIntensity'].min()) / 0.5),\n", - " xbins = dict(start=0, end=100, size=.5),\n", - " name='Histogram of Values',\n", - " hovertemplate = \" %{y} Buildings
with GHG Intensity between %{x}\"\n", - " ),\n", - " row=1,col=1\n", - ")\n", - "\n", - "\n", - "# Add a light red box to the first plot\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=50,\n", - " x1=100,\n", - " y0=0,\n", - " y1=1500,\n", - " fillcolor=\"rgba(255, 0, 0, 0.1)\", # Light red with transparency\n", - " line=dict(width=0),\n", - " row=1, col=1\n", - ")\n", - "\n", - "outlier_subset = df.dropna(subset=['GHGIntensity'])\n", - "outlier_subset = outlier_subset[outlier_subset['GHGIntensity']>50]\n", - "fig.add_trace(\n", - " go.Scatter(\n", - " x=[0] * len(outlier_subset['GHGIntensity']), # Make x an array of zeros with the correct length\n", - " y=outlier_subset['GHGIntensity'],\n", - " mode='markers',\n", - " marker=dict( color='blue', opacity=0.6),\n", - " customdata=df['DataYear'],\n", - " hovertext=outlier_subset['PropertyName'], # Add PropertyName to hovertext\n", - " hovertemplate=\"%{hovertext}
GHG Intensity: %{y} in %{customdata}\",\n", - " name=''\n", - " ),\n", - " row=1, col=2\n", - ")\n", - "\n", - "# Add a light red background to the second subplot\n", - "fig.add_shape(\n", - " type=\"rect\",\n", - " x0=-1,\n", - " x1=1,\n", - " y0=50,\n", - " y1=900,\n", - " fillcolor=\"rgba(255, 0, 0, 0.1)\", # Light red with transparency\n", - " line=dict(width=0),\n", - " layer=\"below\",\n", - " row=1, col=2\n", - ")\n", - "\n", - "\n", - "fig.update_xaxes(visible=False, row=1, col=2)\n", - "fig.update_xaxes(range=[0, 100], row=1,col=1)\n", - "fig.update_xaxes( title_text='' , row=1,col=2)\n", - "fig.update_yaxes( title_text='GHG Intensity', row=1,col=2)\n", - "\n", - "## Add an outline to the bars\n", - "fig.update_traces(marker=dict(line=dict(width=.1, color='black')))\n", - "\n", - "fig.add_annotation(\n", - " x=80,\n", - " y=300,\n", - " text=\"Some buildings had
outlier GHG intensity
levels (up to 800) →\",\n", - " showarrow=False, # No arrow for this annotation\n", - " font=dict(size=10), # Customize font size\n", - ")\n", - "\n", - "# Update layout for better display\n", - "fig.update_layout(\n", - " xaxis_title='GHG Intensity',\n", - " yaxis_title='Count',\n", - " showlegend=False,\n", - " title='Distribution of GHG Intensities',\n", - " height=400,\n", - " width=800\n", - ")\n", - "\n", - "# Show the plot\n", - "#pio.show(fig)\n", - "iplot(fig)\n", - "\n", - "for dir in [static_blog_pth, fig_dir]:\n", - " fig.write_html( os.path.join(dir,'distribution_of_GHG_intensity.html'), include_plotlyjs=\"cdn\" )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compliance type counts over time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GHGIntensity\n", - "0.0 2\n", - "0.1 1\n", - "0.2 4\n", - "0.3 8\n", - "0.4 5\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 141, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "value_counts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[107], line 33\u001b[0m\n\u001b[1;32m 26\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCount of Buildings That Did/Didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt Report Emissions by Year\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 27\u001b[0m xaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYear of Emissions
(One year before data is reported)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 28\u001b[0m yaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCount of Buildings\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 29\u001b[0m legend_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCategory\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# Show the plot\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m#pio.show(fig)\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m \u001b[43miplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mdir\u001b[39m \u001b[38;5;129;01min\u001b[39;00m [static_blog_pth, fig_dir]:\n\u001b[1;32m 37\u001b[0m fig\u001b[38;5;241m.\u001b[39mwrite_html( os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mdir\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreporting_counts_over_time.html\u001b[39m\u001b[38;5;124m'\u001b[39m), include_plotlyjs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcdn\u001b[39m\u001b[38;5;124m\"\u001b[39m )\n", - "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/offline/offline.py:387\u001b[0m, in \u001b[0;36miplot\u001b[0;34m(figure_or_data, show_link, link_text, validate, image, filename, image_width, image_height, config, auto_play, animation_opts)\u001b[0m\n\u001b[1;32m 382\u001b[0m post_script \u001b[38;5;241m=\u001b[39m build_save_image_post_script(\n\u001b[1;32m 383\u001b[0m image, filename, image_height, image_width, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miplot\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 384\u001b[0m )\n\u001b[1;32m 386\u001b[0m \u001b[38;5;66;03m# Show figure\u001b[39;00m\n\u001b[0;32m--> 387\u001b[0m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mauto_play\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto_play\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_script\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_script\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m \u001b[49m\u001b[43manimation_opts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manimation_opts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 394\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 391\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 396\u001b[0m )\n\u001b[1;32m 398\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" - ] - } - ], - "source": [ - "# Count each new column per year\n", - "value_counts = df.groupby('DataYear')['Reported'].value_counts()\n", - "non_reporting_counts = value_counts.xs(0, level='Reported')\n", - "reporting_counts = value_counts.xs(1, level='Reported')\n", - "\n", - "# Create the figure\n", - "fig = go.Figure()\n", - "\n", - "# Add traces for each category\n", - "fig.add_trace(go.Scatter(x=reporting_counts.index, y=reporting_counts.values,\n", - " mode='lines+markers', name='Reported',\n", - " line=dict(width=4, color='rgba(0, 0,255, 0.7)'),\n", - " marker=dict(symbol='circle', size=10)))\n", - "\n", - "fig.add_trace(go.Scatter(x=non_reporting_counts.index, y=non_reporting_counts.values,\n", - " mode='lines+markers', name=\"Didn't Report\",\n", - " line=dict(width=4, color='rgba(255, 0, 0, 0.7)'),\n", - " marker=dict(symbol='circle', size=10)))\n", - "\n", - "fig.add_trace( go.Scatter( x=[2018.5, 2019.5, 2019.5, 2018.5, 2018.5],\n", - " y=[0, 0, np.max(reporting_counts)*1.2, np.max(reporting_counts)*1.2, 0 ],\n", - " fill='toself', mode='lines', name='Covid Data Disruption' )\n", - ")\n", - "\n", - "# Update layout\n", - "fig.update_layout(title=\"Count of Buildings That Did/Didn't Report Emissions by Year\",\n", - " xaxis_title='Year of Emissions
(One year before data is reported)',\n", - " yaxis_title='Count of Buildings',\n", - " legend_title='Category')\n", - "\n", - "# Show the plot\n", - "#pio.show(fig)\n", - "iplot(fig)\n", - "\n", - "\n", - "for dir in [static_blog_pth, fig_dir]:\n", - " fig.write_html( os.path.join(dir,'reporting_counts_over_time.html'), include_plotlyjs=\"cdn\" )\n" - ] - } - ], - "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": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/data/analysis/anomaly_detection.ipynb b/src/data/analysis/anomaly_detection.ipynb new file mode 100644 index 0000000..2698ceb --- /dev/null +++ b/src/data/analysis/anomaly_detection.ipynb @@ -0,0 +1,13896 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection\n", + "\n", + "**The problem:** When we started looking into grading buildings by their GHG emissions intensity,\n", + "all of the buildings that we were going to be giving an A grade appear to be outliers, missing data\n", + "or having faulty data.\n", + "\n", + "The A buildings from our [first PR](https://github.com/vkoves/electrify-chicago/pull/140/commits/14546521270ade9e47623f615af4e6868c6c9cfc) are as follows:\n", + "\n", + "- https://electrifychicago.net/building/1830-n-winchester-ave/ (ID 117024)\n", + " Multi-family housing. Reported 0 natural gas use for the past two years despite non-zero use before.\n", + "\n", + "- https://electrifychicago.net/building/830-n-michigan-ave/ (ID 124236)\n", + " Topshop and UNIQLO building, may be largely vacant, had precipitous declines\n", + "\n", + "- https://electrifychicago.net/building/u-s-cellular-plaza-8430-goby-llc/ (ID 160142)\n", + " Large decline in electricity use (3x from 2017-202), never reported gas use. Could be correct?\n", + "\n", + "- https://electrifychicago.net/building/moody-bible-institute-solheim-center/ (ID 165717)\n", + " Moody's gym, went from 2M kBTUs of natural gas to 0 in 2021 and 2022.\n", + "\n", + "- https://electrifychicago.net/building/newberry-plaza-townhouse-owners-association/ (ID 172137)\n", + " Similarly went to 0 gas from 800k KBTU,\n", + "\n", + "- https://electrifychicago.net/building/u-s-cellular-plaza-8420-goby-llc/ (ID 251770)\n", + "\n", + "- https://electrifychicago.net/building/4434-4444-n-damen-ave/ (ID 254001)\n", + " Robey Condominiums, multi-family housing. Reported 0 natural gas use for the past two years\n", + " despite non-zero use before.\n", + "\n", + "\n", + "## Dependencies\n", + "\n", + "This notebook requires:\n", + "\n", + "- pandas\n", + "- numpy\n", + "- plotly\n", + "- statsmodels\n", + "- nbformat\n", + "\n", + "To install, _in this `src/data/analysis` directory_, run:\n", + "\n", + "```\n", + "pip install -r requirements.txt\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import plotly.subplots as sp\n", + "import plotly.graph_objects as go\n", + "import plotly.express as px\n", + "from plotly.offline import iplot\n", + "import plotly.io as pio\n", + "from plotly.subplots import make_subplots\n", + "import statsmodels.api as sm\n", + "import os\n", + "from pathlib import Path\n", + "import json\n", + "\n", + "from IPython.display import Image\n", + "\n", + "from plotly.offline import init_notebook_mode\n", + "init_notebook_mode(connected=True)\n", + "\n", + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set pathing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success: Found 'electrify-chicago' as the base directory.\n", + "Current working directory is correctly set to '/home/viktor/Documents/electrify-chicago/src/data/analysis'.\n" + ] + } + ], + "source": [ + "# get static dir for saving images\n", + "current_dir = Path.cwd()\n", + "project_root = current_dir\n", + "\n", + "while True:\n", + " if os.path.basename(project_root) == 'electrify-chicago':\n", + " print(\"Success: Found 'electrify-chicago' as the base directory.\")\n", + " break\n", + " new_root = os.path.dirname(project_root)\n", + " if new_root == project_root: # Reached the filesystem root\n", + " raise FileNotFoundError(\"Error: 'electrify-chicago' directory not found in the path hierarchy.\")\n", + " project_root = new_root\n", + "static_blog_pth = os.path.join(project_root, 'static', 'blog', 'GHGIntensityPredictCompliance')\n", + "os.makedirs(static_blog_pth, exist_ok=True)\n", + "\n", + "expected_dir_name = \"analysis\"\n", + "fig_dir = os.path.join(current_dir, 'output', 'compliance_analysis')\n", + "\n", + "# Check if the current directory is the \"analysis\" folder\n", + "if current_dir.name != expected_dir_name:\n", + " raise AssertionError(f\"Expected working directory to be '{expected_dir_name}', but got '{current_dir.name}'.\\n\"\n", + " f\"Please ensure you are in the correct directory.\")\n", + "\n", + "print(f\"Current working directory is correctly set to '{current_dir}'.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notebook options and custom plotting function" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "reduce_memory = True # option to display plotly as static images to reduce memory, if possible\n", + "export_to_blog = False # if true, saves plots and regressions to blog static folder for website publishing\n", + "\n", + "if export_to_blog:\n", + " dirs = [static_blog_pth, fig_dir]\n", + "else:\n", + " dirs = [fig_dir]\n", + "\n", + "def show_fig(fig, reduce_memory):\n", + " if reduce_memory:\n", + " try:\n", + " png_image = pio.to_image(fig, format='png')\n", + " return (png_image, reduce_memory)\n", + "\n", + " except:\n", + " print(\"Error exporting plotly to png, displaying html graph instead\")\n", + " reduce_memory = False\n", + "\n", + " if not reduce_memory:\n", + " return (iplot(fig), reduce_memory)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 3749 unique building ids\n" + ] + }, + { + "data": { + "text/html": [ + "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReported
02520642020Submitted Data64028.01160.918.11.02.0NaN2384738.9NaN7438787.05594040.1NaN240.8323.6246.0329.91
12324582020Submitted Data627680.04871.77.81.01.022.016397682.843537490.6NaNNaNNaN95.5146.0100.3150.71
22546162020Submitted Data555524.04581.48.21.02.049.028606427.72199940.1NaNNaNNaN55.5148.356.7151.81
31038122020Submitted Data130007.01092.18.41.03.061.06489281.31493523.2NaNNaNNaN61.4151.863.0154.81
42540732020Submitted Data83000.0295.83.61.04.0100.01614582.3825006.6NaNNaNNaN29.464.929.664.31
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" + ], + "text/plain": [ + " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", + "0 252064 2020 Submitted Data 64028.0 1160.9 \n", + "1 232458 2020 Submitted Data 627680.0 4871.7 \n", + "2 254616 2020 Submitted Data 555524.0 4581.4 \n", + "3 103812 2020 Submitted Data 130007.0 1092.1 \n", + "4 254073 2020 Submitted Data 83000.0 295.8 \n", + "\n", + " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", + "0 18.1 1.0 2.0 NaN \n", + "1 7.8 1.0 1.0 22.0 \n", + "2 8.2 1.0 2.0 49.0 \n", + "3 8.4 1.0 3.0 61.0 \n", + "4 3.6 1.0 4.0 100.0 \n", + "\n", + " ElectricityUse NaturalGasUse DistrictSteamUse DistrictChilledWaterUse \\\n", + "0 2384738.9 NaN 7438787.0 5594040.1 \n", + "1 16397682.8 43537490.6 NaN NaN \n", + "2 28606427.7 2199940.1 NaN NaN \n", + "3 6489281.3 1493523.2 NaN NaN \n", + "4 1614582.3 825006.6 NaN NaN \n", + "\n", + " AllOtherFuelUse SiteEUI SourceEUI WeatherNormalizedSiteEUI \\\n", + "0 NaN 240.8 323.6 246.0 \n", + "1 NaN 95.5 146.0 100.3 \n", + "2 NaN 55.5 148.3 56.7 \n", + "3 NaN 61.4 151.8 63.0 \n", + "4 NaN 29.4 64.9 29.6 \n", + "\n", + " WeatherNormalizedSourceEUI Reported \n", + "0 329.9 1 \n", + "1 150.7 1 \n", + "2 151.8 1 \n", + "3 154.8 1 \n", + "4 64.3 1 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Construct the path to the CSV file (one level above the current directory)\n", + "data_path = os.path.join( current_dir.parent, 'dist', 'benchmarking-all-years.csv')\n", + "\n", + "df = pd.read_csv(data_path)\n", + "\n", + "# Create the \"reported\" column\n", + "df['Reported'] = df['GHGIntensity'].notna().astype(int)\n", + "\n", + "print(f\"There are {df['ID'].unique().shape[0]} unique building ids\")\n", + "\n", + "df['DataYear'] = df['DataYear'].astype(int)\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in Building Benchmark Data to get Building Names" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDPropertyName
0100001Presence SMEMC St Elizabeth Campus
1100002Clemente Community Academy HS -CPS
2100019Dixon Building
3100068Joffco Square
4100148The Jeffery Cyril Building
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" + ], + "text/plain": [ + " ID PropertyName\n", + "0 100001 Presence SMEMC St Elizabeth Campus\n", + "1 100002 Clemente Community Academy HS -CPS\n", + "2 100019 Dixon Building\n", + "3 100068 Joffco Square\n", + "4 100148 The Jeffery Cyril Building" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names_path = os.path.join( current_dir.parent, 'dist', 'building-benchmarks.csv')\n", + "\n", + "building_names = pd.read_csv(names_path)[['ID', 'PropertyName' ]]\n", + "building_names.drop_duplicates(keep='first')\n", + "building_names.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merge names to data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName
02520642020Submitted Data64028.01160.918.11.02.0NaN2384738.9NaN7438787.05594040.1NaN240.8323.6246.0329.91Mansueto Library
12324582020Submitted Data627680.04871.77.81.01.022.016397682.843537490.6NaNNaNNaN95.5146.0100.3150.71Harper Square Cooperative
22546162020Submitted Data555524.04581.48.21.02.049.028606427.72199940.1NaNNaNNaN55.5148.356.7151.81Former Coyne College
31038122020Submitted Data130007.01092.18.41.03.061.06489281.31493523.2NaNNaNNaN61.4151.863.0154.81400 W Superior St
42540732020Submitted Data83000.0295.83.61.04.0100.01614582.3825006.6NaNNaNNaN29.464.929.664.31Blue Moon Lofts
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" + ], + "text/plain": [ + " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", + "0 252064 2020 Submitted Data 64028.0 1160.9 \n", + "1 232458 2020 Submitted Data 627680.0 4871.7 \n", + "2 254616 2020 Submitted Data 555524.0 4581.4 \n", + "3 103812 2020 Submitted Data 130007.0 1092.1 \n", + "4 254073 2020 Submitted Data 83000.0 295.8 \n", + "\n", + " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", + "0 18.1 1.0 2.0 NaN \n", + "1 7.8 1.0 1.0 22.0 \n", + "2 8.2 1.0 2.0 49.0 \n", + "3 8.4 1.0 3.0 61.0 \n", + "4 3.6 1.0 4.0 100.0 \n", + "\n", + " ElectricityUse NaturalGasUse DistrictSteamUse DistrictChilledWaterUse \\\n", + "0 2384738.9 NaN 7438787.0 5594040.1 \n", + "1 16397682.8 43537490.6 NaN NaN \n", + "2 28606427.7 2199940.1 NaN NaN \n", + "3 6489281.3 1493523.2 NaN NaN \n", + "4 1614582.3 825006.6 NaN NaN \n", + "\n", + " AllOtherFuelUse SiteEUI SourceEUI WeatherNormalizedSiteEUI \\\n", + "0 NaN 240.8 323.6 246.0 \n", + "1 NaN 95.5 146.0 100.3 \n", + "2 NaN 55.5 148.3 56.7 \n", + "3 NaN 61.4 151.8 63.0 \n", + "4 NaN 29.4 64.9 29.6 \n", + "\n", + " WeatherNormalizedSourceEUI Reported PropertyName \n", + "0 329.9 1 Mansueto Library \n", + "1 150.7 1 Harper Square Cooperative \n", + "2 151.8 1 Former Coyne College \n", + "3 154.8 1 400 W Superior St \n", + "4 64.3 1 Blue Moon Lofts " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.merge( df, building_names, how='left', on='ID')\n", + "df['PropertyName'] = df['PropertyName'].fillna(\"[Building Name Unavailable]\").replace(\"\", \"[Building Name Unavailable]\")\n", + "df = df[df['ReportingStatus'].isin(['Submitted Data'])]\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check that every building/year combo exists only once" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "group_counts = df.groupby(['ID', 'DataYear']).size()\n", + "\n", + "# Assert that the maximum count in any group is at most 1\n", + "assert group_counts.max() <= 1, \"There are buildings with more than one row in a given year!\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the latest year we have data for " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2022" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get buildings with zero natural gas use in past year\n", + "latestYear = df['DataYear'].max()\n", + "latestYear" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName
177531758912022Submitted Data172500.0452.43.01.04.0100.02069532.43384519.40.00.0NaN36.462.336.462.31[Building Name Unavailable]
177552512452022Submitted Data249095.01434.05.82.03.574.03345590.218702028.90.00.0NaN88.5116.494.1121.113800 N. Lake Shore Drive
212842566582022Submitted Data393938.01948.44.98.04.075.06388293.520841006.30.00.0NaN69.1101.071.9102.61Midpointe Apartments
212852500622022Submitted Data66285.0525.47.91.02.0NaN2649529.23322169.70.00.0NaN90.1164.591.9165.61RJ Quinn Academy
212861015452022Submitted Data51163.0300.35.91.02.050.0541560.14310877.60.00.0NaN94.8118.1101.2123.81[Building Name Unavailable]
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" + ], + "text/plain": [ + " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", + "17753 175891 2022 Submitted Data 172500.0 452.4 \n", + "17755 251245 2022 Submitted Data 249095.0 1434.0 \n", + "21284 256658 2022 Submitted Data 393938.0 1948.4 \n", + "21285 250062 2022 Submitted Data 66285.0 525.4 \n", + "21286 101545 2022 Submitted Data 51163.0 300.3 \n", + "\n", + " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", + "17753 3.0 1.0 4.0 100.0 \n", + "17755 5.8 2.0 3.5 74.0 \n", + "21284 4.9 8.0 4.0 75.0 \n", + "21285 7.9 1.0 2.0 NaN \n", + "21286 5.9 1.0 2.0 50.0 \n", + "\n", + " ElectricityUse NaturalGasUse DistrictSteamUse \\\n", + "17753 2069532.4 3384519.4 0.0 \n", + "17755 3345590.2 18702028.9 0.0 \n", + "21284 6388293.5 20841006.3 0.0 \n", + "21285 2649529.2 3322169.7 0.0 \n", + "21286 541560.1 4310877.6 0.0 \n", + "\n", + " DistrictChilledWaterUse AllOtherFuelUse SiteEUI SourceEUI \\\n", + "17753 0.0 NaN 36.4 62.3 \n", + "17755 0.0 NaN 88.5 116.4 \n", + "21284 0.0 NaN 69.1 101.0 \n", + "21285 0.0 NaN 90.1 164.5 \n", + "21286 0.0 NaN 94.8 118.1 \n", + "\n", + " WeatherNormalizedSiteEUI WeatherNormalizedSourceEUI Reported \\\n", + "17753 36.4 62.3 1 \n", + "17755 94.1 121.1 1 \n", + "21284 71.9 102.6 1 \n", + "21285 91.9 165.6 1 \n", + "21286 101.2 123.8 1 \n", + "\n", + " PropertyName \n", + "17753 [Building Name Unavailable] \n", + "17755 3800 N. Lake Shore Drive \n", + "21284 Midpointe Apartments \n", + "21285 RJ Quinn Academy \n", + "21286 [Building Name Unavailable] " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "latestData = df[df['DataYear'] == latestYear]\n", + "latestData.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDDataYearReportingStatusGrossFloorAreaTotalGHGEmissionsGHGIntensityNumberOfBuildingsChicagoEnergyRatingENERGYSTARScoreElectricityUseNaturalGasUseDistrictSteamUseDistrictChilledWaterUseAllOtherFuelUseSiteEUISourceEUIWeatherNormalizedSiteEUIWeatherNormalizedSourceEUIReportedPropertyName
212871604382022Submitted Data1484327.09938.87.11.04.075.063482520.50.00.029929202.7NaN66.4145.864.4144.91155 North Wacker
212971001792022Submitted Data450612.0NaNNaN1.00.0NaN15191302.20.00.00.0NaNNaNNaNNaNNaN0Saint Anthony Hospital - Main Hospital
213221569422022Submitted Data201402.01318.26.51.03.068.02852269.00.014194821.60.0NaN84.6124.489.7130.61Burton-Judson Courts
213542501472022Submitted Data186957.71766.09.41.01.0NaN13408061.50.00.00.0NaN71.7200.871.7200.81820 W Jackson Blvd
213661018672022Submitted Data641962.04402.26.91.03.067.033422010.00.00.00.0NaN52.1145.852.9148.21125 South Wacker
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" + ], + "text/plain": [ + " ID DataYear ReportingStatus GrossFloorArea TotalGHGEmissions \\\n", + "21287 160438 2022 Submitted Data 1484327.0 9938.8 \n", + "21297 100179 2022 Submitted Data 450612.0 NaN \n", + "21322 156942 2022 Submitted Data 201402.0 1318.2 \n", + "21354 250147 2022 Submitted Data 186957.7 1766.0 \n", + "21366 101867 2022 Submitted Data 641962.0 4402.2 \n", + "\n", + " GHGIntensity NumberOfBuildings ChicagoEnergyRating ENERGYSTARScore \\\n", + "21287 7.1 1.0 4.0 75.0 \n", + "21297 NaN 1.0 0.0 NaN \n", + "21322 6.5 1.0 3.0 68.0 \n", + "21354 9.4 1.0 1.0 NaN \n", + "21366 6.9 1.0 3.0 67.0 \n", + "\n", + " ElectricityUse NaturalGasUse DistrictSteamUse \\\n", + "21287 63482520.5 0.0 0.0 \n", + "21297 15191302.2 0.0 0.0 \n", + "21322 2852269.0 0.0 14194821.6 \n", + "21354 13408061.5 0.0 0.0 \n", + "21366 33422010.0 0.0 0.0 \n", + "\n", + " DistrictChilledWaterUse AllOtherFuelUse SiteEUI SourceEUI \\\n", + "21287 29929202.7 NaN 66.4 145.8 \n", + "21297 0.0 NaN NaN NaN \n", + "21322 0.0 NaN 84.6 124.4 \n", + "21354 0.0 NaN 71.7 200.8 \n", + "21366 0.0 NaN 52.1 145.8 \n", + "\n", + " WeatherNormalizedSiteEUI WeatherNormalizedSourceEUI Reported \\\n", + "21287 64.4 144.9 1 \n", + "21297 NaN NaN 0 \n", + "21322 89.7 130.6 1 \n", + "21354 71.7 200.8 1 \n", + "21366 52.9 148.2 1 \n", + "\n", + " PropertyName \n", + "21287 155 North Wacker \n", + "21297 Saint Anthony Hospital - Main Hospital \n", + "21322 Burton-Judson Courts \n", + "21354 820 W Jackson Blvd \n", + "21366 125 South Wacker " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noGasUse = latestData[latestData['NaturalGasUse'].isin([0, np.nan])]\n", + "noGasUse.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Count of \"Gas Free\" Buildings Latest Year" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "235" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noGasUse['ID'].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loop Through Gas Free Buildings And See If They Used Gas in Previous Years" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_23063/3249074751.py:2: UserWarning:\n", + "\n", + "Boolean Series key will be reindexed to match DataFrame index.\n", + "\n", + "/tmp/ipykernel_23063/3249074751.py:2: UserWarning:\n", + "\n", + "Boolean Series key will be reindexed to match DataFrame index.\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "array([254001, 165664, 165819, 159892, 260149, 175817, 242935, 256639,\n", + " 174228, 175914, 135050, 103602, 102987, 113670, 172256, 115942,\n", + " 175754, 254378, 175995, 116550, 260135, 175492, 159425, 100179,\n", + " 172145, 137144, 101757, 138730, 172565, 101448, 101396, 260116,\n", + " 157988, 105441, 252312, 172772, 115605, 165717, 116644, 255944,\n", + " 172540, 172157, 159423, 175885, 251883, 238480, 117179, 175334,\n", + " 172393, 251959, 175882, 160137, 254035, 254387, 255012, 242279,\n", + " 162325, 256614, 260101, 129344])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "noGasUseIds = noGasUse['ID']\n", + "usedGasBefore = df[df['DataYear'] < latestYear][df['NaturalGasUse'] > 0][df['ID'].isin(noGasUseIds)]\n", + "gasAnomalyIds = usedGasBefore['ID'].unique()\n", + "gasAnomalyIds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Did this catch our A buildings?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{165717, 254001}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AGradedBuildingIds = [117024, 124236, 160142, 165717, 172137, 251770, 254001]\n", + "AGradedBuildingIds = set(AGradedBuildingIds)\n", + "AGradedBuildingIds.intersection(gasAnomalyIds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis: Unique values for GHG Intensity\n", + "\n", + "Conclusion: strange that some values are highly represented while others are not. How are these calculated? " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Round GHG Intensity values to 1 digit\n", + "df['GHGIntensity'] = df['GHGIntensity'].round(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Distribution of GHG Intensities" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": false + }, + "data": [ + { + "hovertemplate": " %{y} Buildings
with GHG Intensity between %{x}", + "marker": { + "line": { + "color": "black", + "width": 0.1 + } + }, + "name": "Histogram of Values", + "type": "histogram", + "x": [ + 18.1, + 7.8, + 8.2, + 8.4, + 3.6, + 11.6, + 9.7, + 6.2, + 7.1, + 17.7, + 8.5, + 6.9, + 5.4, + 10.2, + 5.3, + 13.3, + 4.7, + 7.4, + 6.1, + 5.9, + 5.8, + 8.5, + 6.4, + 6.8, + 4.4, + 16.5, + 7, + 3.3, + 18.8, + 7.2, + 5.4, + 5.3, + 8.2, + 8.6, + 4, + 7.4, + 2.5, + 9.4, + 7.9, + 6.4, + 7.1, + 10.5, + 5.7, + 20.2, + 5.1, + 7.5, + 6.6, + 9.2, + 4.4, + 10.4, + 5.1, + 4.3, + 6.9, + 10.3, + 32.7, + 7.1, + 6.7, + 6.8, + null, + 4.1, + 5.8, + 6.4, + 6.7, + 7.3, + 6.6, + 5.5, + 7.4, + 5.1, + 15.8, + 17, + 4.5, + 6.2, + 8.8, + 9.5, + 9.5, + 5.2, + 4.7, + 7.9, + 5.3, + 7.7, + 3.4, + 9, + 5.4, + 0, + 4.7, + 7.3, + 6.2, + 8.2, + 7.1, + 7.8, + 6.1, + 6.2, + 7.7, + 2.9, + 6.2, + 8.4, + 6.3, + 6.3, + 6.5, + 4, + 6.6, + 9.1, + 8.5, + 8.4, + 5.7, + 8.1, + 4.8, + 4.8, + 7.9, + 3.7, + 6.5, + 8.1, + 4.7, + 8.6, + 10.1, + 4.6, + 9.1, + 7.9, + 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data\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m 1048\u001b[0m encoding \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mtext_encoding(encoding)\n\u001b[0;32m-> 1049\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 1050\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f\u001b[38;5;241m.\u001b[39mwrite(data)\n", + "File \u001b[0;32m/usr/lib/python3.12/pathlib.py:1015\u001b[0m, in \u001b[0;36mPath.open\u001b[0;34m(self, mode, buffering, encoding, errors, newline)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1014\u001b[0m encoding \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mtext_encoding(encoding)\n\u001b[0;32m-> 1015\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffering\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/viktor/Documents/electrify-chicago/src/data/analysis/output/compliance_analysis/distribution_of_GHG_intensity.html'" + ] + } + ], + "source": [ + "\n", + "fig = sp.make_subplots(\n", + " rows=1,\n", + " cols=2,\n", + " column_widths=[0.8, 0.2],\n", + " horizontal_spacing=0.2,\n", + " subplot_titles = [\n", + " '',\n", + " 'GHG Intensity Outliers
(GHG Intensity values > 50)'\n", + " ]\n", + ")\n", + "\n", + "\n", + "fig.add_trace( go.Histogram(\n", + " x=df['GHGIntensity'],\n", + " #nbinsx=int((df['GHGIntensity'].max() - df['GHGIntensity'].min()) / 0.5),\n", + " xbins = dict(start=0, end=100, size=.5),\n", + " name='Histogram of Values',\n", + " hovertemplate = \" %{y} Buildings
with GHG Intensity between %{x}\"\n", + " ),\n", + " row=1,col=1\n", + ")\n", + "\n", + "\n", + "# Add a light red box to the first plot\n", + "fig.add_shape(\n", + " type=\"rect\",\n", + " x0=50,\n", + " x1=100,\n", + " y0=0,\n", + " y1=1500,\n", + " fillcolor=\"rgba(255, 0, 0, 0.1)\", # Light red with transparency\n", + " line=dict(width=0),\n", + " row=1, col=1\n", + ")\n", + "\n", + "outlier_subset = df.dropna(subset=['GHGIntensity'])\n", + "outlier_subset = outlier_subset[outlier_subset['GHGIntensity']>50]\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=[0] * len(outlier_subset['GHGIntensity']), # Make x an array of zeros with the correct length\n", + " y=outlier_subset['GHGIntensity'],\n", + " mode='markers',\n", + " marker=dict( color='blue', opacity=0.6),\n", + " customdata=df['DataYear'],\n", + " hovertext=outlier_subset['PropertyName'], # Add PropertyName to hovertext\n", + " hovertemplate=\"%{hovertext}
GHG Intensity: %{y} in %{customdata}\",\n", + " name=''\n", + " ),\n", + " row=1, col=2\n", + ")\n", + "\n", + "# Add a light red background to the second subplot\n", + "fig.add_shape(\n", + " type=\"rect\",\n", + " x0=-1,\n", + " x1=1,\n", + " y0=50,\n", + " y1=900,\n", + " fillcolor=\"rgba(255, 0, 0, 0.1)\", # Light red with transparency\n", + " line=dict(width=0),\n", + " layer=\"below\",\n", + " row=1, col=2\n", + ")\n", + "\n", + "\n", + "fig.update_xaxes(visible=False, row=1, col=2)\n", + "fig.update_xaxes(range=[0, 100], row=1,col=1)\n", + "fig.update_xaxes( title_text='' , row=1,col=2)\n", + "fig.update_yaxes( title_text='GHG Intensity', row=1,col=2)\n", + "\n", + "## Add an outline to the bars\n", + "fig.update_traces(marker=dict(line=dict(width=.1, color='black')))\n", + "\n", + "fig.add_annotation(\n", + " x=80,\n", + " y=300,\n", + " text=\"Some buildings had
outlier GHG intensity
levels (up to 800) →\",\n", + " showarrow=False, # No arrow for this annotation\n", + " font=dict(size=10), # Customize font size\n", + ")\n", + "\n", + "# Update layout for better display\n", + "fig.update_layout(\n", + " xaxis_title='GHG Intensity',\n", + " yaxis_title='Count',\n", + " showlegend=False,\n", + " title='Distribution of GHG Intensities',\n", + " height=400,\n", + " width=800\n", + ")\n", + "\n", + "# Show the plot\n", + "#pio.show(fig)\n", + "iplot(fig)\n", + "\n", + "for dir in [static_blog_pth, fig_dir]:\n", + " fig.write_html( os.path.join(dir,'distribution_of_GHG_intensity.html'), include_plotlyjs=\"cdn\" )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compliance type counts over time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GHGIntensity\n", + "0.0 2\n", + "0.1 1\n", + "0.2 4\n", + "0.3 8\n", + "0.4 5\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "value_counts.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[107], line 33\u001b[0m\n\u001b[1;32m 26\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCount of Buildings That Did/Didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt Report Emissions by Year\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 27\u001b[0m xaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYear of Emissions
(One year before data is reported)\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 28\u001b[0m yaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCount of Buildings\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 29\u001b[0m legend_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCategory\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# Show the plot\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;66;03m#pio.show(fig)\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m \u001b[43miplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mdir\u001b[39m \u001b[38;5;129;01min\u001b[39;00m [static_blog_pth, fig_dir]:\n\u001b[1;32m 37\u001b[0m fig\u001b[38;5;241m.\u001b[39mwrite_html( os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mdir\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreporting_counts_over_time.html\u001b[39m\u001b[38;5;124m'\u001b[39m), include_plotlyjs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcdn\u001b[39m\u001b[38;5;124m\"\u001b[39m )\n", + "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/offline/offline.py:387\u001b[0m, in \u001b[0;36miplot\u001b[0;34m(figure_or_data, show_link, link_text, validate, image, filename, image_width, image_height, config, auto_play, animation_opts)\u001b[0m\n\u001b[1;32m 382\u001b[0m post_script \u001b[38;5;241m=\u001b[39m build_save_image_post_script(\n\u001b[1;32m 383\u001b[0m image, filename, image_height, image_width, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miplot\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 384\u001b[0m )\n\u001b[1;32m 386\u001b[0m \u001b[38;5;66;03m# Show figure\u001b[39;00m\n\u001b[0;32m--> 387\u001b[0m \u001b[43mpio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 388\u001b[0m \u001b[43m \u001b[49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 390\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 391\u001b[0m \u001b[43m \u001b[49m\u001b[43mauto_play\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto_play\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_script\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_script\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 393\u001b[0m \u001b[43m \u001b[49m\u001b[43manimation_opts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manimation_opts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 394\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.8/site-packages/plotly/io/_renderers.py:394\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 391\u001b[0m )\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 395\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 396\u001b[0m )\n\u001b[1;32m 398\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 400\u001b[0m \u001b[38;5;66;03m# external renderers\u001b[39;00m\n", + "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed" + ] + } + ], + "source": [ + "# Count each new column per year\n", + "value_counts = df.groupby('DataYear')['Reported'].value_counts()\n", + "non_reporting_counts = value_counts.xs(0, level='Reported')\n", + "reporting_counts = value_counts.xs(1, level='Reported')\n", + "\n", + "# Create the figure\n", + "fig = go.Figure()\n", + "\n", + "# Add traces for each category\n", + "fig.add_trace(go.Scatter(x=reporting_counts.index, y=reporting_counts.values,\n", + " mode='lines+markers', name='Reported',\n", + " line=dict(width=4, color='rgba(0, 0,255, 0.7)'),\n", + " marker=dict(symbol='circle', size=10)))\n", + "\n", + "fig.add_trace(go.Scatter(x=non_reporting_counts.index, y=non_reporting_counts.values,\n", + " mode='lines+markers', name=\"Didn't Report\",\n", + " line=dict(width=4, color='rgba(255, 0, 0, 0.7)'),\n", + " marker=dict(symbol='circle', size=10)))\n", + "\n", + "fig.add_trace( go.Scatter( x=[2018.5, 2019.5, 2019.5, 2018.5, 2018.5],\n", + " y=[0, 0, np.max(reporting_counts)*1.2, np.max(reporting_counts)*1.2, 0 ],\n", + " fill='toself', mode='lines', name='Covid Data Disruption' )\n", + ")\n", + "\n", + "# Update layout\n", + "fig.update_layout(title=\"Count of Buildings That Did/Didn't Report Emissions by Year\",\n", + " xaxis_title='Year of Emissions
(One year before data is reported)',\n", + " yaxis_title='Count of Buildings',\n", + " legend_title='Category')\n", + "\n", + "# Show the plot\n", + "#pio.show(fig)\n", + "iplot(fig)\n", + "\n", + "\n", + "for dir in [static_blog_pth, fig_dir]:\n", + " fig.write_html( os.path.join(dir,'reporting_counts_over_time.html'), include_plotlyjs=\"cdn\" )\n" + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From c78dcd1db730ef2f01a73eb44bc62c9b6ef3fe7a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Viktor=20K=C3=B6ves?= <3187531+vkoves@users.noreply.github.com> Date: Tue, 21 Jan 2025 21:41:56 -0600 Subject: [PATCH 4/4] Make anomaly detection notebook properly catch buildings --- src/data/analysis/anomaly_detection.ipynb | 30502 +++++++++++++++++++- 1 file changed, 30376 insertions(+), 126 deletions(-) diff --git a/src/data/analysis/anomaly_detection.ipynb b/src/data/analysis/anomaly_detection.ipynb index 2698ceb..333f589 100644 --- a/src/data/analysis/anomaly_detection.ipynb +++ b/src/data/analysis/anomaly_detection.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -399,7 +399,7 @@ "4 64.3 1 " ] }, - "execution_count": 5, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -429,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -496,7 +496,7 @@ "4 100148 The Jeffery Cyril Building" ] }, - "execution_count": 6, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -518,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -721,7 +721,7 @@ "4 64.3 1 Blue Moon Lofts " ] }, - "execution_count": 7, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -729,10 +729,30 @@ "source": [ "df = pd.merge( df, building_names, how='left', on='ID')\n", "df['PropertyName'] = df['PropertyName'].fillna(\"[Building Name Unavailable]\").replace(\"\", \"[Building Name Unavailable]\")\n", - "df = df[df['ReportingStatus'].isin(['Submitted Data'])]\n", + "df = df[df['ReportingStatus'].isin(['Submitted Data', 'Submitted'])]\n", "df.head()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test That A Building Properly Exists Across All Years\n", + "\n", + "Test Newberry Plaza (ID 172137), should have 2016 - 2022 data" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "newberryData = df[df['ID'] == 172137].sort_values(by='DataYear')\n", + "\n", + "assert newberryData['ID'].count() == 7, \"There is not 7 years of data for Newberry Plaza, something is wrong!\"" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -742,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -761,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -770,7 +790,7 @@ "2022" ] }, - "execution_count": 9, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -783,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -993,7 +1013,7 @@ "21286 [Building Name Unavailable] " ] }, - "execution_count": 10, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -1005,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -1215,7 +1235,7 @@ "21366 125 South Wacker " ] }, - "execution_count": 11, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -1234,22 +1254,35 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 112, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Got 235 buildings that did not use gas in 2022\n" + ] + }, { "data": { "text/plain": [ - "235" + "21643 172137\n", + "Name: ID, dtype: int64" ] }, - "execution_count": 12, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "noGasUse['ID'].count()" + "print('Got ' + str(noGasUse['ID'].count())\n", + " + ' buildings that did not use gas in ' + str(latestYear))\n", + "\n", + "# Check for Newberry Plaza Townhouse\n", + "buildingToValidateId = 172137;\n", + "noGasUse['ID'][noGasUse['ID'].astype(str).str.contains(str(buildingToValidateId))]" ] }, { @@ -1261,21 +1294,18 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 121, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/tmp/ipykernel_23063/3249074751.py:2: UserWarning:\n", - "\n", - "Boolean Series key will be reindexed to match DataFrame index.\n", + "Got 94 gas-anomaly buildings (used gas before, but not in latest year)! \n", "\n", - "/tmp/ipykernel_23063/3249074751.py:2: UserWarning:\n", + "Newberry Plaza (ID 172137) in data? True\n", "\n", - "Boolean Series key will be reindexed to match DataFrame index.\n", - "\n" + "Gas Anomaly IDs:\n" ] }, { @@ -1288,18 +1318,38 @@ " 157988, 105441, 252312, 172772, 115605, 165717, 116644, 255944,\n", " 172540, 172157, 159423, 175885, 251883, 238480, 117179, 175334,\n", " 172393, 251959, 175882, 160137, 254035, 254387, 255012, 242279,\n", - " 162325, 256614, 260101, 129344])" + " 162325, 256614, 260101, 129344, 160414, 172137, 103962, 101827,\n", + " 101748, 158391, 173618, 101717, 101760, 160130, 125048, 101559,\n", + " 102854, 103761, 210090, 117599, 117024, 252054, 101722, 103573,\n", + " 165439, 105034, 120931, 132278, 165328, 103598, 135660, 119689,\n", + " 103739, 174289, 175484, 104879, 256803, 251379])" ] }, - "execution_count": 13, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import warnings\n", + "\n", + "# Ignore Pandas warnings for this script\n", + "warnings.filterwarnings('ignore')\n", + "\n", "noGasUseIds = noGasUse['ID']\n", "usedGasBefore = df[df['DataYear'] < latestYear][df['NaturalGasUse'] > 0][df['ID'].isin(noGasUseIds)]\n", + "\n", "gasAnomalyIds = usedGasBefore['ID'].unique()\n", + "\n", + "print('Got ' + str(len(gasAnomalyIds))\n", + " + ' gas-anomaly buildings (used gas before, but not in latest year)! \\n')\n", + "\n", + "# Check if our building is in the dataset\n", + "print('Newberry Plaza (ID ' + str(buildingToValidateId) + ') in data? '\n", + " + str(buildingToValidateId in gasAnomalyIds))\n", + "\n", + "print('\\nGas Anomaly IDs:')\n", + "\n", "gasAnomalyIds" ] }, @@ -1312,21 +1362,23 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{165717, 254001}" + "{117024, 165717, 172137, 254001}" ] }, - "execution_count": 14, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "###\n", + "### Should catch 117024 ✅, 165717 ✅, 172137✅, 254001 ✅\n", "AGradedBuildingIds = [117024, 124236, 160142, 165717, 172137, 251770, 254001]\n", "AGradedBuildingIds = set(AGradedBuildingIds)\n", "AGradedBuildingIds.intersection(gasAnomalyIds)" @@ -1350,7 +1402,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ @@ -1367,7 +1419,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ 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