03_Demo_households_temporal_disaggregations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from disaggregator import config, data, spatial, temporal, plot\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Power by 'Time Use Survey' (ZVE, only 2015)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get spatial data in [MWh] that shall be disaggregated temporally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nuts3\n",
       "DE111    1.162713e+06\n",
       "DE112    6.942694e+05\n",
       "DE113    9.678796e+05\n",
       "DE114    4.479099e+05\n",
       "DE115    9.955292e+05\n",
       "             ...     \n",
       "DEG0K    1.156523e+05\n",
       "DEG0L    1.437641e+05\n",
       "DEG0M    1.262784e+05\n",
       "DEG0N    5.985672e+04\n",
       "DEG0P    1.821599e+05\n",
       "Name: value, Length: 401, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spat = spatial.disagg_households_power('population', weight_by_income=True) * 1e3\n",
    "df_spat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate temporal distribution keys based on the ZVE. They will be saved in your `data_out` folder. This step can take up to 60 Minutes but is only necessary once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Creating ZVE-profile for Stuttgart (DE111)\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 1\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 2\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 3\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 4\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 5\n",
      "disaggregator.temporal: INFO     ...creating hourly load profile for entire year...\n",
      "disaggregator.temporal: INFO     Creating ZVE-profile for Böblingen (DE112)\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 1\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 2\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 3\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 4\n",
      "disaggregator.temporal: INFO     Calc load profile for household-size: 5\n",
      "disaggregator.temporal: INFO     ...creating hourly load profile for entire year...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-7fb1a56b28c9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtemporal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_zve_load_profiles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mZ:\\PRO\\PRO_DemandRegio\\Arbeitsbereich_indiv\\PV\\Git\\demandregio_github\\disaggregator\\temporal.py\u001b[0m in \u001b[0;36mmake_zve_load_profiles\u001b[1;34m(return_profile_by_typeday, return_profile_by_application, **kwargs)\u001b[0m\n\u001b[0;32m    248\u001b[0m             \u001b[0mind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_new\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Hour'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m             \u001b[0mcol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf_new\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'TypeDay'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_new\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Season'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 250\u001b[1;33m             \u001b[0mdf_new\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'value'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_erg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    251\u001b[0m         \u001b[1;31m# generate distribution keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m         \u001b[0mdf_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_new\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'value'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdf_new\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'value'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    669\u001b[0m         \u001b[0miloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 670\u001b[1;33m         \u001b[0miloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    671\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    672\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[1;34m(self, indexer, value)\u001b[0m\n\u001b[0;32m   1763\u001b[0m                 \u001b[1;31m# scalar value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1764\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mloc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0milocs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1765\u001b[1;33m                     \u001b[0misetter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1766\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1767\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36misetter\u001b[1;34m(loc, v)\u001b[0m\n\u001b[0;32m   1688\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1689\u001b[0m                     \u001b[1;31m# set the item, possibly having a dtype change\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1690\u001b[1;33m                     \u001b[0mser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1691\u001b[0m                     \u001b[0mser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1692\u001b[0m                     \u001b[0mser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_update_cacher\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclear\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mcopy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m   5652\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5653\u001b[0m         \"\"\"\n\u001b[1;32m-> 5654\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdeep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5655\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5656\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"copy\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mcopy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m    801\u001b[0m             \u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    802\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 803\u001b[1;33m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"copy\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdeep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdeep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    804\u001b[0m         \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    805\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m    404\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    407\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mcopy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m    671\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    672\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 673\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    674\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_block_same_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "temporal.make_zve_load_profiles()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then load the temporal distribution keys based on the ZVE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.000174</td>\n",
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       "    <tr>\n",
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       "      <td>0.000121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00011</td>\n",
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       "      <td>0.000138</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEG0N</th>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEG0P</th>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000079</td>\n",
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       "           0         1         2         3         4         5         6     \\\n",
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       "DE113  0.000054  0.000045  0.000042  0.000041  0.000047  0.000076  0.000134   \n",
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       "DE115  0.000054  0.000045  0.000042  0.000041  0.000047  0.000076  0.000134   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
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       "DE115  0.000144  0.000132  0.000121  ...  0.00011  0.000111  0.000136   \n",
       "...         ...       ...       ...  ...      ...       ...       ...   \n",
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       "\n",
       "           8753      8754      8755      8756      8757      8758      8759  \n",
       "DE111  0.000173  0.000189  0.000191  0.000184  0.000174  0.000131  0.000079  \n",
       "DE112  0.000173  0.000189  0.000191  0.000184  0.000174  0.000131  0.000079  \n",
       "DE113  0.000173  0.000189  0.000191  0.000184  0.000174  0.000131  0.000079  \n",
       "DE114  0.000173  0.000189  0.000191  0.000184  0.000174  0.000131  0.000079  \n",
       "DE115  0.000173  0.000189  0.000191  0.000184  0.000174  0.000131  0.000079  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "DEG0K  0.000174  0.000188  0.000191  0.000184  0.000174  0.000131  0.000079  \n",
       "DEG0L  0.000174  0.000188  0.000191  0.000184  0.000173  0.000131  0.000079  \n",
       "DEG0M  0.000174  0.000188  0.000191  0.000184  0.000173  0.000131  0.000079  \n",
       "DEG0N  0.000173  0.000188  0.000191  0.000184  0.000174  0.000131  0.000079  \n",
       "DEG0P  0.000173  0.000188  0.000191  0.000184  0.000174  0.000131  0.000079  \n",
       "\n",
       "[401 rows x 8760 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = (pd.read_csv(config.data_out('ZVE_timeseries_AllRegions_2015.csv'),\n",
    "                       engine='c', index_col=0, parse_dates=True,\n",
    "                       infer_datetime_format=True)\n",
    "              .pipe(data.transpose_spatiotemporal))\n",
    "df_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `temporal.disagg_temporal()` needs two inputs:\n",
    "1. **a spatial dataset**  \n",
    "    either as `pd.Series` or `pd.DataFrame`: in both cases _NUTS-3-index'ed_\n",
    "\n",
    "\n",
    "2. **a temporal dataset**  \n",
    "    if passed as `pd.DataFrame`: _NUTS-3-index'ed_  \n",
    "    if passed as `pd.Series`: _pd.DateTimeIndex'ed_\n",
    "\n",
    "Pre-check:\n",
    "* Both `df_spat` and `df_temp` are `pd.DataFrame`s\n",
    "* Both have NUTS-3-indices.\n",
    "\n",
    "So now we can directly perfom the disaggregation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>116.463387</td>\n",
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       "      <td>107.554479</td>\n",
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       "      <td>184.921195</td>\n",
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       "      <td>40.651284</td>\n",
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       "      <td>131.013930</td>\n",
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       "      <td>22.046708</td>\n",
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       "      <td>20.067565</td>\n",
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       "      <td>20.584787</td>\n",
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       "      <td>17.372849</td>\n",
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       "      <td>19.858781</td>\n",
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       "      <td>5.976532</td>\n",
       "      <td>9.622400</td>\n",
       "      <td>16.888233</td>\n",
       "      <td>18.073631</td>\n",
       "      <td>16.527329</td>\n",
       "      <td>15.258049</td>\n",
       "      <td>...</td>\n",
       "      <td>13.889377</td>\n",
       "      <td>14.536011</td>\n",
       "      <td>17.456427</td>\n",
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       "      <td>24.056387</td>\n",
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       "      <td>7.298413</td>\n",
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       "      <td>6.583629</td>\n",
       "      <td>6.802883</td>\n",
       "      <td>8.234557</td>\n",
       "      <td>10.374306</td>\n",
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       "      <td>10.386395</td>\n",
       "      <td>7.818490</td>\n",
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       "    <tr>\n",
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       "      <td>8.623073</td>\n",
       "      <td>13.886662</td>\n",
       "      <td>24.375760</td>\n",
       "      <td>26.133508</td>\n",
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       "      <td>22.203549</td>\n",
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       "      <td>20.694584</td>\n",
       "      <td>25.060210</td>\n",
       "      <td>31.562803</td>\n",
       "      <td>34.308922</td>\n",
       "      <td>34.726370</td>\n",
       "      <td>33.461871</td>\n",
       "      <td>31.609157</td>\n",
       "      <td>23.794159</td>\n",
       "      <td>14.431925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>401 rows × 8760 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1          2          3          4          5     \\\n",
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       "...          ...        ...        ...        ...        ...        ...   \n",
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       "\n",
       "             6           7           8           9     ...        8750  \\\n",
       "DE111  155.867967  167.129661  152.972696  140.248072  ...  127.884639   \n",
       "DE112   93.078833   99.804531   91.436438   83.775718  ...   76.361392   \n",
       "DE113  129.753235  139.128452  127.261214  116.463387  ...  106.455208   \n",
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       "DE115  133.450182  143.091755  131.013930  120.206915  ...  109.496528   \n",
       "...           ...         ...         ...         ...  ...         ...   \n",
       "DEG0K   15.475622   16.567142   15.158041   13.976416  ...   12.720572   \n",
       "DEG0L   19.234237   20.584787   18.828011   17.372849  ...   15.812597   \n",
       "DEG0M   16.888233   18.073631   16.527329   15.258049  ...   13.889377   \n",
       "DEG0N    8.009632    8.584622    7.885118    7.298413  ...    6.583629   \n",
       "DEG0P   24.375760   26.133508   23.996802   22.203549  ...   20.035718   \n",
       "\n",
       "             8751        8752        8753        8754        8755        8756  \\\n",
       "DE111  129.204791  158.526482  200.867852  219.436620  222.137802  214.089070   \n",
       "DE112   77.085460   94.527074  119.874115  131.041281  132.655255  127.849916   \n",
       "DE113  107.554479  131.965348  167.266655  182.672132  184.921195  178.221492   \n",
       "DE114   49.815212   61.112960   77.477955   84.534197   85.574870   82.474331   \n",
       "DE115  110.672853  135.776451  171.978498  187.875086  190.187102  183.295229   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "DEG0K   13.186998   15.955136   20.100347   21.788890   22.046708   21.243819   \n",
       "DEG0L   16.418333   19.858781   24.989838   27.089108   27.400320   26.402020   \n",
       "DEG0M   14.536011   17.456427   21.962006   23.783845   24.056387   23.178993   \n",
       "DEG0N    6.802883    8.234557   10.374306   11.273542   11.410698   10.995180   \n",
       "DEG0P   20.694584   25.060210   31.562803   34.308922   34.726370   33.461871   \n",
       "\n",
       "             8757        8758       8759  \n",
       "DE111  202.246599  152.218293  92.287071  \n",
       "DE112  120.778150   90.901379  55.110654  \n",
       "DE113  168.363208  126.716053  76.825033  \n",
       "DE114   77.912231   58.639579  35.551998  \n",
       "DE115  173.155909  130.324063  79.013796  \n",
       "...           ...         ...        ...  \n",
       "DEG0K   20.067565   15.106155   9.162467  \n",
       "DEG0L   24.940035   18.774261  11.387739  \n",
       "DEG0M   21.895219   16.482779   9.998707  \n",
       "DEG0N   10.386395    7.818490   4.742183  \n",
       "DEG0P   31.609157   23.794159  14.431925  \n",
       "\n",
       "[401 rows x 8760 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elc_tempo_ZVE = temporal.disagg_temporal(df_spat, df_temp)\n",
    "df_elc_tempo_ZVE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the data has successfully been disaggregated.  \n",
    "However, regions are in rows and time-steps are in columns -- this is not really intuitive. Better would be the other way round, right? We can achieve this by passing `time_indexed=True` to give us a transponed version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DE111</th>\n",
       "      <th>DE112</th>\n",
       "      <th>DE113</th>\n",
       "      <th>DE114</th>\n",
       "      <th>DE115</th>\n",
       "      <th>DE116</th>\n",
       "      <th>DE117</th>\n",
       "      <th>DE118</th>\n",
       "      <th>DE119</th>\n",
       "      <th>DE11A</th>\n",
       "      <th>...</th>\n",
       "      <th>DEG0F</th>\n",
       "      <th>DEG0G</th>\n",
       "      <th>DEG0H</th>\n",
       "      <th>DEG0I</th>\n",
       "      <th>DEG0J</th>\n",
       "      <th>DEG0K</th>\n",
       "      <th>DEG0L</th>\n",
       "      <th>DEG0M</th>\n",
       "      <th>DEG0N</th>\n",
       "      <th>DEG0P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.243096</td>\n",
       "      <td>37.168059</td>\n",
       "      <td>51.814067</td>\n",
       "      <td>23.977962</td>\n",
       "      <td>53.291912</td>\n",
       "      <td>40.905465</td>\n",
       "      <td>15.877155</td>\n",
       "      <td>31.947945</td>\n",
       "      <td>10.068429</td>\n",
       "      <td>17.453757</td>\n",
       "      <td>...</td>\n",
       "      <td>8.054676</td>\n",
       "      <td>6.167067</td>\n",
       "      <td>4.450422</td>\n",
       "      <td>8.252664</td>\n",
       "      <td>6.443149</td>\n",
       "      <td>6.184371</td>\n",
       "      <td>7.686875</td>\n",
       "      <td>6.750356</td>\n",
       "      <td>3.200796</td>\n",
       "      <td>9.740952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.126232</td>\n",
       "      <td>31.126070</td>\n",
       "      <td>43.391955</td>\n",
       "      <td>20.080572</td>\n",
       "      <td>44.630502</td>\n",
       "      <td>34.258926</td>\n",
       "      <td>13.297695</td>\n",
       "      <td>26.757555</td>\n",
       "      <td>8.433337</td>\n",
       "      <td>14.619123</td>\n",
       "      <td>...</td>\n",
       "      <td>6.748838</td>\n",
       "      <td>5.167463</td>\n",
       "      <td>3.728710</td>\n",
       "      <td>6.914674</td>\n",
       "      <td>5.398940</td>\n",
       "      <td>5.181835</td>\n",
       "      <td>6.441060</td>\n",
       "      <td>5.656935</td>\n",
       "      <td>2.681904</td>\n",
       "      <td>8.161778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.009933</td>\n",
       "      <td>29.265026</td>\n",
       "      <td>40.797721</td>\n",
       "      <td>18.880060</td>\n",
       "      <td>41.962479</td>\n",
       "      <td>32.211398</td>\n",
       "      <td>12.503040</td>\n",
       "      <td>25.158548</td>\n",
       "      <td>7.929553</td>\n",
       "      <td>13.745765</td>\n",
       "      <td>...</td>\n",
       "      <td>6.346301</td>\n",
       "      <td>4.859305</td>\n",
       "      <td>3.506253</td>\n",
       "      <td>6.502229</td>\n",
       "      <td>5.077018</td>\n",
       "      <td>4.872785</td>\n",
       "      <td>6.056989</td>\n",
       "      <td>5.319789</td>\n",
       "      <td>2.521948</td>\n",
       "      <td>7.674979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.478387</td>\n",
       "      <td>28.350340</td>\n",
       "      <td>39.522726</td>\n",
       "      <td>18.290048</td>\n",
       "      <td>40.651284</td>\n",
       "      <td>31.205260</td>\n",
       "      <td>12.112576</td>\n",
       "      <td>24.372856</td>\n",
       "      <td>7.682060</td>\n",
       "      <td>13.316700</td>\n",
       "      <td>...</td>\n",
       "      <td>6.148695</td>\n",
       "      <td>4.708045</td>\n",
       "      <td>3.397035</td>\n",
       "      <td>6.299755</td>\n",
       "      <td>4.919010</td>\n",
       "      <td>4.721075</td>\n",
       "      <td>5.868472</td>\n",
       "      <td>5.154349</td>\n",
       "      <td>2.443429</td>\n",
       "      <td>7.436016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55.075697</td>\n",
       "      <td>32.887385</td>\n",
       "      <td>45.847246</td>\n",
       "      <td>21.216790</td>\n",
       "      <td>47.155769</td>\n",
       "      <td>36.197121</td>\n",
       "      <td>14.049972</td>\n",
       "      <td>28.271282</td>\n",
       "      <td>8.910340</td>\n",
       "      <td>15.446021</td>\n",
       "      <td>...</td>\n",
       "      <td>7.130278</td>\n",
       "      <td>5.459496</td>\n",
       "      <td>3.939479</td>\n",
       "      <td>7.305491</td>\n",
       "      <td>5.704034</td>\n",
       "      <td>5.474694</td>\n",
       "      <td>6.805049</td>\n",
       "      <td>5.976532</td>\n",
       "      <td>2.833481</td>\n",
       "      <td>8.623073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>222.137802</td>\n",
       "      <td>132.655255</td>\n",
       "      <td>184.921195</td>\n",
       "      <td>85.574870</td>\n",
       "      <td>190.187102</td>\n",
       "      <td>145.966569</td>\n",
       "      <td>56.652676</td>\n",
       "      <td>113.996411</td>\n",
       "      <td>35.919919</td>\n",
       "      <td>62.269330</td>\n",
       "      <td>...</td>\n",
       "      <td>28.715183</td>\n",
       "      <td>21.983851</td>\n",
       "      <td>15.867765</td>\n",
       "      <td>29.421521</td>\n",
       "      <td>22.966677</td>\n",
       "      <td>22.046708</td>\n",
       "      <td>27.400320</td>\n",
       "      <td>24.056387</td>\n",
       "      <td>11.410698</td>\n",
       "      <td>34.726370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>214.089070</td>\n",
       "      <td>127.849916</td>\n",
       "      <td>178.221492</td>\n",
       "      <td>82.474331</td>\n",
       "      <td>183.295229</td>\n",
       "      <td>140.674461</td>\n",
       "      <td>54.598159</td>\n",
       "      <td>109.862357</td>\n",
       "      <td>34.616282</td>\n",
       "      <td>60.009690</td>\n",
       "      <td>...</td>\n",
       "      <td>27.669618</td>\n",
       "      <td>21.183064</td>\n",
       "      <td>15.290306</td>\n",
       "      <td>28.350323</td>\n",
       "      <td>22.129864</td>\n",
       "      <td>21.243819</td>\n",
       "      <td>26.402020</td>\n",
       "      <td>23.178993</td>\n",
       "      <td>10.995180</td>\n",
       "      <td>33.461871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>202.246599</td>\n",
       "      <td>120.778150</td>\n",
       "      <td>168.363208</td>\n",
       "      <td>77.912231</td>\n",
       "      <td>173.155909</td>\n",
       "      <td>132.892042</td>\n",
       "      <td>51.577525</td>\n",
       "      <td>103.784245</td>\n",
       "      <td>32.700858</td>\n",
       "      <td>56.689247</td>\n",
       "      <td>...</td>\n",
       "      <td>26.137623</td>\n",
       "      <td>20.010122</td>\n",
       "      <td>14.443807</td>\n",
       "      <td>26.780660</td>\n",
       "      <td>20.904432</td>\n",
       "      <td>20.067565</td>\n",
       "      <td>24.940035</td>\n",
       "      <td>21.895219</td>\n",
       "      <td>10.386395</td>\n",
       "      <td>31.609157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>152.218293</td>\n",
       "      <td>90.901379</td>\n",
       "      <td>126.716053</td>\n",
       "      <td>58.639579</td>\n",
       "      <td>130.324063</td>\n",
       "      <td>100.021561</td>\n",
       "      <td>38.820303</td>\n",
       "      <td>78.114146</td>\n",
       "      <td>24.613234</td>\n",
       "      <td>42.668594</td>\n",
       "      <td>...</td>\n",
       "      <td>19.675369</td>\n",
       "      <td>15.063030</td>\n",
       "      <td>10.872531</td>\n",
       "      <td>20.159370</td>\n",
       "      <td>15.736385</td>\n",
       "      <td>15.106155</td>\n",
       "      <td>18.774261</td>\n",
       "      <td>16.482779</td>\n",
       "      <td>7.818490</td>\n",
       "      <td>23.794159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>92.287071</td>\n",
       "      <td>55.110654</td>\n",
       "      <td>76.825033</td>\n",
       "      <td>35.551998</td>\n",
       "      <td>79.013796</td>\n",
       "      <td>60.644336</td>\n",
       "      <td>23.537738</td>\n",
       "      <td>47.362572</td>\n",
       "      <td>14.924596</td>\n",
       "      <td>25.872470</td>\n",
       "      <td>...</td>\n",
       "      <td>11.933700</td>\n",
       "      <td>9.136486</td>\n",
       "      <td>6.594222</td>\n",
       "      <td>12.227185</td>\n",
       "      <td>9.545127</td>\n",
       "      <td>9.162467</td>\n",
       "      <td>11.387739</td>\n",
       "      <td>9.998707</td>\n",
       "      <td>4.742183</td>\n",
       "      <td>14.431925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DE111       DE112       DE113      DE114       DE115       DE116  \\\n",
       "0      62.243096   37.168059   51.814067  23.977962   53.291912   40.905465   \n",
       "1      52.126232   31.126070   43.391955  20.080572   44.630502   34.258926   \n",
       "2      49.009933   29.265026   40.797721  18.880060   41.962479   32.211398   \n",
       "3      47.478387   28.350340   39.522726  18.290048   40.651284   31.205260   \n",
       "4      55.075697   32.887385   45.847246  21.216790   47.155769   36.197121   \n",
       "...          ...         ...         ...        ...         ...         ...   \n",
       "8755  222.137802  132.655255  184.921195  85.574870  190.187102  145.966569   \n",
       "8756  214.089070  127.849916  178.221492  82.474331  183.295229  140.674461   \n",
       "8757  202.246599  120.778150  168.363208  77.912231  173.155909  132.892042   \n",
       "8758  152.218293   90.901379  126.716053  58.639579  130.324063  100.021561   \n",
       "8759   92.287071   55.110654   76.825033  35.551998   79.013796   60.644336   \n",
       "\n",
       "          DE117       DE118      DE119      DE11A  ...      DEG0F      DEG0G  \\\n",
       "0     15.877155   31.947945  10.068429  17.453757  ...   8.054676   6.167067   \n",
       "1     13.297695   26.757555   8.433337  14.619123  ...   6.748838   5.167463   \n",
       "2     12.503040   25.158548   7.929553  13.745765  ...   6.346301   4.859305   \n",
       "3     12.112576   24.372856   7.682060  13.316700  ...   6.148695   4.708045   \n",
       "4     14.049972   28.271282   8.910340  15.446021  ...   7.130278   5.459496   \n",
       "...         ...         ...        ...        ...  ...        ...        ...   \n",
       "8755  56.652676  113.996411  35.919919  62.269330  ...  28.715183  21.983851   \n",
       "8756  54.598159  109.862357  34.616282  60.009690  ...  27.669618  21.183064   \n",
       "8757  51.577525  103.784245  32.700858  56.689247  ...  26.137623  20.010122   \n",
       "8758  38.820303   78.114146  24.613234  42.668594  ...  19.675369  15.063030   \n",
       "8759  23.537738   47.362572  14.924596  25.872470  ...  11.933700   9.136486   \n",
       "\n",
       "          DEG0H      DEG0I      DEG0J      DEG0K      DEG0L      DEG0M  \\\n",
       "0      4.450422   8.252664   6.443149   6.184371   7.686875   6.750356   \n",
       "1      3.728710   6.914674   5.398940   5.181835   6.441060   5.656935   \n",
       "2      3.506253   6.502229   5.077018   4.872785   6.056989   5.319789   \n",
       "3      3.397035   6.299755   4.919010   4.721075   5.868472   5.154349   \n",
       "4      3.939479   7.305491   5.704034   5.474694   6.805049   5.976532   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "8755  15.867765  29.421521  22.966677  22.046708  27.400320  24.056387   \n",
       "8756  15.290306  28.350323  22.129864  21.243819  26.402020  23.178993   \n",
       "8757  14.443807  26.780660  20.904432  20.067565  24.940035  21.895219   \n",
       "8758  10.872531  20.159370  15.736385  15.106155  18.774261  16.482779   \n",
       "8759   6.594222  12.227185   9.545127   9.162467  11.387739   9.998707   \n",
       "\n",
       "          DEG0N      DEG0P  \n",
       "0      3.200796   9.740952  \n",
       "1      2.681904   8.161778  \n",
       "2      2.521948   7.674979  \n",
       "3      2.443429   7.436016  \n",
       "4      2.833481   8.623073  \n",
       "...         ...        ...  \n",
       "8755  11.410698  34.726370  \n",
       "8756  10.995180  33.461871  \n",
       "8757  10.386395  31.609157  \n",
       "8758   7.818490  23.794159  \n",
       "8759   4.742183  14.431925  \n",
       "\n",
       "[8760 rows x 401 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elc_tempo_ZVE = temporal.disagg_temporal(df_spat, df_temp, time_indexed=True)\n",
    "df_elc_tempo_ZVE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's take out one region, e.g. Berlin (DE300), and plot it as a line graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 4))\n",
    "df_elc_tempo_ZVE['DE300'].plot(ax=ax)\n",
    "plot.set_ax_format(ax=ax, xlim=(0, 8760), ylim=(0, None), ylabel='[MWh]', xlabel='Hour of Year',\n",
    "                   axtitle='Power Consumption of Private Households in Berlin')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This depictation however does not provide a good visualization of what is happening on shorter timescales, i.e. within one day.\n",
    "\n",
    "Therefore, we choose to plot it as a heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot.heatmap_timeseries(df_elc_tempo_ZVE['DE300'], clabel='[MWh]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Power by 'Standard Load Profiles' (SLP, all years)\n",
    "update Dec 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     ... assigning load-profiles\n"
     ]
    }
   ],
   "source": [
    "df_elec_temp_slp = temporal.disagg_temporal_power_housholds_slp(use_nuts3code=True, by='population', weight_by_income=False,  year=2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>DEF01</th>\n",
       "      <th>DEF02</th>\n",
       "      <th>DEF03</th>\n",
       "      <th>DEF04</th>\n",
       "      <th>DEF05</th>\n",
       "      <th>DEF06</th>\n",
       "      <th>DEF07</th>\n",
       "      <th>DEF08</th>\n",
       "      <th>DEF09</th>\n",
       "      <th>DEF0A</th>\n",
       "      <th>...</th>\n",
       "      <th>DEG0F</th>\n",
       "      <th>DEG0G</th>\n",
       "      <th>DEG0H</th>\n",
       "      <th>DEG0I</th>\n",
       "      <th>DEG0J</th>\n",
       "      <th>DEG0K</th>\n",
       "      <th>DEG0L</th>\n",
       "      <th>DEG0M</th>\n",
       "      <th>DEG0N</th>\n",
       "      <th>DEG0P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00</th>\n",
       "      <td>2.521633</td>\n",
       "      <td>7.226888</td>\n",
       "      <td>6.345100</td>\n",
       "      <td>2.323727</td>\n",
       "      <td>3.899931</td>\n",
       "      <td>5.662803</td>\n",
       "      <td>4.810766</td>\n",
       "      <td>5.855720</td>\n",
       "      <td>9.021537</td>\n",
       "      <td>3.764580</td>\n",
       "      <td>...</td>\n",
       "      <td>3.216371</td>\n",
       "      <td>2.409696</td>\n",
       "      <td>1.667102</td>\n",
       "      <td>3.206336</td>\n",
       "      <td>2.528733</td>\n",
       "      <td>2.433873</td>\n",
       "      <td>2.966796</td>\n",
       "      <td>2.709474</td>\n",
       "      <td>1.244561</td>\n",
       "      <td>3.686856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:15:00</th>\n",
       "      <td>3.014247</td>\n",
       "      <td>8.638699</td>\n",
       "      <td>7.584649</td>\n",
       "      <td>2.777679</td>\n",
       "      <td>4.661802</td>\n",
       "      <td>6.769060</td>\n",
       "      <td>5.750575</td>\n",
       "      <td>6.999666</td>\n",
       "      <td>10.783941</td>\n",
       "      <td>4.500010</td>\n",
       "      <td>...</td>\n",
       "      <td>3.844706</td>\n",
       "      <td>2.880443</td>\n",
       "      <td>1.992780</td>\n",
       "      <td>3.832711</td>\n",
       "      <td>3.022734</td>\n",
       "      <td>2.909343</td>\n",
       "      <td>3.546375</td>\n",
       "      <td>3.238784</td>\n",
       "      <td>1.487693</td>\n",
       "      <td>4.407102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:30:00</th>\n",
       "      <td>2.793776</td>\n",
       "      <td>8.006839</td>\n",
       "      <td>7.029886</td>\n",
       "      <td>2.574512</td>\n",
       "      <td>4.320825</td>\n",
       "      <td>6.273952</td>\n",
       "      <td>5.329961</td>\n",
       "      <td>6.487690</td>\n",
       "      <td>9.995172</td>\n",
       "      <td>4.170867</td>\n",
       "      <td>...</td>\n",
       "      <td>3.563493</td>\n",
       "      <td>2.669759</td>\n",
       "      <td>1.847022</td>\n",
       "      <td>3.552376</td>\n",
       "      <td>2.801643</td>\n",
       "      <td>2.696546</td>\n",
       "      <td>3.286983</td>\n",
       "      <td>3.001890</td>\n",
       "      <td>1.378879</td>\n",
       "      <td>4.084754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:45:00</th>\n",
       "      <td>2.583640</td>\n",
       "      <td>7.404599</td>\n",
       "      <td>6.501127</td>\n",
       "      <td>2.380868</td>\n",
       "      <td>3.995831</td>\n",
       "      <td>5.802052</td>\n",
       "      <td>4.929064</td>\n",
       "      <td>5.999713</td>\n",
       "      <td>9.243378</td>\n",
       "      <td>3.857152</td>\n",
       "      <td>...</td>\n",
       "      <td>3.295462</td>\n",
       "      <td>2.468951</td>\n",
       "      <td>1.708097</td>\n",
       "      <td>3.285181</td>\n",
       "      <td>2.590915</td>\n",
       "      <td>2.493723</td>\n",
       "      <td>3.039750</td>\n",
       "      <td>2.776101</td>\n",
       "      <td>1.275165</td>\n",
       "      <td>3.777516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 01:00:00</th>\n",
       "      <td>2.380394</td>\n",
       "      <td>6.822104</td>\n",
       "      <td>5.989705</td>\n",
       "      <td>2.193573</td>\n",
       "      <td>3.681492</td>\n",
       "      <td>5.345624</td>\n",
       "      <td>4.541311</td>\n",
       "      <td>5.527736</td>\n",
       "      <td>8.516232</td>\n",
       "      <td>3.553723</td>\n",
       "      <td>...</td>\n",
       "      <td>3.036219</td>\n",
       "      <td>2.274727</td>\n",
       "      <td>1.573727</td>\n",
       "      <td>3.026747</td>\n",
       "      <td>2.387096</td>\n",
       "      <td>2.297550</td>\n",
       "      <td>2.800623</td>\n",
       "      <td>2.557714</td>\n",
       "      <td>1.174852</td>\n",
       "      <td>3.480351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        DEF01     DEF02     DEF03     DEF04     DEF05  \\\n",
       "2016-01-01 00:00:00  2.521633  7.226888  6.345100  2.323727  3.899931   \n",
       "2016-01-01 00:15:00  3.014247  8.638699  7.584649  2.777679  4.661802   \n",
       "2016-01-01 00:30:00  2.793776  8.006839  7.029886  2.574512  4.320825   \n",
       "2016-01-01 00:45:00  2.583640  7.404599  6.501127  2.380868  3.995831   \n",
       "2016-01-01 01:00:00  2.380394  6.822104  5.989705  2.193573  3.681492   \n",
       "\n",
       "                        DEF06     DEF07     DEF08      DEF09     DEF0A  ...  \\\n",
       "2016-01-01 00:00:00  5.662803  4.810766  5.855720   9.021537  3.764580  ...   \n",
       "2016-01-01 00:15:00  6.769060  5.750575  6.999666  10.783941  4.500010  ...   \n",
       "2016-01-01 00:30:00  6.273952  5.329961  6.487690   9.995172  4.170867  ...   \n",
       "2016-01-01 00:45:00  5.802052  4.929064  5.999713   9.243378  3.857152  ...   \n",
       "2016-01-01 01:00:00  5.345624  4.541311  5.527736   8.516232  3.553723  ...   \n",
       "\n",
       "                        DEG0F     DEG0G     DEG0H     DEG0I     DEG0J  \\\n",
       "2016-01-01 00:00:00  3.216371  2.409696  1.667102  3.206336  2.528733   \n",
       "2016-01-01 00:15:00  3.844706  2.880443  1.992780  3.832711  3.022734   \n",
       "2016-01-01 00:30:00  3.563493  2.669759  1.847022  3.552376  2.801643   \n",
       "2016-01-01 00:45:00  3.295462  2.468951  1.708097  3.285181  2.590915   \n",
       "2016-01-01 01:00:00  3.036219  2.274727  1.573727  3.026747  2.387096   \n",
       "\n",
       "                        DEG0K     DEG0L     DEG0M     DEG0N     DEG0P  \n",
       "2016-01-01 00:00:00  2.433873  2.966796  2.709474  1.244561  3.686856  \n",
       "2016-01-01 00:15:00  2.909343  3.546375  3.238784  1.487693  4.407102  \n",
       "2016-01-01 00:30:00  2.696546  3.286983  3.001890  1.378879  4.084754  \n",
       "2016-01-01 00:45:00  2.493723  3.039750  2.776101  1.275165  3.777516  \n",
       "2016-01-01 01:00:00  2.297550  2.800623  2.557714  1.174852  3.480351  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_elec_temp_slp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and can then be plotted directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot.heatmap_timeseries(df_elec_temp_slp.resample('H').sum()['DE300'], clabel='[MWh]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gas by 'Standard Load Profiles' (SLP, all years)\n",
    "update Dec 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use ``disagg_temporal_gas_households()`` to get hourly gas demand of household per region in **MWh**. Function uses spatial function ``disagg_households_gas()``, which distinguishes between gas demand for heating, cooking and warm water. However, demands are summed up for SLP.\n",
    "Demands are then split between demand for _single family houses (EFH)_ and _multi family houses (MFH)_ with the help of ``data.percentage_EFH_MFH()``, which calculates the percentages of EFH and MFH per NUTS-3 based on the available living spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.data: INFO     Dropping old NUTS-v2013 regions.\n",
      "disaggregator.temporal: INFO     Working on state: SH.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: HH.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: NI.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: HB.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: NW.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: HE.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: RP.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: BW.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: BY.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: SL.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: BE.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: BB.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: MV.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: SN.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: ST.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n",
      "disaggregator.temporal: INFO     Working on state: TH.\n",
      "disaggregator.spatial: INFO     Calculating regional gas demands top-down.\n",
      "disaggregator.temporal: INFO     ... creating state-specific load-profiles\n"
     ]
    }
   ],
   "source": [
    "# use function disagg_daily_gas_slp_households similar as for CTS to create gas SLP for all years\n",
    "# specify year either through config file (restart kernel after) or specify directly in function\n",
    "df_households_gas = temporal.disagg_temporal_gas_households(use_nuts3code=True, year=2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ags_lk</th>\n",
       "      <th>DE111</th>\n",
       "      <th>DE112</th>\n",
       "      <th>DE113</th>\n",
       "      <th>DE114</th>\n",
       "      <th>DE115</th>\n",
       "      <th>DE116</th>\n",
       "      <th>DE117</th>\n",
       "      <th>DE118</th>\n",
       "      <th>DE119</th>\n",
       "      <th>DE11A</th>\n",
       "      <th>...</th>\n",
       "      <th>DEG0F</th>\n",
       "      <th>DEG0G</th>\n",
       "      <th>DEG0H</th>\n",
       "      <th>DEG0I</th>\n",
       "      <th>DEG0J</th>\n",
       "      <th>DEG0K</th>\n",
       "      <th>DEG0L</th>\n",
       "      <th>DEG0M</th>\n",
       "      <th>DEG0N</th>\n",
       "      <th>DEG0P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>247.970136</td>\n",
       "      <td>171.311272</td>\n",
       "      <td>281.113367</td>\n",
       "      <td>125.872131</td>\n",
       "      <td>266.244686</td>\n",
       "      <td>208.347180</td>\n",
       "      <td>62.342533</td>\n",
       "      <td>165.684524</td>\n",
       "      <td>53.524665</td>\n",
       "      <td>94.085525</td>\n",
       "      <td>...</td>\n",
       "      <td>49.787493</td>\n",
       "      <td>47.218522</td>\n",
       "      <td>27.872201</td>\n",
       "      <td>52.347202</td>\n",
       "      <td>37.868065</td>\n",
       "      <td>38.051811</td>\n",
       "      <td>49.844194</td>\n",
       "      <td>38.265077</td>\n",
       "      <td>21.422530</td>\n",
       "      <td>65.052095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 01:00:00</th>\n",
       "      <td>255.266509</td>\n",
       "      <td>175.778068</td>\n",
       "      <td>278.463519</td>\n",
       "      <td>129.143872</td>\n",
       "      <td>273.282283</td>\n",
       "      <td>213.908530</td>\n",
       "      <td>64.065576</td>\n",
       "      <td>169.735732</td>\n",
       "      <td>54.840805</td>\n",
       "      <td>96.421562</td>\n",
       "      <td>...</td>\n",
       "      <td>51.091460</td>\n",
       "      <td>48.366397</td>\n",
       "      <td>28.600643</td>\n",
       "      <td>53.714487</td>\n",
       "      <td>38.810643</td>\n",
       "      <td>39.008105</td>\n",
       "      <td>51.138051</td>\n",
       "      <td>39.291432</td>\n",
       "      <td>22.033458</td>\n",
       "      <td>66.648314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 02:00:00</th>\n",
       "      <td>259.485919</td>\n",
       "      <td>179.241110</td>\n",
       "      <td>278.304506</td>\n",
       "      <td>131.698171</td>\n",
       "      <td>278.573211</td>\n",
       "      <td>217.997188</td>\n",
       "      <td>65.232710</td>\n",
       "      <td>173.341775</td>\n",
       "      <td>55.998686</td>\n",
       "      <td>98.435370</td>\n",
       "      <td>...</td>\n",
       "      <td>52.092371</td>\n",
       "      <td>49.400465</td>\n",
       "      <td>29.162455</td>\n",
       "      <td>54.770412</td>\n",
       "      <td>39.618921</td>\n",
       "      <td>39.811575</td>\n",
       "      <td>52.151174</td>\n",
       "      <td>40.037623</td>\n",
       "      <td>22.416521</td>\n",
       "      <td>68.058786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 03:00:00</th>\n",
       "      <td>279.067315</td>\n",
       "      <td>191.888683</td>\n",
       "      <td>288.205123</td>\n",
       "      <td>140.975311</td>\n",
       "      <td>298.375998</td>\n",
       "      <td>233.576720</td>\n",
       "      <td>69.984907</td>\n",
       "      <td>185.161526</td>\n",
       "      <td>59.828412</td>\n",
       "      <td>105.201809</td>\n",
       "      <td>...</td>\n",
       "      <td>55.776980</td>\n",
       "      <td>52.758734</td>\n",
       "      <td>31.222809</td>\n",
       "      <td>58.638746</td>\n",
       "      <td>42.345931</td>\n",
       "      <td>42.565839</td>\n",
       "      <td>55.822194</td>\n",
       "      <td>42.906565</td>\n",
       "      <td>24.078406</td>\n",
       "      <td>72.708132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 04:00:00</th>\n",
       "      <td>335.122964</td>\n",
       "      <td>228.258824</td>\n",
       "      <td>331.764758</td>\n",
       "      <td>167.656364</td>\n",
       "      <td>355.292939</td>\n",
       "      <td>278.338809</td>\n",
       "      <td>83.620825</td>\n",
       "      <td>219.233280</td>\n",
       "      <td>70.865690</td>\n",
       "      <td>124.695593</td>\n",
       "      <td>...</td>\n",
       "      <td>66.370898</td>\n",
       "      <td>62.441432</td>\n",
       "      <td>37.147172</td>\n",
       "      <td>69.762039</td>\n",
       "      <td>50.201574</td>\n",
       "      <td>50.497132</td>\n",
       "      <td>66.380573</td>\n",
       "      <td>51.147924</td>\n",
       "      <td>28.841430</td>\n",
       "      <td>86.108619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ags_lk                    DE111       DE112       DE113       DE114  \\\n",
       "2017-01-01 00:00:00  247.970136  171.311272  281.113367  125.872131   \n",
       "2017-01-01 01:00:00  255.266509  175.778068  278.463519  129.143872   \n",
       "2017-01-01 02:00:00  259.485919  179.241110  278.304506  131.698171   \n",
       "2017-01-01 03:00:00  279.067315  191.888683  288.205123  140.975311   \n",
       "2017-01-01 04:00:00  335.122964  228.258824  331.764758  167.656364   \n",
       "\n",
       "ags_lk                    DE115       DE116      DE117       DE118      DE119  \\\n",
       "2017-01-01 00:00:00  266.244686  208.347180  62.342533  165.684524  53.524665   \n",
       "2017-01-01 01:00:00  273.282283  213.908530  64.065576  169.735732  54.840805   \n",
       "2017-01-01 02:00:00  278.573211  217.997188  65.232710  173.341775  55.998686   \n",
       "2017-01-01 03:00:00  298.375998  233.576720  69.984907  185.161526  59.828412   \n",
       "2017-01-01 04:00:00  355.292939  278.338809  83.620825  219.233280  70.865690   \n",
       "\n",
       "ags_lk                    DE11A  ...      DEG0F      DEG0G      DEG0H  \\\n",
       "2017-01-01 00:00:00   94.085525  ...  49.787493  47.218522  27.872201   \n",
       "2017-01-01 01:00:00   96.421562  ...  51.091460  48.366397  28.600643   \n",
       "2017-01-01 02:00:00   98.435370  ...  52.092371  49.400465  29.162455   \n",
       "2017-01-01 03:00:00  105.201809  ...  55.776980  52.758734  31.222809   \n",
       "2017-01-01 04:00:00  124.695593  ...  66.370898  62.441432  37.147172   \n",
       "\n",
       "ags_lk                   DEG0I      DEG0J      DEG0K      DEG0L      DEG0M  \\\n",
       "2017-01-01 00:00:00  52.347202  37.868065  38.051811  49.844194  38.265077   \n",
       "2017-01-01 01:00:00  53.714487  38.810643  39.008105  51.138051  39.291432   \n",
       "2017-01-01 02:00:00  54.770412  39.618921  39.811575  52.151174  40.037623   \n",
       "2017-01-01 03:00:00  58.638746  42.345931  42.565839  55.822194  42.906565   \n",
       "2017-01-01 04:00:00  69.762039  50.201574  50.497132  66.380573  51.147924   \n",
       "\n",
       "ags_lk                   DEG0N      DEG0P  \n",
       "2017-01-01 00:00:00  21.422530  65.052095  \n",
       "2017-01-01 01:00:00  22.033458  66.648314  \n",
       "2017-01-01 02:00:00  22.416521  68.058786  \n",
       "2017-01-01 03:00:00  24.078406  72.708132  \n",
       "2017-01-01 04:00:00  28.841430  86.108619  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_households_gas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot df directly..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gas Load Private Households Germany')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot.heatmap_timeseries(df_households_gas.sum(axis=1)/1e3, clabel='[GWh]')\n",
    "ax[0, 0].set_title('Gas Load Private Households Germany')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### older version below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "normalized gas SLP for _single family houses_ (EFH) and _multi family houses (MFH)_ have been uploaded into the database untill 2016 as well. which could be used with ``temporal.disagg_temporal(df_spat, df_temp)`` and ``data.transpose_spatiotemporal()`` to perform the same disaggregation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disaggregator.data: INFO     Merging old Göttingen+Osterode to new NUTS-v2016 region.\n"
     ]
    },
    {
     "data": {
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       "      <td>0.000143</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000140</td>\n",
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       "      <td>0.000142</td>\n",
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       "      <td>0.000197</td>\n",
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       "      <td>0.000169</td>\n",
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       "      <td>0.000194</td>\n",
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       "      <td>0.000179</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000167</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 19:00:00</th>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 20:00:00</th>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000263</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.000230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 21:00:00</th>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 22:00:00</th>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 23:00:00</th>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.000118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "nuts3                   DE111     DE112     DE113     DE114     DE115  \\\n",
       "2015-01-01 00:00:00  0.000175  0.000172  0.000181  0.000177  0.000180   \n",
       "2015-01-01 01:00:00  0.000178  0.000174  0.000184  0.000180  0.000183   \n",
       "2015-01-01 02:00:00  0.000183  0.000177  0.000187  0.000184  0.000187   \n",
       "2015-01-01 03:00:00  0.000193  0.000186  0.000197  0.000194  0.000197   \n",
       "2015-01-01 04:00:00  0.000224  0.000208  0.000222  0.000221  0.000222   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2015-12-31 19:00:00  0.000260  0.000235  0.000273  0.000242  0.000271   \n",
       "2015-12-31 20:00:00  0.000250  0.000228  0.000263  0.000234  0.000262   \n",
       "2015-12-31 21:00:00  0.000221  0.000204  0.000235  0.000208  0.000234   \n",
       "2015-12-31 22:00:00  0.000158  0.000149  0.000172  0.000151  0.000171   \n",
       "2015-12-31 23:00:00  0.000126  0.000119  0.000137  0.000120  0.000136   \n",
       "\n",
       "nuts3                   DE116     DE117     DE118     DE119     DE11A  ...  \\\n",
       "2015-01-01 00:00:00  0.000181  0.000153  0.000157  0.000155  0.000150  ...   \n",
       "2015-01-01 01:00:00  0.000184  0.000158  0.000162  0.000160  0.000155  ...   \n",
       "2015-01-01 02:00:00  0.000187  0.000161  0.000164  0.000162  0.000157  ...   \n",
       "2015-01-01 03:00:00  0.000197  0.000174  0.000177  0.000175  0.000169  ...   \n",
       "2015-01-01 04:00:00  0.000222  0.000207  0.000203  0.000201  0.000194  ...   \n",
       "...                       ...       ...       ...       ...       ...  ...   \n",
       "2015-12-31 19:00:00  0.000260  0.000275  0.000275  0.000260  0.000255  ...   \n",
       "2015-12-31 20:00:00  0.000251  0.000264  0.000266  0.000252  0.000247  ...   \n",
       "2015-12-31 21:00:00  0.000225  0.000235  0.000238  0.000226  0.000221  ...   \n",
       "2015-12-31 22:00:00  0.000164  0.000169  0.000175  0.000165  0.000162  ...   \n",
       "2015-12-31 23:00:00  0.000130  0.000135  0.000139  0.000131  0.000129  ...   \n",
       "\n",
       "nuts3                   DEG0G     DEG0H     DEG0I     DEG0J     DEG0K  \\\n",
       "2015-01-01 00:00:00  0.000136  0.000130  0.000134  0.000134  0.000133   \n",
       "2015-01-01 01:00:00  0.000140  0.000132  0.000138  0.000138  0.000137   \n",
       "2015-01-01 02:00:00  0.000143  0.000135  0.000141  0.000141  0.000140   \n",
       "2015-01-01 03:00:00  0.000154  0.000143  0.000152  0.000151  0.000150   \n",
       "2015-01-01 04:00:00  0.000179  0.000164  0.000180  0.000178  0.000179   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2015-12-31 19:00:00  0.000259  0.000229  0.000261  0.000253  0.000261   \n",
       "2015-12-31 20:00:00  0.000250  0.000220  0.000250  0.000244  0.000250   \n",
       "2015-12-31 21:00:00  0.000223  0.000196  0.000223  0.000217  0.000222   \n",
       "2015-12-31 22:00:00  0.000163  0.000151  0.000161  0.000169  0.000160   \n",
       "2015-12-31 23:00:00  0.000130  0.000129  0.000128  0.000145  0.000128   \n",
       "\n",
       "nuts3                   DEG0L     DEG0M     DEG0N     DEG0P     DE91C  \n",
       "2015-01-01 00:00:00  0.000131  0.000135  0.000129  0.000131  0.000125  \n",
       "2015-01-01 01:00:00  0.000135  0.000139  0.000133  0.000135  0.000128  \n",
       "2015-01-01 02:00:00  0.000138  0.000142  0.000135  0.000137  0.000131  \n",
       "2015-01-01 03:00:00  0.000149  0.000153  0.000145  0.000148  0.000141  \n",
       "2015-01-01 04:00:00  0.000177  0.000182  0.000171  0.000172  0.000167  \n",
       "...                       ...       ...       ...       ...       ...  \n",
       "2015-12-31 19:00:00  0.000264  0.000270  0.000239  0.000255  0.000239  \n",
       "2015-12-31 20:00:00  0.000253  0.000260  0.000231  0.000246  0.000230  \n",
       "2015-12-31 21:00:00  0.000226  0.000231  0.000205  0.000219  0.000205  \n",
       "2015-12-31 22:00:00  0.000174  0.000166  0.000160  0.000172  0.000148  \n",
       "2015-12-31 23:00:00  0.000149  0.000133  0.000137  0.000147  0.000118  \n",
       "\n",
       "[8760 rows x 401 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp_EFH = data.standard_load_profile_gas(typ='EFH') # type='MFH'\n",
    "df_temp_EFH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe has the NUTS-3-regions in columns and the timesteps in rows. Though being intuitive that way, the function ` temporal.disagg_temporal(df_spat, df_temp)` requires us to provide the temporal dataset _NUTS-3-index'ed_ (i.e. with timesteps in columns) for compatibility reasons. To achieve that, we use the function `data.transpose_spatiotemporal()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>8751</th>\n",
       "      <th>8752</th>\n",
       "      <th>8753</th>\n",
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       "      <th>8756</th>\n",
       "      <th>8757</th>\n",
       "      <th>8758</th>\n",
       "      <th>8759</th>\n",
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       "    <tr>\n",
       "      <th>nuts3</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DE111</th>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.000342</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE112</th>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE113</th>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>0.000263</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE114</th>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>0.000341</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE115</th>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000136</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEG0L</th>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEG0M</th>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEG0N</th>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEG0P</th>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE91C</th>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>401 rows × 8760 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6     \\\n",
       "nuts3                                                                         \n",
       "DE111  0.000175  0.000178  0.000183  0.000193  0.000224  0.000290  0.000369   \n",
       "DE112  0.000172  0.000174  0.000177  0.000186  0.000208  0.000267  0.000358   \n",
       "DE113  0.000181  0.000184  0.000187  0.000197  0.000222  0.000286  0.000378   \n",
       "DE114  0.000177  0.000180  0.000184  0.000194  0.000221  0.000284  0.000371   \n",
       "DE115  0.000180  0.000183  0.000187  0.000197  0.000222  0.000285  0.000377   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "DEG0L  0.000131  0.000135  0.000138  0.000149  0.000177  0.000254  0.000328   \n",
       "DEG0M  0.000135  0.000139  0.000142  0.000153  0.000182  0.000262  0.000338   \n",
       "DEG0N  0.000129  0.000133  0.000135  0.000145  0.000171  0.000245  0.000319   \n",
       "DEG0P  0.000131  0.000135  0.000137  0.000148  0.000172  0.000246  0.000323   \n",
       "DE91C  0.000125  0.000128  0.000131  0.000141  0.000167  0.000239  0.000310   \n",
       "\n",
       "           7         8         9     ...      8750      8751      8752  \\\n",
       "nuts3                                ...                                 \n",
       "DE111  0.000342  0.000334  0.000324  ...  0.000230  0.000237  0.000248   \n",
       "DE112  0.000326  0.000312  0.000304  ...  0.000205  0.000209  0.000223   \n",
       "DE113  0.000346  0.000333  0.000324  ...  0.000239  0.000244  0.000259   \n",
       "DE114  0.000341  0.000330  0.000320  ...  0.000213  0.000218  0.000230   \n",
       "DE115  0.000345  0.000332  0.000323  ...  0.000237  0.000242  0.000257   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "DEG0L  0.000305  0.000289  0.000277  ...  0.000243  0.000247  0.000262   \n",
       "DEG0M  0.000314  0.000298  0.000286  ...  0.000238  0.000245  0.000257   \n",
       "DEG0N  0.000296  0.000280  0.000269  ...  0.000220  0.000223  0.000237   \n",
       "DEG0P  0.000298  0.000282  0.000272  ...  0.000233  0.000236  0.000251   \n",
       "DE91C  0.000288  0.000273  0.000262  ...  0.000210  0.000216  0.000228   \n",
       "\n",
       "           8753      8754      8755      8756      8757      8758      8759  \n",
       "nuts3                                                                        \n",
       "DE111  0.000258  0.000261  0.000260  0.000250  0.000221  0.000158  0.000126  \n",
       "DE112  0.000233  0.000235  0.000235  0.000228  0.000204  0.000149  0.000119  \n",
       "DE113  0.000270  0.000273  0.000273  0.000263  0.000235  0.000172  0.000137  \n",
       "DE114  0.000240  0.000243  0.000242  0.000234  0.000208  0.000151  0.000120  \n",
       "DE115  0.000268  0.000271  0.000271  0.000262  0.000234  0.000171  0.000136  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "DEG0L  0.000266  0.000267  0.000264  0.000253  0.000226  0.000174  0.000149  \n",
       "DEG0M  0.000268  0.000271  0.000270  0.000260  0.000231  0.000166  0.000133  \n",
       "DEG0N  0.000241  0.000242  0.000239  0.000231  0.000205  0.000160  0.000137  \n",
       "DEG0P  0.000256  0.000258  0.000255  0.000246  0.000219  0.000172  0.000147  \n",
       "DE91C  0.000237  0.000240  0.000239  0.000230  0.000205  0.000148  0.000118  \n",
       "\n",
       "[401 rows x 8760 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp_EFH = data.transpose_spatiotemporal(df_temp_EFH)\n",
    "df_temp_MFH = data.transpose_spatiotemporal(df_temp_MFH)\n",
    "df_temp_EFH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: It is by design that the pd.DateTimeIndex gets lost while transposing this way for a better handling of the data. The former index is being recovered, one it is transposed again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>nuts3</th>\n",
       "      <th>DE111</th>\n",
       "      <th>DE112</th>\n",
       "      <th>DE113</th>\n",
       "      <th>DE114</th>\n",
       "      <th>DE115</th>\n",
       "      <th>DE116</th>\n",
       "      <th>DE117</th>\n",
       "      <th>DE118</th>\n",
       "      <th>DE119</th>\n",
       "      <th>DE11A</th>\n",
       "      <th>...</th>\n",
       "      <th>DEG0F</th>\n",
       "      <th>DEG0G</th>\n",
       "      <th>DEG0H</th>\n",
       "      <th>DEG0I</th>\n",
       "      <th>DEG0J</th>\n",
       "      <th>DEG0K</th>\n",
       "      <th>DEG0L</th>\n",
       "      <th>DEG0M</th>\n",
       "      <th>DEG0N</th>\n",
       "      <th>DEG0P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.716632</td>\n",
       "      <td>45.234605</td>\n",
       "      <td>59.195138</td>\n",
       "      <td>33.968098</td>\n",
       "      <td>63.725355</td>\n",
       "      <td>49.048145</td>\n",
       "      <td>9.815829</td>\n",
       "      <td>52.906967</td>\n",
       "      <td>16.939199</td>\n",
       "      <td>28.601924</td>\n",
       "      <td>...</td>\n",
       "      <td>11.541571</td>\n",
       "      <td>13.467630</td>\n",
       "      <td>7.010504</td>\n",
       "      <td>11.918055</td>\n",
       "      <td>10.595872</td>\n",
       "      <td>10.228622</td>\n",
       "      <td>11.595045</td>\n",
       "      <td>7.604337</td>\n",
       "      <td>2.221464</td>\n",
       "      <td>17.764242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.034128</td>\n",
       "      <td>45.739080</td>\n",
       "      <td>60.081956</td>\n",
       "      <td>34.480824</td>\n",
       "      <td>64.680042</td>\n",
       "      <td>49.782948</td>\n",
       "      <td>10.113279</td>\n",
       "      <td>54.700424</td>\n",
       "      <td>17.513409</td>\n",
       "      <td>29.571481</td>\n",
       "      <td>...</td>\n",
       "      <td>11.715783</td>\n",
       "      <td>13.928062</td>\n",
       "      <td>7.117128</td>\n",
       "      <td>12.279209</td>\n",
       "      <td>10.914203</td>\n",
       "      <td>10.538580</td>\n",
       "      <td>11.946410</td>\n",
       "      <td>7.834771</td>\n",
       "      <td>2.288203</td>\n",
       "      <td>18.371566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.669120</td>\n",
       "      <td>46.579872</td>\n",
       "      <td>61.190479</td>\n",
       "      <td>35.249913</td>\n",
       "      <td>65.873400</td>\n",
       "      <td>50.701453</td>\n",
       "      <td>10.325743</td>\n",
       "      <td>55.372970</td>\n",
       "      <td>17.728738</td>\n",
       "      <td>29.935064</td>\n",
       "      <td>...</td>\n",
       "      <td>11.977102</td>\n",
       "      <td>14.158278</td>\n",
       "      <td>7.303719</td>\n",
       "      <td>12.537175</td>\n",
       "      <td>11.141582</td>\n",
       "      <td>10.759979</td>\n",
       "      <td>12.197385</td>\n",
       "      <td>7.999367</td>\n",
       "      <td>2.335874</td>\n",
       "      <td>18.675229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.859731</td>\n",
       "      <td>48.934089</td>\n",
       "      <td>64.516049</td>\n",
       "      <td>37.172636</td>\n",
       "      <td>69.453477</td>\n",
       "      <td>53.456967</td>\n",
       "      <td>11.133105</td>\n",
       "      <td>59.632429</td>\n",
       "      <td>19.092487</td>\n",
       "      <td>32.237762</td>\n",
       "      <td>...</td>\n",
       "      <td>12.630398</td>\n",
       "      <td>15.251803</td>\n",
       "      <td>7.703558</td>\n",
       "      <td>13.517448</td>\n",
       "      <td>11.960147</td>\n",
       "      <td>11.601294</td>\n",
       "      <td>13.151089</td>\n",
       "      <td>8.624832</td>\n",
       "      <td>2.507490</td>\n",
       "      <td>20.117624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.510939</td>\n",
       "      <td>54.819633</td>\n",
       "      <td>72.719120</td>\n",
       "      <td>42.299896</td>\n",
       "      <td>78.284331</td>\n",
       "      <td>60.253901</td>\n",
       "      <td>13.215251</td>\n",
       "      <td>68.599712</td>\n",
       "      <td>21.963538</td>\n",
       "      <td>37.085545</td>\n",
       "      <td>...</td>\n",
       "      <td>14.372522</td>\n",
       "      <td>17.726624</td>\n",
       "      <td>8.849762</td>\n",
       "      <td>16.045520</td>\n",
       "      <td>14.097512</td>\n",
       "      <td>13.771001</td>\n",
       "      <td>15.610644</td>\n",
       "      <td>10.237873</td>\n",
       "      <td>2.955596</td>\n",
       "      <td>23.381994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>30.792166</td>\n",
       "      <td>61.911240</td>\n",
       "      <td>89.104422</td>\n",
       "      <td>46.450064</td>\n",
       "      <td>95.672731</td>\n",
       "      <td>70.462767</td>\n",
       "      <td>17.603949</td>\n",
       "      <td>92.839626</td>\n",
       "      <td>28.389489</td>\n",
       "      <td>48.797119</td>\n",
       "      <td>...</td>\n",
       "      <td>20.009782</td>\n",
       "      <td>25.660437</td>\n",
       "      <td>12.320289</td>\n",
       "      <td>23.260219</td>\n",
       "      <td>20.052506</td>\n",
       "      <td>20.094974</td>\n",
       "      <td>23.293658</td>\n",
       "      <td>15.201925</td>\n",
       "      <td>4.132440</td>\n",
       "      <td>34.636785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>29.511699</td>\n",
       "      <td>59.949696</td>\n",
       "      <td>86.104867</td>\n",
       "      <td>44.794418</td>\n",
       "      <td>92.452065</td>\n",
       "      <td>68.090753</td>\n",
       "      <td>16.906763</td>\n",
       "      <td>89.898172</td>\n",
       "      <td>27.490020</td>\n",
       "      <td>47.251072</td>\n",
       "      <td>...</td>\n",
       "      <td>19.308408</td>\n",
       "      <td>24.796620</td>\n",
       "      <td>11.837639</td>\n",
       "      <td>22.339022</td>\n",
       "      <td>19.349634</td>\n",
       "      <td>19.299134</td>\n",
       "      <td>22.381123</td>\n",
       "      <td>14.599869</td>\n",
       "      <td>3.987592</td>\n",
       "      <td>33.494128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>26.158097</td>\n",
       "      <td>53.697274</td>\n",
       "      <td>76.929758</td>\n",
       "      <td>39.827481</td>\n",
       "      <td>82.600616</td>\n",
       "      <td>60.835181</td>\n",
       "      <td>15.024360</td>\n",
       "      <td>80.522289</td>\n",
       "      <td>24.622963</td>\n",
       "      <td>42.323046</td>\n",
       "      <td>...</td>\n",
       "      <td>17.163029</td>\n",
       "      <td>22.154357</td>\n",
       "      <td>10.542103</td>\n",
       "      <td>19.851790</td>\n",
       "      <td>17.199675</td>\n",
       "      <td>17.150364</td>\n",
       "      <td>19.931686</td>\n",
       "      <td>12.974316</td>\n",
       "      <td>3.544526</td>\n",
       "      <td>29.851909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>18.719198</td>\n",
       "      <td>39.353481</td>\n",
       "      <td>56.109319</td>\n",
       "      <td>28.881822</td>\n",
       "      <td>60.245403</td>\n",
       "      <td>44.370613</td>\n",
       "      <td>10.841244</td>\n",
       "      <td>59.012911</td>\n",
       "      <td>18.045596</td>\n",
       "      <td>31.017575</td>\n",
       "      <td>...</td>\n",
       "      <td>13.367359</td>\n",
       "      <td>16.158453</td>\n",
       "      <td>8.128851</td>\n",
       "      <td>14.324610</td>\n",
       "      <td>13.395901</td>\n",
       "      <td>12.375321</td>\n",
       "      <td>15.369011</td>\n",
       "      <td>9.361978</td>\n",
       "      <td>2.760641</td>\n",
       "      <td>23.353049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>14.938773</td>\n",
       "      <td>31.262111</td>\n",
       "      <td>44.640433</td>\n",
       "      <td>22.995081</td>\n",
       "      <td>47.931091</td>\n",
       "      <td>35.301148</td>\n",
       "      <td>8.645108</td>\n",
       "      <td>46.879415</td>\n",
       "      <td>14.335287</td>\n",
       "      <td>24.640129</td>\n",
       "      <td>...</td>\n",
       "      <td>11.428267</td>\n",
       "      <td>12.855625</td>\n",
       "      <td>6.960328</td>\n",
       "      <td>11.422840</td>\n",
       "      <td>11.452668</td>\n",
       "      <td>9.868423</td>\n",
       "      <td>13.159716</td>\n",
       "      <td>7.465500</td>\n",
       "      <td>2.360177</td>\n",
       "      <td>19.996494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "nuts3      DE111      DE112      DE113      DE114      DE115      DE116  \\\n",
       "0      20.716632  45.234605  59.195138  33.968098  63.725355  49.048145   \n",
       "1      21.034128  45.739080  60.081956  34.480824  64.680042  49.782948   \n",
       "2      21.669120  46.579872  61.190479  35.249913  65.873400  50.701453   \n",
       "3      22.859731  48.934089  64.516049  37.172636  69.453477  53.456967   \n",
       "4      26.510939  54.819633  72.719120  42.299896  78.284331  60.253901   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "8755   30.792166  61.911240  89.104422  46.450064  95.672731  70.462767   \n",
       "8756   29.511699  59.949696  86.104867  44.794418  92.452065  68.090753   \n",
       "8757   26.158097  53.697274  76.929758  39.827481  82.600616  60.835181   \n",
       "8758   18.719198  39.353481  56.109319  28.881822  60.245403  44.370613   \n",
       "8759   14.938773  31.262111  44.640433  22.995081  47.931091  35.301148   \n",
       "\n",
       "nuts3      DE117      DE118      DE119      DE11A  ...      DEG0F      DEG0G  \\\n",
       "0       9.815829  52.906967  16.939199  28.601924  ...  11.541571  13.467630   \n",
       "1      10.113279  54.700424  17.513409  29.571481  ...  11.715783  13.928062   \n",
       "2      10.325743  55.372970  17.728738  29.935064  ...  11.977102  14.158278   \n",
       "3      11.133105  59.632429  19.092487  32.237762  ...  12.630398  15.251803   \n",
       "4      13.215251  68.599712  21.963538  37.085545  ...  14.372522  17.726624   \n",
       "...          ...        ...        ...        ...  ...        ...        ...   \n",
       "8755   17.603949  92.839626  28.389489  48.797119  ...  20.009782  25.660437   \n",
       "8756   16.906763  89.898172  27.490020  47.251072  ...  19.308408  24.796620   \n",
       "8757   15.024360  80.522289  24.622963  42.323046  ...  17.163029  22.154357   \n",
       "8758   10.841244  59.012911  18.045596  31.017575  ...  13.367359  16.158453   \n",
       "8759    8.645108  46.879415  14.335287  24.640129  ...  11.428267  12.855625   \n",
       "\n",
       "nuts3      DEG0H      DEG0I      DEG0J      DEG0K      DEG0L      DEG0M  \\\n",
       "0       7.010504  11.918055  10.595872  10.228622  11.595045   7.604337   \n",
       "1       7.117128  12.279209  10.914203  10.538580  11.946410   7.834771   \n",
       "2       7.303719  12.537175  11.141582  10.759979  12.197385   7.999367   \n",
       "3       7.703558  13.517448  11.960147  11.601294  13.151089   8.624832   \n",
       "4       8.849762  16.045520  14.097512  13.771001  15.610644  10.237873   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "8755   12.320289  23.260219  20.052506  20.094974  23.293658  15.201925   \n",
       "8756   11.837639  22.339022  19.349634  19.299134  22.381123  14.599869   \n",
       "8757   10.542103  19.851790  17.199675  17.150364  19.931686  12.974316   \n",
       "8758    8.128851  14.324610  13.395901  12.375321  15.369011   9.361978   \n",
       "8759    6.960328  11.422840  11.452668   9.868423  13.159716   7.465500   \n",
       "\n",
       "nuts3     DEG0N      DEG0P  \n",
       "0      2.221464  17.764242  \n",
       "1      2.288203  18.371566  \n",
       "2      2.335874  18.675229  \n",
       "3      2.507490  20.117624  \n",
       "4      2.955596  23.381994  \n",
       "...         ...        ...  \n",
       "8755   4.132440  34.636785  \n",
       "8756   3.987592  33.494128  \n",
       "8757   3.544526  29.851909  \n",
       "8758   2.760641  23.353049  \n",
       "8759   2.360177  19.996494  \n",
       "\n",
       "[8760 rows x 401 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gas_st_EFH = temporal.disagg_temporal(df_gas_EFH, df_temp_EFH, time_indexed=True)\n",
    "df_gas_st_MFH = temporal.disagg_temporal(df_gas_MFH, df_temp_MFH, time_indexed=True)\n",
    "df_gas_st_EFH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum these consumption for both building types up:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>nuts3</th>\n",
       "      <th>DE111</th>\n",
       "      <th>DE112</th>\n",
       "      <th>DE113</th>\n",
       "      <th>DE114</th>\n",
       "      <th>DE115</th>\n",
       "      <th>DE116</th>\n",
       "      <th>DE117</th>\n",
       "      <th>DE118</th>\n",
       "      <th>DE119</th>\n",
       "      <th>DE11A</th>\n",
       "      <th>...</th>\n",
       "      <th>DEG0F</th>\n",
       "      <th>DEG0G</th>\n",
       "      <th>DEG0H</th>\n",
       "      <th>DEG0I</th>\n",
       "      <th>DEG0J</th>\n",
       "      <th>DEG0K</th>\n",
       "      <th>DEG0L</th>\n",
       "      <th>DEG0M</th>\n",
       "      <th>DEG0N</th>\n",
       "      <th>DEG0P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>206.170388</td>\n",
       "      <td>140.190103</td>\n",
       "      <td>202.667750</td>\n",
       "      <td>105.224867</td>\n",
       "      <td>215.091334</td>\n",
       "      <td>174.672309</td>\n",
       "      <td>46.664946</td>\n",
       "      <td>125.313498</td>\n",
       "      <td>40.966770</td>\n",
       "      <td>71.888898</td>\n",
       "      <td>...</td>\n",
       "      <td>37.601944</td>\n",
       "      <td>31.987246</td>\n",
       "      <td>22.623693</td>\n",
       "      <td>39.149205</td>\n",
       "      <td>27.469076</td>\n",
       "      <td>27.904335</td>\n",
       "      <td>36.820917</td>\n",
       "      <td>28.991814</td>\n",
       "      <td>14.353573</td>\n",
       "      <td>43.707776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>211.500148</td>\n",
       "      <td>143.673574</td>\n",
       "      <td>208.038088</td>\n",
       "      <td>107.955702</td>\n",
       "      <td>220.776207</td>\n",
       "      <td>179.332868</td>\n",
       "      <td>49.481993</td>\n",
       "      <td>132.482112</td>\n",
       "      <td>43.324687</td>\n",
       "      <td>76.071898</td>\n",
       "      <td>...</td>\n",
       "      <td>38.587374</td>\n",
       "      <td>33.730421</td>\n",
       "      <td>23.153930</td>\n",
       "      <td>41.372317</td>\n",
       "      <td>28.951076</td>\n",
       "      <td>29.422889</td>\n",
       "      <td>38.897128</td>\n",
       "      <td>30.684640</td>\n",
       "      <td>15.257009</td>\n",
       "      <td>46.112055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>214.999291</td>\n",
       "      <td>146.748613</td>\n",
       "      <td>211.948810</td>\n",
       "      <td>110.111110</td>\n",
       "      <td>224.925933</td>\n",
       "      <td>182.704969</td>\n",
       "      <td>50.324357</td>\n",
       "      <td>134.735586</td>\n",
       "      <td>44.064635</td>\n",
       "      <td>77.380612</td>\n",
       "      <td>...</td>\n",
       "      <td>39.355704</td>\n",
       "      <td>34.361495</td>\n",
       "      <td>23.643103</td>\n",
       "      <td>42.095773</td>\n",
       "      <td>29.542102</td>\n",
       "      <td>29.946436</td>\n",
       "      <td>39.579315</td>\n",
       "      <td>31.214834</td>\n",
       "      <td>15.566148</td>\n",
       "      <td>46.977266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>231.226693</td>\n",
       "      <td>156.922695</td>\n",
       "      <td>227.043618</td>\n",
       "      <td>117.579106</td>\n",
       "      <td>240.922749</td>\n",
       "      <td>195.765590</td>\n",
       "      <td>54.438691</td>\n",
       "      <td>146.899689</td>\n",
       "      <td>48.051481</td>\n",
       "      <td>84.408961</td>\n",
       "      <td>...</td>\n",
       "      <td>42.037045</td>\n",
       "      <td>37.298965</td>\n",
       "      <td>25.313782</td>\n",
       "      <td>45.519867</td>\n",
       "      <td>31.960713</td>\n",
       "      <td>32.374033</td>\n",
       "      <td>42.796880</td>\n",
       "      <td>33.759688</td>\n",
       "      <td>16.888222</td>\n",
       "      <td>51.002784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>278.556198</td>\n",
       "      <td>181.054589</td>\n",
       "      <td>264.950003</td>\n",
       "      <td>138.233133</td>\n",
       "      <td>281.091092</td>\n",
       "      <td>228.570652</td>\n",
       "      <td>66.599228</td>\n",
       "      <td>174.521929</td>\n",
       "      <td>57.113042</td>\n",
       "      <td>100.409284</td>\n",
       "      <td>...</td>\n",
       "      <td>49.457693</td>\n",
       "      <td>44.664247</td>\n",
       "      <td>29.969928</td>\n",
       "      <td>55.495775</td>\n",
       "      <td>38.607296</td>\n",
       "      <td>39.378124</td>\n",
       "      <td>52.155819</td>\n",
       "      <td>41.222296</td>\n",
       "      <td>20.578529</td>\n",
       "      <td>61.118043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>314.090461</td>\n",
       "      <td>203.074292</td>\n",
       "      <td>317.413566</td>\n",
       "      <td>150.167164</td>\n",
       "      <td>335.982110</td>\n",
       "      <td>262.806997</td>\n",
       "      <td>83.355251</td>\n",
       "      <td>224.816658</td>\n",
       "      <td>70.565079</td>\n",
       "      <td>125.910215</td>\n",
       "      <td>...</td>\n",
       "      <td>66.191563</td>\n",
       "      <td>61.206344</td>\n",
       "      <td>40.016742</td>\n",
       "      <td>75.331808</td>\n",
       "      <td>52.080561</td>\n",
       "      <td>54.080492</td>\n",
       "      <td>72.791854</td>\n",
       "      <td>56.915518</td>\n",
       "      <td>26.838796</td>\n",
       "      <td>85.493281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>306.463190</td>\n",
       "      <td>197.950235</td>\n",
       "      <td>309.299142</td>\n",
       "      <td>146.187917</td>\n",
       "      <td>327.377730</td>\n",
       "      <td>256.125845</td>\n",
       "      <td>81.185021</td>\n",
       "      <td>218.918489</td>\n",
       "      <td>68.720740</td>\n",
       "      <td>122.636583</td>\n",
       "      <td>...</td>\n",
       "      <td>64.034881</td>\n",
       "      <td>59.546183</td>\n",
       "      <td>38.661304</td>\n",
       "      <td>73.244038</td>\n",
       "      <td>50.368402</td>\n",
       "      <td>52.523265</td>\n",
       "      <td>70.319502</td>\n",
       "      <td>55.378941</td>\n",
       "      <td>25.978411</td>\n",
       "      <td>82.748003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8757</th>\n",
       "      <td>279.453315</td>\n",
       "      <td>180.485269</td>\n",
       "      <td>281.524511</td>\n",
       "      <td>132.771521</td>\n",
       "      <td>297.949142</td>\n",
       "      <td>233.200682</td>\n",
       "      <td>73.812184</td>\n",
       "      <td>199.059705</td>\n",
       "      <td>62.503687</td>\n",
       "      <td>111.583484</td>\n",
       "      <td>...</td>\n",
       "      <td>58.202722</td>\n",
       "      <td>54.008123</td>\n",
       "      <td>35.154707</td>\n",
       "      <td>66.408670</td>\n",
       "      <td>45.661581</td>\n",
       "      <td>47.536601</td>\n",
       "      <td>63.918529</td>\n",
       "      <td>50.270176</td>\n",
       "      <td>23.722653</td>\n",
       "      <td>75.045812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8758</th>\n",
       "      <td>237.972461</td>\n",
       "      <td>146.591400</td>\n",
       "      <td>230.479847</td>\n",
       "      <td>108.517966</td>\n",
       "      <td>243.781079</td>\n",
       "      <td>191.273030</td>\n",
       "      <td>61.460372</td>\n",
       "      <td>159.272448</td>\n",
       "      <td>50.085301</td>\n",
       "      <td>89.598399</td>\n",
       "      <td>...</td>\n",
       "      <td>49.653045</td>\n",
       "      <td>43.306549</td>\n",
       "      <td>30.006721</td>\n",
       "      <td>54.412310</td>\n",
       "      <td>38.560801</td>\n",
       "      <td>38.539324</td>\n",
       "      <td>54.468427</td>\n",
       "      <td>41.475497</td>\n",
       "      <td>20.601348</td>\n",
       "      <td>63.098041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>175.916827</td>\n",
       "      <td>109.749918</td>\n",
       "      <td>172.512153</td>\n",
       "      <td>81.296343</td>\n",
       "      <td>182.523920</td>\n",
       "      <td>143.029587</td>\n",
       "      <td>45.872932</td>\n",
       "      <td>120.259720</td>\n",
       "      <td>37.785258</td>\n",
       "      <td>67.515639</td>\n",
       "      <td>...</td>\n",
       "      <td>38.205939</td>\n",
       "      <td>32.764229</td>\n",
       "      <td>23.077802</td>\n",
       "      <td>40.905329</td>\n",
       "      <td>30.023557</td>\n",
       "      <td>29.110732</td>\n",
       "      <td>41.964339</td>\n",
       "      <td>31.083379</td>\n",
       "      <td>15.526047</td>\n",
       "      <td>49.484715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 401 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "nuts3       DE111       DE112       DE113       DE114       DE115       DE116  \\\n",
       "0      206.170388  140.190103  202.667750  105.224867  215.091334  174.672309   \n",
       "1      211.500148  143.673574  208.038088  107.955702  220.776207  179.332868   \n",
       "2      214.999291  146.748613  211.948810  110.111110  224.925933  182.704969   \n",
       "3      231.226693  156.922695  227.043618  117.579106  240.922749  195.765590   \n",
       "4      278.556198  181.054589  264.950003  138.233133  281.091092  228.570652   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "8755   314.090461  203.074292  317.413566  150.167164  335.982110  262.806997   \n",
       "8756   306.463190  197.950235  309.299142  146.187917  327.377730  256.125845   \n",
       "8757   279.453315  180.485269  281.524511  132.771521  297.949142  233.200682   \n",
       "8758   237.972461  146.591400  230.479847  108.517966  243.781079  191.273030   \n",
       "8759   175.916827  109.749918  172.512153   81.296343  182.523920  143.029587   \n",
       "\n",
       "nuts3      DE117       DE118      DE119       DE11A  ...      DEG0F  \\\n",
       "0      46.664946  125.313498  40.966770   71.888898  ...  37.601944   \n",
       "1      49.481993  132.482112  43.324687   76.071898  ...  38.587374   \n",
       "2      50.324357  134.735586  44.064635   77.380612  ...  39.355704   \n",
       "3      54.438691  146.899689  48.051481   84.408961  ...  42.037045   \n",
       "4      66.599228  174.521929  57.113042  100.409284  ...  49.457693   \n",
       "...          ...         ...        ...         ...  ...        ...   \n",
       "8755   83.355251  224.816658  70.565079  125.910215  ...  66.191563   \n",
       "8756   81.185021  218.918489  68.720740  122.636583  ...  64.034881   \n",
       "8757   73.812184  199.059705  62.503687  111.583484  ...  58.202722   \n",
       "8758   61.460372  159.272448  50.085301   89.598399  ...  49.653045   \n",
       "8759   45.872932  120.259720  37.785258   67.515639  ...  38.205939   \n",
       "\n",
       "nuts3      DEG0G      DEG0H      DEG0I      DEG0J      DEG0K      DEG0L  \\\n",
       "0      31.987246  22.623693  39.149205  27.469076  27.904335  36.820917   \n",
       "1      33.730421  23.153930  41.372317  28.951076  29.422889  38.897128   \n",
       "2      34.361495  23.643103  42.095773  29.542102  29.946436  39.579315   \n",
       "3      37.298965  25.313782  45.519867  31.960713  32.374033  42.796880   \n",
       "4      44.664247  29.969928  55.495775  38.607296  39.378124  52.155819   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "8755   61.206344  40.016742  75.331808  52.080561  54.080492  72.791854   \n",
       "8756   59.546183  38.661304  73.244038  50.368402  52.523265  70.319502   \n",
       "8757   54.008123  35.154707  66.408670  45.661581  47.536601  63.918529   \n",
       "8758   43.306549  30.006721  54.412310  38.560801  38.539324  54.468427   \n",
       "8759   32.764229  23.077802  40.905329  30.023557  29.110732  41.964339   \n",
       "\n",
       "nuts3      DEG0M      DEG0N      DEG0P  \n",
       "0      28.991814  14.353573  43.707776  \n",
       "1      30.684640  15.257009  46.112055  \n",
       "2      31.214834  15.566148  46.977266  \n",
       "3      33.759688  16.888222  51.002784  \n",
       "4      41.222296  20.578529  61.118043  \n",
       "...          ...        ...        ...  \n",
       "8755   56.915518  26.838796  85.493281  \n",
       "8756   55.378941  25.978411  82.748003  \n",
       "8757   50.270176  23.722653  75.045812  \n",
       "8758   41.475497  20.601348  63.098041  \n",
       "8759   31.083379  15.526047  49.484715  \n",
       "\n",
       "[8760 rows x 401 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gas_st = df_gas_st_EFH + df_gas_st_MFH\n",
    "df_gas_st"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of selecting one specific region, this time we choose to sum it up for the entire country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser_gas = df_gas_st.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot this one again as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Gas Load Private Households Germany')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot.heatmap_timeseries(ser_gas/1e3, clabel='[GWh]')\n",
    "ax[0, 0].set_title('Gas Load Private Households Germany')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}