gbdb_py_benchmarking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "53302ec4-deee-432f-b6d2-7f4b67cf5911",
   "metadata": {},
   "source": [
    "# GBDB CarbEnMats // Benchmarking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749de6a9-18b7-40fc-99b5-6d66a48e5471",
   "metadata": {
    "tags": []
   },
   "source": [
    "Author: Martin Röck (www.martinroeck.com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "bdb511f0-d55c-4f2e-b37c-1ec9a21bc1a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "script_title = 'GBDB CarbEnMats // Benchmarking'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "901061d7-d693-4961-aa16-a7ca35c153e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running: GBDB CarbEnMats // Benchmarking\n"
     ]
    }
   ],
   "source": [
    "print('Running: ' + script_title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b44c43-2a42-4486-b6a6-a7b7802011db",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5510b28-9bfc-4fcc-99f4-3de91ec8577f",
   "metadata": {},
   "source": [
    "## Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "12508dfa-da1f-4742-abd2-c2800e3e968b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-05T21:33:56.749346Z",
     "start_time": "2021-06-05T21:33:54.444930Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# SETUP\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "96f29568-c4fa-40ce-a208-1637cfdc1306",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') #gets rid of the 'red' warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff0bef2-f615-446a-ab02-f872c2ad0557",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "fff69195-4433-429e-8f68-6b722e1d4849",
   "metadata": {},
   "outputs": [],
   "source": [
    "# File(s)\n",
    "filepath = ''\n",
    "filename = 'gbdb_data'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c81bd6f5-72a2-4e75-8cae-cc8b3a463052",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-19T12:08:33.751168Z",
     "start_time": "2022-08-19T12:08:31.578961Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Read CSV\n",
    "#df = pd.read_csv(filepath + filename +'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3892dd83-7d6d-4a69-a261-fd86f4536892",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-19T12:08:33.751168Z",
     "start_time": "2022-08-19T12:08:31.578961Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Read XLS\n",
    "#df = pd.read_excel(filepath + filename +'.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "43049cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data from the specified sheet into a DataFrame\n",
    "#sheet_name = \"CORE\"\n",
    "sheet_name = \"FULL\"\n",
    "\n",
    "df = pd.read_excel(filepath + filename +'.xlsx', sheet_name=sheet_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c462697f-f770-4208-95e6-30a6e2d208c3",
   "metadata": {},
   "source": [
    "## General Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e50b872c-fcff-4cff-aff8-09d141b1977e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1294 entries, 0 to 1293\n",
      "Columns: 335 entries, Unnamed: 0 to GHG_per_mass\n",
      "dtypes: float64(241), int64(1), object(93)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "fad06432-095c-4de4-9900-67b72fc0cfd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>meta_DOI_URL</th>\n",
       "      <th>meta_authors</th>\n",
       "      <th>meta_year</th>\n",
       "      <th>meta_title</th>\n",
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       "      <th>site_region_local</th>\n",
       "      <th>site_climate_zone</th>\n",
       "      <th>...</th>\n",
       "      <th>GHG_C12_total</th>\n",
       "      <th>GHG_C34_total</th>\n",
       "      <th>GHG_em_sum_total</th>\n",
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       "      <th>GHG_P56_total</th>\n",
       "      <th>GHG_P78_total</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>No data</td>\n",
       "      <td>Alexander Passer, Helmuth Kreiner</td>\n",
       "      <td>No data</td>\n",
       "      <td>AT6 - Karmeliterhof</td>\n",
       "      <td>A57 case study report</td>\n",
       "      <td>Austria</td>\n",
       "      <td>AT</td>\n",
       "      <td>Graz</td>\n",
       "      <td>(D) Continental</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16251.6600</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Austria</td>\n",
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       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76937.8800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>Austria</td>\n",
       "      <td>AT</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>7760.9200</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.003755</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>Austria</td>\n",
       "      <td>AT</td>\n",
       "      <td>No data</td>\n",
       "      <td>No data</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>73264.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 335 columns</p>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0 meta_DOI_URL                       meta_authors meta_year  \\\n",
       "0           0      No data                            No data   No data   \n",
       "1           1      No data  Alexander Passer, Helmuth Kreiner   No data   \n",
       "2           2      No data                            No data   No data   \n",
       "3           3      No data                            No data   No data   \n",
       "4           4      No data                            No data   No data   \n",
       "\n",
       "            meta_title           meta_journal site_country site_country_iso2  \\\n",
       "0              No data                No data      Austria                AT   \n",
       "1  AT6 - Karmeliterhof  A57 case study report      Austria                AT   \n",
       "2              No data                No data      Austria                AT   \n",
       "3              No data                No data      Austria                AT   \n",
       "4              No data                No data      Austria                AT   \n",
       "\n",
       "  site_region_local site_climate_zone  ... GHG_C12_total GHG_C34_total  \\\n",
       "0           No data           No data  ...           NaN           NaN   \n",
       "1              Graz   (D) Continental  ...           NaN           NaN   \n",
       "2           No data           No data  ...           NaN           NaN   \n",
       "3           No data           No data  ...           NaN           NaN   \n",
       "4           No data           No data  ...           NaN           NaN   \n",
       "\n",
       "  GHG_em_sum_total GHG_P1_total GHG_P2_total GHG_P34_total  GHG_P4_total  \\\n",
       "0      101921.4321          NaN          NaN           NaN           NaN   \n",
       "1       16251.6600          NaN          NaN           NaN           NaN   \n",
       "2       76937.8800          NaN          NaN           NaN           NaN   \n",
       "3        7760.9200          NaN          NaN           NaN           NaN   \n",
       "4       73264.3500          NaN          NaN           NaN           NaN   \n",
       "\n",
       "  GHG_P56_total  GHG_P78_total GHG_per_mass  \n",
       "0           NaN            NaN     0.003705  \n",
       "1           NaN            NaN          NaN  \n",
       "2           NaN            NaN     0.004256  \n",
       "3           NaN            NaN     0.003755  \n",
       "4           NaN            NaN     0.003629  \n",
       "\n",
       "[5 rows x 335 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3867b5a5-83a5-4792-89c2-a4ef957287e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>bldg_year_complete</th>\n",
       "      <th>bldg_users_total</th>\n",
       "      <th>bldg_area_gfa</th>\n",
       "      <th>bldg_floors_ag</th>\n",
       "      <th>bldg_floors_bg</th>\n",
       "      <th>bldg_area_value</th>\n",
       "      <th>bldg_height</th>\n",
       "      <th>bldg_footprint</th>\n",
       "      <th>lca_RSP</th>\n",
       "      <th>...</th>\n",
       "      <th>GHG_C12_total</th>\n",
       "      <th>GHG_C34_total</th>\n",
       "      <th>GHG_em_sum_total</th>\n",
       "      <th>GHG_P1_total</th>\n",
       "      <th>GHG_P2_total</th>\n",
       "      <th>GHG_P34_total</th>\n",
       "      <th>GHG_P4_total</th>\n",
       "      <th>GHG_P56_total</th>\n",
       "      <th>GHG_P78_total</th>\n",
       "      <th>GHG_per_mass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1294.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>710.000000</td>\n",
       "      <td>1055.000000</td>\n",
       "      <td>830.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>423.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1268.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>1.005000e+03</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>551.000000</td>\n",
       "      <td>272.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>646.500000</td>\n",
       "      <td>2008.112121</td>\n",
       "      <td>60.261549</td>\n",
       "      <td>4797.306550</td>\n",
       "      <td>4.031325</td>\n",
       "      <td>1.271795</td>\n",
       "      <td>6016.184161</td>\n",
       "      <td>26.789189</td>\n",
       "      <td>851.623548</td>\n",
       "      <td>53.518927</td>\n",
       "      <td>...</td>\n",
       "      <td>2321.905441</td>\n",
       "      <td>1538.185831</td>\n",
       "      <td>4.595358e+04</td>\n",
       "      <td>2237.119107</td>\n",
       "      <td>3560.500249</td>\n",
       "      <td>2383.537179</td>\n",
       "      <td>3070.739160</td>\n",
       "      <td>3600.862111</td>\n",
       "      <td>356.757936</td>\n",
       "      <td>0.007243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>373.689934</td>\n",
       "      <td>14.733587</td>\n",
       "      <td>234.654388</td>\n",
       "      <td>15852.959380</td>\n",
       "      <td>5.440897</td>\n",
       "      <td>0.636497</td>\n",
       "      <td>20307.786749</td>\n",
       "      <td>32.056306</td>\n",
       "      <td>2492.269024</td>\n",
       "      <td>12.087505</td>\n",
       "      <td>...</td>\n",
       "      <td>8259.834536</td>\n",
       "      <td>3675.030928</td>\n",
       "      <td>1.886603e+05</td>\n",
       "      <td>10354.741164</td>\n",
       "      <td>11536.426059</td>\n",
       "      <td>8123.095542</td>\n",
       "      <td>12753.048343</td>\n",
       "      <td>13406.241290</td>\n",
       "      <td>931.348331</td>\n",
       "      <td>0.010428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-16360.000000</td>\n",
       "      <td>-19284.000000</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>14.401800</td>\n",
       "      <td>24.229333</td>\n",
       "      <td>50.030600</td>\n",
       "      <td>1.201600</td>\n",
       "      <td>15.594461</td>\n",
       "      <td>0.932000</td>\n",
       "      <td>0.000583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>323.250000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>138.699997</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>151.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>70.575000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>47.972542</td>\n",
       "      <td>53.138843</td>\n",
       "      <td>1.361207e+03</td>\n",
       "      <td>77.020925</td>\n",
       "      <td>259.127800</td>\n",
       "      <td>142.447500</td>\n",
       "      <td>302.921650</td>\n",
       "      <td>366.735600</td>\n",
       "      <td>59.885000</td>\n",
       "      <td>0.003686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>646.500000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>269.610000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>586.000000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>205.002000</td>\n",
       "      <td>377.200000</td>\n",
       "      <td>2.839800e+03</td>\n",
       "      <td>129.829200</td>\n",
       "      <td>427.050867</td>\n",
       "      <td>321.019100</td>\n",
       "      <td>480.336500</td>\n",
       "      <td>526.161600</td>\n",
       "      <td>85.260000</td>\n",
       "      <td>0.005114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>969.750000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2671.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3537.000000</td>\n",
       "      <td>49.200000</td>\n",
       "      <td>679.425000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1301.308768</td>\n",
       "      <td>2.213640e+04</td>\n",
       "      <td>764.340463</td>\n",
       "      <td>1998.143250</td>\n",
       "      <td>1147.404500</td>\n",
       "      <td>1822.247600</td>\n",
       "      <td>1978.160000</td>\n",
       "      <td>367.887100</td>\n",
       "      <td>0.007044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1293.000000</td>\n",
       "      <td>2025.000000</td>\n",
       "      <td>2991.000000</td>\n",
       "      <td>216000.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>216000.000000</td>\n",
       "      <td>110.200000</td>\n",
       "      <td>19433.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>104465.000000</td>\n",
       "      <td>25747.470000</td>\n",
       "      <td>2.667087e+06</td>\n",
       "      <td>114628.700000</td>\n",
       "      <td>150898.350000</td>\n",
       "      <td>101784.000000</td>\n",
       "      <td>182903.837398</td>\n",
       "      <td>140951.237343</td>\n",
       "      <td>16320.000000</td>\n",
       "      <td>0.147943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  bldg_year_complete  bldg_users_total  bldg_area_gfa  \\\n",
       "count  1294.000000          330.000000        710.000000    1055.000000   \n",
       "mean    646.500000         2008.112121         60.261549    4797.306550   \n",
       "std     373.689934           14.733587        234.654388   15852.959380   \n",
       "min       0.000000         1940.000000          1.670000      21.000000   \n",
       "25%     323.250000         2005.000000          2.000000     138.699997   \n",
       "50%     646.500000         2012.000000          4.000000     269.610000   \n",
       "75%     969.750000         2019.000000         23.000000    2671.000000   \n",
       "max    1293.000000         2025.000000       2991.000000  216000.000000   \n",
       "\n",
       "       bldg_floors_ag  bldg_floors_bg  bldg_area_value  bldg_height  \\\n",
       "count      830.000000      195.000000       423.000000    74.000000   \n",
       "mean         4.031325        1.271795      6016.184161    26.789189   \n",
       "std          5.440897        0.636497     20307.786749    32.056306   \n",
       "min          1.000000        1.000000        21.000000     2.400000   \n",
       "25%          2.000000        1.000000       151.200000     3.000000   \n",
       "50%          2.000000        1.000000       586.000000     8.100000   \n",
       "75%          4.000000        1.000000      3537.000000    49.200000   \n",
       "max         40.000000        5.000000    216000.000000   110.200000   \n",
       "\n",
       "       bldg_footprint      lca_RSP  ...  GHG_C12_total  GHG_C34_total  \\\n",
       "count       62.000000  1268.000000  ...     275.000000     205.000000   \n",
       "mean       851.623548    53.518927  ...    2321.905441    1538.185831   \n",
       "std       2492.269024    12.087505  ...    8259.834536    3675.030928   \n",
       "min         21.000000    14.000000  ...  -16360.000000  -19284.000000   \n",
       "25%         70.575000    50.000000  ...      47.972542      53.138843   \n",
       "50%        192.000000    50.000000  ...     205.002000     377.200000   \n",
       "75%        679.425000    50.000000  ...    1763.000000    1301.308768   \n",
       "max      19433.000000   100.000000  ...  104465.000000   25747.470000   \n",
       "\n",
       "       GHG_em_sum_total   GHG_P1_total   GHG_P2_total  GHG_P34_total  \\\n",
       "count      1.005000e+03     650.000000     670.000000     670.000000   \n",
       "mean       4.595358e+04    2237.119107    3560.500249    2383.537179   \n",
       "std        1.886603e+05   10354.741164   11536.426059    8123.095542   \n",
       "min        8.000000e+01      14.401800      24.229333      50.030600   \n",
       "25%        1.361207e+03      77.020925     259.127800     142.447500   \n",
       "50%        2.839800e+03     129.829200     427.050867     321.019100   \n",
       "75%        2.213640e+04     764.340463    1998.143250    1147.404500   \n",
       "max        2.667087e+06  114628.700000  150898.350000  101784.000000   \n",
       "\n",
       "        GHG_P4_total  GHG_P56_total  GHG_P78_total  GHG_per_mass  \n",
       "count     666.000000     645.000000     551.000000    272.000000  \n",
       "mean     3070.739160    3600.862111     356.757936      0.007243  \n",
       "std     12753.048343   13406.241290     931.348331      0.010428  \n",
       "min         1.201600      15.594461       0.932000      0.000583  \n",
       "25%       302.921650     366.735600      59.885000      0.003686  \n",
       "50%       480.336500     526.161600      85.260000      0.005114  \n",
       "75%      1822.247600    1978.160000     367.887100      0.007044  \n",
       "max    182903.837398  140951.237343   16320.000000      0.147943  \n",
       "\n",
       "[8 rows x 242 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ef207ff",
   "metadata": {},
   "source": [
    "# Benchmarking // Whole Life Carbon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30d76272",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "55a2b990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GHG_A123_m2a_harm</th>\n",
       "      <td>24.0</td>\n",
       "      <td>5.467654</td>\n",
       "      <td>6.228475</td>\n",
       "      <td>6.644924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_A45_m2a_harm</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.380667</td>\n",
       "      <td>0.752923</td>\n",
       "      <td>0.794030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_B1234_m2a_harm</th>\n",
       "      <td>23.0</td>\n",
       "      <td>3.012792</td>\n",
       "      <td>3.946062</td>\n",
       "      <td>4.403784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_B67_m2a_harm</th>\n",
       "      <td>191.0</td>\n",
       "      <td>1.374600</td>\n",
       "      <td>4.292308</td>\n",
       "      <td>10.401850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_C12_m2a_harm</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.203112</td>\n",
       "      <td>0.226940</td>\n",
       "      <td>0.240334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_C34_m2a_harm</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.197095</td>\n",
       "      <td>0.204283</td>\n",
       "      <td>0.240584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count       25%       50%        75%\n",
       "GHG_A123_m2a_harm    24.0  5.467654  6.228475   6.644924\n",
       "GHG_A45_m2a_harm     23.0  0.380667  0.752923   0.794030\n",
       "GHG_B1234_m2a_harm   23.0  3.012792  3.946062   4.403784\n",
       "GHG_B67_m2a_harm    191.0  1.374600  4.292308  10.401850\n",
       "GHG_C12_m2a_harm     24.0  0.203112  0.226940   0.240334\n",
       "GHG_C34_m2a_harm     18.0  0.197095  0.204283   0.240584"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# List of variables to compute statistics for\n",
    "variables = [\n",
    "    'GHG_A123_m2a_harm', \n",
    "    'GHG_A45_m2a_harm',\n",
    "    'GHG_B1234_m2a_harm',\n",
    "    'GHG_B67_m2a_harm',\n",
    "    'GHG_C12_m2a_harm',\n",
    "    'GHG_C34_m2a_harm',]\n",
    "\n",
    "# Filter the dataset\n",
    "filtered_df = df[(df['site_region_geoscheme'] == 'Western Europe') \n",
    "                 & (df['bldg_use_subtype'] == 'Single family house')\n",
    "                 #& (df['bldg_energy_class_general'] == 'New Standard') \n",
    "                 #& (df['bldg_struct_type'] == 'massive brick')\n",
    "                 ]\n",
    "\n",
    "# Initialize an empty DataFrame to store the results\n",
    "results_df = pd.DataFrame()\n",
    "\n",
    "# Loop through each variable to compute the descriptive statistics\n",
    "for var in variables:\n",
    "    descriptive_stats = filtered_df[var].describe(percentiles=[.25, .5, .75])\n",
    "    percentiles = descriptive_stats[['count', '25%', '50%', '75%']]\n",
    "    percentiles_df = percentiles.to_frame().T\n",
    "    results_df = pd.concat([results_df, percentiles_df])\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "7dd4bf93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GHG_A123_m2a_harm</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.031171</td>\n",
       "      <td>4.102981</td>\n",
       "      <td>4.265320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_A45_m2a_harm</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.380667</td>\n",
       "      <td>0.389260</td>\n",
       "      <td>0.404856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_B1234_m2a_harm</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.099536</td>\n",
       "      <td>3.121530</td>\n",
       "      <td>3.187904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_B67_m2a_harm</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1.135000</td>\n",
       "      <td>1.379100</td>\n",
       "      <td>19.051566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_C12_m2a_harm</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.137066</td>\n",
       "      <td>0.141438</td>\n",
       "      <td>0.147865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_C34_m2a_harm</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.198964</td>\n",
       "      <td>0.204283</td>\n",
       "      <td>0.210111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count       25%       50%        75%\n",
       "GHG_A123_m2a_harm     6.0  4.031171  4.102981   4.265320\n",
       "GHG_A45_m2a_harm      6.0  0.380667  0.389260   0.404856\n",
       "GHG_B1234_m2a_harm    6.0  3.099536  3.121530   3.187904\n",
       "GHG_B67_m2a_harm     23.0  1.135000  1.379100  19.051566\n",
       "GHG_C12_m2a_harm      6.0  0.137066  0.141438   0.147865\n",
       "GHG_C34_m2a_harm      6.0  0.198964  0.204283   0.210111"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# List of variables to compute statistics for\n",
    "variables = [\n",
    "    'GHG_A123_m2a_harm', \n",
    "    'GHG_A45_m2a_harm',\n",
    "    'GHG_B1234_m2a_harm',\n",
    "    'GHG_B67_m2a_harm',\n",
    "    'GHG_C12_m2a_harm',\n",
    "    'GHG_C34_m2a_harm',]\n",
    "\n",
    "# Filter the dataset\n",
    "filtered_df = df[(df['site_region_geoscheme'] == 'Western Europe') \n",
    "                 & (df['bldg_use_subtype'] == 'Single family house')\n",
    "                 #& (df['bldg_energy_class_general'] == 'New Advanced') \n",
    "                 & (df['bldg_struct_type'] == 'frame wood')\n",
    "                 ]\n",
    "\n",
    "# Initialize an empty DataFrame to store the results\n",
    "results_df = pd.DataFrame()\n",
    "\n",
    "# Loop through each variable to compute the descriptive statistics\n",
    "for var in variables:\n",
    "    descriptive_stats = filtered_df[var].describe(percentiles=[.25, .5, .75])\n",
    "    percentiles = descriptive_stats[['count', '25%', '50%', '75%']]\n",
    "    percentiles_df = percentiles.to_frame().T\n",
    "    results_df = pd.concat([results_df, percentiles_df])\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f3858d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frame wood    26\n",
       "Name: bldg_struct_type, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df['bldg_struct_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "d4c8c41f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GHG_A123_m2a_harm</th>\n",
       "      <td>45.0</td>\n",
       "      <td>6.180000</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>8.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_A45_m2a_harm</th>\n",
       "      <td>36.0</td>\n",
       "      <td>0.585000</td>\n",
       "      <td>0.905000</td>\n",
       "      <td>1.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_B1234_m2a_harm</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>1.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_B67_m2a_harm</th>\n",
       "      <td>43.0</td>\n",
       "      <td>19.350000</td>\n",
       "      <td>34.407143</td>\n",
       "      <td>52.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_C12_m2a_harm</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHG_C34_m2a_harm</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>1.430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count        25%        50%     75%\n",
       "GHG_A123_m2a_harm    45.0   6.180000   8.250000   8.530\n",
       "GHG_A45_m2a_harm     36.0   0.585000   0.905000   1.330\n",
       "GHG_B1234_m2a_harm   21.0   0.666667   1.680000   1.710\n",
       "GHG_B67_m2a_harm     43.0  19.350000  34.407143  52.335\n",
       "GHG_C12_m2a_harm     33.0   0.190000   0.380000   0.480\n",
       "GHG_C34_m2a_harm      4.0   0.710000   1.070000   1.430"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# List of variables to compute statistics for\n",
    "variables = [\n",
    "    'GHG_A123_m2a_harm', \n",
    "    'GHG_A45_m2a_harm',\n",
    "    'GHG_B1234_m2a_harm',\n",
    "    'GHG_B67_m2a_harm',\n",
    "    'GHG_C12_m2a_harm',\n",
    "    'GHG_C34_m2a_harm',]\n",
    "\n",
    "# Filter the dataset\n",
    "filtered_df = df[(df['site_region_geoscheme'] == 'Eastern Asia') \n",
    "                 & (df['bldg_use_subtype'] == 'Multi-family house')\n",
    "                 #& (df['bldg_energy_class_general'] == 'New Standard') \n",
    "                 #& (df['bldg_struct_type'] == 'frame wood')\n",
    "                 ]\n",
    "\n",
    "# Initialize an empty DataFrame to store the results\n",
    "results_df = pd.DataFrame()\n",
    "\n",
    "# Loop through each variable to compute the descriptive statistics\n",
    "for var in variables:\n",
    "    descriptive_stats = filtered_df[var].describe(percentiles=[.25, .5, .75])\n",
    "    percentiles = descriptive_stats[['count', '25%', '50%', '75%']]\n",
    "    percentiles_df = percentiles.to_frame().T\n",
    "    results_df = pd.concat([results_df, percentiles_df])\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a1a257ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    count   25%     50%        75%\n",
      "GHG_A123_m2a_harm    38.0  3.06   3.635   4.375000\n",
      "GHG_A45_m2a_harm     29.0  0.29   0.410   0.410000\n",
      "GHG_B1234_m2a_harm   11.0  1.03   1.190   4.876923\n",
      "GHG_B67_m2a_harm     48.0  7.48  29.925  32.665000\n",
      "GHG_C12_m2a_harm      0.0   NaN     NaN        NaN\n",
      "GHG_C34_m2a_harm      0.0   NaN     NaN        NaN\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# List of variables to compute statistics for\n",
    "variables = [\n",
    "    'GHG_A123_m2a_harm', \n",
    "    'GHG_A45_m2a_harm',\n",
    "    'GHG_B1234_m2a_harm',\n",
    "    'GHG_B67_m2a_harm',\n",
    "    'GHG_C12_m2a_harm',\n",
    "    'GHG_C34_m2a_harm',]\n",
    "\n",
    "# Filter the dataset\n",
    "filtered_df = df[(df['site_region_geoscheme'] == 'Australia and New Zealand') \n",
    "                 #& (df['bldg_use_subtype'] == 'Office')\n",
    "                 #& (df['bldg_energy_class_general'] == 'New Standard') \n",
    "                 #& (df['bldg_struct_type'] == 'massive brick')\n",
    "                 ]\n",
    "\n",
    "# Initialize an empty DataFrame to store the results\n",
    "results_df = pd.DataFrame()\n",
    "\n",
    "# Loop through each variable to compute the descriptive statistics\n",
    "for var in variables:\n",
    "    descriptive_stats = filtered_df[var].describe(percentiles=[.25, .5, .75])\n",
    "    percentiles = descriptive_stats[['count', '25%', '50%', '75%']]\n",
    "    percentiles_df = percentiles.to_frame().T\n",
    "    results_df = pd.concat([results_df, percentiles_df])\n",
    "\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad363da1",
   "metadata": {},
   "source": [
    "# Benchmarking // Material Intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "7f5d5c31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                count         25%         50%          75%\n",
      "mi_fr_concrete   28.0  849.378004  849.378004  1097.013946\n",
      "mi_fr_steel      25.0   17.039864   17.039864    50.984624\n",
      "mi_fr_wood       16.0    6.734521    6.734521     6.734521\n",
      "mi_fr_brick       6.0   58.331922   81.368591    90.318877\n",
      "mi_fr_glass      15.0   12.476675   12.476675    12.476675\n",
      "mi_fr_plastics    0.0         NaN         NaN          NaN\n",
      "mi_fr_aluminum    0.0         NaN         NaN          NaN\n",
      "mi_fr_copper      0.0         NaN         NaN          NaN\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# List of variables to compute statistics for\n",
    "variables = [\n",
    "    'mi_fr_concrete', \n",
    "    'mi_fr_steel',\n",
    "    'mi_fr_wood',\n",
    "    'mi_fr_brick',\n",
    "    'mi_fr_glass',\n",
    "    'mi_fr_plastics',\n",
    "    'mi_fr_aluminum',\n",
    "    'mi_fr_copper',\n",
    "    ]\n",
    "    \n",
    "\n",
    "# Filter the dataset\n",
    "filtered_df = df[\n",
    "    #(df['site_region_geoscheme'] == 'Australia and New Zealand') \n",
    "    #& (df['bldg_use_subtype'] == 'Office')\n",
    "    #& (df['bldg_energy_class_general'] == 'New Standard')\n",
    "    #&\n",
    "    (df['bldg_struct_type'] == 'massive concrete')\n",
    "    ]\n",
    "\n",
    "# Initialize an empty DataFrame to store the results\n",
    "results_df = pd.DataFrame()\n",
    "\n",
    "# Loop through each variable to compute the descriptive statistics\n",
    "for var in variables:\n",
    "    descriptive_stats = filtered_df[var].describe(percentiles=[.25, .5, .75])\n",
    "    percentiles = descriptive_stats[['count', '25%', '50%', '75%']]\n",
    "    percentiles_df = percentiles.to_frame().T\n",
    "    results_df = pd.concat([results_df, percentiles_df])\n",
    "\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "579f123f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished: GBDB CarbEnMats // Benchmarking\n"
     ]
    }
   ],
   "source": [
    "print('Finished: ' + script_title)"
   ]
  }
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