Dataset/Quantitative Summaries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"contents\"></a>Contents\n",
    "<!-- - [Overview](#overview) -->\n",
    "- [37NTF-A](#37ntf-a)\n",
    "    - [Total appliance ratings per priority level among all consumers](#37ntf-a--trpl)\n",
    "    - [Total appliance counts per priority level among all consumers](#37ntf-a--acpl)\n",
    "- [37NTF-B](#37ntf-b)\n",
    "    - [Total appliance ratings per priority level among all consumers](#37ntf-b--trpl)\n",
    "    - [Total appliance counts per priority level among all consumers](#37ntf-b--acpl)\n",
    "- [37NTF-C](#37ntf-c)\n",
    "    - [Total appliance ratings per priority level among all consumers](#37ntf-c--trpl)\n",
    "    - [Total appliance counts per priority level among all consumers](#37ntf-c--acpl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantitative Summaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"37ntf-a\"></a>37NTF-A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib, numpy, pandas\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\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></th>\n",
       "      <th>Consumer ID</th>\n",
       "      <th>kW</th>\n",
       "      <th>No. of appliances</th>\n",
       "      <th>No. of priority levels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>701-1</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>701-2</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>701-3</td>\n",
       "      <td>350</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>712-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>713-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>714-1</td>\n",
       "      <td>17</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>714-2</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>718-1</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>720-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>722-2</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>722-3</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>724-2</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>725-2</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>727-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>728-1</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>728-2</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>728-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>729-1</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>730-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>731-2</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>732-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>733-1</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>734-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>735-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>736-2</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>737-1</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>738-1</td>\n",
       "      <td>126</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>740-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>741-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>742-1</td>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>742-2</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>744-1</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Consumer ID   kW  No. of appliances  No. of priority levels\n",
       "0        701-1  140                100                       5\n",
       "1        701-2  140                100                       5\n",
       "2        701-3  350                100                       5\n",
       "3        712-3   85                100                       5\n",
       "4        713-3   85                100                       5\n",
       "5        714-1   17                100                       5\n",
       "6        714-2   21                100                       5\n",
       "7        718-1   85                100                       5\n",
       "8        720-3   85                100                       5\n",
       "9        722-2  140                100                       5\n",
       "10       722-3   21                100                       5\n",
       "11       724-2   42                100                       5\n",
       "12       725-2   42                100                       5\n",
       "13       727-3   42                100                       5\n",
       "14       728-1   42                100                       5\n",
       "15       728-2   42                100                       5\n",
       "16       728-3   42                100                       5\n",
       "17       729-1   42                100                       5\n",
       "18       730-3   85                100                       5\n",
       "19       731-2   85                100                       5\n",
       "20       732-3   42                100                       5\n",
       "21       733-1   85                100                       5\n",
       "22       734-3   42                100                       5\n",
       "23       735-3   85                100                       5\n",
       "24       736-2   42                100                       5\n",
       "25       737-1  140                100                       5\n",
       "26       738-1  126                100                       5\n",
       "27       740-3   85                100                       5\n",
       "28       741-3   42                100                       5\n",
       "29       742-1    8                100                       5\n",
       "30       742-2   85                100                       5\n",
       "31       744-1   42                100                       5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve and load dataset from spreadsheet file\n",
    "dataset_workbook_path = \"./37NTF-A.xlsx\"\n",
    "dataset_overview = pandas.read_excel(dataset_workbook_path, sheet_name=\"Overview\")\n",
    "dataset_overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Containers...\n",
    "# ...for consumer IDs\n",
    "consumer_IDs = dataset_overview[\"Consumer ID\"].values\n",
    "# ...for total (sum of) appliance ratings per priority level among all consumers\n",
    "consumer_TRPL = numpy.zeros(\n",
    "    shape=(len(dataset_overview[\"Consumer ID\"]), dataset_overview[\"No. of priority levels\"].max())\n",
    ")\n",
    "# ...for appliance count per priority level among all consumers\n",
    "consumer_ACPL = numpy.zeros(\n",
    "    shape=(len(dataset_overview[\"Consumer ID\"]), dataset_overview[\"No. of priority levels\"].max())\n",
    ")\n",
    "# ...for priority level labels\n",
    "prio_level_labels = [f\"$p_c={p+1}$\" for p in range(consumer_TRPL.shape[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(consumer_IDs)):\n",
    "    dataset_consumer = pandas.read_excel(dataset_workbook_path, sheet_name=consumer_IDs[i])\n",
    "    \n",
    "    for j, prio_level in enumerate(dataset_consumer.columns):\n",
    "        consumer_TRPL[i,j] = dataset_consumer[prio_level].dropna().sum(axis=0)\n",
    "        consumer_ACPL[i,j] = len(dataset_consumer[prio_level].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Workspace cleanup\n",
    "del i, dataset_consumer, j, prio_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"37ntf-a--trpl\"></a>Total appliance ratings per priority level among all consumers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRPL_df = pandas.DataFrame(\n",
    "    [consumer_TRPL[i,:] for i in range(consumer_TRPL.shape[0])],\n",
    "    columns = prio_level_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_TRPL = TRPL_df.plot.bar(stacked=True, fontsize=7, colormap=\"tab10\")\n",
    "\n",
    "#ax_TRPL.set_xlabel(\"Consumer IDs\", fontsize=7)\n",
    "ax_TRPL.set_ylabel(\"$L_{c}$ (kW)\", fontsize=7)\n",
    "\n",
    "ax_TRPL.set_xticklabels(consumer_IDs, rotation=60)\n",
    "\n",
    "ax_TRPL.grid(True, which=\"both\", axis=\"y\")\n",
    "ax_TRPL.legend(loc=\"best\", ncol=5, prop={\"size\":7});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax_TRPL.figure.savefig(\n",
    "    fname=\"37NTF-A TRPL.png\", dpi=300,\n",
    "    transparent=True, bbox_inches=\"tight\", pad_inches=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"37ntf-a--acpl\"></a>Total appliance counts per priority level among all consumers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACPL_df = pandas.DataFrame(\n",
    "    [consumer_ACPL[i,:] for i in range(consumer_ACPL.shape[0])],\n",
    "    columns = prio_level_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_ACPL = ACPL_df.plot.bar(stacked=True, fontsize=7, colormap=\"Set3\")\n",
    "\n",
    "#ax_ACPL.set_xlabel(\"Consumer IDs\", fontsize=7)\n",
    "ax_ACPL.set_ylabel(\"No. of appliances\", fontsize=7)\n",
    "\n",
    "ax_ACPL.set_xticklabels(consumer_IDs, rotation=60)\n",
    "\n",
    "ax_ACPL.grid(True, which=\"both\", axis=\"y\")\n",
    "ax_ACPL.legend(loc=\"best\", ncol=5, prop={\"size\":7});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax_ACPL.figure.savefig(\n",
    "    fname=\"37NTF-A ACPL.png\", dpi=300,\n",
    "    transparent=True, bbox_inches=\"tight\", pad_inches=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"37ntf-b\"></a>37NTF-B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib, numpy, pandas\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>Consumer ID</th>\n",
       "      <th>kW</th>\n",
       "      <th>No. of appliances</th>\n",
       "      <th>No. of priority levels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>701-1</td>\n",
       "      <td>140</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>701-2</td>\n",
       "      <td>140</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>701-3</td>\n",
       "      <td>350</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>712-3</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>713-3</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>714-1</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>714-2</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>718-1</td>\n",
       "      <td>85</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>720-3</td>\n",
       "      <td>85</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>722-2</td>\n",
       "      <td>140</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>722-3</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>724-2</td>\n",
       "      <td>42</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>725-2</td>\n",
       "      <td>42</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>727-3</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>728-1</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>728-2</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>728-3</td>\n",
       "      <td>42</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>729-1</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>730-3</td>\n",
       "      <td>85</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>731-2</td>\n",
       "      <td>85</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>732-3</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>733-1</td>\n",
       "      <td>85</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>734-3</td>\n",
       "      <td>42</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>735-3</td>\n",
       "      <td>85</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>736-2</td>\n",
       "      <td>42</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>737-1</td>\n",
       "      <td>140</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>738-1</td>\n",
       "      <td>126</td>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>740-3</td>\n",
       "      <td>85</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>741-3</td>\n",
       "      <td>42</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>742-1</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>742-2</td>\n",
       "      <td>85</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>744-1</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Consumer ID   kW  No. of appliances  No. of priority levels\n",
       "0        701-1  140                 53                       5\n",
       "1        701-2  140                 58                       5\n",
       "2        701-3  350                100                       5\n",
       "3        712-3   85                 42                       5\n",
       "4        713-3   85                 39                       5\n",
       "5        714-1   17                 16                       5\n",
       "6        714-2   21                 23                       5\n",
       "7        718-1   85                 34                       5\n",
       "8        720-3   85                 43                       5\n",
       "9        722-2  140                 50                       5\n",
       "10       722-3   21                 20                       5\n",
       "11       724-2   42                 28                       5\n",
       "12       725-2   42                 28                       5\n",
       "13       727-3   42                 24                       5\n",
       "14       728-1   42                 22                       5\n",
       "15       728-2   42                 30                       5\n",
       "16       728-3   42                 28                       5\n",
       "17       729-1   42                 22                       5\n",
       "18       730-3   85                 38                       5\n",
       "19       731-2   85                 43                       5\n",
       "20       732-3   42                 30                       5\n",
       "21       733-1   85                 35                       5\n",
       "22       734-3   42                 29                       5\n",
       "23       735-3   85                 34                       5\n",
       "24       736-2   42                 28                       5\n",
       "25       737-1  140                 50                       5\n",
       "26       738-1  126                 54                       5\n",
       "27       740-3   85                 41                       5\n",
       "28       741-3   42                 28                       5\n",
       "29       742-1    8                 12                       5\n",
       "30       742-2   85                 34                       5\n",
       "31       744-1   42                 26                       5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve and load dataset from spreadsheet file\n",
    "dataset_workbook_path = \"./37NTF-B.xlsx\"\n",
    "dataset_overview = pandas.read_excel(dataset_workbook_path, sheet_name=\"Overview\")\n",
    "dataset_overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Containers...\n",
    "# ...for consumer IDs\n",
    "consumer_IDs = dataset_overview[\"Consumer ID\"].values\n",
    "# ...for total (sum of) appliance ratings per priority level among all consumers\n",
    "consumer_TRPL = numpy.zeros(\n",
    "    shape=(len(dataset_overview[\"Consumer ID\"]), dataset_overview[\"No. of priority levels\"].max())\n",
    ")\n",
    "# ...for appliance count per priority level among all consumers\n",
    "consumer_ACPL = numpy.zeros(\n",
    "    shape=(len(dataset_overview[\"Consumer ID\"]), dataset_overview[\"No. of priority levels\"].max())\n",
    ")\n",
    "# ...for priority level labels\n",
    "prio_level_labels = [f\"$p_c={p+1}$\" for p in range(consumer_TRPL.shape[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(consumer_IDs)):\n",
    "    dataset_consumer = pandas.read_excel(dataset_workbook_path, sheet_name=consumer_IDs[i])\n",
    "    \n",
    "    for j, prio_level in enumerate(dataset_consumer.columns):\n",
    "        consumer_TRPL[i,j] = dataset_consumer[prio_level].dropna().sum(axis=0)\n",
    "        consumer_ACPL[i,j] = len(dataset_consumer[prio_level].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Workspace cleanup\n",
    "del i, dataset_consumer, j, prio_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"37ntf-b--trpl\"></a>Total appliance ratings per priority level among all consumers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRPL_df = pandas.DataFrame(\n",
    "    [consumer_TRPL[i,:] for i in range(consumer_TRPL.shape[0])],\n",
    "    columns = prio_level_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_TRPL = TRPL_df.plot.bar(stacked=True, fontsize=7, colormap=\"tab10\")\n",
    "\n",
    "#ax_TRPL.set_xlabel(\"Consumer IDs\", fontsize=7)\n",
    "ax_TRPL.set_ylabel(\"$L_{c}$ (kW)\", fontsize=7)\n",
    "\n",
    "ax_TRPL.set_xticklabels(consumer_IDs, rotation=60)\n",
    "\n",
    "ax_TRPL.grid(True, which=\"both\", axis=\"y\")\n",
    "ax_TRPL.legend(loc=\"best\", ncol=5, prop={\"size\":7});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax_TRPL.figure.savefig(\n",
    "    fname=\"37NTF-B TRPL.png\", dpi=300,\n",
    "    transparent=True, bbox_inches=\"tight\", pad_inches=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"37ntf-b--acpl\"></a>Total appliance counts per priority level among all consumers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACPL_df = pandas.DataFrame(\n",
    "    [consumer_ACPL[i,:] for i in range(consumer_ACPL.shape[0])],\n",
    "    columns = prio_level_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zfhGRgtGMvwrNgERaT6GPywZcY9CMX0SkYAo547/mXccN2PbhQajHYDjilhEDNzbpnZnt8j2+evdq54jt7xDb5wfzLKLZZWvGLyJSMB014x/M2aw0Vsfd+y3SQjTjFxEpmI6a8Q+mXNboMly917q0VNLo/tEu12vy0AnHmmb8IiIF0/Iz/ixX5UUqyuEzVuq6FlFn2e1yL3se9dT1vPpoxi8iUjAa+EVECia3pR4zOwaYDmwH3ATsA0wGtgbOcHfPq2wRkU7ViIvLuQ387n4bcJuZbQ9cCWzj7ieb2dnAQcD/y6tsERFJZnlPvM3ss8AK4Dh3n2dm/wKMcvf/iImdC8wF6O7u3n/p0qXw55XxicdPGLgtLjYm7um1qwds6x4+MraYF9c/OWDb6G0m1hyXyaY/Dtw2ZJfY0FV/XjVgW9f4roGBKfdRXHsgoU0p6xlXR4ivZ9r2ZMlZTz1j82WJzeG5zNLnGp0zy35PXc+4fQTx+yllP84yftTT9qT+kVYjn8upU6c+6O49lcrLc6nHgE8D3wF+CpwZ/ddE4Jdxj3H3xcBigJ6eHu/t7WXjwnNj8w898d0DtsXFxsVd+ev7BmybufcBseV86/fvH7Ctd/IpNcdlEnsnyKzY0LsW3j2w/BN7B2xLu4/i2gMJbUpZz7g6Qnw907YnS8566hmbL0tsDs9llj7X6JxZ9nvqeia+C3vgfkrbj7OMH/W0Pal/pJXHc1lJnrdzngO8HRgNvA54yMyuBoYB1+VYrkisdnnjTbvcptloRX5TWLPlucZ/DXBNXvlFRKQ2Lf8GLimmTpv1tsvZht4YVQy6j19EpGA046+iXWZAgzlDHszZbB5fCp9lX3bamUmRtcux3gia8YuIFIxm/IMhhw/sagd5zM4lHZ2ZNE4nfOicZvwiIgWjGb+0pCKtt7Yjnb21N834RUQKRjP+FtYu93532vpxHmcbnbaPpL1pxi8iUjCa8XcIrYlXp1l357jmXccN2PbhJpXdCceaZvwiIgWjGb/EyuNL7vPI2S7aZZY4mDPpjpTDe3YaceaqGb+ISMFoxt8gWWZ0ae/W0Zp0MaXtS+1yFpGW3hvQPJrxi4gUTEfN+PNYn8wj52DO5PNoT6fNPEUapVXfi6MZv4hIwbT8jD9uhgq606DTpT0zGew7hdrlbKfR9cyy39tlHw3mHU3NLlszfhGRgmn5GX8naocZUCeeabXDfpfGyqMfd8J7HTTjFxEpGA38IiIFo6WeFtZpSxOduHzUCaf9kp9WfROmZvwiIgWjGX+H0MyztWV5ftLGttrth80sv8gasRKgGb+ISMFoxj8INDsXKYZWvU6nGb+ISMFoxi9tLctaczusneel1a4HtOLHbxSJZvwiIgWjGX+DtMtHQuehXeop0mytOi5oxi8iUjCa8YtIS9B7A5pHM34RkYJp6ozfzEYC1wHrgT53/2ozyxcRkebP+GcAy919DjDwyyhFRCR35u7NK8xsPvAdd/+5md3q7ieV/f9cYG705+7Ao2UpxgHPpigqbZxytn7OTmuPcqp/5J3zNe6+Y8VHunvTfoBZwFHR70trePwDjYxTztbP2WntUU71j8HIWf7T7Lt6VgDXmtl04PYmly0iIjT54q67rwZOa2aZIiKypXa7nXNxg+OUs/Vzdlp7lLNzym6nnFto6sVdEREZfO024xcRkTpp4BcRKZhCDPxmZo2MyyvnYMqjnoPZ9sHe740uv136ZrvUM4t2qWcWbTnwWyRF3BAAr3Ihw8y2SxMXxW6TQ85cDsCU+yhVe2qRR85qsrYnhwE6dfkpn58s/Shtf8+8j6rVNWO70x5DqdqTpZ4Z49LWM7djKC9tdXHXzCYCQ9398ejvIe6+KSH2DcChQDfwSWB93BNjZvsC/e8gXhjdcppU/huBI4BtgMvcfUNCXKqcZtYNbOXuf47+3srdNyfE7gHsC4xy9xvNzBLak2UfpW3PbsBrANz9B0llR7F7Az3AJndfEhdTQ85UsWnbE8Wm2vdp93uW8jOUvS/p+2ba/p5lH6XqSxlzpt1HqdqTsZ55HBtZ2j6GMOa+EP2d9LxnOTZS5SzXbjP+TwHXm9kF0c7YZGZbJ8R+BHgIeAYYAQxPiP0QcBPwPPAhM7vIzJLe3/AR4D+BdcBcMzvfzOL2YdqcnwQWmNmpAO6+uX+Wk1D2emBsNMMYkRCbdR+lac+/Ej5C4y3R38MrzJjOB/4K7GRmJ5vZ+xLisuRMG5u2PZB+36fd71nKT1t21r6Zpr9n2Udp+1KWnGlj07YnSz3zODaytP3jwBlmdhi8/LzXe7ylzbmFthn4zWx7YJm7/xOwGlhiZrcD+8TE7kF4O/OPgKeB/YAzCDuzNG5H4HZ3fxTYBVgEjAV2jsk5EfgO8AAwBfgasB0wppacZvbqKNfFwEgz+7iZ3Vhexyh2CvAbd19BGFT3IMwEJ9Wxj9K2563AY+7+eWCNmU0AjgHGx+TcA7gLuAOYCtwHjDOzEWVxWXKmik3bnig21b5Pu9+zlJ+h7Cx9M21/z7KPUvWljDnT7qNU7clYzzyOjSxt3w14gfAi8VozO83MPsXAYzjLsZEqZ6w0n+vQKj+E07T+308D/m9C3DBg2+j3CdGO+VxSTsCA4dGTd1WFuOGEF8sxwJuAa2vNGeUpbc95wIqEfCOB7aLf9wTuAT5b5z5K1R5CRx4b/f5m4AfA5RX2+6go586EJZLr68yZKjbj85Nq32fd7yn3Z5bnPW3fTNXfs+yjtH0p435Pu49SH79Z+3yDj40sbd8aGBL9vjNwKfB/6jw2UuWMfWyaoFb44ZXrESOjnfMmYNcqjxkeHTifB/ap1HEIA9ZM4HWVyo9+HwHMAXaLiRuSJmdJe7aOOvphcfnKn+jo3+XAG5I6dpp9lLY9JTFbRW36IbBHiudrG+Bc4PWNyFktlrBmnqo9Jc9Rqn1fbb9nKb8/rlrZaftRlv6e5TlP25ey9qOMfS6xPaXHT8p65nZspNyfVvb3ycDujTg20ubcIjZNUCv8lBwI84C3pow9FzgQ2KlK3IeiuGEpOsKZUacZU96xyg7+j8TlLI2J/j4L2DfpyS/7+xzgjZQNpoRBdkjJ3+dW20clsWfHtScm7jTgtUBPipynAq8Ddq4Sd3pSTl4ZIPsP8DlxscDo/oO/0vPT/3yXPUex+z4m7pwo54AXsZjYtPvz7JRlx/ajrP09TR9O6MfnxfWlmL6Z2G5KZtpl+7P8GBpWVvb5FdoztnSfkDAu5HlslPTRM9I85yXHxuuBbarEZTne3psmZ+lPy3/nrpm9yd1/4eFCzLbA8+5+b8rY//WwTlgt7rm4uCj2XcAbzewO4L+Bze7+i5jQOVHczcCDhLXhuJzzzOy/CeuCf4/a8/OE5pfHPufuv4yJOx04wMwWE9bVH6mwj44irGv+DriNsI8GtMfMjgBeBfQBfwO2d/fHEnKWx45199/FxL0vivkfwoA9JCkncIiZPQQMMbN1AAmx/xaV+QkzGx3FxT0/ENbnDzCzLxGey2cS9n153F8y5Hy6wv7sAX4LfAN4NqHs44ApZvaQu3+N8Fwm9c20/T1tH4b4fhzXl+L6ZlLOw8zsYWAT8BfCfo+LPRb4i5k97e6/JvkYAriEcI3kbGAD8EJCPfM4Nl7r7o95uJA6mvCiGtv2hGPjtyniKh1v5cfR0LicFaV9hRiMH+AgwpO1GHhztK1/zbX81ClVbMacbwW+RLi4NCva1h2T82DgBsLFp48BVwF7lsYRTlmHETrU+4B3Ap8mOn0vy5cl9hDC11lOjMr+NNFMMqY9bwO+QrhV7Argy4QvbSjPeSBwK+E2tWOiba9KyJkqlrBccT/h7OpQ4APAhISc+xM69kXAgui52iUm5z8A/06YlR4YbRuakHMq8AVgN8KM91JgSkzOVHEZc/YSLs6+Abgz2u87J+z3mwgDwGXAjcCkOvt7qj5c0o+/QIP6cfT3W4AfE+4+uQJYBkyMqefuhKW0Cwh339xSIec/AFcTvt+jt+z/8j423kr4ePkLSvbN9nUeG1mOt9THUaWfVr+rZyRh3erLhFullhBO//CopTXEZsm5F3CDuz8EjDezA4DjY2JHETriTsD/Aj8idJCX4zxYBywhnE73EDrwxPJ8WWKj//8DYfDbSOgUMxLasxn4T3d/wt3PI9yRsGtM7EHAlcC9wO7R3QOHJuRMFevufye8OG4ATiAMGGMScvYSljwedvcFhIFjh5jY/QkH6beAD5jZQe6+MSHn9lGeM4EdCTPVg2Ni08Zlid0J+D7hBeI6wl1Pce1ZAzxMOMM4H/gu4XmPK3tb4N2EF4o5Ffrx3oQL7NX6MEAX8DnS9eNbCNcpKvVNCC92WwE/j/rcHcT3ubWEb5N61t0viOJ6E3LuRnhRvAc4x8xm9d/uWBa7NfB7wnJZmmOjL8WxMZnwHP4UOD66i+a0hJwHE/bnfwF7VDiO0sb1H0eLSXccJUv7CjEYP8Dwsr9/CkyvJzZjzp1Kfr8gemIOiInrv8DUP4v7BHBIQs6hhAt1VwFHAjtWaH/VWMIA8DbCKfqhwDUVyt6GMCs9h3DwfAV4W0xcd8nvVxBmqQcm5MwSO5ww6FxJWOtMurg2ieib2qK/vwwcGhM3uuT3fYELKVt7Lvn/HQgD5bmE6w+foWy2mCUuY87J0X5/D2EQvB6YFvNcb0346tH3Ey763gIcFJNvCGXruYQXnekxca9K2YeHACOq9WNeOaMaShh4KvXNIYQXkf1Ktt0U1/bo35MJs/IZhLOUAf0oyjm27Dm4ABgZsz+3I8z65xDOzhKPjegxpcfGEmKOjbJ69L/YH5EQM7Lk98RjI21c1uOo0k+m4Gb9EGbQVxHW5+YRbmUbDryr1tiMObchnG6X3v51OGW38kVxry6Ley3w3oS44dHf04HjE9qeKrY/ruTv3QgXo94bE7tt1KGOIMzqZhPWpo8t74DA5cCMkm0nEn87aqrYkrJPIAx6pwCnJB0oUc7pJdveApwbE/eZ0ueOcFfFSQk5ryA6OAm3yH0aeH8tcTXk/AwwjVcuwL4amF0W1wXcTFjj3iHaR28Djo4pu4vwQvjuqP+OIyy9lPf3LsLgeTKv3B0zLeG57M95ctSvRhCWPOYkxL0ninlP6fOfEDszKn8EYfZ/TkLbZxJmricRZsDvqFLP/rYbcGRC3LFRew4kXICdE5NzW8LSzT9Gf59D6MczE+IOK9m2E8nH23JKXuCinItqiSsr/5+jv88g4ThK89OSH9lgZicSduoSwoH/ane/sZ7YjDkvJrww9J/SvUSYhT7t0Vujy+Kuj+JWEQ7sp939pbK4fQlLHfe6+9/NbLxHb9mPKbtqbEnZNwA/cfcXzOzV7v5UTM6zCLOfkYTlk1vNbERpHcvihgN/9PARBa8DVrn707XERnFdhI77W3f/ipmNitq1xVvRS3JuCzwR5ZwQ5Xwxoewn3f2m8jYn5Py9u3/JzMYRLkYmlZ0YV2POkVHcTdHFwJfcfX1J3JyoLV2EAfJxYIlHy1ZlZc/hlfdLjCUs891Ymq8s57aEF5Mnga8Da0r3ZVnsyCj2ccKbkf4eU8/+srsIF4pXeMxHSZTlHEtYcvl6lHNNlbbf4vEfuVDepj8QlrHKn5/+uFGE5bhfAbe5+6qYnK8iLNOuJiw3PeTuA77gpCRuFWG2/0t3/2x5XFns3whncb8CriXM7J/NGhdT/kbgp+7+xbjy02i5Nf7oQB9DuANgvbt/B3iDmR1Sa2zGnJOBFwl3i7yRcNp5GeH+3BcS4vYhrLVdAbypbNDvj7soijvJzK4mzEKSyq4YW1b23sCxUdxbKGNmuxMu/l1KuFA6xszmEmb/SXGfINxNM5dwga180E8VWxL3acLFve2iuP63l3tCzoUlOd9UNuiXlz3UzOaY2YwqbV8IDItyvrVK2bFxdeRc0F9PwlJD6WC6KzDO3RcR1m3vIZy9TYtpT3/stYS+fCfhDU69CXGLCGvXdxImJLvHDPqlsZsI1xV2IVykjqtnf9l9hDe1HVShnv0574xy7lU26Ce1/e1Vcva3aQLhTDwpbiPwPcKZxj/G5NyZMJB+grC8toFwzeCwCnFnE94te6qZxT1HpbHnED5u4z3A/mWDfqq4hPLXEa5pbVHPLFpu4Ces0x5MmCV8OTqohhPW0mqNzZLzw4R7Z3/t7lcQOs+bCe8eTBN3V5W4dVHcnSnKTootj1sfxX0vJudcYGszGxbNIPeNfr5bJW7/KO6eFDmTYtOWXSnn3SniphAuxlXLOSXKWb6f0sbVk7O/nt8vi5sH7GVmQ939M4Qzx1GEtfhy5bEPRLHltyfGxY2McqfNeV+VuPtrqOf9ObS9PDYu50gG7iMIt4ReT5gU3ko4E9uHgX2uPG59FNeXIud6d9+bcBNALXFZ6plaSw38ZvY2wmD3dcJgdyrwJ+Dq8tPJtLE15NwIfMfMjo429wHnebianktczjmHEO6Q6J8Z/S/wh7KzklRxytnYnFHcJuDbhGs5EO6Q+X5C36waq5ypcx5M+PylO6LHfJQwuAJ41rjBzpmZ13hxII8fwptXdiN87sRSyi6w1BJbY84dga8CpzUjrok5T64nTjkbmzMmLvFiXdpY5cycs5swi55dT9xg58z6U3eCRv6w5VvvxxDedDGqntg6c16UMmddcU3MuSBlztg45Wxszjr7Zh79vcg5/y1lzti4wc6Z9aeuB+f1Q7hFy6hwH23W2MHM2WntUU71D+VszZxpf1rydk4REclPS13cFRGR/GngFxEpGA38IiIFo4FfRKRgNPCLiBSMBn4RkYL5/zzCuwrwEn2EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_ACPL = ACPL_df.plot.bar(stacked=True, fontsize=7, colormap=\"Set3\")\n",
    "\n",
    "#ax_ACPL.set_xlabel(\"Consumer IDs\", fontsize=7)\n",
    "ax_ACPL.set_ylabel(\"No. of appliances\", fontsize=7)\n",
    "\n",
    "ax_ACPL.set_xticklabels(consumer_IDs, rotation=60)\n",
    "\n",
    "ax_ACPL.grid(True, which=\"both\", axis=\"y\")\n",
    "ax_ACPL.legend(loc=\"best\", ncol=5, prop={\"size\":7});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax_ACPL.figure.savefig(\n",
    "    fname=\"37NTF-B ACPL.png\", dpi=300,\n",
    "    transparent=True, bbox_inches=\"tight\", pad_inches=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"37ntf-c\"></a>37NTF-C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib, numpy, pandas\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>Consumer ID</th>\n",
       "      <th>kW</th>\n",
       "      <th>No. of appliances</th>\n",
       "      <th>No. of priority levels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>701-1</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>701-2</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>701-3</td>\n",
       "      <td>350</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>712-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>713-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>714-1</td>\n",
       "      <td>17</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>714-2</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>718-1</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>720-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>722-2</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>722-3</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>724-2</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>725-2</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>727-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>728-1</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>728-2</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>728-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>729-1</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>730-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>731-2</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>732-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>733-1</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>734-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>735-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>736-2</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>737-1</td>\n",
       "      <td>140</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>738-1</td>\n",
       "      <td>126</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>740-3</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>741-3</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>742-1</td>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>742-2</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>744-1</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Consumer ID   kW  No. of appliances  No. of priority levels\n",
       "0        701-1  140                100                       2\n",
       "1        701-2  140                100                       2\n",
       "2        701-3  350                100                       5\n",
       "3        712-3   85                100                       2\n",
       "4        713-3   85                100                       1\n",
       "5        714-1   17                100                       2\n",
       "6        714-2   21                100                       5\n",
       "7        718-1   85                100                       2\n",
       "8        720-3   85                100                       4\n",
       "9        722-2  140                100                       3\n",
       "10       722-3   21                100                       4\n",
       "11       724-2   42                100                       4\n",
       "12       725-2   42                100                       4\n",
       "13       727-3   42                100                       2\n",
       "14       728-1   42                100                       1\n",
       "15       728-2   42                100                       4\n",
       "16       728-3   42                100                       3\n",
       "17       729-1   42                100                       3\n",
       "18       730-3   85                100                       4\n",
       "19       731-2   85                100                       1\n",
       "20       732-3   42                100                       4\n",
       "21       733-1   85                100                       1\n",
       "22       734-3   42                100                       3\n",
       "23       735-3   85                100                       1\n",
       "24       736-2   42                100                       1\n",
       "25       737-1  140                100                       5\n",
       "26       738-1  126                100                       5\n",
       "27       740-3   85                100                       1\n",
       "28       741-3   42                100                       5\n",
       "29       742-1    8                100                       1\n",
       "30       742-2   85                100                       2\n",
       "31       744-1   42                100                       1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve and load dataset from spreadsheet file\n",
    "dataset_workbook_path = \"./37NTF-C.xlsx\"\n",
    "dataset_overview = pandas.read_excel(dataset_workbook_path, sheet_name=\"Overview\")\n",
    "dataset_overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Containers...\n",
    "# ...for consumer IDs\n",
    "consumer_IDs = dataset_overview[\"Consumer ID\"].values\n",
    "# ...for total (sum of) appliance ratings per priority level among all consumers\n",
    "consumer_TRPL = numpy.zeros(\n",
    "    shape=(len(dataset_overview[\"Consumer ID\"]), dataset_overview[\"No. of priority levels\"].max())\n",
    ")\n",
    "# ...for appliance count per priority level among all consumers\n",
    "consumer_ACPL = numpy.zeros(\n",
    "    shape=(len(dataset_overview[\"Consumer ID\"]), dataset_overview[\"No. of priority levels\"].max())\n",
    ")\n",
    "# ...for priority level labels\n",
    "prio_level_labels = [f\"$p_c={p+1}$\" for p in range(consumer_TRPL.shape[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(consumer_IDs)):\n",
    "    dataset_consumer = pandas.read_excel(dataset_workbook_path, sheet_name=consumer_IDs[i])\n",
    "    \n",
    "    for j, prio_level in enumerate(dataset_consumer.columns):\n",
    "        consumer_TRPL[i,j] = dataset_consumer[prio_level].dropna().sum(axis=0)\n",
    "        consumer_ACPL[i,j] = len(dataset_consumer[prio_level].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Workspace cleanup\n",
    "del i, dataset_consumer, j, prio_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"37ntf-b--trpl\"></a>Total appliance ratings per priority level among all consumers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRPL_df = pandas.DataFrame(\n",
    "    [consumer_TRPL[i,:] for i in range(consumer_TRPL.shape[0])],\n",
    "    columns = prio_level_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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22C7LOlLiANozlpdFkecItgHOByaZ2VxgiZldAwwDTknCrjKzKcAdRfVDRAa+luUPNroLTa2wQuDuLwGzqxbfXHX/pKLaFxGROLp8VESk5FQIRERKToVARKTkVAhEREpOXzoHrF4+v9FdEBFpGBWCnCbcNKHLsmUndP3Mwfizl3ZZ9vT8KYX0KU1sP9Ouv05bJo0T+xwVMeeaJWcRGtnP2O23twzoQqB3+iIi3dM5AhGRkhvQewTSe248bEXq8jP6uB8i0vu0RyAiUnIqBCIiJadCICJScjpHUHL61kYR0R6BiEjJqRCIiJScDg2J9HM6fNd7tC7TaY9ARKTkmm6PQF8bISLSu7RHICJScioEIiIlp0IgIlJyTXeOQESkWtqXIuoLEeNpj0BEpORUCERESq7PDg2ZWTvwDeBR4BZgf2BnYHNgtrt7X/VFRETeYX31+mtmhwBnA88D3wK+5u7TzexU4Lfu/n9THjMLmAXQ2tq6/y233MKyZ1al5p+w48guy9Ji64kDeOylx7os22ubverKWYTYfj7/1BNdlrXusmtUvqyc0rtin6Mi5lyz5KxnvkPfrc9YseOJMXny5Ifcva1WTF8Wgs3cfaOZtQI3A8vc/XQz++/ACHf/X7Ue39bW5g8++GDqD0pD+o9Kx/74dJ4fqR5oP15/6bTDuyw7Y9GdUfmyckrvauSca5acsesozzxu5Dbcm22bWbeFoM/OEbj7xuTPl4FVwJjk/k7Ayr7qh4iIbKovzxFMBT4BjAKuAPYzs8uBIcDVfdUPERHZVJ8VAndfDCyuWNTRV21LNl1/LSL6QFlOzfKld83Qz3nz5kUvj11WxDHg2LYh/pxLbFxR8owpRtp4oL4x5Tkf2Ejb3/tIl2XPTZ7YZ+33xvkEfY5ARKTktEcgUZphD0NEekZ7BCIiJadCICJScjo0JA2T5/djG/lbs/qdWxnotEcgIlJy2iMQ6YY+azFw6KKHdCoEIr2oWYqGDndJJRUCEWl6jXynP/u+JV0X9uEHynqDCkHJaVe5MfLsOTTDc5Q2Huife0PSlQqBiEgdBsJhNl01JCJSctojaCKN/nKrWL39hWYiUiwVAul1sbvKeY4rN8vVOM1C61MqqRD0A7E/szcQrk5oRs1wslakHioEMqA0y4t2s/RTykGFoIkMhKsTRKT/USGQXhd7/DnPu2K9g5b+aiCcb1EhEOlFZS1YZR33QKFC0A/EbkQD4Z2HSJn1xu8LF0GFQHqd3h32f3qOpJIKQRPRxivS/wyE7bKhhcDMhgNXA28AHe7+o0b2R0SkjBr9XUNTgdvcfSZwRIP7IiJSSubujWvcbC5wt7s/YmY3u/txVf8/C5iV3N0deLwqxRjgxYimYuOUs//nHGjjUU7Nj6Jzvtvdt635SHdv2A04Hjg8+fuWHjz+wd6MU87+n3OgjUc5NT8akbP61uiTxYuBq8xsCnBHg/siIlJKDS0E7r4GOKmRfRARKbtGnyyu14JejlPO/p9zoI1HOQdO282UcxMNPVksIiKN1+x7BCIiUicVAhGRkitlITAz6824onI2UhH9bOTYG73ee7v9ZpmbzdLPPJqln3kMiEJgiYi4QQDezYkRM9sqJi6J3aKAnIVskJHrKGo8PVFEzu7kHU8BL9jR7Uc+P3nmUex8z72OuutrznHHbkNR48nTz5xxsf0sbBsqSlOfLDaznYDB7v5Ucn+Qu2/IiN0L+AjQCnwTeCPtiTKziUDnJ5y/nlzimtX+vsAngS2AC939zYy4qJxm1gps5u5/Se5v5u4bM2L3ACYCI9z9OjOzjPHkWUex49kNeDeAu/8sq+0kdh+gDdjg7gvTYnqQMyo2djxJbNS6j13vedrP0fZE4udm7HzPs46i5lLOnLHrKGo8OftZxLaRZ+yjCK/BLyf3s573PNtGVM5qzb5H8C3gGjM7J1k5G8xs84zYLwMPAy8Aw4ChGbFfAq4H/gZ8yczOM7Osz1t8GbgXeB2YZWZnmVnaOo3N+U1gnpmdCODuGzvfBWW0/QYwOnkHMiwjNu86ihnP/yR85ccHkvtDa7yjOgt4BdjOzKab2Rcz4vLkjI2NHQ/Er/vY9Z6n/di2887NmPmeZx3FzqU8OWNjY8eTp59FbBt5xv5PwGwz+xi8/bzXu73F5txE0xYCM9sauNXdPwGsARaa2R3AhJTYPQgfv/4l8DywHzCbsHIr47YF7nD3x4F3AVcCo4HtU3LuBNwNPAhMAhYBWwGjepLTzHZMcn0NGG5m/2Rm11X3MYmdBDzq7osJL7J7EN4pjq9jHcWO50DgSXf/DrDWzMYBRwJjU3LuAfwcWApMBv4dGGNmw+rIGRUbO54kNmrdx673PO3naDvP3Iyd73nWUdRcypkzdh1FjSdnP4vYNvKMfTfgZULReI+ZnWRm36LrNpxn24jKmSrmeyj6642wW9f590nA/8mIGwJsmfw9LllR/5yVEzBgaPJkXlYjbiihmI4C3gdc1dOcSZ7K8ZwJLM7INxzYKvl7T+A+4NI611HUeAgTe3Ty9/uBnwEX1VjvI5Kc2xMOqVxTZ86o2JzPT9S6z7veI9dnnuc9dm5Gzfc86yh2LuVc77HrKHr7zTvne3nbyDP2zYFByd/bAxcA/7vObSMqZ+pjY4L64413zm8MT1bW+4BdunnM0GRD+g4wodZEIryATQN2rdV+8vcwYCawW0rcoJicFePZPJn4H0vLV/3EJ//eBuyVNdFj1lHseCpiNkvG9G/AHhHP1xaEX9Z8b2/k7C6WcMw9ajwVz1HUuu9uvedpvzOuu7Zj51Ge+Z7nOY+dS3nnUc45lzmeyu0nsp+FbRuR69Oq7k8Hdu+NbSM25yaxMUH98VaxYZwOHBgZewZwALBdN3FfSuKGREyMk5NJNKp6olW9GHw5LWdlTHL/FGBi1mSouj8H2JeqF1fCi+6givtndLeOKmJPTRtPStxJwHuAtoicJwK7Att3E/eFrJy884LZucHPTIsFRna+GNR6fjqf76rnKHXdp8TNSXJ2KWopsbHr89TItlPnUd75HjOHM+bxmWlzKWVuZo6binfiVeuzehsaUtX2WTXGM7pynZDxulDktlExR2fHPOcV28Z7gS26icuzvX0+JmflrdHfPpqbmb3P3X/r4cTOlsDf3P3+yNi/ejjO2F3cS2lxSeyngX3NbCnwH8BGd/9tSujMJO5G4CHCseW0nKeb2X8Qjiu+loznkYzhV8e+5O6/S4n7AvBBM1tAOC6/vMY6OpxwXPQJYAlhHXUZj5l9EtgB6ABeBbZ29yczclbHjnb3J1LivpjE/CfhBXxQVk7gEDN7GBhkZq8DZMR+JWnzG2Y2MolLe34gHN//oJl9n/BcvpCx7qvjnsuR8/ka67MN+APwE+DFjLY/A0wys4fdfRHhucyam7HzPXYOQ/o8TptLaXMzK+fHzOwxYAPwHGG9p8UeBTxnZs+7++/J3oYAziecYzkVeBN4OaOfRWwb73H3Jz2cmB1JKLKpY8/YNv4QEVdre6vejgan5awptmL0hxtwEOHJWwC8P1nWecy2elcrKjZnzgOB7xNOVh2fLGtNyXkwcC3hZNZXgcuAPSvjCLu4QwgT7IvA3wPzSXb3q/LliT2E8POfOyVtzyd5p5kyng8DPyRcmnYJcAPhRyyqcx4A3Ey4LO7IZNkOGTmjYgmHNx4g7H19BPhHYFxGzv0JE/08YF7yXL0rJeffATcR3rUekCwbnJFzMvBdYDfCO+ILgEkpOaPicuZsJ5zs3Qu4J1nv22es9+sJLwgXAtcB4+uc71FzuGIef5demsfJ/Q8AvyJc3XIJcCuwU0o/dyccejuHcHXPD2rk/DvgcsLvm7RX/V/R28aBhK/TP6di3Wxd57aRZ3uL3o5q3ZrtqqHhhONeNxAuzVpI2F3Ek5H3IDZPzr2Ba939YWCsmX0QODoldgRhYm4H/BX4JWHCvB3nwevAQsLudxthQu9UnS9PbPL/TxNeDN8iTJKpGePZCNzr7ivc/UzCFQ+7pMQeBHwbuB/YPbk64SMZOaNi3f01QrF8EziG8AIyKiNnO+EQyWPuPo/wQrJNSuz+hI32TuAfzewgd38rI+fWSZ6TgW0J72QPTomNjcsTux3wU0LBuJpwVVXaeNYCjxH2QM4C/oXwvKe1vSXwWULhmFljHu9DOGHf3RwGaAH+mbh5/APCeY5acxNC8dsMeCSZc0tJn3PrCb+29aK7n5PEtWfk3I1QJO8D5pjZ8Z2XV1bFbg78kXB4LWbb6IjYNnYmPIe/Bo5OrtI5KSPnwYT1+f+APWpsR7FxndvRAuK2o2yxFaM/3IChVfd/DUypJzZnzu0q/j4neaI+mBLXecKq813eN4BDMnIOJpz4uww4DNi2xvi7jSW8IHyYsEv/EeCKGm1vQXjXOoewMf0Q+HBKXGvF35cQ3sUekJEzT+xQwovQtwnHSrNO1o0n+SW75P4NwEdS4kZW/D0ROJeqY9cV/78N4YXzDML5i4upejeZJy5nzp2T9X4C4UXxGuDQlOd6c8JPtf4D4STyD4CDUvINoup4MKEITUmJ2yFyDg8ChnU3j3lnj2sw4YWo1twcRCgq+1Usuz5t7Mm/0wnv2qcS9mK6zKMk5+iq5+AcYHjK+tyKsFcwk7D3lrltJI+p3DYWkrJtVPWjs/h/MiNmeMXfmdtGbFze7ajWLVdwo26Ed9iXEY7vnU64dG4o8OmexubMuQVh97zycrOPU3XpYBK3Y1Xce4DPZ8QNTe5PAY7OGHtUbGdcxf3dCCe3Pp8Su2UywT5JeNc3g3Bs+6jqCQlcBEytWHYs6Ze/RsVWtH0M4UXwc8DnsjacJOeUimUfAM5Iibu48rkjXLVxXEbOS0g2VsIlefOBf+hJXA9yXgwcyjsndHcEZlTFtQA3Eo6Rb5Osow8Dn0ppu4VQGD+bzN8xhEM11fO9hfBiOp13rr45NOO57Mw5PZlXwwiHSGZmxJ2QxJxQ+fxnxE5L2h9G2DuYkzH2aYR3tscR3iEf0U0/O8duwGEZcUcl4zmAcEJ3ZkrOLQmHej6U3J9DmMfTMuI+VrFsO7K3t9uoKHhJzit7ElfV/n9L7s8mYzuKuTXFV0yY2bGElbyQ8EKwo7tfV09szpxfIxSKzl3AdYR3qc978lHuqrhrkrjVhA39eXdfVxU3kXBo5H53f83MxnryFQMpbXcbW9H2tcD/d/eXzWxHd38mJecphHdHwwmHW242s2GVfayKGwr82cNXKuwKrHb353sSm8S1ECbyH9z9h2Y2IhnXJh+dr8i5JbAiyTkuybkqo+0/ufv11WPOyPlHd/++mY0hnNzMajszroc5hydx1ycnF9e5+xsVcTOTsbQQXjCfAhZ6cpirqu2ZvPN5jdGEw4LXVearyrklobj8CfgxsLZyXVbFDk9inyJ8OOq1lH52tt1COPG82FO++qIq52jCIZofJznXdjP2H3j6V0RUj+lpwmGv6uenM24E4fDdMmCJu69OybkD4bDuGsLhqYfdvcsPvlTErSbsDfzO3S+tjquKfZWwl7cMuIrwzv/FvHEp7b8F/Nrdv5fWfox+f44g2fBHEa4weMPd7wb2MrNDehqbM+fOwCrC1Sj7EnZTLyRcH/xyRtwEwrG6S4D3VRWBzrjzkrjjzOxywruUrLZrxla1vQ9wVBL3AaqY2e6Ek4kXEE68jjKzWYS9g6y4bxCu1plFOGFXXQSiYivi5hNOFm6VxHV+HN4zcn69Iuf7qopAdduDzWymmU3tZuxfB4YkOQ/spu3UuDpyzuvsJ+HQROWL6y7AGHe/knDc9z7C3t2hKePpjL2KMJfvIXzgqj0j7krCse97CG9Qdk8pApWxGwjnJd5FOOmd1s/OtjsIH7I7qEY/O3Pek+Tcu6oIZI39o93k7BzTOMKeelbcW8C/EvZEPpSSc3vCC+s3CIfj3iScc/hYjbhTCZ/mPdHM0p6jytg5hK8HOQHYv6oIRMVltP864ZzYJv3Mo98XAsJx3oMJ7yJuSDayoYRjcT2NzZPzfxCu3f29u19CmEzvJ3y6MSbu593EvZ7E3RPRdlZsddwbSdy/puScBWxuZkOSd5gTk9u/dBO3fxJ3X0TOrNjYtmvl/EVE3CTCyb3uck5Kclavp9i4enJ29vOnVXGnA3ub2WB3v5iwZzmCcCy/WnXsg0ls9eWQaXHDk9yxOf+9m7gHetDPBwoYe3VsWs7hdF1HEC5BvYbwJvFmwp7aBLrOueq4N5K4joicb7j7PoSLCnoSl6ef0fp1ITCzDxNe/H5MePE7EXgWuLx69zM2tgc53wLuNrNPJYs7gDM9nK0vJK7gnIMIV2B0vnP6K/B01V5LVJxy9m7OJG4DcBfhXBCEK3B+mjE3u41VzuicBxO+P2pp8pizCS+2AJ43rtE5c/MenlzoixvhwzS7Eb434xaqTtj0JLaHObcFfgSc1BdxfZhzej1xytm7OVPiMk/+xcYqZ+6crYR32TPqiWt0zry3uhMUeWPTrwoYRfgQyIh6YuvMeV5kzrri+jDnvMicqXHK2bs565ybRcz3Muf8SmTO1LhG58x7q+vBfXUjXBJm1LiON29sI3MOtPEop+aHcvbPnLG3prh8VEREitOvTxaLiEjxVAhEREpOhUBEpORUCERESk6FQESk5FQIRERK7r8AvcUIDTVif4AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_TRPL = TRPL_df.plot.bar(stacked=True, fontsize=7, colormap=\"tab10\")\n",
    "\n",
    "#ax_TRPL.set_xlabel(\"Consumer IDs\", fontsize=7)\n",
    "ax_TRPL.set_ylabel(\"$L_{c}$ (kW)\", fontsize=7)\n",
    "\n",
    "ax_TRPL.set_xticklabels(consumer_IDs, rotation=60)\n",
    "\n",
    "ax_TRPL.grid(True, which=\"both\", axis=\"y\")\n",
    "ax_TRPL.legend(loc=\"best\", ncol=5, prop={\"size\":7});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax_TRPL.figure.savefig(\n",
    "    fname=\"37NTF-C TRPL.png\", dpi=300,\n",
    "    transparent=True, bbox_inches=\"tight\", pad_inches=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"37ntf-b--acpl\"></a>Total appliance counts per priority level among all consumers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACPL_df = pandas.DataFrame(\n",
    "    [consumer_ACPL[i,:] for i in range(consumer_ACPL.shape[0])],\n",
    "    columns = prio_level_labels\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_ACPL = ACPL_df.plot.bar(stacked=True, fontsize=7, colormap=\"Set3\")\n",
    "\n",
    "#ax_ACPL.set_xlabel(\"Consumer IDs\", fontsize=7)\n",
    "ax_ACPL.set_ylabel(\"No. of appliances\", fontsize=7)\n",
    "\n",
    "ax_ACPL.set_xticklabels(consumer_IDs, rotation=60)\n",
    "\n",
    "ax_ACPL.grid(True, which=\"both\", axis=\"y\")\n",
    "ax_ACPL.legend(loc=\"best\", ncol=5, prop={\"size\":7});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax_ACPL.figure.savefig(\n",
    "    fname=\"37NTF-C ACPL.png\", dpi=300,\n",
    "    transparent=True, bbox_inches=\"tight\", pad_inches=0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}