USA Power Outages Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PowerGrid Insights: Illuminating America's Outage Landscape\n",
    "\n",
    "**Name(s)**: Eric Sun & Sunan Xu\n",
    "\n",
    "**Website Link**: https://ericsun153.github.io/Illuminating_US_Outage_Landscape/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question Identification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do demographic factors of West Coast states influence the duration and affecting range of power outages, and what mitigation strategies can be employed to reduce the risk of outages in these regions?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-31T23:36:28.652554Z",
     "start_time": "2019-10-31T23:36:27.180520Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = 'png'\n",
    "pd.options.plotting.backend = 'plotly'\n",
    "\n",
    "\n",
    "import matplotlib as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the file type of our dataset is xlsx, we first convert it into csv file using the **openpyxl** library and store it into a csv file called 'outage.csv'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: openpyxl in ./.local/lib/python3.9/site-packages (3.1.2)\n",
      "Requirement already satisfied: et-xmlfile in ./.local/lib/python3.9/site-packages (from openpyxl) (1.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Major power outage events in the continental U.S.</th>\n",
       "      <th>Unnamed: 1</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Time period: January 2000 - July 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>Regions affected: Outages reported in this dat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>variables</td>\n",
       "      <td>OBS</td>\n",
       "      <td>YEAR</td>\n",
       "      <td>MONTH</td>\n",
       "      <td>U.S._STATE</td>\n",
       "      <td>POSTAL.CODE</td>\n",
       "      <td>NERC.REGION</td>\n",
       "      <td>CLIMATE.REGION</td>\n",
       "      <td>ANOMALY.LEVEL</td>\n",
       "      <td>CLIMATE.CATEGORY</td>\n",
       "      <td>...</td>\n",
       "      <td>POPPCT_URBAN</td>\n",
       "      <td>POPPCT_UC</td>\n",
       "      <td>POPDEN_URBAN</td>\n",
       "      <td>POPDEN_UC</td>\n",
       "      <td>POPDEN_RURAL</td>\n",
       "      <td>AREAPCT_URBAN</td>\n",
       "      <td>AREAPCT_UC</td>\n",
       "      <td>PCT_LAND</td>\n",
       "      <td>PCT_WATER_TOT</td>\n",
       "      <td>PCT_WATER_INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1530</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>cold</td>\n",
       "      <td>...</td>\n",
       "      <td>59.9</td>\n",
       "      <td>19.9</td>\n",
       "      <td>2192.2</td>\n",
       "      <td>1868.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>2.40176525502843</td>\n",
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       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1531</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>59.9</td>\n",
       "      <td>19.9</td>\n",
       "      <td>2192.2</td>\n",
       "      <td>1868.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97.5996492121418</td>\n",
       "      <td>2.40176525502843</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1532</td>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>SD</td>\n",
       "      <td>RFC</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>0.5</td>\n",
       "      <td>warm</td>\n",
       "      <td>...</td>\n",
       "      <td>56.65</td>\n",
       "      <td>26.73</td>\n",
       "      <td>2038.3</td>\n",
       "      <td>1905.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>98.3077441776026</td>\n",
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       "      <td>1.69225582239743</td>\n",
       "    </tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>1533</td>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>SD</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>0.5</td>\n",
       "      <td>warm</td>\n",
       "      <td>...</td>\n",
       "      <td>56.65</td>\n",
       "      <td>26.73</td>\n",
       "      <td>2038.3</td>\n",
       "      <td>1905.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>98.3077441776026</td>\n",
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       "      <td>1.69225582239743</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1534</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>ASCC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66.02</td>\n",
       "      <td>21.56</td>\n",
       "      <td>1802.6</td>\n",
       "      <td>1276</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>85.7611544611833</td>\n",
       "      <td>14.2388455388167</td>\n",
       "      <td>2.90118187392543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1540 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Major power outage events in the continental U.S. Unnamed: 1 Unnamed: 2  \\\n",
       "0                 Time period: January 2000 - July 2016        NaN        NaN   \n",
       "1     Regions affected: Outages reported in this dat...        NaN        NaN   \n",
       "2                                                   NaN        NaN        NaN   \n",
       "3                                                   NaN        NaN        NaN   \n",
       "4                                             variables        OBS       YEAR   \n",
       "...                                                 ...        ...        ...   \n",
       "1535                                                NaN       1530       2011   \n",
       "1536                                                NaN       1531       2006   \n",
       "1537                                                NaN       1532       2009   \n",
       "1538                                                NaN       1533       2009   \n",
       "1539                                                NaN       1534       2000   \n",
       "\n",
       "     Unnamed: 3    Unnamed: 4   Unnamed: 5   Unnamed: 6          Unnamed: 7  \\\n",
       "0           NaN           NaN          NaN          NaN                 NaN   \n",
       "1           NaN           NaN          NaN          NaN                 NaN   \n",
       "2           NaN           NaN          NaN          NaN                 NaN   \n",
       "3           NaN           NaN          NaN          NaN                 NaN   \n",
       "4         MONTH    U.S._STATE  POSTAL.CODE  NERC.REGION      CLIMATE.REGION   \n",
       "...         ...           ...          ...          ...                 ...   \n",
       "1535         12  North Dakota           ND          MRO  West North Central   \n",
       "1536        NaN  North Dakota           ND          MRO  West North Central   \n",
       "1537          8  South Dakota           SD          RFC  West North Central   \n",
       "1538          8  South Dakota           SD          MRO  West North Central   \n",
       "1539        NaN        Alaska           AK         ASCC                 NaN   \n",
       "\n",
       "         Unnamed: 8        Unnamed: 9  ...   Unnamed: 47 Unnamed: 48  \\\n",
       "0               NaN               NaN  ...           NaN         NaN   \n",
       "1               NaN               NaN  ...           NaN         NaN   \n",
       "2               NaN               NaN  ...           NaN         NaN   \n",
       "3               NaN               NaN  ...           NaN         NaN   \n",
       "4     ANOMALY.LEVEL  CLIMATE.CATEGORY  ...  POPPCT_URBAN   POPPCT_UC   \n",
       "...             ...               ...  ...           ...         ...   \n",
       "1535           -0.9              cold  ...          59.9        19.9   \n",
       "1536            NaN               NaN  ...          59.9        19.9   \n",
       "1537            0.5              warm  ...         56.65       26.73   \n",
       "1538            0.5              warm  ...         56.65       26.73   \n",
       "1539            NaN               NaN  ...         66.02       21.56   \n",
       "\n",
       "       Unnamed: 49 Unnamed: 50   Unnamed: 51    Unnamed: 52 Unnamed: 53  \\\n",
       "0              NaN         NaN           NaN            NaN         NaN   \n",
       "1              NaN         NaN           NaN            NaN         NaN   \n",
       "2              NaN         NaN           NaN            NaN         NaN   \n",
       "3              NaN         NaN           NaN            NaN         NaN   \n",
       "4     POPDEN_URBAN   POPDEN_UC  POPDEN_RURAL  AREAPCT_URBAN  AREAPCT_UC   \n",
       "...            ...         ...           ...            ...         ...   \n",
       "1535        2192.2      1868.2           3.9           0.27         0.1   \n",
       "1536        2192.2      1868.2           3.9           0.27         0.1   \n",
       "1537        2038.3      1905.4           4.7            0.3        0.15   \n",
       "1538        2038.3      1905.4           4.7            0.3        0.15   \n",
       "1539        1802.6        1276           0.4           0.05        0.02   \n",
       "\n",
       "           Unnamed: 54       Unnamed: 55       Unnamed: 56  \n",
       "0                  NaN               NaN               NaN  \n",
       "1                  NaN               NaN               NaN  \n",
       "2                  NaN               NaN               NaN  \n",
       "3                  NaN               NaN               NaN  \n",
       "4             PCT_LAND     PCT_WATER_TOT  PCT_WATER_INLAND  \n",
       "...                ...               ...               ...  \n",
       "1535  97.5996492121418  2.40176525502843  2.40176525502843  \n",
       "1536  97.5996492121418  2.40176525502843  2.40176525502843  \n",
       "1537  98.3077441776026  1.69225582239743  1.69225582239743  \n",
       "1538  98.3077441776026  1.69225582239743  1.69225582239743  \n",
       "1539  85.7611544611833  14.2388455388167  2.90118187392543  \n",
       "\n",
       "[1540 rows x 57 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlsx_file = 'outage.xlsx'\n",
    "csv_file = 'outage.csv'\n",
    "\n",
    "df = pd.read_excel(xlsx_file)\n",
    "\n",
    "# Write the DataFrame to a CSV file\n",
    "df.to_csv(csv_file, index=False)\n",
    "df = pd.read_csv('outage.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | Major power outage events in the continental U.S.                                                           |   Unnamed: 1 |   Unnamed: 2 |   Unnamed: 3 |   Unnamed: 4 |   Unnamed: 5 |   Unnamed: 6 |   Unnamed: 7 |   Unnamed: 8 |   Unnamed: 9 |   Unnamed: 10 |   Unnamed: 11 |   Unnamed: 12 |   Unnamed: 13 |   Unnamed: 14 |   Unnamed: 15 |   Unnamed: 16 |   Unnamed: 17 |   Unnamed: 18 |   Unnamed: 19 |   Unnamed: 20 |   Unnamed: 21 |   Unnamed: 22 |   Unnamed: 23 |   Unnamed: 24 |   Unnamed: 25 |   Unnamed: 26 |   Unnamed: 27 |   Unnamed: 28 |   Unnamed: 29 |   Unnamed: 30 |   Unnamed: 31 |   Unnamed: 32 |   Unnamed: 33 |   Unnamed: 34 |   Unnamed: 35 |   Unnamed: 36 |   Unnamed: 37 |   Unnamed: 38 |   Unnamed: 39 |   Unnamed: 40 |   Unnamed: 41 |   Unnamed: 42 |   Unnamed: 43 |   Unnamed: 44 |   Unnamed: 45 |   Unnamed: 46 |   Unnamed: 47 |   Unnamed: 48 |   Unnamed: 49 |   Unnamed: 50 |   Unnamed: 51 |   Unnamed: 52 |   Unnamed: 53 |   Unnamed: 54 |   Unnamed: 55 |   Unnamed: 56 |\n",
      "|---:|:------------------------------------------------------------------------------------------------------------|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|\n",
      "|  0 | Time period: January 2000 - July 2016                                                                       |          nan |          nan |          nan |          nan |          nan |          nan |          nan |          nan |          nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |\n",
      "|  1 | Regions affected: Outages reported in this data file affected a single U.S. state at the time of occurrence |          nan |          nan |          nan |          nan |          nan |          nan |          nan |          nan |          nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |\n",
      "|  2 | nan                                                                                                         |          nan |          nan |          nan |          nan |          nan |          nan |          nan |          nan |          nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |           nan |\n"
     ]
    }
   ],
   "source": [
    "print(df.head(4).to_markdown(index=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First 5 rows are the header of the dataset, we drop them and filter it into correct format in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>OBS</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>U.S._STATE</th>\n",
       "      <th>POSTAL.CODE</th>\n",
       "      <th>NERC.REGION</th>\n",
       "      <th>CLIMATE.REGION</th>\n",
       "      <th>ANOMALY.LEVEL(numeric)</th>\n",
       "      <th>CLIMATE.CATEGORY</th>\n",
       "      <th>OUTAGE.START.DATE(Day of the week, Month Day, Year)</th>\n",
       "      <th>...</th>\n",
       "      <th>POPPCT_URBAN(%)</th>\n",
       "      <th>POPPCT_UC(%)</th>\n",
       "      <th>POPDEN_URBAN(persons per square mile)</th>\n",
       "      <th>POPDEN_UC(persons per square mile)</th>\n",
       "      <th>POPDEN_RURAL(persons per square mile)</th>\n",
       "      <th>AREAPCT_URBAN(%)</th>\n",
       "      <th>AREAPCT_UC(%)</th>\n",
       "      <th>PCT_LAND(%)</th>\n",
       "      <th>PCT_WATER_TOT(%)</th>\n",
       "      <th>PCT_WATER_INLAND(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>normal</td>\n",
       "      <td>2011-07-01 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>73.27</td>\n",
       "      <td>15.28</td>\n",
       "      <td>2279</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>91.5926658691451</td>\n",
       "      <td>8.40733413085488</td>\n",
       "      <td>5.47874298334407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>normal</td>\n",
       "      <td>2014-05-11 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>73.27</td>\n",
       "      <td>15.28</td>\n",
       "      <td>2279</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>91.5926658691451</td>\n",
       "      <td>8.40733413085488</td>\n",
       "      <td>5.47874298334407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>cold</td>\n",
       "      <td>2010-10-26 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>73.27</td>\n",
       "      <td>15.28</td>\n",
       "      <td>2279</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>91.5926658691451</td>\n",
       "      <td>8.40733413085488</td>\n",
       "      <td>5.47874298334407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>normal</td>\n",
       "      <td>2012-06-19 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>73.27</td>\n",
       "      <td>15.28</td>\n",
       "      <td>2279</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>91.5926658691451</td>\n",
       "      <td>8.40733413085488</td>\n",
       "      <td>5.47874298334407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>1.2</td>\n",
       "      <td>warm</td>\n",
       "      <td>2015-07-18 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>73.27</td>\n",
       "      <td>15.28</td>\n",
       "      <td>2279</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>91.5926658691451</td>\n",
       "      <td>8.40733413085488</td>\n",
       "      <td>5.47874298334407</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>1529</th>\n",
       "      <td>1530.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>cold</td>\n",
       "      <td>2011-12-06 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>59.9</td>\n",
       "      <td>19.9</td>\n",
       "      <td>2192.2</td>\n",
       "      <td>1868.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97.5996492121418</td>\n",
       "      <td>2.40176525502843</td>\n",
       "      <td>2.40176525502843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>1531.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>59.9</td>\n",
       "      <td>19.9</td>\n",
       "      <td>2192.2</td>\n",
       "      <td>1868.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.1</td>\n",
       "      <td>97.5996492121418</td>\n",
       "      <td>2.40176525502843</td>\n",
       "      <td>2.40176525502843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>1532.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>SD</td>\n",
       "      <td>RFC</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>0.5</td>\n",
       "      <td>warm</td>\n",
       "      <td>2009-08-29 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>56.65</td>\n",
       "      <td>26.73</td>\n",
       "      <td>2038.3</td>\n",
       "      <td>1905.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>98.3077441776026</td>\n",
       "      <td>1.69225582239743</td>\n",
       "      <td>1.69225582239743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>1533.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>SD</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>0.5</td>\n",
       "      <td>warm</td>\n",
       "      <td>2009-08-29 00:00:00</td>\n",
       "      <td>...</td>\n",
       "      <td>56.65</td>\n",
       "      <td>26.73</td>\n",
       "      <td>2038.3</td>\n",
       "      <td>1905.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>98.3077441776026</td>\n",
       "      <td>1.69225582239743</td>\n",
       "      <td>1.69225582239743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>1534.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>ASCC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>66.02</td>\n",
       "      <td>21.56</td>\n",
       "      <td>1802.6</td>\n",
       "      <td>1276</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>85.7611544611833</td>\n",
       "      <td>14.2388455388167</td>\n",
       "      <td>2.90118187392543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1534 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         OBS    YEAR  MONTH    U.S._STATE POSTAL.CODE NERC.REGION  \\\n",
       "0        1.0  2011.0    7.0     Minnesota          MN         MRO   \n",
       "1        2.0  2014.0    5.0     Minnesota          MN         MRO   \n",
       "2        3.0  2010.0   10.0     Minnesota          MN         MRO   \n",
       "3        4.0  2012.0    6.0     Minnesota          MN         MRO   \n",
       "4        5.0  2015.0    7.0     Minnesota          MN         MRO   \n",
       "...      ...     ...    ...           ...         ...         ...   \n",
       "1529  1530.0  2011.0   12.0  North Dakota          ND         MRO   \n",
       "1530  1531.0  2006.0    NaN  North Dakota          ND         MRO   \n",
       "1531  1532.0  2009.0    8.0  South Dakota          SD         RFC   \n",
       "1532  1533.0  2009.0    8.0  South Dakota          SD         MRO   \n",
       "1533  1534.0  2000.0    NaN        Alaska          AK        ASCC   \n",
       "\n",
       "          CLIMATE.REGION ANOMALY.LEVEL(numeric) CLIMATE.CATEGORY  \\\n",
       "0     East North Central                   -0.3           normal   \n",
       "1     East North Central                   -0.1           normal   \n",
       "2     East North Central                   -1.5             cold   \n",
       "3     East North Central                   -0.1           normal   \n",
       "4     East North Central                    1.2             warm   \n",
       "...                  ...                    ...              ...   \n",
       "1529  West North Central                   -0.9             cold   \n",
       "1530  West North Central                    NaN              NaN   \n",
       "1531  West North Central                    0.5             warm   \n",
       "1532  West North Central                    0.5             warm   \n",
       "1533                 NaN                    NaN              NaN   \n",
       "\n",
       "     OUTAGE.START.DATE(Day of the week, Month Day, Year)  ... POPPCT_URBAN(%)  \\\n",
       "0                                   2011-07-01 00:00:00   ...           73.27   \n",
       "1                                   2014-05-11 00:00:00   ...           73.27   \n",
       "2                                   2010-10-26 00:00:00   ...           73.27   \n",
       "3                                   2012-06-19 00:00:00   ...           73.27   \n",
       "4                                   2015-07-18 00:00:00   ...           73.27   \n",
       "...                                                 ...   ...             ...   \n",
       "1529                                2011-12-06 00:00:00   ...            59.9   \n",
       "1530                                                NaN   ...            59.9   \n",
       "1531                                2009-08-29 00:00:00   ...           56.65   \n",
       "1532                                2009-08-29 00:00:00   ...           56.65   \n",
       "1533                                                NaN   ...           66.02   \n",
       "\n",
       "     POPPCT_UC(%) POPDEN_URBAN(persons per square mile)  \\\n",
       "0           15.28                                  2279   \n",
       "1           15.28                                  2279   \n",
       "2           15.28                                  2279   \n",
       "3           15.28                                  2279   \n",
       "4           15.28                                  2279   \n",
       "...           ...                                   ...   \n",
       "1529         19.9                                2192.2   \n",
       "1530         19.9                                2192.2   \n",
       "1531        26.73                                2038.3   \n",
       "1532        26.73                                2038.3   \n",
       "1533        21.56                                1802.6   \n",
       "\n",
       "     POPDEN_UC(persons per square mile) POPDEN_RURAL(persons per square mile)  \\\n",
       "0                                1700.5                                  18.2   \n",
       "1                                1700.5                                  18.2   \n",
       "2                                1700.5                                  18.2   \n",
       "3                                1700.5                                  18.2   \n",
       "4                                1700.5                                  18.2   \n",
       "...                                 ...                                   ...   \n",
       "1529                             1868.2                                   3.9   \n",
       "1530                             1868.2                                   3.9   \n",
       "1531                             1905.4                                   4.7   \n",
       "1532                             1905.4                                   4.7   \n",
       "1533                               1276                                   0.4   \n",
       "\n",
       "     AREAPCT_URBAN(%) AREAPCT_UC(%)       PCT_LAND(%)  PCT_WATER_TOT(%)  \\\n",
       "0                2.14           0.6  91.5926658691451  8.40733413085488   \n",
       "1                2.14           0.6  91.5926658691451  8.40733413085488   \n",
       "2                2.14           0.6  91.5926658691451  8.40733413085488   \n",
       "3                2.14           0.6  91.5926658691451  8.40733413085488   \n",
       "4                2.14           0.6  91.5926658691451  8.40733413085488   \n",
       "...               ...           ...               ...               ...   \n",
       "1529             0.27           0.1  97.5996492121418  2.40176525502843   \n",
       "1530             0.27           0.1  97.5996492121418  2.40176525502843   \n",
       "1531              0.3          0.15  98.3077441776026  1.69225582239743   \n",
       "1532              0.3          0.15  98.3077441776026  1.69225582239743   \n",
       "1533             0.05          0.02  85.7611544611833  14.2388455388167   \n",
       "\n",
       "     PCT_WATER_INLAND(%)  \n",
       "0       5.47874298334407  \n",
       "1       5.47874298334407  \n",
       "2       5.47874298334407  \n",
       "3       5.47874298334407  \n",
       "4       5.47874298334407  \n",
       "...                  ...  \n",
       "1529    2.40176525502843  \n",
       "1530    2.40176525502843  \n",
       "1531    1.69225582239743  \n",
       "1532    1.69225582239743  \n",
       "1533    2.90118187392543  \n",
       "\n",
       "[1534 rows x 56 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_to_skip = list(range(5))\n",
    "df = pd.read_csv('outage.csv', skiprows=rows_to_skip, index_col='OBS')\n",
    "\n",
    "# Combine the units line and column names, drop unecessary rows and columns\n",
    "column = np.array(df.columns).astype('str')\n",
    "unites = np.array(df.iloc[0].fillna('')).astype('str')\n",
    "unites = [\"(\" + i + \")\" for i in unites]\n",
    "for i in range(len(unites)):\n",
    "    if unites[i] == '()':\n",
    "        unites[i] = ''\n",
    "combined_column = np.core.defchararray.add(column, unites)\n",
    "df.columns = combined_column\n",
    "df = df.reset_index().drop(0).drop('variables(Units)', axis=1).reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    |   OBS |   YEAR |   MONTH | U.S._STATE   | POSTAL.CODE   | NERC.REGION   | CLIMATE.REGION     |   ANOMALY.LEVEL(numeric) | CLIMATE.CATEGORY   | OUTAGE.START.DATE(Day of the week, Month Day, Year)   | OUTAGE.START.TIME(Hour:Minute:Second (AM / PM))   | OUTAGE.RESTORATION.DATE(Day of the week, Month Day, Year)   | OUTAGE.RESTORATION.TIME(Hour:Minute:Second (AM / PM))   | CAUSE.CATEGORY     | CAUSE.CATEGORY.DETAIL   |   HURRICANE.NAMES |   OUTAGE.DURATION(mins) |   DEMAND.LOSS.MW(Megawatt) |   CUSTOMERS.AFFECTED |   RES.PRICE(cents / kilowatt-hour) |   COM.PRICE(cents / kilowatt-hour) |   IND.PRICE(cents / kilowatt-hour) |   TOTAL.PRICE(cents / kilowatt-hour) |   RES.SALES(Megawatt-hour) |   COM.SALES(Megawatt-hour) |   IND.SALES(Megawatt-hour) |   TOTAL.SALES(Megawatt-hour) |   RES.PERCEN(%) |   COM.PERCEN(%) |   IND.PERCEN(%) |   RES.CUSTOMERS |   COM.CUSTOMERS |   IND.CUSTOMERS |   TOTAL.CUSTOMERS |   RES.CUST.PCT(%) |   COM.CUST.PCT(%) |   IND.CUST.PCT(%) |   PC.REALGSP.STATE(USD) |   PC.REALGSP.USA(USD) |   PC.REALGSP.REL(fraction) |   PC.REALGSP.CHANGE(%) |   UTIL.REALGSP(USD) |   TOTAL.REALGSP(USD) |   UTIL.CONTRI(%) |   PI.UTIL.OFUSA(%) |   POPULATION |   POPPCT_URBAN(%) |   POPPCT_UC(%) |   POPDEN_URBAN(persons per square mile) |   POPDEN_UC(persons per square mile) |   POPDEN_RURAL(persons per square mile) |   AREAPCT_URBAN(%) |   AREAPCT_UC(%) |   PCT_LAND(%) |   PCT_WATER_TOT(%) |   PCT_WATER_INLAND(%) |\n",
      "|---:|------:|-------:|--------:|:-------------|:--------------|:--------------|:-------------------|-------------------------:|:-------------------|:------------------------------------------------------|:--------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|:-------------------|:------------------------|------------------:|------------------------:|---------------------------:|---------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|-------------------------------------:|---------------------------:|---------------------------:|---------------------------:|-----------------------------:|----------------:|----------------:|----------------:|----------------:|----------------:|----------------:|------------------:|------------------:|------------------:|------------------:|------------------------:|----------------------:|---------------------------:|-----------------------:|--------------------:|---------------------:|-----------------:|-------------------:|-------------:|------------------:|---------------:|----------------------------------------:|-------------------------------------:|----------------------------------------:|-------------------:|----------------:|--------------:|-------------------:|----------------------:|\n",
      "|  0 |     1 |   2011 |       7 | Minnesota    | MN            | MRO           | East North Central |                     -0.3 | normal             | 2011-07-01 00:00:00                                   | 17:00:00                                          | 2011-07-03 00:00:00                                         | 20:00:00                                                | severe weather     | nan                     |               nan |                    3060 |                        nan |                70000 |                              11.6  |                               9.18 |                               6.81 |                                 9.28 |                    2332915 |                    2114774 |                    2113291 |                      6562520 |         35.5491 |         32.225  |         32.2024 |     2.30874e+06 |          276286 |           10673 |       2.5957e+06  |           88.9448 |           10.644  |          0.411181 |                   51268 |                 47586 |                    1.07738 |                    1.6 |                4802 |               274182 |          1.75139 |                2.2 |  5.34812e+06 |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 |\n",
      "|  1 |     2 |   2014 |       5 | Minnesota    | MN            | MRO           | East North Central |                     -0.1 | normal             | 2014-05-11 00:00:00                                   | 18:38:00                                          | 2014-05-11 00:00:00                                         | 18:39:00                                                | intentional attack | vandalism               |               nan |                       1 |                        nan |                  nan |                              12.12 |                               9.71 |                               6.49 |                                 9.28 |                    1586986 |                    1807756 |                    1887927 |                      5284231 |         30.0325 |         34.2104 |         35.7276 |     2.34586e+06 |          284978 |            9898 |       2.64074e+06 |           88.8335 |           10.7916 |          0.37482  |                   53499 |                 49091 |                    1.08979 |                    1.9 |                5226 |               291955 |          1.79    |                2.2 |  5.45712e+06 |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 |\n",
      "|  2 |     3 |   2010 |      10 | Minnesota    | MN            | MRO           | East North Central |                     -1.5 | cold               | 2010-10-26 00:00:00                                   | 20:00:00                                          | 2010-10-28 00:00:00                                         | 22:00:00                                                | severe weather     | heavy wind              |               nan |                    3000 |                        nan |                70000 |                              10.87 |                               8.19 |                               6.07 |                                 8.15 |                    1467293 |                    1801683 |                    1951295 |                      5222116 |         28.0977 |         34.501  |         37.366  |     2.30029e+06 |          276463 |           10150 |       2.58690e+06 |           88.9206 |           10.687  |          0.392361 |                   50447 |                 47287 |                    1.06683 |                    2.7 |                4571 |               267895 |          1.70627 |                2.1 |  5.3109e+06  |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 |\n",
      "|  3 |     4 |   2012 |       6 | Minnesota    | MN            | MRO           | East North Central |                     -0.1 | normal             | 2012-06-19 00:00:00                                   | 04:30:00                                          | 2012-06-20 00:00:00                                         | 23:00:00                                                | severe weather     | thunderstorm            |               nan |                    2550 |                        nan |                68200 |                              11.79 |                               9.25 |                               6.71 |                                 9.19 |                    1851519 |                    1941174 |                    1993026 |                      5787064 |         31.9941 |         33.5433 |         34.4393 |     2.31734e+06 |          278466 |           11010 |       2.60681e+06 |           88.8954 |           10.6822 |          0.422355 |                   51598 |                 48156 |                    1.07148 |                    0.6 |                5364 |               277627 |          1.93209 |                2.2 |  5.38044e+06 |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 |\n",
      "|  4 |     5 |   2015 |       7 | Minnesota    | MN            | MRO           | East North Central |                      1.2 | warm               | 2015-07-18 00:00:00                                   | 02:00:00                                          | 2015-07-19 00:00:00                                         | 07:00:00                                                | severe weather     | nan                     |               nan |                    1740 |                        250 |               250000 |                              13.07 |                              10.16 |                               7.74 |                                10.43 |                    2028875 |                    2161612 |                    1777937 |                      5970339 |         33.9826 |         36.2059 |         29.7795 |     2.37467e+06 |          289044 |            9812 |       2.67353e+06 |           88.8216 |           10.8113 |          0.367005 |                   54431 |                 49844 |                    1.09203 |                    1.7 |                4873 |               292023 |          1.6687  |                2.2 |  5.48959e+06 |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 |\n"
     ]
    }
   ],
   "source": [
    "print(df.head().to_markdown(index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>OUTAGE.RESTORATION</th>\n",
       "      <th>OUTAGE.START</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-07-03 20:00:00</td>\n",
       "      <td>2011-07-01 17:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-05-11 18:39:00</td>\n",
       "      <td>2014-05-11 18:38:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-10-28 22:00:00</td>\n",
       "      <td>2010-10-26 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-06-20 23:00:00</td>\n",
       "      <td>2012-06-19 04:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-07-19 07:00:00</td>\n",
       "      <td>2015-07-18 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>2011-12-06 20:00:00</td>\n",
       "      <td>2011-12-06 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>2009-08-29 23:53:00</td>\n",
       "      <td>2009-08-29 22:54:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>2009-08-29 14:01:00</td>\n",
       "      <td>2009-08-29 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1534 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      OUTAGE.RESTORATION        OUTAGE.START\n",
       "0    2011-07-03 20:00:00 2011-07-01 17:00:00\n",
       "1    2014-05-11 18:39:00 2014-05-11 18:38:00\n",
       "2    2010-10-28 22:00:00 2010-10-26 20:00:00\n",
       "3    2012-06-20 23:00:00 2012-06-19 04:30:00\n",
       "4    2015-07-19 07:00:00 2015-07-18 02:00:00\n",
       "...                  ...                 ...\n",
       "1529 2011-12-06 20:00:00 2011-12-06 08:00:00\n",
       "1530                 NaT                 NaT\n",
       "1531 2009-08-29 23:53:00 2009-08-29 22:54:00\n",
       "1532 2009-08-29 14:01:00 2009-08-29 11:00:00\n",
       "1533                 NaT                 NaT\n",
       "\n",
       "[1534 rows x 2 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['YEAR'] = df['YEAR'].astype('int')\n",
    "# Combine 'OUTAGE.START.DATE' and 'OUTAGE.START.TIME' into a new pd.Timestamp column called 'OUTAGE.START'\n",
    "start_time = df['OUTAGE.START.TIME(Hour:Minute:Second (AM / PM))']\n",
    "start_date = df['OUTAGE.START.DATE(Day of the week, Month Day, Year)']\n",
    "df[\"OUTAGE.START\"] = pd.to_datetime(start_date + \" \" + start_time)\n",
    "# combine 'OUTAGE.RESTORATION.DATE' and 'OUTAGE.RESTORATION.TIME' into a new pd.Timestamp column called 'OUTAGE.RESTORATION'.\n",
    "end_time = df['OUTAGE.RESTORATION.TIME(Hour:Minute:Second (AM / PM))']\n",
    "end_date = df['OUTAGE.RESTORATION.DATE(Day of the week, Month Day, Year)']\n",
    "df[\"OUTAGE.RESTORATION\"] = pd.to_datetime(end_date + \" \" + end_time)\n",
    "df[['OUTAGE.RESTORATION', 'OUTAGE.START']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1534 entries, 0 to 1533\n",
      "Data columns (total 58 columns):\n",
      " #   Column                                                     Non-Null Count  Dtype         \n",
      "---  ------                                                     --------------  -----         \n",
      " 0   OBS                                                        1534 non-null   float64       \n",
      " 1   YEAR                                                       1534 non-null   int32         \n",
      " 2   MONTH                                                      1525 non-null   float64       \n",
      " 3   U.S._STATE                                                 1534 non-null   object        \n",
      " 4   POSTAL.CODE                                                1534 non-null   object        \n",
      " 5   NERC.REGION                                                1534 non-null   object        \n",
      " 6   CLIMATE.REGION                                             1528 non-null   object        \n",
      " 7   ANOMALY.LEVEL(numeric)                                     1525 non-null   object        \n",
      " 8   CLIMATE.CATEGORY                                           1525 non-null   object        \n",
      " 9   OUTAGE.START.DATE(Day of the week, Month Day, Year)        1525 non-null   object        \n",
      " 10  OUTAGE.START.TIME(Hour:Minute:Second (AM / PM))            1525 non-null   object        \n",
      " 11  OUTAGE.RESTORATION.DATE(Day of the week, Month Day, Year)  1476 non-null   object        \n",
      " 12  OUTAGE.RESTORATION.TIME(Hour:Minute:Second (AM / PM))      1476 non-null   object        \n",
      " 13  CAUSE.CATEGORY                                             1534 non-null   object        \n",
      " 14  CAUSE.CATEGORY.DETAIL                                      1063 non-null   object        \n",
      " 15  HURRICANE.NAMES                                            72 non-null     object        \n",
      " 16  OUTAGE.DURATION(mins)                                      1476 non-null   object        \n",
      " 17  DEMAND.LOSS.MW(Megawatt)                                   829 non-null    object        \n",
      " 18  CUSTOMERS.AFFECTED                                         1091 non-null   float64       \n",
      " 19  RES.PRICE(cents / kilowatt-hour)                           1512 non-null   object        \n",
      " 20  COM.PRICE(cents / kilowatt-hour)                           1512 non-null   object        \n",
      " 21  IND.PRICE(cents / kilowatt-hour)                           1512 non-null   object        \n",
      " 22  TOTAL.PRICE(cents / kilowatt-hour)                         1512 non-null   object        \n",
      " 23  RES.SALES(Megawatt-hour)                                   1512 non-null   object        \n",
      " 24  COM.SALES(Megawatt-hour)                                   1512 non-null   object        \n",
      " 25  IND.SALES(Megawatt-hour)                                   1512 non-null   object        \n",
      " 26  TOTAL.SALES(Megawatt-hour)                                 1512 non-null   object        \n",
      " 27  RES.PERCEN(%)                                              1512 non-null   object        \n",
      " 28  COM.PERCEN(%)                                              1512 non-null   object        \n",
      " 29  IND.PERCEN(%)                                              1512 non-null   object        \n",
      " 30  RES.CUSTOMERS                                              1534 non-null   float64       \n",
      " 31  COM.CUSTOMERS                                              1534 non-null   float64       \n",
      " 32  IND.CUSTOMERS                                              1534 non-null   float64       \n",
      " 33  TOTAL.CUSTOMERS                                            1534 non-null   float64       \n",
      " 34  RES.CUST.PCT(%)                                            1534 non-null   object        \n",
      " 35  COM.CUST.PCT(%)                                            1534 non-null   object        \n",
      " 36  IND.CUST.PCT(%)                                            1534 non-null   object        \n",
      " 37  PC.REALGSP.STATE(USD)                                      1534 non-null   object        \n",
      " 38  PC.REALGSP.USA(USD)                                        1534 non-null   object        \n",
      " 39  PC.REALGSP.REL(fraction)                                   1534 non-null   object        \n",
      " 40  PC.REALGSP.CHANGE(%)                                       1534 non-null   object        \n",
      " 41  UTIL.REALGSP(USD)                                          1534 non-null   object        \n",
      " 42  TOTAL.REALGSP(USD)                                         1534 non-null   object        \n",
      " 43  UTIL.CONTRI(%)                                             1534 non-null   object        \n",
      " 44  PI.UTIL.OFUSA(%)                                           1534 non-null   object        \n",
      " 45  POPULATION                                                 1534 non-null   float64       \n",
      " 46  POPPCT_URBAN(%)                                            1534 non-null   object        \n",
      " 47  POPPCT_UC(%)                                               1534 non-null   object        \n",
      " 48  POPDEN_URBAN(persons per square mile)                      1534 non-null   object        \n",
      " 49  POPDEN_UC(persons per square mile)                         1524 non-null   object        \n",
      " 50  POPDEN_RURAL(persons per square mile)                      1524 non-null   object        \n",
      " 51  AREAPCT_URBAN(%)                                           1534 non-null   object        \n",
      " 52  AREAPCT_UC(%)                                              1534 non-null   object        \n",
      " 53  PCT_LAND(%)                                                1534 non-null   object        \n",
      " 54  PCT_WATER_TOT(%)                                           1534 non-null   object        \n",
      " 55  PCT_WATER_INLAND(%)                                        1534 non-null   object        \n",
      " 56  OUTAGE.START                                               1525 non-null   datetime64[ns]\n",
      " 57  OUTAGE.RESTORATION                                         1476 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(8), int32(1), object(47)\n",
      "memory usage: 689.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should then convert all the columns into proper data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ANOMALY.LEVEL'] = df['ANOMALY.LEVEL(numeric)'].astype(float)\n",
    "df = df.drop(columns=['ANOMALY.LEVEL(numeric)', 'OUTAGE.START.DATE(Day of the week, Month Day, Year)',\n",
    "                      'OUTAGE.START.TIME(Hour:Minute:Second (AM / PM))', \n",
    "                      'OUTAGE.RESTORATION.DATE(Day of the week, Month Day, Year)',\n",
    "                      'OUTAGE.RESTORATION.TIME(Hour:Minute:Second (AM / PM))'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_convert = {'OUTAGE.DURATION(mins)': float, 'DEMAND.LOSS.MW(Megawatt)': float,\n",
    "                      'RES.PRICE(cents / kilowatt-hour)': float, 'COM.PRICE(cents / kilowatt-hour)': float,\n",
    "                     'COM.PRICE(cents / kilowatt-hour)':float, 'IND.PRICE(cents / kilowatt-hour)': float,\n",
    "                     'TOTAL.PRICE(cents / kilowatt-hour)':float, 'RES.SALES(Megawatt-hour)': float,\n",
    "                     'COM.SALES(Megawatt-hour)':float, 'IND.SALES(Megawatt-hour)':float, 'TOTAL.PRICE(cents / kilowatt-hour)':float,\n",
    "                     'RES.PERCEN(%)':float, 'COM.PERCEN(%)':float, 'IND.PERCEN(%)':float, 'RES.CUST.PCT(%)':float,\n",
    "                     'COM.CUST.PCT(%)':float, 'IND.CUST.PCT(%)':float, 'PC.REALGSP.STATE(USD)':float, 'PC.REALGSP.USA(USD)':float,\n",
    "                     'PC.REALGSP.REL(fraction)':float, 'PC.REALGSP.CHANGE(%)':float, 'UTIL.REALGSP(USD)':float, 'TOTAL.REALGSP(USD)':float,\n",
    "                     'UTIL.CONTRI(%)':float, 'PI.UTIL.OFUSA(%)':float, 'POPPCT_URBAN(%)':float, 'POPPCT_UC(%)':float, \n",
    "                     'POPDEN_URBAN(persons per square mile)':float, 'POPDEN_UC(persons per square mile)':float, \n",
    "                     'POPDEN_RURAL(persons per square mile)':float, 'AREAPCT_URBAN(%)':float, 'AREAPCT_UC(%)':float,\n",
    "                     'PCT_LAND(%)':float, 'PCT_WATER_TOT(%)':float, 'PCT_WATER_INLAND(%)':float,'TOTAL.SALES(Megawatt-hour)':float}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>OBS</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>U.S._STATE</th>\n",
       "      <th>POSTAL.CODE</th>\n",
       "      <th>NERC.REGION</th>\n",
       "      <th>CLIMATE.REGION</th>\n",
       "      <th>CLIMATE.CATEGORY</th>\n",
       "      <th>CAUSE.CATEGORY</th>\n",
       "      <th>CAUSE.CATEGORY.DETAIL</th>\n",
       "      <th>...</th>\n",
       "      <th>POPDEN_UC(persons per square mile)</th>\n",
       "      <th>POPDEN_RURAL(persons per square mile)</th>\n",
       "      <th>AREAPCT_URBAN(%)</th>\n",
       "      <th>AREAPCT_UC(%)</th>\n",
       "      <th>PCT_LAND(%)</th>\n",
       "      <th>PCT_WATER_TOT(%)</th>\n",
       "      <th>PCT_WATER_INLAND(%)</th>\n",
       "      <th>OUTAGE.START</th>\n",
       "      <th>OUTAGE.RESTORATION</th>\n",
       "      <th>ANOMALY.LEVEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>normal</td>\n",
       "      <td>severe weather</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.60</td>\n",
       "      <td>91.592666</td>\n",
       "      <td>8.407334</td>\n",
       "      <td>5.478743</td>\n",
       "      <td>2011-07-01 17:00:00</td>\n",
       "      <td>2011-07-03 20:00:00</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>normal</td>\n",
       "      <td>intentional attack</td>\n",
       "      <td>vandalism</td>\n",
       "      <td>...</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.60</td>\n",
       "      <td>91.592666</td>\n",
       "      <td>8.407334</td>\n",
       "      <td>5.478743</td>\n",
       "      <td>2014-05-11 18:38:00</td>\n",
       "      <td>2014-05-11 18:39:00</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>cold</td>\n",
       "      <td>severe weather</td>\n",
       "      <td>heavy wind</td>\n",
       "      <td>...</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.60</td>\n",
       "      <td>91.592666</td>\n",
       "      <td>8.407334</td>\n",
       "      <td>5.478743</td>\n",
       "      <td>2010-10-26 20:00:00</td>\n",
       "      <td>2010-10-28 22:00:00</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>normal</td>\n",
       "      <td>severe weather</td>\n",
       "      <td>thunderstorm</td>\n",
       "      <td>...</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.60</td>\n",
       "      <td>91.592666</td>\n",
       "      <td>8.407334</td>\n",
       "      <td>5.478743</td>\n",
       "      <td>2012-06-19 04:30:00</td>\n",
       "      <td>2012-06-20 23:00:00</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MN</td>\n",
       "      <td>MRO</td>\n",
       "      <td>East North Central</td>\n",
       "      <td>warm</td>\n",
       "      <td>severe weather</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1700.5</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.60</td>\n",
       "      <td>91.592666</td>\n",
       "      <td>8.407334</td>\n",
       "      <td>5.478743</td>\n",
       "      <td>2015-07-18 02:00:00</td>\n",
       "      <td>2015-07-19 07:00:00</td>\n",
       "      <td>1.2</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>1529</th>\n",
       "      <td>1530.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>12.0</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>cold</td>\n",
       "      <td>public appeal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1868.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.10</td>\n",
       "      <td>97.599649</td>\n",
       "      <td>2.401765</td>\n",
       "      <td>2.401765</td>\n",
       "      <td>2011-12-06 08:00:00</td>\n",
       "      <td>2011-12-06 20:00:00</td>\n",
       "      <td>-0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>1531.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ND</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fuel supply emergency</td>\n",
       "      <td>Coal</td>\n",
       "      <td>...</td>\n",
       "      <td>1868.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.10</td>\n",
       "      <td>97.599649</td>\n",
       "      <td>2.401765</td>\n",
       "      <td>2.401765</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>1532.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>8.0</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>SD</td>\n",
       "      <td>RFC</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>warm</td>\n",
       "      <td>islanding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1905.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>98.307744</td>\n",
       "      <td>1.692256</td>\n",
       "      <td>1.692256</td>\n",
       "      <td>2009-08-29 22:54:00</td>\n",
       "      <td>2009-08-29 23:53:00</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>1533.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>8.0</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>SD</td>\n",
       "      <td>MRO</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>warm</td>\n",
       "      <td>islanding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1905.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.15</td>\n",
       "      <td>98.307744</td>\n",
       "      <td>1.692256</td>\n",
       "      <td>1.692256</td>\n",
       "      <td>2009-08-29 11:00:00</td>\n",
       "      <td>2009-08-29 14:01:00</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>1534.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>ASCC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>equipment failure</td>\n",
       "      <td>failure</td>\n",
       "      <td>...</td>\n",
       "      <td>1276.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>85.761154</td>\n",
       "      <td>14.238846</td>\n",
       "      <td>2.901182</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1534 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         OBS  YEAR  MONTH    U.S._STATE POSTAL.CODE NERC.REGION  \\\n",
       "0        1.0  2011    7.0     Minnesota          MN         MRO   \n",
       "1        2.0  2014    5.0     Minnesota          MN         MRO   \n",
       "2        3.0  2010   10.0     Minnesota          MN         MRO   \n",
       "3        4.0  2012    6.0     Minnesota          MN         MRO   \n",
       "4        5.0  2015    7.0     Minnesota          MN         MRO   \n",
       "...      ...   ...    ...           ...         ...         ...   \n",
       "1529  1530.0  2011   12.0  North Dakota          ND         MRO   \n",
       "1530  1531.0  2006    NaN  North Dakota          ND         MRO   \n",
       "1531  1532.0  2009    8.0  South Dakota          SD         RFC   \n",
       "1532  1533.0  2009    8.0  South Dakota          SD         MRO   \n",
       "1533  1534.0  2000    NaN        Alaska          AK        ASCC   \n",
       "\n",
       "          CLIMATE.REGION CLIMATE.CATEGORY         CAUSE.CATEGORY  \\\n",
       "0     East North Central           normal         severe weather   \n",
       "1     East North Central           normal     intentional attack   \n",
       "2     East North Central             cold         severe weather   \n",
       "3     East North Central           normal         severe weather   \n",
       "4     East North Central             warm         severe weather   \n",
       "...                  ...              ...                    ...   \n",
       "1529  West North Central             cold          public appeal   \n",
       "1530  West North Central              NaN  fuel supply emergency   \n",
       "1531  West North Central             warm              islanding   \n",
       "1532  West North Central             warm              islanding   \n",
       "1533                 NaN              NaN      equipment failure   \n",
       "\n",
       "     CAUSE.CATEGORY.DETAIL  ... POPDEN_UC(persons per square mile)  \\\n",
       "0                      NaN  ...                             1700.5   \n",
       "1                vandalism  ...                             1700.5   \n",
       "2               heavy wind  ...                             1700.5   \n",
       "3             thunderstorm  ...                             1700.5   \n",
       "4                      NaN  ...                             1700.5   \n",
       "...                    ...  ...                                ...   \n",
       "1529                   NaN  ...                             1868.2   \n",
       "1530                  Coal  ...                             1868.2   \n",
       "1531                   NaN  ...                             1905.4   \n",
       "1532                   NaN  ...                             1905.4   \n",
       "1533               failure  ...                             1276.0   \n",
       "\n",
       "      POPDEN_RURAL(persons per square mile)  AREAPCT_URBAN(%)  AREAPCT_UC(%)  \\\n",
       "0                                      18.2              2.14           0.60   \n",
       "1                                      18.2              2.14           0.60   \n",
       "2                                      18.2              2.14           0.60   \n",
       "3                                      18.2              2.14           0.60   \n",
       "4                                      18.2              2.14           0.60   \n",
       "...                                     ...               ...            ...   \n",
       "1529                                    3.9              0.27           0.10   \n",
       "1530                                    3.9              0.27           0.10   \n",
       "1531                                    4.7              0.30           0.15   \n",
       "1532                                    4.7              0.30           0.15   \n",
       "1533                                    0.4              0.05           0.02   \n",
       "\n",
       "      PCT_LAND(%)  PCT_WATER_TOT(%)  PCT_WATER_INLAND(%)        OUTAGE.START  \\\n",
       "0       91.592666          8.407334             5.478743 2011-07-01 17:00:00   \n",
       "1       91.592666          8.407334             5.478743 2014-05-11 18:38:00   \n",
       "2       91.592666          8.407334             5.478743 2010-10-26 20:00:00   \n",
       "3       91.592666          8.407334             5.478743 2012-06-19 04:30:00   \n",
       "4       91.592666          8.407334             5.478743 2015-07-18 02:00:00   \n",
       "...           ...               ...                  ...                 ...   \n",
       "1529    97.599649          2.401765             2.401765 2011-12-06 08:00:00   \n",
       "1530    97.599649          2.401765             2.401765                 NaT   \n",
       "1531    98.307744          1.692256             1.692256 2009-08-29 22:54:00   \n",
       "1532    98.307744          1.692256             1.692256 2009-08-29 11:00:00   \n",
       "1533    85.761154         14.238846             2.901182                 NaT   \n",
       "\n",
       "      OUTAGE.RESTORATION  ANOMALY.LEVEL  \n",
       "0    2011-07-03 20:00:00           -0.3  \n",
       "1    2014-05-11 18:39:00           -0.1  \n",
       "2    2010-10-28 22:00:00           -1.5  \n",
       "3    2012-06-20 23:00:00           -0.1  \n",
       "4    2015-07-19 07:00:00            1.2  \n",
       "...                  ...            ...  \n",
       "1529 2011-12-06 20:00:00           -0.9  \n",
       "1530                 NaT            NaN  \n",
       "1531 2009-08-29 23:53:00            0.5  \n",
       "1532 2009-08-29 14:01:00            0.5  \n",
       "1533                 NaT            NaN  \n",
       "\n",
       "[1534 rows x 54 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.astype(columns_to_convert)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1534 entries, 0 to 1533\n",
      "Data columns (total 54 columns):\n",
      " #   Column                                 Non-Null Count  Dtype         \n",
      "---  ------                                 --------------  -----         \n",
      " 0   OBS                                    1534 non-null   float64       \n",
      " 1   YEAR                                   1534 non-null   int32         \n",
      " 2   MONTH                                  1525 non-null   float64       \n",
      " 3   U.S._STATE                             1534 non-null   object        \n",
      " 4   POSTAL.CODE                            1534 non-null   object        \n",
      " 5   NERC.REGION                            1534 non-null   object        \n",
      " 6   CLIMATE.REGION                         1528 non-null   object        \n",
      " 7   CLIMATE.CATEGORY                       1525 non-null   object        \n",
      " 8   CAUSE.CATEGORY                         1534 non-null   object        \n",
      " 9   CAUSE.CATEGORY.DETAIL                  1063 non-null   object        \n",
      " 10  HURRICANE.NAMES                        72 non-null     object        \n",
      " 11  OUTAGE.DURATION(mins)                  1476 non-null   float64       \n",
      " 12  DEMAND.LOSS.MW(Megawatt)               829 non-null    float64       \n",
      " 13  CUSTOMERS.AFFECTED                     1091 non-null   float64       \n",
      " 14  RES.PRICE(cents / kilowatt-hour)       1512 non-null   float64       \n",
      " 15  COM.PRICE(cents / kilowatt-hour)       1512 non-null   float64       \n",
      " 16  IND.PRICE(cents / kilowatt-hour)       1512 non-null   float64       \n",
      " 17  TOTAL.PRICE(cents / kilowatt-hour)     1512 non-null   float64       \n",
      " 18  RES.SALES(Megawatt-hour)               1512 non-null   float64       \n",
      " 19  COM.SALES(Megawatt-hour)               1512 non-null   float64       \n",
      " 20  IND.SALES(Megawatt-hour)               1512 non-null   float64       \n",
      " 21  TOTAL.SALES(Megawatt-hour)             1512 non-null   float64       \n",
      " 22  RES.PERCEN(%)                          1512 non-null   float64       \n",
      " 23  COM.PERCEN(%)                          1512 non-null   float64       \n",
      " 24  IND.PERCEN(%)                          1512 non-null   float64       \n",
      " 25  RES.CUSTOMERS                          1534 non-null   float64       \n",
      " 26  COM.CUSTOMERS                          1534 non-null   float64       \n",
      " 27  IND.CUSTOMERS                          1534 non-null   float64       \n",
      " 28  TOTAL.CUSTOMERS                        1534 non-null   float64       \n",
      " 29  RES.CUST.PCT(%)                        1534 non-null   float64       \n",
      " 30  COM.CUST.PCT(%)                        1534 non-null   float64       \n",
      " 31  IND.CUST.PCT(%)                        1534 non-null   float64       \n",
      " 32  PC.REALGSP.STATE(USD)                  1534 non-null   float64       \n",
      " 33  PC.REALGSP.USA(USD)                    1534 non-null   float64       \n",
      " 34  PC.REALGSP.REL(fraction)               1534 non-null   float64       \n",
      " 35  PC.REALGSP.CHANGE(%)                   1534 non-null   float64       \n",
      " 36  UTIL.REALGSP(USD)                      1534 non-null   float64       \n",
      " 37  TOTAL.REALGSP(USD)                     1534 non-null   float64       \n",
      " 38  UTIL.CONTRI(%)                         1534 non-null   float64       \n",
      " 39  PI.UTIL.OFUSA(%)                       1534 non-null   float64       \n",
      " 40  POPULATION                             1534 non-null   float64       \n",
      " 41  POPPCT_URBAN(%)                        1534 non-null   float64       \n",
      " 42  POPPCT_UC(%)                           1534 non-null   float64       \n",
      " 43  POPDEN_URBAN(persons per square mile)  1534 non-null   float64       \n",
      " 44  POPDEN_UC(persons per square mile)     1524 non-null   float64       \n",
      " 45  POPDEN_RURAL(persons per square mile)  1524 non-null   float64       \n",
      " 46  AREAPCT_URBAN(%)                       1534 non-null   float64       \n",
      " 47  AREAPCT_UC(%)                          1534 non-null   float64       \n",
      " 48  PCT_LAND(%)                            1534 non-null   float64       \n",
      " 49  PCT_WATER_TOT(%)                       1534 non-null   float64       \n",
      " 50  PCT_WATER_INLAND(%)                    1534 non-null   float64       \n",
      " 51  OUTAGE.START                           1525 non-null   datetime64[ns]\n",
      " 52  OUTAGE.RESTORATION                     1476 non-null   datetime64[ns]\n",
      " 53  ANOMALY.LEVEL                          1525 non-null   float64       \n",
      "dtypes: datetime64[ns](2), float64(43), int32(1), object(8)\n",
      "memory usage: 641.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_values = df.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('OBS', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    |   YEAR |   MONTH | U.S._STATE   | POSTAL.CODE   | NERC.REGION   | CLIMATE.REGION     | CLIMATE.CATEGORY   | CAUSE.CATEGORY     | CAUSE.CATEGORY.DETAIL   |   HURRICANE.NAMES |   OUTAGE.DURATION(mins) |   DEMAND.LOSS.MW(Megawatt) |   CUSTOMERS.AFFECTED |   RES.PRICE(cents / kilowatt-hour) |   COM.PRICE(cents / kilowatt-hour) |   IND.PRICE(cents / kilowatt-hour) |   TOTAL.PRICE(cents / kilowatt-hour) |   RES.SALES(Megawatt-hour) |   COM.SALES(Megawatt-hour) |   IND.SALES(Megawatt-hour) |   TOTAL.SALES(Megawatt-hour) |   RES.PERCEN(%) |   COM.PERCEN(%) |   IND.PERCEN(%) |   RES.CUSTOMERS |   COM.CUSTOMERS |   IND.CUSTOMERS |   TOTAL.CUSTOMERS |   RES.CUST.PCT(%) |   COM.CUST.PCT(%) |   IND.CUST.PCT(%) |   PC.REALGSP.STATE(USD) |   PC.REALGSP.USA(USD) |   PC.REALGSP.REL(fraction) |   PC.REALGSP.CHANGE(%) |   UTIL.REALGSP(USD) |   TOTAL.REALGSP(USD) |   UTIL.CONTRI(%) |   PI.UTIL.OFUSA(%) |   POPULATION |   POPPCT_URBAN(%) |   POPPCT_UC(%) |   POPDEN_URBAN(persons per square mile) |   POPDEN_UC(persons per square mile) |   POPDEN_RURAL(persons per square mile) |   AREAPCT_URBAN(%) |   AREAPCT_UC(%) |   PCT_LAND(%) |   PCT_WATER_TOT(%) |   PCT_WATER_INLAND(%) | OUTAGE.START        | OUTAGE.RESTORATION   |   ANOMALY.LEVEL |\n",
      "|---:|-------:|--------:|:-------------|:--------------|:--------------|:-------------------|:-------------------|:-------------------|:------------------------|------------------:|------------------------:|---------------------------:|---------------------:|-----------------------------------:|-----------------------------------:|-----------------------------------:|-------------------------------------:|---------------------------:|---------------------------:|---------------------------:|-----------------------------:|----------------:|----------------:|----------------:|----------------:|----------------:|----------------:|------------------:|------------------:|------------------:|------------------:|------------------------:|----------------------:|---------------------------:|-----------------------:|--------------------:|---------------------:|-----------------:|-------------------:|-------------:|------------------:|---------------:|----------------------------------------:|-------------------------------------:|----------------------------------------:|-------------------:|----------------:|--------------:|-------------------:|----------------------:|:--------------------|:---------------------|----------------:|\n",
      "|  0 |   2011 |       7 | Minnesota    | MN            | MRO           | East North Central | normal             | severe weather     | nan                     |               nan |                    3060 |                        nan |                70000 |                              11.6  |                               9.18 |                               6.81 |                                 9.28 |                2.33292e+06 |                2.11477e+06 |                2.11329e+06 |                  6.56252e+06 |         35.5491 |         32.225  |         32.2024 |     2.30874e+06 |          276286 |           10673 |       2.5957e+06  |           88.9448 |           10.644  |          0.411181 |                   51268 |                 47586 |                    1.07738 |                    1.6 |                4802 |               274182 |          1.75139 |                2.2 |  5.34812e+06 |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 | 2011-07-01 17:00:00 | 2011-07-03 20:00:00  |            -0.3 |\n",
      "|  1 |   2014 |       5 | Minnesota    | MN            | MRO           | East North Central | normal             | intentional attack | vandalism               |               nan |                       1 |                        nan |                  nan |                              12.12 |                               9.71 |                               6.49 |                                 9.28 |                1.58699e+06 |                1.80776e+06 |                1.88793e+06 |                  5.28423e+06 |         30.0325 |         34.2104 |         35.7276 |     2.34586e+06 |          284978 |            9898 |       2.64074e+06 |           88.8335 |           10.7916 |          0.37482  |                   53499 |                 49091 |                    1.08979 |                    1.9 |                5226 |               291955 |          1.79    |                2.2 |  5.45712e+06 |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 | 2014-05-11 18:38:00 | 2014-05-11 18:39:00  |            -0.1 |\n",
      "|  2 |   2010 |      10 | Minnesota    | MN            | MRO           | East North Central | cold               | severe weather     | heavy wind              |               nan |                    3000 |                        nan |                70000 |                              10.87 |                               8.19 |                               6.07 |                                 8.15 |                1.46729e+06 |                1.80168e+06 |                1.9513e+06  |                  5.22212e+06 |         28.0977 |         34.501  |         37.366  |     2.30029e+06 |          276463 |           10150 |       2.58690e+06 |           88.9206 |           10.687  |          0.392361 |                   50447 |                 47287 |                    1.06683 |                    2.7 |                4571 |               267895 |          1.70627 |                2.1 |  5.3109e+06  |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 | 2010-10-26 20:00:00 | 2010-10-28 22:00:00  |            -1.5 |\n",
      "|  3 |   2012 |       6 | Minnesota    | MN            | MRO           | East North Central | normal             | severe weather     | thunderstorm            |               nan |                    2550 |                        nan |                68200 |                              11.79 |                               9.25 |                               6.71 |                                 9.19 |                1.85152e+06 |                1.94117e+06 |                1.99303e+06 |                  5.78706e+06 |         31.9941 |         33.5433 |         34.4393 |     2.31734e+06 |          278466 |           11010 |       2.60681e+06 |           88.8954 |           10.6822 |          0.422355 |                   51598 |                 48156 |                    1.07148 |                    0.6 |                5364 |               277627 |          1.93209 |                2.2 |  5.38044e+06 |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 | 2012-06-19 04:30:00 | 2012-06-20 23:00:00  |            -0.1 |\n",
      "|  4 |   2015 |       7 | Minnesota    | MN            | MRO           | East North Central | warm               | severe weather     | nan                     |               nan |                    1740 |                        250 |               250000 |                              13.07 |                              10.16 |                               7.74 |                                10.43 |                2.02888e+06 |                2.16161e+06 |                1.77794e+06 |                  5.97034e+06 |         33.9826 |         36.2059 |         29.7795 |     2.37467e+06 |          289044 |            9812 |       2.67353e+06 |           88.8216 |           10.8113 |          0.367005 |                   54431 |                 49844 |                    1.09203 |                    1.7 |                4873 |               292023 |          1.6687  |                2.2 |  5.48959e+06 |             73.27 |          15.28 |                                    2279 |                               1700.5 |                                    18.2 |               2.14 |             0.6 |       91.5927 |            8.40733 |               5.47874 | 2015-07-18 02:00:00 | 2015-07-19 07:00:00  |             1.2 |\n"
     ]
    }
   ],
   "source": [
    "print(df.head().to_markdown(index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>CUSTOMERS.AFFECTED</th>\n",
       "      <th>CAUSE.CATEGORY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70000.0</td>\n",
       "      <td>severe weather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>intentional attack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70000.0</td>\n",
       "      <td>severe weather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68200.0</td>\n",
       "      <td>severe weather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>250000.0</td>\n",
       "      <td>severe weather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>34500.0</td>\n",
       "      <td>public appeal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>NaN</td>\n",
       "      <td>fuel supply emergency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>NaN</td>\n",
       "      <td>islanding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>NaN</td>\n",
       "      <td>islanding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>14273.0</td>\n",
       "      <td>equipment failure</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1534 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CUSTOMERS.AFFECTED         CAUSE.CATEGORY\n",
       "0                70000.0         severe weather\n",
       "1                    NaN     intentional attack\n",
       "2                70000.0         severe weather\n",
       "3                68200.0         severe weather\n",
       "4               250000.0         severe weather\n",
       "...                  ...                    ...\n",
       "1529             34500.0          public appeal\n",
       "1530                 NaN  fuel supply emergency\n",
       "1531                 NaN              islanding\n",
       "1532                 NaN              islanding\n",
       "1533             14273.0      equipment failure\n",
       "\n",
       "[1534 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing1 = df[[\"CUSTOMERS.AFFECTED\", 'CAUSE.CATEGORY']]\n",
    "missing1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   CUSTOMERS.AFFECTED | CAUSE.CATEGORY     |\n",
      "|---------------------:|:-------------------|\n",
      "|                70000 | severe weather     |\n",
      "|                  nan | intentional attack |\n",
      "|                70000 | severe weather     |\n",
      "|                68200 | severe weather     |\n",
      "|               250000 | severe weather     |\n",
      "|                60000 | severe weather     |\n",
      "|                63000 | severe weather     |\n",
      "|               300000 | severe weather     |\n",
      "|                 5941 | intentional attack |\n",
      "|               400000 | severe weather     |\n"
     ]
    }
   ],
   "source": [
    "print(missing1.head(10).to_markdown(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   CUSTOMERS.AFFECTED |   TOTAL.PRICE(cents / kilowatt-hour) |\n",
      "|---------------------:|-------------------------------------:|\n",
      "|                70000 |                                 9.28 |\n",
      "|                  nan |                                 9.28 |\n",
      "|                70000 |                                 8.15 |\n",
      "|                68200 |                                 9.19 |\n",
      "|               250000 |                                10.43 |\n",
      "|                60000 |                                 8.28 |\n",
      "|                63000 |                                 9.12 |\n",
      "|               300000 |                                 7.36 |\n",
      "|                 5941 |                                 9.03 |\n",
      "|               400000 |                                10    |\n"
     ]
    }
   ],
   "source": [
    "missing2 = df[[\"CUSTOMERS.AFFECTED\", 'TOTAL.PRICE(cents / kilowatt-hour)']]\n",
    "missing2\n",
    "print(missing2.head(10).to_markdown(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   OUTAGE.DURATION(mins) | CLIMATE.REGION     |\n",
      "|------------------------:|:-------------------|\n",
      "|                    3060 | East North Central |\n",
      "|                       1 | East North Central |\n",
      "|                    3000 | East North Central |\n",
      "|                    2550 | East North Central |\n",
      "|                    1740 | East North Central |\n",
      "|                    1860 | East North Central |\n",
      "|                    2970 | East North Central |\n",
      "|                    3960 | East North Central |\n",
      "|                     155 | East North Central |\n",
      "|                    3621 | East North Central |\n"
     ]
    }
   ],
   "source": [
    "test1 = df[['OUTAGE.DURATION(mins)','CLIMATE.REGION']]\n",
    "test1\n",
    "print(test1.head(10).to_markdown(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| CLIMATE.REGION     |   CUSTOMERS.AFFECTED |\n",
      "|:-------------------|---------------------:|\n",
      "| East North Central |                70000 |\n",
      "| East North Central |                  nan |\n",
      "| East North Central |                70000 |\n",
      "| East North Central |                68200 |\n",
      "| East North Central |               250000 |\n",
      "| East North Central |                60000 |\n",
      "| East North Central |                63000 |\n",
      "| East North Central |               300000 |\n",
      "| East North Central |                 5941 |\n",
      "| East North Central |               400000 |\n"
     ]
    }
   ],
   "source": [
    "test2 = df[['CLIMATE.REGION', 'CUSTOMERS.AFFECTED']]\n",
    "test2\n",
    "print(test2.head(10).to_markdown(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can first find some relationship or patterns within columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California              210\n",
       "Texas                   127\n",
       "Washington               97\n",
       "Michigan                 95\n",
       "New York                 71\n",
       "Maryland                 58\n",
       "Pennsylvania             57\n",
       "Illinois                 46\n",
       "Florida                  45\n",
       "Ohio                     43\n",
       "Indiana                  43\n",
       "Utah                     41\n",
       "Delaware                 41\n",
       "North Carolina           40\n",
       "Louisiana                40\n",
       "Virginia                 37\n",
       "New Jersey               35\n",
       "Tennessee                34\n",
       "Arizona                  28\n",
       "Oregon                   26\n",
       "Arkansas                 25\n",
       "Oklahoma                 24\n",
       "Wisconsin                20\n",
       "Maine                    19\n",
       "Massachusetts            18\n",
       "Connecticut              18\n",
       "Georgia                  17\n",
       "Missouri                 17\n",
       "Colorado                 15\n",
       "Minnesota                15\n",
       "New Hampshire            14\n",
       "Kentucky                 13\n",
       "District of Columbia     10\n",
       "Idaho                     9\n",
       "Vermont                   9\n",
       "Kansas                    9\n",
       "Iowa                      8\n",
       "New Mexico                8\n",
       "South Carolina            8\n",
       "Nevada                    7\n",
       "Wyoming                   6\n",
       "Alabama                   6\n",
       "Hawaii                    5\n",
       "Nebraska                  4\n",
       "Mississippi               4\n",
       "West Virginia             4\n",
       "Montana                   3\n",
       "North Dakota              2\n",
       "South Dakota              2\n",
       "Alaska                    1\n",
       "Name: U.S._STATE, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_states = df['U.S._STATE'].value_counts()\n",
    "all_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(all_states, color=all_states.values)\n",
    "fig.update_layout(title='State Power Outage Count', xaxis_title='States', yaxis_title='Values')\n",
    "# fig.write_html('state_power_outage_count.html', include_plotlyjs='cdn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_counts = df['POSTAL.CODE'].value_counts().reset_index()\n",
    "state_counts.columns = ['POSTAL.CODE', 'Frequency']\n",
    "\n",
    "# Create the geospatial graph\n",
    "fig = px.choropleth(state_counts, \n",
    "                    locations='POSTAL.CODE', \n",
    "                    locationmode='USA-states',\n",
    "                    color='Frequency',\n",
    "                    scope='usa',\n",
    "                    labels={'Frequency': 'Frequency'},\n",
    "                    title='State Frequency Map')\n",
    "\n",
    "fig.show()\n",
    "# fig.write_html('state_frequency_map.html', include_plotlyjs='cdn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(df['CAUSE.CATEGORY'].value_counts(), x=df['CAUSE.CATEGORY'].value_counts().index, y=df['CAUSE.CATEGORY'].value_counts().values)\n",
    "fig.update_layout(title='Count of Outages by Cause Category', xaxis_title='Cause Category', yaxis_title='Count')\n",
    "fig.show()\n",
    "# fig.write_html('outage_count_by_category.html', include_plotlyjs='cdn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.box(df, x='CLIMATE.CATEGORY', y='OUTAGE.DURATION(mins)', title='Outage Duration by Climate Category')\n",
    "fig.update_layout(xaxis_title='Climate Category', yaxis_title='Outage Duration (minutes)')\n",
    "fig.show()\n",
    "# fig.write_html('duration_vs_category.html', include_plotlyjs='cdn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship between residential sales and commericial sales, find some pattern when set the color to population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(df, x='RES.SALES(Megawatt-hour)', y='COM.SALES(Megawatt-hour)', title='Residential vs Commercial Sales', trendline='ols',color = 'POPULATION')\n",
    "fig.update_layout(xaxis_title='Residential Sales (Megawatt-hour)', yaxis_title='Commercial Sales (Megawatt-hour)')\n",
    "fig.show()\n",
    "\n",
    "# fig.write_html('resales_vs_comsales.html', include_plotlyjs='cdn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation between each column (Correlation Heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.corr()  # Assuming you want to calculate correlations between all numeric columns\n",
    "fig = px.imshow(corr_matrix, color_continuous_scale='RdBu', title='Correlation Heatmap')\n",
    "fig.show()\n",
    "# fig.write_html('correlation_heatmap.html', include_plotlyjs='cdn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.pie(df, names='NERC.REGION', title='NERC Region Distribution')\n",
    "fig.show()\n",
    "# fig.write_html('region_pie_chary.html', include_plotlyjs='cdn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivoting and Aggregation -- Interesting Aggregates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tabulate\n",
      "  Downloading tabulate-0.9.0-py3-none-any.whl (35 kB)\n",
      "Installing collected packages: tabulate\n",
      "Successfully installed tabulate-0.9.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>CUSTOMERS.AFFECTED</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>4270581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1431411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>6382586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>12463108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>13592556.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>13552084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>10152092.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>5973433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>19964926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>6813477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>10108129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>16438669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>12703973.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>7018398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>8022197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>5629211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1993908.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CUSTOMERS.AFFECTED\n",
       "YEAR                    \n",
       "2000           4270581.0\n",
       "2001           1431411.0\n",
       "2002           6382586.0\n",
       "2003          12463108.0\n",
       "2004          13592556.0\n",
       "2005          13552084.0\n",
       "2006          10152092.0\n",
       "2007           5973433.0\n",
       "2008          19964926.0\n",
       "2009           6813477.0\n",
       "2010          10108129.0\n",
       "2011          16438669.0\n",
       "2012          12703973.0\n",
       "2013           7018398.0\n",
       "2014           8022197.0\n",
       "2015           5629211.0\n",
       "2016           1993908.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total customers affected per year:\n",
    "total_customers_affected_per_year = pd.DataFrame(df.groupby('YEAR')['CUSTOMERS.AFFECTED'].sum())\n",
    "total_customers_affected_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   YEAR |   CUSTOMERS.AFFECTED |\n",
      "|-------:|---------------------:|\n",
      "|   2000 |          4.27058e+06 |\n",
      "|   2001 |          1.43141e+06 |\n",
      "|   2002 |          6.38259e+06 |\n",
      "|   2003 |          1.24631e+07 |\n",
      "|   2004 |          1.35926e+07 |\n",
      "|   2005 |          1.35521e+07 |\n",
      "|   2006 |          1.01521e+07 |\n",
      "|   2007 |          5.97343e+06 |\n",
      "|   2008 |          1.99649e+07 |\n",
      "|   2009 |          6.81348e+06 |\n",
      "|   2010 |          1.01081e+07 |\n",
      "|   2011 |          1.64387e+07 |\n",
      "|   2012 |          1.2704e+07  |\n",
      "|   2013 |          7.0184e+06  |\n",
      "|   2014 |          8.0222e+06  |\n",
      "|   2015 |          5.62921e+06 |\n",
      "|   2016 |          1.99391e+06 |\n"
     ]
    }
   ],
   "source": [
    "print(total_customers_affected_per_year.to_markdown(index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>CAUSE.CATEGORY</th>\n",
       "      <th>equipment failure</th>\n",
       "      <th>fuel supply emergency</th>\n",
       "      <th>intentional attack</th>\n",
       "      <th>islanding</th>\n",
       "      <th>public appeal</th>\n",
       "      <th>severe weather</th>\n",
       "      <th>system operability disruption</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U.S._STATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1421.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>138.500000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>639.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25726.500000</td>\n",
       "      <td>384.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>547.833333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1063.714286</td>\n",
       "      <td>2701.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>524.809524</td>\n",
       "      <td>6154.60</td>\n",
       "      <td>946.458333</td>\n",
       "      <td>214.857143</td>\n",
       "      <td>2028.111111</td>\n",
       "      <td>2928.373134</td>\n",
       "      <td>363.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2727.250000</td>\n",
       "      <td>279.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>49.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2262.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.918919</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2153.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>159.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4764.111111</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>554.500000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4320.000000</td>\n",
       "      <td>6420.192308</td>\n",
       "      <td>205.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1422.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>997.500000</td>\n",
       "      <td>237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>307.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>179.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149.000000</td>\n",
       "      <td>2761.00</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>1650.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12240.00</td>\n",
       "      <td>421.875000</td>\n",
       "      <td>125.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4523.291667</td>\n",
       "      <td>4671.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5657.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3353.666667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>561.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>913.000000</td>\n",
       "      <td>9346.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>652.000000</td>\n",
       "      <td>12570.00</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4480.111111</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>176.333333</td>\n",
       "      <td>28170.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1359.214286</td>\n",
       "      <td>7186.928571</td>\n",
       "      <td>1144.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1676.00</td>\n",
       "      <td>82.666667</td>\n",
       "      <td>881.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1669.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>225.320000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4006.937500</td>\n",
       "      <td>304.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2891.00</td>\n",
       "      <td>384.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1556.571429</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>26435.333333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3635.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1078.000000</td>\n",
       "      <td>4831.650602</td>\n",
       "      <td>2610.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>369.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3585.545455</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4483.818182</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>3221.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>553.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1597.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>91.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6372.863636</td>\n",
       "      <td>748.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.00</td>\n",
       "      <td>174.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>247.000000</td>\n",
       "      <td>16687.25</td>\n",
       "      <td>309.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2655.000000</td>\n",
       "      <td>6034.575758</td>\n",
       "      <td>1176.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1063.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1738.933333</td>\n",
       "      <td>82.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>720.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>327.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4322.269231</td>\n",
       "      <td>1744.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>75.666667</td>\n",
       "      <td>984.000000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>4206.466667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>394.105263</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2295.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>376.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1526.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4314.000000</td>\n",
       "      <td>329.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3135.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>404.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>1386.350000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>405.600000</td>\n",
       "      <td>13920.00</td>\n",
       "      <td>298.769231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1140.411765</td>\n",
       "      <td>3854.890625</td>\n",
       "      <td>810.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>142.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2275.000000</td>\n",
       "      <td>957.000000</td>\n",
       "      <td>537.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.444444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>683.500000</td>\n",
       "      <td>1132.281250</td>\n",
       "      <td>241.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>1204.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>371.870968</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>5473.550000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9305.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>33971.25</td>\n",
       "      <td>459.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>1527.428571</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CAUSE.CATEGORY        equipment failure  fuel supply emergency  \\\n",
       "U.S._STATE                                                       \n",
       "Alabama                        0.000000                   0.00   \n",
       "Arizona                      138.500000                   0.00   \n",
       "Arkansas                     105.000000                   0.00   \n",
       "California                   524.809524                6154.60   \n",
       "Colorado                       0.000000                   0.00   \n",
       "Connecticut                    0.000000                   0.00   \n",
       "Delaware                      50.000000                   0.00   \n",
       "District of Columbia         159.000000                   0.00   \n",
       "Florida                      554.500000                   0.00   \n",
       "Georgia                        0.000000                   0.00   \n",
       "Hawaii                         0.000000                   0.00   \n",
       "Idaho                          0.000000                   0.00   \n",
       "Illinois                     149.000000                2761.00   \n",
       "Indiana                        1.000000               12240.00   \n",
       "Iowa                           0.000000                   0.00   \n",
       "Kansas                         0.000000                   0.00   \n",
       "Kentucky                     652.000000               12570.00   \n",
       "Louisiana                    176.333333               28170.00   \n",
       "Maine                          0.000000                1676.00   \n",
       "Maryland                       0.000000                   0.00   \n",
       "Massachusetts                  0.000000                2891.00   \n",
       "Michigan                   26435.333333                   0.00   \n",
       "Minnesota                      0.000000                   0.00   \n",
       "Mississippi                    0.000000                   0.00   \n",
       "Missouri                       0.000000                   0.00   \n",
       "Montana                        0.000000                   0.00   \n",
       "Nebraska                       0.000000                   0.00   \n",
       "Nevada                         0.000000                   0.00   \n",
       "New Hampshire                  0.000000                   0.00   \n",
       "New Jersey                     0.000000                   0.00   \n",
       "New Mexico                     0.000000                  76.00   \n",
       "New York                     247.000000               16687.25   \n",
       "North Carolina                 0.000000                   0.00   \n",
       "North Dakota                   0.000000                   0.00   \n",
       "Ohio                           0.000000                   0.00   \n",
       "Oklahoma                       0.000000                   0.00   \n",
       "Oregon                       200.000000                   0.00   \n",
       "Pennsylvania                 376.000000                   0.00   \n",
       "South Carolina                 0.000000                   0.00   \n",
       "South Dakota                   0.000000                   0.00   \n",
       "Tennessee                    404.000000                   0.00   \n",
       "Texas                        405.600000               13920.00   \n",
       "Utah                          15.000000                   0.00   \n",
       "Vermont                        0.000000                   0.00   \n",
       "Virginia                       0.000000                   0.00   \n",
       "Washington                  1204.000000                   1.00   \n",
       "West Virginia                  0.000000                   0.00   \n",
       "Wisconsin                      0.000000               33971.25   \n",
       "Wyoming                       61.000000                   0.00   \n",
       "\n",
       "CAUSE.CATEGORY        intentional attack   islanding  public appeal  \\\n",
       "U.S._STATE                                                            \n",
       "Alabama                        77.000000    0.000000       0.000000   \n",
       "Arizona                       639.600000    0.000000       0.000000   \n",
       "Arkansas                      547.833333    3.000000    1063.714286   \n",
       "California                    946.458333  214.857143    2028.111111   \n",
       "Colorado                      117.000000    2.000000       0.000000   \n",
       "Connecticut                    49.125000    0.000000       0.000000   \n",
       "Delaware                       38.918919    0.000000       0.000000   \n",
       "District of Columbia            0.000000    0.000000       0.000000   \n",
       "Florida                        50.000000    0.000000    4320.000000   \n",
       "Georgia                       108.000000    0.000000       0.000000   \n",
       "Hawaii                          0.000000    0.000000       0.000000   \n",
       "Idaho                         307.500000    0.000000    1548.000000   \n",
       "Illinois                     1450.000000    0.000000     120.000000   \n",
       "Indiana                       421.875000  125.333333       0.000000   \n",
       "Iowa                         5657.800000    0.000000       0.000000   \n",
       "Kansas                        561.000000    0.000000     913.000000   \n",
       "Kentucky                      108.000000    0.000000       0.000000   \n",
       "Louisiana                       0.000000    0.000000    1359.214286   \n",
       "Maine                          82.666667  881.000000       0.000000   \n",
       "Maryland                      225.320000    0.000000       0.000000   \n",
       "Massachusetts                 384.250000    0.000000       0.000000   \n",
       "Michigan                     3635.250000    1.000000    1078.000000   \n",
       "Minnesota                     369.500000    0.000000       0.000000   \n",
       "Mississippi                    12.000000    0.000000       0.000000   \n",
       "Missouri                      408.000000    0.000000       0.000000   \n",
       "Montana                        93.000000   34.500000       0.000000   \n",
       "Nebraska                        0.000000    0.000000     159.000000   \n",
       "Nevada                        553.285714    0.000000       0.000000   \n",
       "New Hampshire                  60.000000    0.000000       0.000000   \n",
       "New Jersey                     91.125000    0.000000       0.000000   \n",
       "New Mexico                    174.500000    0.000000       0.000000   \n",
       "New York                      309.083333    0.000000    2655.000000   \n",
       "North Carolina               1063.750000    0.000000       0.000000   \n",
       "North Dakota                    0.000000    0.000000     720.000000   \n",
       "Ohio                          327.285714    0.000000       0.000000   \n",
       "Oklahoma                       75.666667  984.000000     704.000000   \n",
       "Oregon                        394.105263    0.000000       0.000000   \n",
       "Pennsylvania                 1526.833333    0.000000       0.000000   \n",
       "South Carolina                  0.000000    0.000000       0.000000   \n",
       "South Dakota                    0.000000  120.000000       0.000000   \n",
       "Tennessee                     171.000000    0.000000    2700.000000   \n",
       "Texas                         298.769231    0.000000    1140.411765   \n",
       "Utah                          142.285714    0.000000    2275.000000   \n",
       "Vermont                        35.444444    0.000000       0.000000   \n",
       "Virginia                        2.000000    0.000000     683.500000   \n",
       "Washington                    371.870968   73.333333     248.000000   \n",
       "West Virginia                   1.000000    0.000000       0.000000   \n",
       "Wisconsin                     459.000000    0.000000     388.000000   \n",
       "Wyoming                         0.333333   32.000000       0.000000   \n",
       "\n",
       "CAUSE.CATEGORY        severe weather  system operability disruption  \n",
       "U.S._STATE                                                           \n",
       "Alabama                  1421.750000                       0.000000  \n",
       "Arizona                 25726.500000                     384.500000  \n",
       "Arkansas                 2701.800000                       0.000000  \n",
       "California               2928.373134                     363.666667  \n",
       "Colorado                 2727.250000                     279.750000  \n",
       "Connecticut              2262.600000                       0.000000  \n",
       "Delaware                 2153.500000                       0.000000  \n",
       "District of Columbia     4764.111111                       0.000000  \n",
       "Florida                  6420.192308                     205.700000  \n",
       "Georgia                  1422.750000                       0.000000  \n",
       "Hawaii                    997.500000                     237.000000  \n",
       "Idaho                       0.000000                     179.666667  \n",
       "Illinois                 1650.700000                       0.000000  \n",
       "Indiana                  4523.291667                    4671.600000  \n",
       "Iowa                     3353.666667                       0.000000  \n",
       "Kansas                   9346.000000                       0.000000  \n",
       "Kentucky                 4480.111111                       0.000000  \n",
       "Louisiana                7186.928571                    1144.666667  \n",
       "Maine                    1669.400000                       0.000000  \n",
       "Maryland                 4006.937500                     304.000000  \n",
       "Massachusetts            1556.571429                      67.000000  \n",
       "Michigan                 4831.650602                    2610.000000  \n",
       "Minnesota                3585.545455                       0.000000  \n",
       "Mississippi                 0.000000                     300.000000  \n",
       "Missouri                 4483.818182                      65.000000  \n",
       "Montana                     0.000000                       0.000000  \n",
       "Nebraska                 3221.333333                       0.000000  \n",
       "Nevada                      0.000000                       0.000000  \n",
       "New Hampshire            1597.500000                       0.000000  \n",
       "New Jersey               6372.863636                     748.500000  \n",
       "New Mexico                  0.000000                       0.000000  \n",
       "New York                 6034.575758                    1176.571429  \n",
       "North Carolina           1738.933333                      82.200000  \n",
       "North Dakota                0.000000                       0.000000  \n",
       "Ohio                     4322.269231                    1744.500000  \n",
       "Oklahoma                 4206.466667                       0.000000  \n",
       "Oregon                   2295.800000                       0.000000  \n",
       "Pennsylvania             4314.000000                     329.000000  \n",
       "South Carolina           3135.000000                       0.000000  \n",
       "South Dakota                0.000000                       0.000000  \n",
       "Tennessee                1386.350000                      20.000000  \n",
       "Texas                    3854.890625                     810.800000  \n",
       "Utah                      957.000000                     537.500000  \n",
       "Vermont                     0.000000                       0.000000  \n",
       "Virginia                 1132.281250                     241.000000  \n",
       "Washington               5473.550000                      25.000000  \n",
       "West Virginia            9305.000000                       0.000000  \n",
       "Wisconsin                1527.428571                       0.000000  \n",
       "Wyoming                   106.000000                       0.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the average outage duration per U.S. state and cause category:\n",
    "pivot_table_outage_duration = df.pivot_table(index='U.S._STATE', columns='CAUSE.CATEGORY', values='OUTAGE.DURATION(mins)', aggfunc='mean', fill_value=0)\n",
    "pivot_table_outage_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| U.S._STATE   |   equipment failure |   fuel supply emergency |   intentional attack |   islanding |   public appeal |   severe weather |   system operability disruption |\n",
      "|:-------------|--------------------:|------------------------:|---------------------:|------------:|----------------:|-----------------:|--------------------------------:|\n",
      "| Alabama      |                0    |                     0   |               77     |       0     |            0    |          1421.75 |                           0     |\n",
      "| Arizona      |              138.5  |                     0   |              639.6   |       0     |            0    |         25726.5  |                         384.5   |\n",
      "| Arkansas     |              105    |                     0   |              547.833 |       3     |         1063.71 |          2701.8  |                           0     |\n",
      "| California   |              524.81 |                  6154.6 |              946.458 |     214.857 |         2028.11 |          2928.37 |                         363.667 |\n",
      "| Colorado     |                0    |                     0   |              117     |       2     |            0    |          2727.25 |                         279.75  |\n"
     ]
    }
   ],
   "source": [
    "print(pivot_table_outage_duration.head().to_markdown(index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>CAUSE.CATEGORY</th>\n",
       "      <th>equipment failure</th>\n",
       "      <th>fuel supply emergency</th>\n",
       "      <th>intentional attack</th>\n",
       "      <th>islanding</th>\n",
       "      <th>public appeal</th>\n",
       "      <th>severe weather</th>\n",
       "      <th>system operability disruption</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U.S._STATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>64</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CAUSE.CATEGORY        equipment failure  fuel supply emergency  \\\n",
       "U.S._STATE                                                       \n",
       "Alabama                               0                      0   \n",
       "Alaska                                0                      0   \n",
       "Arizona                               4                      0   \n",
       "Arkansas                              1                      0   \n",
       "California                           21                     10   \n",
       "Colorado                              0                      0   \n",
       "Connecticut                           0                      0   \n",
       "Delaware                              1                      0   \n",
       "District of Columbia                  1                      0   \n",
       "Florida                               4                      0   \n",
       "Georgia                               0                      0   \n",
       "Hawaii                                0                      0   \n",
       "Idaho                                 0                      0   \n",
       "Illinois                              1                      1   \n",
       "Indiana                               1                      1   \n",
       "Iowa                                  0                      0   \n",
       "Kansas                                0                      0   \n",
       "Kentucky                              1                      2   \n",
       "Louisiana                             3                      1   \n",
       "Maine                                 0                      1   \n",
       "Maryland                              0                      0   \n",
       "Massachusetts                         0                      1   \n",
       "Michigan                              3                      0   \n",
       "Minnesota                             0                      0   \n",
       "Mississippi                           0                      0   \n",
       "Missouri                              0                      0   \n",
       "Montana                               0                      0   \n",
       "Nebraska                              0                      0   \n",
       "Nevada                                0                      0   \n",
       "New Hampshire                         0                      0   \n",
       "New Jersey                            0                      0   \n",
       "New Mexico                            0                      1   \n",
       "New York                              2                     12   \n",
       "North Carolina                        0                      0   \n",
       "North Dakota                          0                      0   \n",
       "Ohio                                  0                      0   \n",
       "Oklahoma                              0                      0   \n",
       "Oregon                                1                      0   \n",
       "Pennsylvania                          1                      0   \n",
       "South Carolina                        0                      0   \n",
       "South Dakota                          0                      0   \n",
       "Tennessee                             2                      0   \n",
       "Texas                                 5                      3   \n",
       "Utah                                  1                      0   \n",
       "Vermont                               0                      0   \n",
       "Virginia                              0                      0   \n",
       "Washington                            1                      1   \n",
       "West Virginia                         0                      0   \n",
       "Wisconsin                             0                      4   \n",
       "Wyoming                               1                      0   \n",
       "\n",
       "CAUSE.CATEGORY        intentional attack  islanding  public appeal  \\\n",
       "U.S._STATE                                                           \n",
       "Alabama                                1          0              0   \n",
       "Alaska                                 0          0              0   \n",
       "Arizona                               15          0              0   \n",
       "Arkansas                               6          1              7   \n",
       "California                            24         28              9   \n",
       "Colorado                               5          1              0   \n",
       "Connecticut                            8          0              0   \n",
       "Delaware                              37          0              0   \n",
       "District of Columbia                   0          0              0   \n",
       "Florida                                2          0              3   \n",
       "Georgia                                1          0              0   \n",
       "Hawaii                                 0          0              0   \n",
       "Idaho                                  4          0              1   \n",
       "Illinois                               1          0              1   \n",
       "Indiana                                8          3              0   \n",
       "Iowa                                   5          0              0   \n",
       "Kansas                                 3          0              1   \n",
       "Kentucky                               1          0              0   \n",
       "Louisiana                              0          0             14   \n",
       "Maine                                  6          1              0   \n",
       "Maryland                              25          0              0   \n",
       "Massachusetts                          8          0              0   \n",
       "Michigan                               4          1              1   \n",
       "Minnesota                              4          0              0   \n",
       "Mississippi                            3          0              0   \n",
       "Missouri                               3          0              0   \n",
       "Montana                                1          2              0   \n",
       "Nebraska                               0          0              1   \n",
       "Nevada                                 7          0              0   \n",
       "New Hampshire                         12          0              0   \n",
       "New Jersey                             8          0              0   \n",
       "New Mexico                             6          0              0   \n",
       "New York                              12          0              4   \n",
       "North Carolina                         4          0              0   \n",
       "North Dakota                           0          0              1   \n",
       "Ohio                                  14          0              0   \n",
       "Oklahoma                               3          1              3   \n",
       "Oregon                                19          0              0   \n",
       "Pennsylvania                           6          0              0   \n",
       "South Carolina                         0          0              0   \n",
       "South Dakota                           0          2              0   \n",
       "Tennessee                              6          0              1   \n",
       "Texas                                 13          0             17   \n",
       "Utah                                  35          0              1   \n",
       "Vermont                                9          0              0   \n",
       "Virginia                               1          0              2   \n",
       "Washington                            62          3              1   \n",
       "West Virginia                          1          0              0   \n",
       "Wisconsin                              7          0              1   \n",
       "Wyoming                                3          1              0   \n",
       "\n",
       "CAUSE.CATEGORY        severe weather  system operability disruption  \n",
       "U.S._STATE                                                           \n",
       "Alabama                            4                              0  \n",
       "Alaska                             0                              0  \n",
       "Arizona                            4                              2  \n",
       "Arkansas                          10                              0  \n",
       "California                        67                             39  \n",
       "Colorado                           4                              4  \n",
       "Connecticut                       10                              0  \n",
       "Delaware                           2                              0  \n",
       "District of Columbia               9                              0  \n",
       "Florida                           26                             10  \n",
       "Georgia                           16                              0  \n",
       "Hawaii                             4                              1  \n",
       "Idaho                              0                              3  \n",
       "Illinois                          40                              0  \n",
       "Indiana                           24                              5  \n",
       "Iowa                               3                              0  \n",
       "Kansas                             3                              0  \n",
       "Kentucky                           9                              0  \n",
       "Louisiana                         14                              6  \n",
       "Maine                             10                              0  \n",
       "Maryland                          32                              1  \n",
       "Massachusetts                      7                              2  \n",
       "Michigan                          83                              3  \n",
       "Minnesota                         11                              0  \n",
       "Mississippi                        0                              1  \n",
       "Missouri                          11                              1  \n",
       "Montana                            0                              0  \n",
       "Nebraska                           3                              0  \n",
       "Nevada                             0                              0  \n",
       "New Hampshire                      2                              0  \n",
       "New Jersey                        22                              2  \n",
       "New Mexico                         0                              1  \n",
       "New York                          33                              7  \n",
       "North Carolina                    30                              5  \n",
       "North Dakota                       0                              0  \n",
       "Ohio                              26                              2  \n",
       "Oklahoma                          15                              0  \n",
       "Oregon                             5                              0  \n",
       "Pennsylvania                      48                              2  \n",
       "South Carolina                     8                              0  \n",
       "South Dakota                       0                              0  \n",
       "Tennessee                         20                              2  \n",
       "Texas                             64                             20  \n",
       "Utah                               2                              2  \n",
       "Vermont                            0                              0  \n",
       "Virginia                          32                              1  \n",
       "Washington                        20                              1  \n",
       "West Virginia                      3                              0  \n",
       "Wisconsin                          7                              0  \n",
       "Wyoming                            1                              0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the outage frequency per U.S. state and cause category:\n",
    "pivot_table_outage_frequency = df.pivot_table(index='U.S._STATE', columns='CAUSE.CATEGORY', values='OUTAGE.DURATION(mins)', aggfunc='count', fill_value=0)\n",
    "pivot_table_outage_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>YEAR</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
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       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLIMATE.REGION</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>239567</td>\n",
       "      <td>0</td>\n",
       "      <td>95000</td>\n",
       "      <td>1203000</td>\n",
       "      <td>281000</td>\n",
       "      <td>246990</td>\n",
       "      <td>471932</td>\n",
       "      <td>629590</td>\n",
       "      <td>653000</td>\n",
       "      <td>383000</td>\n",
       "      <td>400000</td>\n",
       "      <td>500000</td>\n",
       "      <td>346000</td>\n",
       "      <td>283451</td>\n",
       "      <td>420000</td>\n",
       "      <td>115000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East North Central</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190000</td>\n",
       "      <td>2100000</td>\n",
       "      <td>250000</td>\n",
       "      <td>300000</td>\n",
       "      <td>315000</td>\n",
       "      <td>215000</td>\n",
       "      <td>358000</td>\n",
       "      <td>137000</td>\n",
       "      <td>285000</td>\n",
       "      <td>197166</td>\n",
       "      <td>140000</td>\n",
       "      <td>400000</td>\n",
       "      <td>164000</td>\n",
       "      <td>250000</td>\n",
       "      <td>160895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northeast</th>\n",
       "      <td>0</td>\n",
       "      <td>130000</td>\n",
       "      <td>224912</td>\n",
       "      <td>3125350</td>\n",
       "      <td>380000</td>\n",
       "      <td>143000</td>\n",
       "      <td>492955</td>\n",
       "      <td>300000</td>\n",
       "      <td>249408</td>\n",
       "      <td>132000</td>\n",
       "      <td>360000</td>\n",
       "      <td>760113</td>\n",
       "      <td>850000</td>\n",
       "      <td>75000</td>\n",
       "      <td>715000</td>\n",
       "      <td>263000</td>\n",
       "      <td>56645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwest</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200000</td>\n",
       "      <td>187000</td>\n",
       "      <td>0</td>\n",
       "      <td>700000</td>\n",
       "      <td>160000</td>\n",
       "      <td>8000</td>\n",
       "      <td>93300</td>\n",
       "      <td>123535</td>\n",
       "      <td>9000</td>\n",
       "      <td>426000</td>\n",
       "      <td>105000</td>\n",
       "      <td>0</td>\n",
       "      <td>500000</td>\n",
       "      <td>56000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>2000000</td>\n",
       "      <td>114000</td>\n",
       "      <td>1881134</td>\n",
       "      <td>192000</td>\n",
       "      <td>500000</td>\n",
       "      <td>1100000</td>\n",
       "      <td>489478</td>\n",
       "      <td>300000</td>\n",
       "      <td>2504366</td>\n",
       "      <td>800000</td>\n",
       "      <td>500000</td>\n",
       "      <td>1069730</td>\n",
       "      <td>262000</td>\n",
       "      <td>881000</td>\n",
       "      <td>57200</td>\n",
       "      <td>454000</td>\n",
       "      <td>415103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southeast</th>\n",
       "      <td>160000</td>\n",
       "      <td>600000</td>\n",
       "      <td>130000</td>\n",
       "      <td>340000</td>\n",
       "      <td>2775093</td>\n",
       "      <td>3241437</td>\n",
       "      <td>126000</td>\n",
       "      <td>107000</td>\n",
       "      <td>584384</td>\n",
       "      <td>217000</td>\n",
       "      <td>145157</td>\n",
       "      <td>285465</td>\n",
       "      <td>880000</td>\n",
       "      <td>283000</td>\n",
       "      <td>677858</td>\n",
       "      <td>186035</td>\n",
       "      <td>203345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Southwest</th>\n",
       "      <td>500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90000</td>\n",
       "      <td>30000</td>\n",
       "      <td>0</td>\n",
       "      <td>65000</td>\n",
       "      <td>0</td>\n",
       "      <td>74031</td>\n",
       "      <td>140000</td>\n",
       "      <td>31000</td>\n",
       "      <td>204000</td>\n",
       "      <td>30379</td>\n",
       "      <td>35230</td>\n",
       "      <td>0</td>\n",
       "      <td>5763</td>\n",
       "      <td>85179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>32000</td>\n",
       "      <td>430984</td>\n",
       "      <td>1500000</td>\n",
       "      <td>241000</td>\n",
       "      <td>505000</td>\n",
       "      <td>1667316</td>\n",
       "      <td>1271893</td>\n",
       "      <td>671189</td>\n",
       "      <td>2606931</td>\n",
       "      <td>859554</td>\n",
       "      <td>1700000</td>\n",
       "      <td>165000</td>\n",
       "      <td>125000</td>\n",
       "      <td>148000</td>\n",
       "      <td>1400000</td>\n",
       "      <td>80000</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West North Central</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120212</td>\n",
       "      <td>0</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>126000</td>\n",
       "      <td>35500</td>\n",
       "      <td>0</td>\n",
       "      <td>34500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "YEAR                   2000    2001     2002     2003     2004     2005  \\\n",
       "CLIMATE.REGION                                                            \n",
       "Central              239567       0    95000  1203000   281000   246990   \n",
       "East North Central        0       0   190000  2100000   250000   300000   \n",
       "Northeast                 0  130000   224912  3125350   380000   143000   \n",
       "Northwest                 0       0        0   200000   187000        0   \n",
       "South               2000000  114000  1881134   192000   500000  1100000   \n",
       "Southeast            160000  600000   130000   340000  2775093  3241437   \n",
       "Southwest            500000       0        0    90000    30000        0   \n",
       "West                  32000  430984  1500000   241000   505000  1667316   \n",
       "West North Central        0       0        0        0   120212        0   \n",
       "\n",
       "YEAR                   2006    2007     2008    2009     2010     2011  \\\n",
       "CLIMATE.REGION                                                           \n",
       "Central              471932  629590   653000  383000   400000   500000   \n",
       "East North Central   315000  215000   358000  137000   285000   197166   \n",
       "Northeast            492955  300000   249408  132000   360000   760113   \n",
       "Northwest            700000  160000     8000   93300   123535     9000   \n",
       "South                489478  300000  2504366  800000   500000  1069730   \n",
       "Southeast            126000  107000   584384  217000   145157   285465   \n",
       "Southwest             65000       0    74031  140000    31000   204000   \n",
       "West                1271893  671189  2606931  859554  1700000   165000   \n",
       "West North Central    15000       0   126000   35500        0    34500   \n",
       "\n",
       "YEAR                  2012    2013     2014    2015    2016  \n",
       "CLIMATE.REGION                                               \n",
       "Central             346000  283451   420000  115000       0  \n",
       "East North Central  140000  400000   164000  250000  160895  \n",
       "Northeast           850000   75000   715000  263000   56645  \n",
       "Northwest           426000  105000        0  500000   56000  \n",
       "South               262000  881000    57200  454000  415103  \n",
       "Southeast           880000  283000   677858  186035  203345  \n",
       "Southwest            30379   35230        0    5763   85179  \n",
       "West                125000  148000  1400000   80000  110000  \n",
       "West North Central       0       0        0       0       0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the maximum customers affected per climate region and year:\n",
    "pivot_table_customers_affected = df.pivot_table(index='CLIMATE.REGION', columns='YEAR', values='CUSTOMERS.AFFECTED', aggfunc='max', fill_value=0)\n",
    "pivot_table_customers_affected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| CLIMATE.REGION     |       2000 |   2001 |             2002 |             2003 |             2004 |             2005 |             2006 |   2007 |             2008 |   2009 |         2010 |             2011 |   2012 |   2013 |         2014 |   2015 |   2016 |\n",
      "|:-------------------|-----------:|-------:|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|-------:|-----------------:|-------:|-------------:|-----------------:|-------:|-------:|-------------:|-------:|-------:|\n",
      "| Central            | 239567     |      0 |  95000           |      1.203e+06   | 281000           | 246990           | 471932           | 629590 | 653000           | 383000 | 400000       | 500000           | 346000 | 283451 | 420000       | 115000 |      0 |\n",
      "| East North Central |      0     |      0 | 190000           |      2.1e+06     | 250000           | 300000           | 315000           | 215000 | 358000           | 137000 | 285000       | 197166           | 140000 | 400000 | 164000       | 250000 | 160895 |\n",
      "| Northeast          |      0     | 130000 | 224912           |      3.12535e+06 | 380000           | 143000           | 492955           | 300000 | 249408           | 132000 | 360000       | 760113           | 850000 |  75000 | 715000       | 263000 |  56645 |\n",
      "| Northwest          |      0     |      0 |      0           | 200000           | 187000           |      0           | 700000           | 160000 |   8000           |  93300 | 123535       |   9000           | 426000 | 105000 |      0       | 500000 |  56000 |\n",
      "| South              |      2e+06 | 114000 |      1.88113e+06 | 192000           | 500000           |      1.1e+06     | 489478           | 300000 |      2.50437e+06 | 800000 | 500000       |      1.06973e+06 | 262000 | 881000 |  57200       | 454000 | 415103 |\n",
      "| Southeast          | 160000     | 600000 | 130000           | 340000           |      2.77509e+06 |      3.24144e+06 | 126000           | 107000 | 584384           | 217000 | 145157       | 285465           | 880000 | 283000 | 677858       | 186035 | 203345 |\n",
      "| Southwest          | 500000     |      0 |      0           |  90000           |  30000           |      0           |  65000           |      0 |  74031           | 140000 |  31000       | 204000           |  30379 |  35230 |      0       |   5763 |  85179 |\n",
      "| West               |  32000     | 430984 |      1.5e+06     | 241000           | 505000           |      1.66732e+06 |      1.27189e+06 | 671189 |      2.60693e+06 | 859554 |      1.7e+06 | 165000           | 125000 | 148000 |      1.4e+06 |  80000 | 110000 |\n",
      "| West North Central |      0     |      0 |      0           |      0           | 120212           |      0           |  15000           |      0 | 126000           |  35500 |      0       |  34500           |      0 |      0 |      0       |      0 |      0 |\n"
     ]
    }
   ],
   "source": [
    "print(pivot_table_customers_affected.to_markdown(index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>CAUSE.CATEGORY.DETAIL</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Hydro</th>\n",
       "      <th>Natural Gas</th>\n",
       "      <th>100 MW loadshed</th>\n",
       "      <th>Coal</th>\n",
       "      <th>HVSubstation interruption</th>\n",
       "      <th>Hydro</th>\n",
       "      <th>Petroleum</th>\n",
       "      <th>breaker trip</th>\n",
       "      <th>cables</th>\n",
       "      <th>...</th>\n",
       "      <th>transmission trip</th>\n",
       "      <th>uncontrolled loss</th>\n",
       "      <th>vandalism</th>\n",
       "      <th>voltage reduction</th>\n",
       "      <th>wildfire</th>\n",
       "      <th>wind</th>\n",
       "      <th>wind storm</th>\n",
       "      <th>wind/rain</th>\n",
       "      <th>winter</th>\n",
       "      <th>winter storm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLIMATE.CATEGORY</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cold</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.250000e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.747059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.836364</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-0.527778</td>\n",
       "      <td>-0.963636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>9.251859e-18</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.275000</td>\n",
       "      <td>-0.215730</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>warm</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.144444</td>\n",
       "      <td>1.189474</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.958065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "CAUSE.CATEGORY.DETAIL   Coal   Hydro   Natural Gas  100 MW loadshed  \\\n",
       "CLIMATE.CATEGORY                                                      \n",
       "cold                    -0.5     0.0          -0.6              0.0   \n",
       "normal                  -0.2    -0.4          -0.3             -0.2   \n",
       "warm                     0.0     0.0           0.0              0.0   \n",
       "\n",
       "CAUSE.CATEGORY.DETAIL          Coal  HVSubstation interruption  Hydro  \\\n",
       "CLIMATE.CATEGORY                                                        \n",
       "cold                  -8.250000e-01                        0.0 -1.200   \n",
       "normal                 9.251859e-18                       -0.1  0.025   \n",
       "warm                   0.000000e+00                        0.0  0.000   \n",
       "\n",
       "CAUSE.CATEGORY.DETAIL  Petroleum  breaker trip  cables  ...  \\\n",
       "CLIMATE.CATEGORY                                        ...   \n",
       "cold                         0.0     -0.500000     0.0  ...   \n",
       "normal                      -0.4      0.066667     0.3  ...   \n",
       "warm                         0.0      0.000000     0.0  ...   \n",
       "\n",
       "CAUSE.CATEGORY.DETAIL  transmission trip  uncontrolled loss  vandalism  \\\n",
       "CLIMATE.CATEGORY                                                         \n",
       "cold                               -0.85          -0.500000  -0.747059   \n",
       "normal                             -0.25          -0.275000  -0.215730   \n",
       "warm                                0.00           1.144444   1.189474   \n",
       "\n",
       "CAUSE.CATEGORY.DETAIL  voltage reduction  wildfire  wind  wind storm  \\\n",
       "CLIMATE.CATEGORY                                                       \n",
       "cold                                 0.0 -0.836364  0.00         0.0   \n",
       "normal                              -0.3 -0.100000 -0.20        -0.3   \n",
       "warm                                 0.0  0.620000  0.55         0.0   \n",
       "\n",
       "CAUSE.CATEGORY.DETAIL  wind/rain    winter  winter storm  \n",
       "CLIMATE.CATEGORY                                          \n",
       "cold                   -1.500000 -0.527778     -0.963636  \n",
       "normal                  0.066667 -0.400000      0.020000  \n",
       "warm                    0.825000  0.525000      0.958065  \n",
       "\n",
       "[3 rows x 51 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the average anomaly level per climate category and cause category detail:\n",
    "pivot_table_anomaly_level = df.pivot_table(index='CLIMATE.CATEGORY', columns='CAUSE.CATEGORY.DETAIL', \n",
    "                                           values='ANOMALY.LEVEL', aggfunc='mean', fill_value=0)\n",
    "pivot_table_anomaly_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessment of Missingness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NMAR Analysis & Missingness Dependency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hurr = pd.DataFrame(df['HURRICANE.NAMES'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    |   HURRICANE.NAMES |\n",
      "|---:|------------------:|\n",
      "|  0 |               nan |\n",
      "|  1 |               nan |\n",
      "|  2 |               nan |\n",
      "|  3 |               nan |\n",
      "|  4 |               nan |\n"
     ]
    }
   ],
   "source": [
    "print(df_hurr.head().to_markdown(index=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-31T23:36:28.662099Z",
     "start_time": "2019-10-31T23:36:28.660016Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Does missingness of customers affected depend on the cause category? \n",
    "df['CAUSE.CATEGORY']\n",
    "df.groupby('CAUSE.CATEGORY').count()\n",
    "df_ca_missing = df[df['CUSTOMERS.AFFECTED'].isna()]\n",
    "df_ca_not_missing = df[~df['CUSTOMERS.AFFECTED'].isna()]\n",
    "missing_count = df_ca_missing.groupby('CAUSE.CATEGORY').count()['YEAR']\n",
    "missing_proportion = pd.Series(np.array(missing_count)/((np.array(missing_count)).sum()), index=missing_count.index)\n",
    "not_missing_count = df_ca_not_missing.groupby('CAUSE.CATEGORY').count()['YEAR']\n",
    "not_missing_proportion = pd.Series(np.array(not_missing_count)/((np.array(not_missing_count)).sum()), index=not_missing_count.index)\n",
    "observed_tvd_statistic = abs(not_missing_proportion - missing_proportion).sum()\n",
    "observed_dataframe = pd.concat([not_missing_proportion, missing_proportion], axis=1, keys=['not_missing_proportion', 'missing_proportion'])\n",
    "observed_dataframe.plot(kind='barh', title='Causes by missingess of user affected', barmode='group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cause_one_permutation(df):\n",
    "    df = df.copy()\n",
    "    df['CUSTOMERS.AFFECTED'] = np.random.permutation(df['CUSTOMERS.AFFECTED'])\n",
    "    df['CAUSE.CATEGORY']\n",
    "    df.groupby('CAUSE.CATEGORY').count()\n",
    "    df_ca_missing = df[df['CUSTOMERS.AFFECTED'].isna()]\n",
    "    df_ca_not_missing = df[~df['CUSTOMERS.AFFECTED'].isna()]\n",
    "    missing_count = df_ca_missing.groupby('CAUSE.CATEGORY').count()['YEAR']\n",
    "    missing_proportion = pd.Series(np.array(missing_count)/((np.array(missing_count)).sum()), index=missing_count.index)\n",
    "    not_missing_count = df_ca_not_missing.groupby('CAUSE.CATEGORY').count()['YEAR']\n",
    "    not_missing_proportion = pd.Series(np.array(not_missing_count)/((np.array(not_missing_count)).sum()), index=not_missing_count.index)\n",
    "    observed_tvd_statistic = abs(not_missing_proportion - missing_proportion).sum()\n",
    "    return observed_tvd_statistic\n",
    "# do permutation 1000 times\n",
    "result = []\n",
    "for i in range(1000):\n",
    "    result.append(cause_one_permutation(df))\n",
    "fig = px.histogram(pd.DataFrame(result), x=0, nbins=50, histnorm='probability', \n",
    "                   title='Empirical Distribution of the TVD')\n",
    "fig.add_vline(x=observed_tvd_statistic, line_color='red')\n",
    "fig.update_layout(xaxis_range=[0, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PvzdjKbYsn5Toy1ZuyzV8/k2VdW3zTO0Uv+xEiOYu3Qi5ZS8SLeUB8hS0ZCW/XDxeZZyMCq+7vMIdTPf7izi2aFRDZYWWrvkGv22aFf9lL+cm/MKdu2wjxn6wKNlcEi6QR+u9gga1KiUSIXldsnb/nruYam2zW2CSZm+Xr/tOZaZEXuT2t8jLnKUb8e3nk9Ut1y+++Rlvjvgw1YfR5L3dwivCKOUY85bFtZcLBhH4XDmyqy/0PkOnpSq80dEx6staxPfZxk+gSoXSKFf6XnU72n1ImYeUGciX/tNPVlG3USVjnvBISXjlNm/CuuIlq75W2TAZo3pg8KmuaFirUqKLBbllK/89qfB66stIfGSOrV4Zgty35YBkY0Vs5UGkhNlzyXwu/Hyzkv8nqpZVFxE5s2dN87NC3lu4DOmb+KLnjeEzsHXnPvywamqKQpVSp6nt0iDnyp0JKQmpVqmMairlLiKc6xeMSdTVsrVb1O1/tzxJbETopZwo4SFlJ1JKJHcZfMnwusVR1t3ufX8p6Y+JjVF/xxJf9y4gnoTX6GeJ/C3LhVzS3RKqN5XdCR7FO73bpRgnuYuxZNU3+OKb7fjn9HncDA9HbEysKtWQCxu5m2BEePf8cQStuwxNdnEobWXNtniqBvp1a60+Ew4f+ydZXBIOLmkCQWJ5+O9/1OdfwsP92fTz+hk4dPQk2nQbnizJ4K41ZobXzl/pHBsJkEBqBGwvvCJYUte584uPEt2add92dWc0k37ZyW1QefDm7IVLGNDjBTx4/13IlCmjun0nAu2t8EpmVTKw7sNd8+kWJPmSXP/1T9i6+oNUBc2dIUltJwl3jZ9kx0JDQxP1I6UNeW7LqSQuLYFJmJWS80RoRUTdmXDJAEm2WERdvoDlVqpk5yYMeTXVv5KEwiviIxlF+dKvVK6UuuhwZ6zTEl7pXCRT6hIlOy41wZKxa1SnMvp2ba2yvHJIyYBkyrft3KeynZItk9vqUt8qR0rCKzW4bZvViR9/QuGV299S1yrZa1krCQ+pU23WMHFJg5G+jMRHpHf+Z1/im62/qd0rJDPZrlV9tGtZT4mvrE+5bb5644/4Zc8BlYGuUr403ur+XKI7Eu7xuteG1LS7Swjcr0l5z8ovflAZ7ZSEKq31InXm998TtyuACy5VLpB0ZwZ5IFEuFJd8+E6irjZ8vV3d+nZfZElsyjxQPFG2XRrIGpH67hljevskvFJH/0yHAbj7zqJqLdxetIC6WyElIPsP/G1YeI1+lqT2t6yE9/Fyqd5lkfKcJau/USUWks3Nnj2Liv0Lr43ySnhl7UvpUIbQULj+P1PuBi9xkAtiqa1+sccodfcnrR1tkgqvxFKy9wkvNKVvEXNZY2vnjcapM+fRqe94deEiz0O4D9lOrVXnIczw0idIgAQCkoDthdctsqlleEW8WjV+IlmGd++fR/Bs56HqwbHmT1WPD4483CZZTm+FN2mG99Df/6Bxu/4Y/kZHVbNoRHjdtaRJb3UnlrDO6lZm947JHzSS+r2E5RkJ27kzdvLlJ7f/3Yf79nrCW5iSRb1y9braU7da0x6YPrpXig+ruftIKLzy3+RLU6StcrkHVJZSJFy+QD0Jb8Lxyu3fDd9sV3v31qpaLtl+qyKEe/44jOlzV6qHxKQmUbKH3gqvZAAfqt1R3QWQ0gX3ERERiUfrv4LnmtZJVNKQlvBKXxUaeB8fuUiQzN+cpV+oCw25I5DwkLISydBKKUFkZCQ2Lhof/yBYwvPK138FjeqknOHdvut/8RdD3pQ0pLRLQ9JPMpEmKbNJLcMrkuSOTUoZ3mrPvIaqFcvg3f6vwH3HIelDayKFcgcmpYfW3J8BGxaOVaUb7sNdD280w2v0s8RX4ZX4SAZYnhlwH3IBJ/HwJsPrFkupma/+2H/C6e5T7szIA39SUiXnJs3WJoxfUuHt9vZEHDxyEh+OTblOXC529h84qv7Gk5aRyQODL/UZS+ENyK96DpoESMCWwivZU3dd3i+/H1CZjKQfvu4HROQ8qXl0f5nJLTnJFrrrZaeO7IlaVeNq5ySDIZIq5QhSJyp1o0ZLGh6vWCbRl4T7/d2ZUyPC667BS/jlJ+OSWkQZi9ymlMyKPLSXsN5Uzjl28ox6eEZqUlM63MIrfUidofuQ+lp57ae10+LbihxJLarsjrB287ZUH1ZLTXjltvSwiXPVmCUrKWUlcqQlvJLp2rXvLzxVu3Ki4csDc/v/PIo180arh/7koa17ixeLP0fm3aDtG6p+WuqofRFe2SNYygUkVu5j7aZtkPdOWtLgSXiNxEcutq5evZHsIqJWy16oWaUser/SEt/+tBuPVyiTaEcGtwx+t2JKinsdd+w1BucuXkm0y4b7ga77Styuykrk0C28UrcpD/klreGVGnqJ2fcrpypBl9jIhZTs1SwPGMohNbZ1W/dVmVl5CM5d+77ykxHxcZaa1SdavK6ywCkJr/sBLnfdvfvv4ekX30b+vLmweekE9V5JPwPkvyVcL0Y/S3wRXlXj++TLaNO0jnqwz31InbN8VngjvHIxVrXJa/F3HxL+wciFdok7CqsLTvePvbg/y+S8y1euo2PvMXipzVPqATQR3oSfp1LfLhf8wizh/sxy50fugOXKkU31ITW8si9wwrnIBZlctLGkgeJAAiQQiARsJ7zy4JJ8iX40tg/y5smlbq9K9ufk6bMqO3ZP8WLY+8dh9XS/iK47U+HOZEoWSf67PLlfu1Vv9ctf8mT31es3MXbaItxeOD+WrtmCsYO6qMyi3BY1skuD9CfZwHo1K6gdI/qPmqlqYEXU5DAivHKeZEjkdv7Ani+qB4KkDEKetJcn86Vm0r0LQPOGNdD6mVrqtv8PP+/BuOmL0adzKyVoaQmv1Ip2bddEbWG2a+9fGDR2Fp6qU1lluhMeIgt/n/xXPQWe2sNqqQmvlCbIg0iSmVw84x2UKRm3XVpawuuWfbml36T+4+qi5OCRExg8drYSWakZlfpCeWhKbuvffVcRdbt2wfIvsW7zT+pWqzwA5ovwirBJOYlkzJ6sUR5//nVMPQV/+Ngpr0oaRBCMxEdqRT/4ZKWSBanNFTmRNSYPS04cKg8jPYQnn+2DUvfdqXaokLV15txFleGV7dpWfZJ42zh3HGSnDhHu1k1qqd0RoqKilPRIacTcybKfbtzDZ7qFV8qH5IcRZAs+2apLtk2THzR4b8YS9Hy5Rfz6kdiER0Tg0YfuR9cXGyMyKlqt2192/4kNn45Tu6pIeYJcwLR5Jk4MZQ2N+eBTfLttt4pvSsLrfqhLdtmQ0hXZIk3ayIXRl9/txMrZI1C0cH6s+fJHdQHr/gyQnVmS7pJi5LPEF+F1cz956izeH/W6usCSLcqkzvizdd+qmEmpg+w0kTQ+SR9ak75kfcq6lYsjKSuQ7PeyNVvUnQK5cJMSLXm4rH7bN9RnpMRBPo+mz12ltkdc+clIVUaT9PNUzpFdGiRT/nqnliomfxw6rnbTeODeO5TMyiH1wdt37cfgXu3U56k8cCklOvI3Q+ENxK96jpkESMB2wvv7/kPo9c4HKsvZ/tkGKjMkH+xy69u9D6+Ih2wPJrImH+Aqs3H1Ojr3G4//HTyGhx+8W226/91Pu5VEnDh9DkUK5lU7EdSvWRHtXn8X/ztwFIN6tVNbaRkRXhEyETR5Kl1ETHaGkHIKuYXrjfDKAywTZizFVz/8ovbzlfby9HrCfXjl1uEHc1aqW4tSLyqZL3nQS0o3UjvcGWcR0CkfL1dfUFLnKP3K2JNuTSUZO6mN/mpZyr+clvB9kpY0yGvyoNu+A0eViLoPTyUNEr+PF65Vt1Ql2y63ZeUCQvaKlcy1O85btv2m9qbNli0LSt17F7q2axz/4wi+CK8Sr2mLIFndiMhIPFL6Xgzs+YJ6iE7qsmXfUveOD54yvDJXT/GRbN/sxRtUZk8y21KPLaUozzWtHf/gkjwYNOGjZaqe8vr1W8ibJycqlS2ltvMSCUntkIvBGfNW4c/DJ9QWVVK+8mqHZ9QDfu5Dt/BKv5JZlL9BeXApPDxSXay1aVpblYq4D4mNXGCKOM1fvglnz11UD8DJvtc1qzwSf55cnMpuARcuXVEPosoesLItnuw77a4TTvrDE5KZlPIZySDLnN/s/hyyZg5Dp37j1boRUc6X97ZknwEp7YPt6bPEV+GVizW5oyLCKQ90yj7Fr73UDGPe/xTL1n6rHpyUXUqMCK/Aklr2RSu+Ug8MZs2aGSXvvgOdX2yc6McpZB2Nm74Ekr2WLHvpksVVjbd7p4iUPk+lvlz24ZV1fONWuPo7rFP9Ubzavmn854T8PQybMFddbMtR4ZGS6o6QPMyWdJcZfo2SAAmQQCAQsJ3w2g2ae1uylGov7TZWo+MR2Wz20mAl7VKDHOyH7BAhX+AJb+HKjyJIbanseCA7KfAwT8CXX0cz/67sgQRIgARIgAQ8E6DwemAUTMIr2WXZwF4yhPLzzXLr3P2Lc56XSuCeIZlBKV2RUgnZg1hqFGUvUslQr58/Js1fNgvcWaf/yCm86c+c70gCJEACJGCMAIXXQcIru0S8+/5CdbtTbukn/LU1Y8slMM+SEgPZj1lqKeUhKqmvlBpIqWGUvYl56CFA4dXDkb2QAAmQAAnoJ0Dh1c+UPZIACZAACZAACZAACdiIAIXXRsHgUEiABEiABEiABEiABPQToPDqZ8oeSYAESIAESIAESIAEbESAwmujYHAoJEACJEACJEACJEAC+glQePUzZY8kQAIkQAIkQAIkQAI2IkDhtVEwOBQSIAESIAESIAESIAH9BCi8+pmyRxIgARIgARIgARIgARsRoPDaKBgcCgmQAAmQAAmQAAmQgH4CFF79TNkjCZAACZAACZAACZCAjQhQeG0UDA6FBEiABEiABEiABEhAPwEKr36m7JEESIAESIAESIAESMBGBCi8NgoGh0ICJEACJEACJEACJKCfAIVXP1P2SAIkQAIkQAIkQAIkYCMCFF4bBYNDIQESIAESIAESIAES0E+AwqufKXskARIgARIgARIgARKwEQEKr42CwaGQAAmQAAmQAAmQAAnoJ0Dh1c+UPZIACZAACZAACZAACdiIAIXXRsHgUEiABEiABEiABEiABPQToPDqZ8oeSYAESIAESIAESIAEbESAwmujYHAoJEACJEACJEACJEAC+glQePUzZY8kQAIkQAIkQAIkQAI2IkDhtVEwOBQSIAESIAESIAESIAH9BCi8+pmyRxIgARIgARIgARIgARsRoPDaKBgcCgmQAAmQAAmQAAmQgH4CFF79TNkjCZAACZAACZAACZCAjQhQeG0UDA6FBEiABEiABEiABEhAPwEKr36m7JEESIAESIAESIAESMBGBCi8NgoGh0ICJEACJEACJEACJKCfAIVXP1P2SAIkQAIkQAIkQAIkYCMCFF4bBYNDIQESIAESIAESIAES0E+AwqufKXskARIgARIgARIgARKwEQEKr42CwaGQAAmQAAmQAAmQAAnoJ0Dh1c+UPZIACZAACZAACZAACdiIAIXXRsHgUEiABEiABEiABEiABPQToPDqZ8oeSYAESIAESIAESIAEbESAwmujYHAoJEACJEACJEACJEAC+glQePUzZY8kQAIkQAIkQAIkQAI2IkDhtVEwOBQSIAESIAESIAESIAH9BCi8+pmyRxIgARIgARIgARIgARsRoPDaKBgcCgmQAAmQAAmQAAmQgH4CFF79TNkjCZAACZAACZAACZCAjQhQeG0UDA6FBEiABEiABEiABEhAPwEKr36m7JEESIAESIAESIAESMBGBCi8NgoGh0ICJEACJEACJEACJKCfAIVXP1P2SAIkQAIkQAIkQAIkYCMCFF4bBYNDIQESIAESIAESIAES0E+AwqufKXskARIgARIgARIgARKwEQEKr8lg/HP+pskevGse4gIK5M6C0xfS9329G2VgnJ0lLBSZM4Xi4tWIwBiwjUeZK6cCZUkAACAASURBVFtGREXH4vqtKBuPMjCGlv+2zLh0NRyR0bGBMWCbjtLlAgrlzoJT/Kw0HaHMYaHImikUFwLws7JI3iym588OgoMAhddkHCm8JgH6sTmFVx98Cq8+lhRePSwpvHo4Si8UXn0s2ZP/CFB4TbKn8JoE6MfmFF598Cm8+lhSePWwpPDq4Ujh1ceRPfmXAIXXJH8Kr0mAfmxO4dUHn8KrjyWFVw9LCq8ejhRefRzZk38JUHhN8qfwmgTox+YUXn3wKbz6WFJ49bCk8OrhSOHVx5E9+ZcAhdckfwqvSYB+bE7h1QefwquPJYVXD0sKrx6OFF59HNmTfwlQeE3yp/CaBOjH5hReffApvPpYUnj1sKTw6uFI4dXHkT35lwCF1yR/Cq9JgH5sTuHVB5/Cq48lhVcPSwqvHo4U3sQcY2Nj0XvIB/h2224M6vUiqlYog25vT8RfR09i9ZyRuKNoQX3gvezpiRavo1/XNmhYu5KXLdM+/avvf8WgcbOwdfUHPvd75doNPNaoG1Z+MgL3Fi/mcz9mGlJ4zdADQOE1CdCPzSm8+uBTePWxpPDqYUnh1cORwpuY474/j6JV5yFK3O4qVggLP9+MJau/xqJpg5Eje1aEhob4DP67n3YrYb7r9kI+9WGV8F68fBVHjp1GuTL3+jQuaRQVHY1ffz+I0iWLI2uWTD73Y6YhhdcMPQqvT/QyfrkUCDf/wxkx9z+C6PseTnMMIaf+RujOLSmekyE0BBkyuHArPNqneVjSKGMYIuu3saRrKzul8OqjS+HVw5LCq4cjhTcxx59+2Y8ub03Ab5s+Vi9MmbUce/84go/G9TUN/PnuI/Hyc0+hZpVHfOrLKuH1aTA2bEThNRkUZni9B5ilbzO4rl/1vmEKLcI7D0H0I1VT7Cvk2EGETeqLkJs3tLxXenQSG5YZNyevSY+30voeFF59OCm8elhSePVwdKLwrv7yR8xcsBYnT59Dntw50b5VfTzf/El8v30PegyagoiISGTJHIbyD5fEtp37EBMbg0xhGfHZzGHIkCEUIycvwO79fyE6OgZ1a1RA/x7Pq/Pl+PvEvxg2cS5+2/uX6rtj6wZo80xtdOj1Ln7e9QfCwjKibo3yGDOgs+p78qzlOHT0JLJlzaLG0f7Z+qqfyKhojJ66EF98vV216db+GcyYtwp9u7Q2VNLQotM76jx5z8N//4PMmcIwbnBXfLpiM3btOYgbt8IxpE97PF6xDJKWNHy64ivMWbIBZ85fQoG8t6Fdq/po26yOGldqryUsaShSMB8qNuyCycNfw9ylX+Ds+cvIljWzmvM9xYuqfhav+hrvz16BiMhItHy6Jm7cDEdMTAyG9u3g88Km8PqMLq4hhdd7gG7hjS5RCggJ9b4DAK5rlxFy+phqG9HhbURVrJWon9DD+xA25W24wm8iNk8BxORJXlcV4nIhJERutfj/J1xdsTEIObQPFF6flkNQNaLw6gknhVcPR6cJ7+Fjp/D0i2/j/VE98XiFMvht3194uc84LPhgIMqULK4ktOvbE+MzvJM/Xo59f8ZleGNiYvFMhwGo/Ggp9HqlFW6Fh+OtkR8hX55cGPnWy+r1Ju3747HypdG1XWMcOXYKnd94D5OGvYaqFUqj0lNdlfRJhvfEqbOqryF9OqBBrUo4ejzu3O4dm+GZ+o9j4eebMHPhOsye+CYK5c+DcdMWYdXGHzHizZcNCW/rrsNw9doNzJvSH7lz5UDH3mPwx1/HMG10L1W6MGPeamz6bieWfzwskfD+deQkWnYegk8/GIj7Styu5t6p33jMnzoA8p2a2muFCuSJr+GVso1ydTuhWqUymDj0NXUx8MbwGbgVEYEpw3vAXTYiQlylfGnMWrQOi1Z+hXo1KuCdPu19XtgUXp/RUXh9RZelb3O4rl9BVJ2WiA3zvZYnZO92hB47GCe9L/RBVJW4K9/QP3YhbNpAuCIjEJO3EKIrxV15Jj3sVNLgio5Cho2LKby+Lqogakfh1RNMCq8ejk4TXsnKXrh0Bfnz3hYPsHH7AWjbtDaebVIrTeH95fcD6NhrDLavn64ypnL8vv8QXuwxCjs2fIh9B46qf5aHv7Jny6Jel6xxgXy34f67b08kvNPnrcKPP+/FgvcHxI/j40/X4fvtv2Pu5LfR/vV3UaZkCfTp0kq9fvnKdVRt0h1jB3YxLLyPPnQf+nVtrdpP+HApftyxVwmuHD/9uh/d+0/Czi8+SiS8u/YexEu9x2Ld/HdRuGBeda4wk9rltF5LmOF1C6/Ibe1q5VQfn639FnOWfoG180Zj6uzP8ePPe7B4xjvqNblQqNumL6pVLEPh1fdn7X1PzPB6z0yX8Mo7h+zfidCjf6hBRLbsqgQ3k/uPpEAxRJevmeoAKbzexy61Fixp0MeSwquHJYVXD0enCa/Md/bi9Vix/ntcvHwNISEuXLpyDX27PIsXW9ZLU3hXbPgeA8fMShH8xkXjVKnAuOmL8d2KKSmekzDD23/0TJWxTXoULpAHm5dOQIO2b6hSgtZN/ru7Wb1pD7zVva1h4ZXMcbuW9dRbfPDJCvy27xBmjo+rRf51zwG06zkae77+JJHwitzK2DZu+RnlHymJGpUfRuN6VZErRzYlvqm9lpLwitBK1lyONV9uVeUbm5e8pxiGR0Rg3KCu8dPv3n8y8ufNReHV92ftfU8UXu+ZuUsaouq0UBlNs0fowd8RcvD3RN1EF74TMWWrpdm1vYQ3Ghk2LmKG1+xiCIL2FF49QaTw6uHoNOFdumYLJn20DNPH9MbDpe5WEJu/PBhN6lX1KLwbvt6OYRPmYtvaaSnCX7tpG959/1P8sGqqR+EdOmEuzl+8rG7xp3TUbtkbrzzfSGWd3YfUxUoJhJFtyaSkoWGtSmpObuHdvf9Q/MN3qQmv+70O/f0Ptmz9DV988zNOnzmvsrFFC+VTL6f0muxg4d6WzJ3hXfLhOyh9f8rCK/W6o97uFD+31wZOQb7cOSm8+v6sve8p0IU3w/4dcB3aj5gHKyK6xAPeA/ChhW7hlSGEHt6PkD9+VaOJKVI81QfZEg6XwutD8FJpEqgZXn+sf0/UKbyeCBl7ncJrjJORszKHhSJrplBcuBph5HRbnVMkb1zpgNFDMpThEZF4751uqolkJp9o/jp6vtzco/C6a08lAyuZWDnkYatb4RHIc1sOiFC2fXWEyvDKv8uxccsO5BQZLP9gopKGOUu+UNudbVg4Nn7o5y5cVlufyQNyIqwVHymJ3p3jShrktRrNeqqsqJXCKw/LXb9+E7flyh73fRsTixadBqNJ/cfxXNM6qb7WtEE1w8Ir5RW79v6F+VP7q/eQvY8btH0Tjz1aisJrdCFbcV6gC2/GpdOR8ZvPEdmqKyKfaGYFomR96ixpSNh5yLEDcF06j+iHHjM0D3sJL2t4DQVN80n+WP+epkDh9UTI2OsUXmOcjJzlJOGVW/sioZ9OG6RkVTKyfx46hieqllVlDWk9tCYsn+08FAXy58bwfh1VXavspHDqzHl8MvEtJW5NOgzEg/fdhV6vtMSJU2fQ9a2JGD+4m3qAS4S1Y5uGaN6wupLuem36ovMLjfFCi7o4f/EKegycgtrVHkW3dk0wbc5KSDZaShAK5M2tSiW++GY7hvV7yVLhXbLqa8xfvglTR/TAncUKqR0e5IE32dHh7PlLqb4m5Q9GM7yyc4T0+dG4Pij/0P34ZMkXWLD8S9Sp9iiF18gfrFXnUHi9J2uV8GbY/BlcEbcQVbsFYjN5LpWg8Hofu9RaBGqGl8Krbw3YrScKr76IOEl4L12+hj5Dp6ltxQrky403X31OZU9HTVmA7h2aouQ9d6S6S4MQl90VRkyaj52747YYq1yulNqWTHZqkOPUv+fx9uiZKtsr/022GnNv6SXbcM1avB6Vyj6AGWN6Q/b8FZGVEoHcubKjUZ0q6NmpOTKEhiohHj5xHr76/hf1Pt07NsW8pRvR+cXGaFTHc9LH15IGqdOdOHOZqrm9fOWaYiTbhnVq20jV8Kb2Wko1vKmVNAgnEfolq79RfcqWcLKdW5YsmTC414s+L2zu0uAzuriGFF7vAVJ4kzPjLg3eryMdLSi8Oijasw8Kr764OEl49VFjT2YI3LwVEb93sfQju19UKlcKnV942uduKbw+owsW4Z2GjN+sQPQDjyKmePrU8GbYtFRtGabroTV3CIMhwytziWz4vMlVmf7NM4WFqlquyKiY9H9zE+/o3uVDdviIrJU+JT2ehsuSBk+EjL1O4TXGychZFF4jlHiOLgIHj5xA046DMGVED7U/8dade9Fz0FQsnj4Ype67y+e3ofD6jC5YhDeuhtcfh9l9eJOOOViE1x+xcPp7pmcNuyfWFF5PhIy9TuE1xsnIWRReI5Tsc45sTyYlD6kd8iMR95UoZp8BpzCSz9d/h5kL1+L02YsoUjCvKpmQH9wwc1B4zdALipIGZnhvhUebXAXmm7tLGqQnZnjN8zTaAzO8RkkF3nkUXn0xo/DqY8me/EeAwmuSPWt4vQfIGt7kzFjD6/060tGCNbw6KNqzDwqvvrhQePWxZE/+I0DhNcmewus9QAovhdf7VWNNCwqvNVzt0CuFV18UKLz6WLIn/xGg8JpkH/jCG1fSkJ4P7VjxwxMSxvga3jotERuWyWNk7bUtGX9pzWPALDgh49L0X/+epsEaXk+EjL1O4TXGychZFF4jlHiO3QlQeE1GKNCFN8OWVQj99TtEP9EEUWWrm6RhrLkVwhty4QxCf/pSDSCmTCVE336vx8FQeD0iMnxCoO7D64/17wkqhdcTIWOvU3iNcTJyFoXXCCWeY3cCFF6TEQp04TU5fZ+a6xbekHOnEPLLN3BF/7clVsyDlRB9Z9rSS+H1KXwpNgpU4dVHQF9PFF49LCm8ejhKLxTelFnevAUcOx5rGHTmzMCdt7sMn88T9RKg8JrkSeH1HqDOGt6Qs/8gdMfXahCRdVog9s77EDZrlPr36NIVEXPHfakO0F7Cy58W9n4lBWcLCq+euFJ49XCk8KbO8dCRWIyeFGUYdIm7XOjfK4Ph83miXgIUXpM8KbzeA9QlvCH/HkfoL9/Gy25k887qnzPs/CZeemMeKI/o4iVTHCSF1/vYpdaCGV59LCm8elhSePVwpPDaQ3i37dyHl/uOQ0hI4gxxq6efwKA0fm533rKNOHjkJIa/0VHfggjQnii8JgNH4fUeoLukIfrRGkCGjN53IC2uXUbovh1xstugLSIbt0/UT0LpjS5RCshfJNn7hIaGIDTUhYgI/+/Di5gYlamODcuMm5PX+MbEj60ovPrgU3j1sKTw6uFI4bWP8A6dMBdffDrWq8BSeP/DReH1aukkP5nC6z1At/B63zJ5i8gmHRBZ/7mUM7i/bEHYxyN1vE269UHhTTfUtn0jCq+e0FB49XCk8AaG8H629lvMWrQeUdHR6pfJ3h3QGYUL5EFC4f1xx16MnbYIERGRyJghA/p2fRbVKz+Ma9dvYsTk+di97xCio6PxyvNPo0WjGvoWkE16ovCaDASF13uAmaYPBm5e975hkhYxZSoj8smWafaT4ddvEbpldYrnhIYAIS4XIqONP3RgetAeOojNGIaI10Zb/Tba+2eGVx9SCq8elhRePRwpvPYX3guXrqJWy15YN/9dFC2UD4PGzoaU7L3Tp30i4W3cfgCG9GmHcmXuw5+HjmPh55swrF9HjJ66EJcuX8Ootzvh6rUbaNl5CKaO6IGS99yhbxHZoCcKr8kgUHhNAvRj8yxhocicKRQXr0b4cRTB8dYUXn1xpPDqYUnh1cORwmsf4X3ljfHInStHogF1a/8MWjephVvhEcicKUy9tnbzNqz84gd8PL5fIuF9qfdY3Hl7IXR4tj5uL1Igvp8azXpi0rDuKFs6bmejcdMXI0umTOjesam+RWSDnii8JoNA4TUJ0I/NKbz64FN49bGk8OphSeHVw5HCax/hHTz+EyyaNijRgLJlzYLMmTJixvzV2PLjb5B1f/nqDVXWMGvCG4mE99yFy5gxbzU2f/8LcmbPin7dWqNapYdQrm4n5MqZDaGhoarvyMgo1KtZEf17tNW3iGzQE4XXZBAovCYB+rE5hVcffAqvPpYUXj0sKbx6OFJ47SO8qT209uW3OzFtzkrMn9ofObJnxaqNP2L1xh+TCW/CmXz9w694a9RH2LZmGp564S2V4Q22EoakkaPwmvxMoPCaBOjH5hReffApvPpYUnj1sKTw6uFI4bW/8C78fDN+3LEH00b3wpVrN9B7yAfqQbTF0wfHZ3j793geHXq9q8S2UP48OP7PGbTo9I4SXilhuHUrHIN7t0NUdAwmfLgUDWtXRpmSxfUtIhv0ROE1GQQKr0mAfmxO4dUHn8KrjyWFVw9LCq8ejhRe+wuvPLTW7e2J6oGzQgXy4LWOzdBj4BQ8XbcKCubLHb8P74oN3+OjBWvVTgxS79vj5eaoU+1RXL9xCyMmzcevew6oyT5esQze7tEWGf6/xEHfSvJvTxRek/wpvCYB+rE5hVcffAqvPpYUXj0sKbx6OFJ47SG8+qLp3J4ovCZjT+E1CdCPzSm8+uBTePWxpPDqYUnh1cORwps6x39OxWLhZ8Z/uKhIIRfatox7MIxH+hOg8JpkTuE1CdCPzSm8+uBTePWxpPDqYUnh1cORwquPI3vyLwEKr0n+FF6TAP3YnMKrDz6FVx9LCq8elhRePRwpvPo4sif/EqDwmuRP4TUJ0I/NKbz64FN49bGk8OphSeHVw5HCq48je/IvAQqvSf4UXpMA/dicwqsPPoVXH0sKrx6WFF49HCm8+jiyJ/8SoPCa5E/hNQnQj80pvPrgU3j1saTw6mFJ4dXDkcKrjyN78i8BCq9J/hRekwD92JzCqw8+hVcfSwqvHpYUXj0cKbz6OLIn/xKg8JrkT+E1CdCPzSm8+uBTePWxpPDqYUnh1cORwpsGxxvXEXXkT8OgXVmyIbTE/YbP54l6CVB4TfKk8JoE6MfmFF598Cm8+lhSePWwpPDq4UjhTZ1j9IG9uDqwi2HQofc9iBwjPjR8Pk/US4DCa5InhdckQD82p/Dqg0/h1ceSwquHJYVXD0cKrz2E95EnX1Y/CSxHTEwsQkJc6p9L3nMnln00RF+wg7gnCq/J4FJ4TQL0Y3MKrz74FF59LCm8elhSePVwpPDaQ3jdozh28l80bj8Av236WF+AHdIThddkoCm8JgH6sTmFVx98Cq8+lhRePSwpvHo4UnjtL7xzl23EwcMn8L+Df6NW1bIIC8uIf06fwzt92qvBz1y4Nv7fDx87hWET5uLfsxeRMWMGjHzzJZR5oIS+xWLjnii8JoND4fUe4IWLLly5HHc7xoojd+5Y5MoVm6jro0dDkr1VpowhCMsYgqs3oqwYRop9FigYi6xZEo8t3d7cwjei8OqDS+HVw5LCq4cjhdf+wrvw882YMW8VFn4wCHcULZBIcBMK7+De7dCi0zt4tvETaNX4Cez54wi695+EzUsnIGOGUH0LxqY9UXhNBobC6z3AL74Mwdafkguo9z2l3KJggVi82iWu1kmO3b+HYPlK697Pm3G3aRWDB0rGeNMkIM6l8OoLE4VXD0sKrx6OFN7AEN4tW3/DzPF91WATZnQT/nu7VvXR7KVB2LHhQ4SGxn0ntuo8BH26PItKZR/Qt2Bs2hOF12RgKLzeA3QLb+bMscgU5n37tFrcuAFERrnw9FMxqPBoDCIigIlTQ3H9ugs5skuh/3+t5QvR5XKpBwCsPtzjovBaTTrw+6fw6okhhVcPRwpvYAjv7n1/YeyguB0jUhPeZg2r47lXh6Ng/jzxk7p5KxyDe72IejUr6lswNu2JwmsyMBRe7wG6hffuErG4vZjebOfZ8yHYt88Fkek+r8dgy3fAj1tDlew+Wi7xe2UIDUGGDC7cCv8vG+z9bIy12LvPhXPnQ0DhNcbLyWdRePVEn8KrhyOFNzCE9/f9hzBmYGc12NmL1+PvE/9iaN8O6t8nzfwMl69cQ+cXG+OZDgPx09pp+hZHAPVE4TUZLAqv9wCtFF4Zza7dIbh82YX77o3BgYNxKd3y5WKQPXviTC6F1/vYpdaCJQ36WFJ49bCk8OrhSOENPOFd8+VWfLbuW8yd/DbCIyLRpuswPFzqbvUQm9TwdmjdAE/VrowLl65i9NQFGNKnA7Jlzaxvwdi0JwqvycBQeL0HaLXwXr8B7Nj5XwF+oUKxKHlf8kwyhdf72FF49TFLrScKrx7GFF49HCm8gSe8N26Gq4fRboZHIF/unLjz9kK4du0mhvRtj6PHT2PIe3Nw+swFVcfbrmU99QCbEw4Kr8koWym87t0M8uSJRc6ccdlJ2Wu6QO4sOH3hpsmR+6+5W3jvuTsWxYrqLWlwz+rAXy78808IQkKBxyrGIGPG5HW66Sq8+104d44lDVavusgo4OSJEBXvokWtr822Yj4UXj1UKbx6OFJ47SW8+qLqvJ4ovCZjbqXwusWwft0YVKkcJ4bBJLxW1PC6wykPrm3f7sJddyFVqU5X4WUNr8m/NGPNz593YfIHocibNwY9X7XmYsrYSHw/i8LrO7uELSm8ejhSeFPnGHP8CG7MmmAYdGixu5Dl5T6Gz+eJeglQeE3yTA/hbVA3Bo8FofDeUyIWxTQ/tOYO58VLLrUdWe7bYvHwQymLD4XX5OJP0NwuNbznL7gw+f3kwrtgSQbcdSdQrHA07rozceb34mUXbt0CIiJcOHUKqFwxbr3If798Ccj0/6VthQumT8bYiPD+9HMISt4P3JbLe6k/+rcLhQpCPdgZzIcvwnvrlgun/0WyNRLMnIzMLXNYKLJmCsWFqxFGTjd0jpu11WuxSN4shsbDk4KfAIXXZIzTQ3iZ4fU+SBRe75mZaWEb4U2S4R37XkZcu55c7Jo2icFdd8Xik7khuHQp+Y+gyK4eV67Jf4+FC3Gv33ZbLNo8GwOrxTct4Z01JwR/H/tvbz0ZZ7/exnYZ2bY9BBs2/tdWxP+5Z2OCVny9Fd7Zc0MhFwPuo0G9GDxWyfsLCjN/R3Ztq1t4138RArlocx+PPByDZk2sYU3hteuqSv9xUXhNMk8P4a1YPgalS8UNVD7Ec+cIw4Ur+q60TSLwuvn2nS7s2++CEzO8tZ+IxZ23B19mLVvmUETHALcijMmX14vGYIPLV6B+ZERKGipXDsG6dW6BSc68RIkYHD4sX7pxYvvfIf/s/m+JZbhQwVh062ztHFMT3j37Q7Dss+Q/oPJo2Vg0eTrtMUm2euLk5L+kFMxS543wJr0YcK+FXj2jkTvJrzYaXIpBdZpO4T31rwvTP0y+FuUitOzD+qWXwhtUS9HUZCi8pvAB6SG8Jodo2+ZW1vDaNcNr22AE2cBEeAvmC8X+P91foMmzuLJN3bVrLsTGxl1Iug/3v7u1NymaYYOt/Snq1IT3p59Csf7L5PO4844YvNQ+bVGQn9aePS+5LFuZWfP3kvJGeD9fFYLfdifn0/FFuROgX8L8zcbb99cpvKldXNSsHo1aNfUnAyi83kY7eM+n8JqMbXoIr9Sh5sz5/xleABkzhiAiMnA/hC9eBK5cdWaGN3/+WGQNwpKyDKFx4hidDr9al9afbJTs0vCPS2V4PQmv/AT1v2eSZ3LjBTipCf//GweT8Eq9csP6gftZktZa8EZ4v97iwpbvkmcdKbxxhHUK7//+CMGipckvLii8JmWEzT0SoPB6RJT2CekhvKzh9T5Ids3w8pfWvI+lNy0S7tLQtIkLH89Svx+dpGRBHvaKhWQ34yRHvnwTSl/c+fI/d/2uewzpkRFNLcN76XIIJkxOLgotW8SgTCnP0jphciguXU6cIQ5mofNGeFPKgMsa6dY5eGucvfm70im88rDae5NDEB6eeC1aVT7CDK83kQ7ucym8JuObHsLLXRq8DxKF13tmZlrY5qG1JLs0bP8lAzasByTxLAIrmej774tFvboxqjZTfpXv110uXLoYghvhMUp9c+cGKlZw4cDBGPx7JgRRkbG47Tag9IOxKPtwrOUPeaX10Nrfx0OwYqUL168DGcOA6lWAypWN1RRLHe83W1y4eNGFLFliUaUSgvp2vTfCK2tfpHfrduDmTRdy547FEzVjWb/7/x8KOoVXupQ63m0/xa1FYV3uYevWIoXXzCd7cLWl8JqMZ3oILzO83geJwus9MzMtbCO83IfXTBiDqq23whtUk9c8Gd3Cq3l4aXZH4U1P2vZ+LwqvyfhYKbw/7wzBnr0utS/og6WC8JfWLNyH9+y5ELUTRI4csXi0LPfhNbnMPTa3i/BeueLCss9D1BZizZ/xfJvf48T8cIKRfXj9MKyAe0sKr76QUXj1sWRP/iNA4TXJ3krhTWlowfRLa1ZuS7Z9R4i6NSlH6VKxyJcvufzwhydMLv4Eze0ivPpm5L+eKLx62FN49XCUXii8+liyJ/8RoPCaZE/h9R6g+yeTrdqW7PiJEBw6/N8DEZkyxaa4gTyF1/vYpdaCwquPJYVXD0sKrx6OFF59HNmTfwlQeE3yp/B6D9BK4Y2MdGHbzyGIiQbaPR+D9V8AUt5Q/K5YyH6lCQ8Kr/exo/DqY5ZaTxRePYwpvHo4Unj1cWRP/iVA4TXJn8LrPUC38BYpHIMC+b1vn1YL2YP17Dl5Ej8GbVvH4OgxF2bPCUVIiJQ2xKj/dx+hISEIDXUhItLYU+5mRnrkqAuXr7jAbcnMUHRGWwqvnjhTePVwpPDq48ie/EuAwmuSP4XXe4Bu4fW+pfEWvXpEQ36wQ46ln4Vi7/7kv1BlvDd9Z1J49bEM1p4ovHoiS+HVw5HCq48je/IvAQqvSf4UXu8Bunef8L6lsRYl7weqPvZf1vbyZRc+W5F8w/7QECDE5UJktP6fs0xtpHVqx+DO29Pv/YwRM38Wa3jNM3T3QOHVw5LCq4cjhVcfR/bkXwIUXpP8KbwmAfqxegoeOwAAIABJREFUeZawUGTOFIqLVyP8OIrgeGsKr744Unj1sKTw6uFI4dXHkT35lwCF1yR/Cq9JgH5sTuHVB5/Cq48lhVcPSwqvHo4UXn0c2ZN/CVB4TfKn8JoE6MfmFF598Cm8+lhSePWwpPDq4Ujh1ceRPfmXAIXXJH8Kr0mAfmxO4dUHn8KrjyWFVw9LCq8ejhRefRzZk38JUHhN8qfwmgTox+YUXn3wKbz6WFJ49bCk8OrhSOHVx5E9+ZcAhdckfwqvSYB+bE7h1QefwquPJYVXD0sKrx6OFF59HNmTfwlQeE3yp/CaBOjH5hReffApvPpYUnj1sKTw6uFI4dXHkT35lwCF1yR/Cq9JgH5sTuHVB5/Cq48lhVcPSwqvHo4UXn0c2ZN/CVB4TfKn8JoE6MfmFF598Cm8+lhSePWwpPDq4Ujh1ceRPfmXAIXXJP8b4f/9opfJrgw3Dw1xITom+H6tyzAATSfKjw3LlyJRmgca4gJkRcZyWZqGyb9v0wjjOyBLPSwD+bMya6ZQPRDYS8AToPAGfAg5ARIgARIgARIgARIggbQIUHi5PkiABEiABEiABEiABIKaAIXXZHiv34oy2QObkwAJkAAJkAAJWEEgW+YMVnTLPgOQAIXXZNDS+6E1k8NlcxIgARIgARJwDIEiebM4Zq6caNoEKLwmVwiF1yRANicBEiABEiABiwhQeC0CG4DdUnhNBo3CaxIgm5MACZAACZCARQQovBaBDcBuKbwmg0bhNQmQzUmABEiABEjAIgIUXovABmC3FF6TQaPwmgTI5iRAAiRAAiRgEQEKr0VgA7BbCq/JoFF4TQJkcxIgARIgARKwiACF1yKwAdgthddk0Ci8JgGyOQmQAAmQAAlYRIDCaxHYAOyWwmsyaBRekwDZnARIgARIgAQsIkDhtQhsAHZL4TUZNAqvSYBsTgIkQAIkQAIWEaDwWgQ2ALul8JoMGoXXJEA2JwESIAESIAGLCFB4LQIbgN1SeE0GjcJrEiCbkwAJkAAJkIBFBCi8FoENwG4pvCaDRuE1CTCN5pmmDfLYeXi34R7P4QkkQAIkQALOJEDhdWbcU5o1hdfkWqDwmgRI4bUOIHsmARIgAYcToPA6fAEkmD6F1+RaoPCaBEjhtQ4geyYBEiABhxOg8Dp8AVB49S0ACq8+lkl7YkmDdWzZMwmQAAk4gQCF1wlRNjZHZniNcUr1LAqvSYDM8FoHkD2TAAmQgMMJUHgdvgCY4dW3ACi8+lgyw2sdS/ZMAiRAAk4kQOF1YtRTnjMzvCbXAoXXJEBmeK0DyJ5JgARIwOEEKLwOXwDM8OpbABRefSyZ4bWOJXsmARIgAScSoPA6MerM8FoSdQqvJVhVp3xozTq27JkESIAEnECAwuuEKBubI0sajHFK9SwKr0mAaTSn8FrHlj2TAAmQgBMIUHidEGVjc6TwGuNE4TXJyZfmFF5fqLENCZAACZCAmwCFl2vBTYDCa3ItMMNrEiAzvNYBZM8kQAIk4HACFF6HL4AE06fwmlwLFF6TACm81gFkzyRAAiTgcAIUXocvAAqvvgVA4dXHMmlPLGmwji17JgESIAEnEKDwOiHKxubIDK8xTqmeReE1CZAZXusAsmcSIAEScDgBCq/DFwAzvPoWAIVXH0tmeK1jyZ5JgARIwIkEKLxOjHrKc2aG1+RaoPCaBMgMr3UA2TMJkAAJOJwAhdfhC4AZXn0LgMKrjyUzvNaxZM8kQAIk4EQCFF4nRp0ZXkuiTuG1BKvqlA+tWceWPZMACZCAEwhQeJ0QZWNzZEmDMU6pnkXhNQkwjeYUXuvYsmcSIAEScAIBCq8TomxsjhReY5wovCY5+dKcwusLNbYhARIgARJwE6Dwci24CVB4Ta4FZnhNAmSG1zqA7JkESIAEHE6AwuvwBZBg+hRek2uBwmsSIIXXOoDsmQRIgAQcToDC6/AFQOHVtwAovPpYJu2JJQ3WsWXPJEACJOAEAhReJ0TZ2ByZ4TXGKdWzKLwmATLDax1A9kwCJEACDidA4XX4AmCGV98CoPDqY8kMr3Us2TMJkAAJOJEAhdeJUU95zszwmlwLFF6TAJnhtQ4geyYBEiABhxOg8Dp8ATDDq28BUHj1sWSG1zqW7JkESIAEnEiAwuvEqDPDa0nUKbyWYFWd8qE169iyZxIgARJwAgEKrxOibGyOLGkwxinVsyi8JgGm0ZzCax1b9kwCJEACTiBA4XVClI3NkcJrjBOF1yQnX5pTeH2hxjYkQAIkQAJuAhRergU3AQqvybXADK9JgMzwWgeQPZMACZCAwwlQeB2+ABJMn8Jrci1QeE0CpPBaB5A9kwAJkIDDCVB4Hb4AKLz6FgCFVx/LpD2xpME6tuyZBEiABJxAgMLrhCgbmyMzvMY4pXoWhdckQGZ4rQPInkmABEjA4QQovA5fAMzw6lsAFF59LJnhtY4leyYBEiABJxKg8Dox6inPmRlek2uBwmsSIDO81gFkzyRAAiTgcAIUXocvAGZ49S0ACq8+lszwWseSPZMACZCAEwlQeJ0YdWZ4LYk6hdcSrKpTPrRmHVv2TAIkQAJOIEDhdUKUjc2RJQ3GOKV6FoXXJMA0mlN4rWPLnkmABEjACQQovE6IsrE5UniNcaLwmuTkS3MKry/U2IYESIAESMBNgMLLteAmQOE1uRaY4TUJkBle6wCyZxIgARJwOAEKr8MXQILpU3hNrgUKr0mAFF7rALJnEiABEnA4AQqvwxcAhVffAqDw6mOZtCeWNFjHlj2TAAmQgBMIUHidEGVjc2SG1xinVM+i8JoEyAyvdQDZMwmQAAk4nACF1+ELgBlefQuAwquPZdKezg0Y7LHzfCOHeTyHJ5AACZAACTiTAIXXmXFPadbM8JpcCxRekwDTaE7htY4teyYBEiABJxCg8DohysbmSOE1xinVsyi8JgFSeK0DyJ5JgARIwOEEKLwOXwAJpk/hNbkWKLwmAVJ4rQPInkmABEjA4QQovA5fABRefQuAwquPZdKeWNJgHVv2TAIkQAJOIEDhdUKUjc2RGV5jnFI9i8JrEiAzvNYBZM8kQAIk4HACFF6HLwBmePUtAAqvPpbM8FrHkj2TAAmQgBMJUHidGPWU58wMr8m1QOE1CZAZXusAsmcSIAEScDgBCq/DFwAzvPoWAIVXH0tmeK1jyZ5JgARIwIkEKLxOjDozvJZEncJrCVbVKR9as44teyYBEiABJxCg8DohysbmyJIGY5xSPYvCaxJgGs0pvNaxZc8kQAIk4AQCFF4nRNnYHB0tvMf/OYNBY2fjz7+OoUihfBjQ8wWUK3NviuTWbt6Goe/NwYg3X0a9mhXiz6HwGltovpxF4fWFGtuQAAmQAAm4CVB4uRbcBBwtvO16jkatx8vh+WZPYuvOfRg0dhY2LXkPGTOEJlohc5Z+gV92/4mz5y+hQ+uGFN50+vuh8KYTaL4NCZAACQQpAQpvkAbWh2k5VnjPX7yC+s+9gW1rP0CG0DjBbdHpHbzRrQ0qli2ZCOUffx3D/Xffjpf7jEOrxk9QeH1YaL40ofD6Qo1tSIAESIAEmOHlGkhKwLHC++uegxg2YS5WfjIinkmfodNQqVwptHq6Zoor5aXeYym86fg3ROFNR9h8KxIgARIIQgLM8AZhUH2ckmOFd+vOvZjy8XIsnvFOPLqBY2bhvhLF8GLLeoaF91ZEtI/o2cwTgYM93/J0Cu6d/K7Hc3gCCZAACZCAMwlkDktcouhMCpy1EHCs8O7aexDvjJ+D1XNGxq+E3kM+QJXypdGiUQ3DwnvhagRXkkUETrwxwGPPxcb+Fz+PJ/MEEiABEiABRxHIkyPMUfPlZFMn4FjhvXj5Kuq06oMfVr2PLJnj/iAatH0Do97uhLKlU96pgSUN6funxJKG9OXNdyMBEiCBYCPAkoZgi6jv83Gs8AoyEdgKj5REp7aNsOGb7arEYcPCsQgNDYFsQ1a5XCnky5Mrni6F1/eF5ktLCq8v1NiGBEiABEjATYDCy7XgJuBo4T15+hz6j56JPw8dx+1FCmBIn/Z48P67FJvqTXtg0rDuKFfmPrV7w19HTyIqKhqhISFwhbgwZsArqFezIrgPr3V/TBRe69iyZxIgARJwAgEKrxOibGyOjhZeY4jSPovCq4Niyn1QeK1jy55JgARIwAkEKLxOiLKxOVJ4jXFK9SwKr0mAaTSn8FrHlj2TAAmQgBMIUHidEGVjc6TwGuNE4TXJyZfmFF5fqLENCZAACZCAmwCFl2vBTYDCa3ItMMNrEiAzvNYBZM8kQAIk4HACFF6HL4AE06fwmlwLFF6TACm81gFkzyRAAiTgcAIUXocvAAqvvgVA4dXHMmlPLGmwji17JgESIAEnEKDwOiHKxubIDK8xTqmeReE1CZAZXusAsmcSIAEScDgBCq/DFwAzvPoWAIVXH0tmeK1jyZ5JgARIwIkEKLxOjHrKc2aG1+RaoPCaBMgMr3UA2TMJkAAJOJwAhdfhCyC9MrytuwzF03WroEGtyshzW46gpE7htS6srOG1ji17JgESIAEnEKDwOiHKxuZoaYZ3+rxV2PTtTvWzvFUrlMHTT1ZBrcfLInOmMGOjC4CzKLzWBYnCax1b9kwCJEACTiBA4XVClI3N0VLhdQ/h2Mkz2PTdTmz6dgcOHzuFOtUeReO6VVGp3ANwuVzGRmrTsyi81gWGwmsdW/ZMAiRAAk4gQOF1QpSNzTFdhNc9lMioaKxY/x3e+3Aprl2/iWKF86NT20Zo/lT1gBVfCq+xhebLWRReX6ixDQmQAAmQgJsAhZdrwU3AcuGNjY3FL78fwJpNW/Hllh0IC8uIRnUeQ5P6j+Pk6bMY+8Ei1KpaDv26tQ7IqFB4rQsbhdc6tuyZBEiABJxAgMLrhCgbm6Olwjtp5mdYu3kbzl24jFpVy6JJvcfxeMUyCA0NiR+dlDjIw20/r59hbMQ2O4vCa11AKLzWsWXPJEACJOAEAhReJ0TZ2BwtFd7WXYfhmfqPo0GtSsiVI1v8iCTrGxUVjYwZMyA6OgaTP/4MvTu3MjZim51F4bUuIBRe69iyZxIgARJwAgEKrxOibGyOlgpvnWf7YPOS95KN5PLV62jw3BvYuuYDY6O08VkUXuuCQ+G1ji17JgESIAEnEKDwOiHKxuZoifD+uGMvtu7YiwXLN+H55k8mG8nxU2ew/df/Yfu66cZGaeOznCy8maYNSjMy4d2Gm4ochdcUPjYmARIgAccToPA6fgnEA7BEeA8dPYk1m7Zh1qJ1qPnYI8loZ84cpvbkrV754YCPBIU39RBSeAN+eXMCJEACJBDQBCi8AR0+rYO3RHjdI3x/9gp079hU64Dt1hmFl8JrtzXJ8ZAACZAACcQRoPByJbgJaBfeg0dOoGih/MiaJRPkn9M67i1eLOAjQeGl8Ab8IuYESIAESCBICVB4gzSwPkxLu/A+WLM9Ppn4FiqWLQn557SOfVvm+DBkezWh8FJ47bUiORoSIAESIAE3AQov14JlGd4r126o7G6G0FDIP6d15MyeNeAjQeGl8Ab8IuYESIAESCBICVB4gzSwPkxLe4bXUxlDwjGypMGHiNmoCXdpsFEwOBQSIAESIIFkBCi8XBSWZXg9lTEkRM+ShsBeiBTewI4fR08CJEACwU6AwhvsETY+P+0ZXk9lDAmHxpIG44Gy45kUXjtGhWMiARIgARJwE6Dwci1YluHlLg3OWVwUXufEmjMlARIggUAkQOENxKhZM2btGV7u0mBNoOzYK4XXjlHhmEiABEiABJjh5RpISkC78HKXBucsMgqvc2LNmZIACZBAIBJghjcQo2bNmLULb0rDvHT5Gv49dxGZwjKiQL7bkDVLZmtm44deuS1Z6tD508J+WJB8SxIgARIggXgCFF4uBjcBS4X3+D9n8MaID/H7/kOJiFetUBpD+3VE4QJ5Aj4SFF4Kb8AvYk6ABEiABIKUAIU3SAPrw7QsFd4XXhuJbFkzo2Prp1CkUF41vBOnzuKTxRsQERmpfpEt0A8KL4U30Ncwx08CJEACwUqAwhuskfV+XpYKb7m6nfD1som4LVf2RCO7dv0mnmjxOnZs+ND7EdusBYXX94B4Knk4N2Cwx87zjRzm8RyeQAIkQAIk4EwCFF5nxj2lWVsqvM90GIhJw7rjrtsLJXrvU2cuoOubE7DykxEBHwkKr+8hpPD6zo4tSYAESIAEPBOg8Hpm5JQztAtvwp8W3n/gbyxa+RVaN6mF4ncUVg+tHTl2Cp+u2Iznm9dFvZoVAp4zhdf3EFJ4fWfHliRAAiRAAp4JUHg9M3LKGdqFlz8t7JSlA3jalswTCQqvJ0J8nQRIgARIwAwBCq8ZesHVVrvwGv1p4Zs3w1Ewf+6ApxmsGV6zMmsksBReI5R4DgmQAAmQgK8EKLy+kgu+dtqF1wgieWitbpu+2Lr6AyOn2/ocCq/v4UkP4V24KCTNAbZtE+P7BNiSBEiABEjA1gQovLYOT7oOzlLhlX14R05egP0HjiI8IjJ+YjdvhePuO4tgxWw+tJau0fbizYIlw0vh9SLoPJUESIAEgowAhTfIAmpiOpYK78t9xyFHtixoUKsyBo+bjVFvvYzd+w/hl98PYOrIHsidK4eJodujKTO8vseBGV7f2bElCZAACZCAZwIUXs+MnHKGpcJboUFnbFk+Wf34RLVnXsP3K6cqrl9+uxNbtu7CqLc7BTxnCq/vIaTw+s6OLUmABEiABDwToPB6ZuSUMywV3scadcPGxeORM3tW1GjWE+sXjFHyGxkVjRpNe2DrGtbw2nWhsaTBrpHhuEiABEiABIwSoPAaJRX851kqvK8NnILr129iyoge6Dd8BgoXzIvnmz+JX6WkYfbn+PbzyQFPOFgzvEZ+5axo0VhT8Zude2Sa7evtHeixf0+/tMYaXo8IeQIJkAAJBC0BCm/QhtbriVkqvOcuXMboqQsxtG8HnD5zAV3efA/yK2vyAxSDe7fDM/Uf93rAdmtA4fU9IidPunxv/P8tKbymEbIDEiABEghaAhTeoA2t1xOzVHiTjiYqOhr/nr2IvLlzInOmMK8Ha8cGFF7fo0Lh9Z0dW5IACZAACXgmQOH1zMgpZ1guvD/8vAff/bQbp8/GZXYL5s+DJ6uXx8Ol7g4KxhTe1MOoQ2g9LRJmeD0R4uskQAIk4FwCFF7nxj7pzC0V3pkL12LKrOUoV+Y+FCmYT733iVNnsWvvQfTr1hrtWtYL+EhQeCm8Ab+IOQESIAESCFICFN4gDawP07JUeGs2fx3D3+iIapUeSjQ0yfrKvrxfL5vow5Dt1YTC61/h9fTgnKcH4/hLa/b6e+JoSIAESEAnAQqvTpqB3ZelwluxYRdsWT4JWbNkTkQpMjIK1Zr2wE9rpwU2PQAUXgpvwC9iToAESIAEgpQAhTdIA+vDtCwVXtmhoXCBvGjXqh5crv+eyF+y6msc+vsU+vdo68OQ7dWEwkvhtdeK5GhIgARIgATcBCi8XAtuAtqF9/XB7yeiu3XnXtyWMzvuur2QemjtyPHT+PfsBdSpVh6j+/OX1uy6FHXsw5seD62xpMGuK4jjIgESIAH/E6Dw+j8GdhmBduGVrK6RIyoqGoN6vWjkVFufwwwvM7y2XqAcHAmQAAk4mACF18HBTzJ17cLrNLROFl47xJoZXjtEgWMgARIgAXsSoPDaMy7+GJWlwhsTE4vP1n2LVV/8gH/+PYfwiEjcUbQgmjWsjlZP1/THfLW/J4VXO1KvOqTweoWLJ5MACZCAowhQeB0V7jQna6nwTpu7CotXfqUEt/gdhdVADv/9D5av+w5d2zVB22Z1Aj4SFF57h3Bj6RFpDpDbktk7fhwdCZAACZghQOE1Qy+42loqvHVb98WUET1Q8p47ElHb++cRvDXyI6ydNzrgaVJ47R1CT8LrafQUYk+E+DoJkAAJ2JcAhde+sUnvkVkqvBUadMbWNdOQMUNoonlFRkWjytOvYseGGek9X+3vR+HVjlRrhxRerTjZGQmQAAkEFAEKb0CFy9LBWiq8z3YeimebPKFKGhIey9ZuwdLVW7DsoyGWTi49Oqfwpgdl39+Dwus7O7YkARIggUAnQOEN9AjqG7+lwvvzrj/wyhvjUeKOwrjnrqKIRSwOHj6Jo8dPYdKw11CzyiP6ZuKnnii8fgJv8G0pvAZB8TQSIAESCEICFN4gDKqPU7JUeGVM/569iDWbtuLEP2cRERm3S0PD2pXU/wfDQeG1dxQpvPaOD0dHAiRAAlYSoPBaSTew+rZUeBd+vlmVM2TJHBZYVLwYLYXXC1h+OJXC6wfofEsSIAESsAkBCq9NAmGDYVgqvFWbdMfC9weqnxUO1oPCa+/IUnjtHR+OjgRIgASsJEDhtZJuYPVtqfCu2PA9Nny9HY3rVkWxIvmRIcluDaXvLx5YtFIYLYXX3iGk8No7PhwdCZAACVhJgMJrJd3A6ttS4X2wZvs0aezbMiewaFF4Ay5eFN6ACxkHTAIkQALaCFB4taEM+I4sFd5r128iY8YMqULKFJYx4AEyw2vvEFJ47R0fjo4ESIAErCRA4bWSbmD1banwCoobN2/hp1/248z5SwhxuVAwfx5UKvcAMmcKjgfZKLz2XvAUXnvHh6MjARIgASsJUHitpBtYfVsqvD/8vAevD56qiOS5LSdiYmJw7uIVhGXMgKkje6JS2QcCi1YKo6Xw2juEFF57x4ejIwESIAErCVB4raQbWH1bKrx1WvXGqx2a4um6VZAhNO7nhW+FR2D24g2QB9o2LR4fWLQovAEXLwpvwIWMAyYBEiABbQQovNpQBnxHlgrvk637pii14RGReKxRN/z65cyAB8gMr71DSOG1d3w4OhIgARKwkgCF10q6gdW3pcL7Up+xGNq3A4oVzp+Iyq69B/HRgrWY/m6vwKLFDG/AxYvCG3Ah44BJgARIQBsBCq82lAHfkaXC++H8NVi6+hvUerwsihUpgKioaPx94jSktrfFUzWQK2f2eIBtm9UJSJjM8No7bBRee8eHoyMBEiABKwlQeK2kG1h9Wyq8jdsPQIbQEENEPp813NB5djuJwmu3iCQeD4XX3vHh6EiABEjASgIUXivpBlbflgqvXVB8/Ok6LF75FSIio1Cnenn079E2/iG6hGNM7bzWXYfhj4N/Ay6XOj1n9qz4bsUU9c8UXrtEOeVxUHjtHR+OjgRIgASsJEDhtZJuYPUd9ML7864/8M742Zg/dQCyZsmE1wZOQe3HH8VzTWsnilRa5z31wluYPOw13FO8aLLoUnjtveDTQ3jPDRicJoR8I4fZGxJHRwIkQAJBSoDCG6SB9WFaQS+8wyfOQ6ECedCpbSOF55utuzBnyReYO/ntRLjSOq9Gs55Y8uE7KJQ/D4XXh0XmzyYUXn/S53uTAAmQgH8JUHj9y99O7x70wis7RbRuUgtPVi+vuB8+dgodXn8X334+OVEc0jqvbN1OqF7pIcjuEvny5MLrnVqgeuWHVXtmeO20nJOPhcJr7/hwdCRAAiRgJQEKr5V0A6vvoBfetq+OQJcXG6NapYdUZE79ex7PdByI7eumJ4pUaudtWzMNg8bOQv0nKuGx8qXw7dbdeGvUh1gzb7TK+F6/FRVYETc42sO9EmfADTaz3WmbHxppakyvdPD80KUnViUmjjY1BjYmARIgARLwjUC2zBl8a8hWQUcg6IX35b7j0LJRTdSrWUEF79DRk5D/9s1nkxIF0+h50qhDr3fR/KkaaFTnMVy+Hhl0i0Im9Hff/kExr81lzAnvS+3iHlRM6/DE6s7xozx1wddJgARIgAQsIJArW0YLemWXgUgg6IV31JQFyJk9G7p3bKris3bTNqzc+AM+Ht8vUbxSO2/K8B44eOQEHi51d/z5z3cfiRda1FUSzZIGey97ljTYOz4cHQmQAAlYSYAlDVbSDay+g154f91zAG8Mn4EFHwxEtiyZIbW6bZ6pjaYNqmH7rv8hV45sKHnPHUjtvCeqlIX8RPLk4d1RpXxpfL/9d/QbPgPr5r+LvLlzUnhtvt4pvDYPEIdHAiRAAhYSoPBaCDfAug564ZV4fLJ4A+Yv/xLR0TFoWLsy+nVtjZAQF/oMnYZ7ixdTNb5pnffttt0YP30xzpy/hKKF8uGNV9ugcrlSqg0zvPZe8WaF18js6u0dmOZp3JbMCEWeQwIkQAL6CVB49TMN1B4dIbxWBofCayVd831TeM0zZA8kQAIkEKgEKLyBGjn946bwmmRK4TUJ0OLmFF6LAbN7EiABErAxAQqvjYOTzkOj8JoETuE1CdDi5hReiwGzexIgARKwMQEKr42Dk85Do/CaBE7hNQnQz811CLHZGt5M0wZ5pBDebbjHc8ycYIcxmBk/25IACZBASgQovFwXbgIUXpNrgcJrEqCfm1N44wJA4fXzQuTbkwAJWEKAwmsJ1oDslMJrMmwUXpMA/dycwkvh9fMS5NuTAAlYSIDCayHcAOuawmsyYBRekwBt3tyIELOkweZB5PBIgAQcS4DC69jQJ5s4hdfkWqDwmgRo8+YU3v8CZHUdsc2XAodHAiQQgAQovAEYNIuGTOE1CZbCaxKgzZtTeCm8Nl+iHB4JkEAaBCi8XB5uAhRek2uBwmsSoM2bU3gpvDZfohweCZAAhZdrwAABCq8BSGmdQuE1CdDmzSm8FF6bL1EOjwRIgMLLNWCAAIXXACQKr0lIAdycwkvhDeDly6GTgOMJsKTB8UsgHgCF1+RaYIbXJECbN6fwUnhtvkQ5PBIgAWZ4uQYMEKDwGoDEDK9JSAHcnMJL4Q3g5cuhk4DjCTDD6/glwAyvriXADK8uksHbT76Rw9KcXHr8ypmR9/AUAW5L5okQXycBErAbAQqv3SLiv/Eww2uSPYXXJEAHNKfwOiDInCIJkIAtCVB4bRkWvwyKwmsSO4XXJEAHNKfwOiDInCIJkIAtCVB4bRkWvwyKwmuyodtKAAAgAElEQVQSO4XXJEAHNKfwOiDInCIJkIAtCVB4bRkWvwyKwmsSO4XXJEAHNKfwOiDInCIJkIAtCVB4bRkWvwyKwmsSO4XXJEAHNKfwOiDInCIJkIAtCVB4bRkWvwyKwmsSO4XXJEAHNPckvOcGDPZIwVMfnjrgLg2eCPF1EiCBYCRA4Q3GqPo2Jwqvb9ziW1F4TQJ0QHNPskrhdcAi4BRJgAT8QoDC6xfstnxTCq/JsFB4TQJ0QHMKrwOCzCmSAAnYkgCF15Zh8cugKLwmsVN4TQJ0QHNPv9ZWb+9AjxQ8SbOnDljS4IkQXycBEghGAhTeYIyqb3Oi8PrGLb4VhdckQAc0p/A6IMicIgmQgC0JUHhtGRa/DIrCaxI7hdckQAc0p/A6IMicIgmQgC0JUHhtGRa/DIrCaxI7hdckQAc0p/A6IMicIgmQgC0JUHhtGRa/DIrCaxI7hdckQAc0DwThPXnS5TESZuuIPb4BTyABEiABzQQovJqBBnB3FF6TwaPwmgTogOYUXgcEmVMkARKwJQEKry3D4pdBUXhNYqfwmgTogOYUXgcEmVMkARKwJQEKry3D4pdBUXhNYqfwmgTogOYUXgcEmVMkARKwJQEKry3D4pdBUXhNYqfwmgTogOYUXgcEmVMkARKwJQEKry3D4pdBUXhNYg9U4V24KCTNmRv5MQST6BzTnMLrmFBzoiRAAjYjQOG1WUD8OBwKr0n4FF6TAB3QnMLrgCBziiRAArYkQOG1ZVj8MigKr0nsFF6TAB3QnMLrgCBziiRAArYkQOG1ZVj8MigKr0nsFF6TAB3QXIfweuqjbZuYNElmmjYozde5D68DFiKnSAIOJEDhdWDQU5kyhdfkWqDwmgTogOaeZNVIvbSnPii8DlhInCIJkIDXBCi8XiML2gYUXpOhpfCaBOiA5p5klcLrgEXAKZIACfiFAIXXL9ht+aYUXpNhofCaBOiA5hReBwSZUyQBErAlAQqvLcPil0FReE1ip/CaBOiA5nYQ3nMDBpsm7WkensoqTA+AHZAACZCAlwQovF4CC+LTKbwmg0vhNQmQzQ0RMCubOoTX00DzjRyW5imeHpwL7zbc01vwdRIgARLwigCF1ytcQX0yhddkeCm8JgGyuSECFF5DmHgSCZAACSQiQOHlgnAToPCaXAsUXpMA2dwQAU/C66kTIw/GeerD0+ueMryessye2nt6f75OAiRAAkkJUHi5Jii8mtYAhVcTSHaTJgEKLxcICZAACXhPgMLrPbNgbcEMr8nIUnhNAmRzQwQovIYw8SQSIAESSESAwssFwQyvpjVA4dUEkt0ww+vhoTcuERIgARLwlgCF11tiwXs+M7wmY0vhNQmQzQ0RYIbXECaeRAIkQALM8HINpEiAwmtyYVB4TQJkc0MEKLyGMIFbnxnjxLNIwCkEmOF1SqQ9z5PC65lRmmdQeE0CZHNDBCi8hjBReI1h4lkk4BgCFF7HhNrjRCm8HhGlfQKF1yRANjdEgMJrCBOF1xgmnkUCjiFA4XVMqD1OlMLrERGF9//auw/wGq8/DuBfYlNqFJVSRdGiSilFbYJasYmR2HskYiVERJGInYiVitkQW+xZlFbVqFGzVlNB7VWC/p/f8b+3yU1yR24ieW++7/P0eSo57/ue8znH9b3nnvdcK4l4eiIIMPCah8glDeY5sRQFUosAA29q6WnT7WTgNW1ktIRWZ3hNfQmAlSw8PRUKmPriCFNjztT55pAy8JqjxDIUSD0CDLypp69NtZSB15SQid8z8FoJyNNtRsBUYGXgtZmuZkMooBkBBl7NdFWSV5SB10rilBp4l32f1mjL3sZXzVpJy9M1JsDAq7EOY3UpkAoEGHhTQSeb2UQGXjOh4ivGwGslIE+3GQEGXpvpSjaEAjYjwMBrM11pdUMYeK0kZOC1EpCn24yAvf2/RtsSEZHG6O9NnS8nP+/rY/QaXMNrM8OJDaFAoggw8CYKo01chIHXym5k4LUSkKdT4P8CDLwcChSgQGILMPAmtqh2r8fAa2XfMfBaCcjTKcDAyzFAAQokkQADbxLBavCyDLxWdhoDr5WAPJ0CDLwcAxSgQBIJMPAmEawGL8vAa2WnMfBaCcjTKcDAyzFAAQokkQADbxLBavCyDLxWdhoDr5WAPJ0CiRh438Zev+wwClBAOwIMvNrpq6SuKQOvlcIMvFYC8nQKMPByDFCAAkkkwMCbRLAavCwDr5WdxsBrJSBPp4AFAilhr18LqsuiFKBAMgsw8CZzB6Sg2zPwWtkZDLxWAvJ0ClggwMBrARaLUoACYODlINAJMPBaORYYeK0E5OkUsECAgdcCLBalAAUYeDkG9AIMvFYOBgZeKwF5OgUsEGDgtQCLRSlAAQZejgEG3sQaAwy8iSXJ61DAtAADr2kjlqAABf4T4JIGjgadAGd4rRwLDLxWAvJ0ClggwMBrARaLUoACnOHlGOAMb2KNAQbexJLkdShgWoCB17QRS1CAApzh5RiILcAZXitHBQOvlYA8nQIWCDDwWoDFohSgAGd4OQY4w5tYY4CBN7EkeR0KmBawt//XaKGIiDRGf2/qfDn5eV8f0xVhCQpQQBMCXMOriW56K5XkDK+VzMkReJd9n9bKWgMOpzytvgYvQAFbFDA1i2yLbWabKGCrAgy8ttqzlreLgdcMs+t/3cJov+9w7uI1FMifBx6DOqF8mY/VmQy8ZgCyCAU0JMDAq6HOYlUpYEKAgZdDRCfAwGvGWOgyaCJqVyuPji3q4eCR0xjtF4wdK6YgfTo7Bl4z/FiEAloSYODVUm+xrhQwLsDAyxHCwGvmGLhz7yEadBiGQ+GBSGdnp85q1cMLw/q2x5flSiZJ4E2MJQummsclDaaE+PvUKrCt9HijTXdq/zrZaUy9RqSEOiY7EitAAYAPrXEU6AU4w2tiMBw9eQHjpi7CuoX//SPo5j0blcp/ijZNajLw8i8TBWxMwBYCrzldwlD8RolvHswZLYlTJjmsOcObOH1nC1dh4DXRiwePnMLMBasROsdLX9LTNxjFi3yAzq0dkmQMzJr3MkmuG/2itY6PTPJ78AYU0KLAns8nGq32gJ7pkr1ZifEakRLakeyQAExZ0inxeonWiWfJK1kuwMBrwuzYqQvw8g/BhpBv9SVdxwaiSoXSaNW4huXiPIMCFKAABShAAQpQ4K0KMPCa4L734BHqtnHDgfUByJwpgyrd0GkYJozsgXKl3+zUwIMCFKAABShAAQpQIOUKMPCa0TfdXP1Q8fOS6OHUGFv2/KyWOGxZ5gc7O+v3wzXj9ixCAQpQgAIUoAAFKGCFAAOvGXgRkX9j1MT5OHfpOgoWyIuxbs4oVaKwGWeyCAUoQAEKUIACFKBAcgsw8CZ3D5h5/xcvolCufg+kT//fAzO1q5bD1LH9zLwCi/1+4SqGeAWi2pdl4Dm4kx7E2BeLUC1ugfCdh+A9JQTjh3eHQ82KqhDHqOWjZfePxzB17krcvnMfJYsVgvdQFxQumF9daMHyTQhdtwsvol6ibvUKGDXQSb81ouV3sv0zZi9aj7CNe/D8RRS+rvQZvFydkSVzRuUbsnIr0qb97xO5FXO8UKJoQdtHSUAL7z94DO+pIfjp1zPInDkjnFrUQ7f2jdSV+FqZAFCekmIEGHhTTFcYr8jfdx+gmYsHflwfoJEap6xqysOH46cvQbGP7PFO1iwxAq+xLxZJWa1IGbWR8PDriXMqpLm0a6QPvByjlvVP5O27aObsgaBJrvi8VDHM+m4Njp++gIXTRuDwsbPw8v8OS2Z5qNA2wHMm6lT7Ah0c61h2k1RSetveXzBjwSqETB+JbFkzY4DnDHzxWQn07dJMvTH7uEhB2pk5FoZ/OxdZMmfCqAFOkDHars84BHw7SD2zwtdKMxFZLEUKMPCmyG6JXanL126gz4hp2LrcTyM1TlnVvBZxE3ly5cDisO2QYKab4TX1xSIpqxUpozZnL15Ts2Pd3SajTdNa+sDLMWpZ/0iYOHH6IhxqfqlOlE8g+o6chj2rpsNn2mLkz5tLPTcgx56DxxCyYisWzeB2gnEpnzx7GVFRUShfprj69aKwbThz7gp8PXth6Lgg1KhcFk3qV7Gsg1Jp6Z37f1XhNnfO7Eqgp7s/GtauhOqVyxr9EqZUysVma0iAgVcjnfXbmUtqlqfIh+/jwh8RKFGsIEYP7qz/+FMjzUj2as5ZvCFG4DX1xSLJXuEUXAF5mDN64OUYta6zgr/fjDPnr2CKV190c/NDu2a1Ua96BXXRP67dgMvgSfhhzQzrbpJKzu49fCpqVfkcbZvVRq9hU/D69b+48mck0gBo3aSm/o1EKuFIUDP//fdfHDt1EW7egVga4Imbt+8Z/RKmBN2EJ1HgLQow8L5FbGtudelKBJas2oHObRxgnz8PZoesww+HTsT4Bjhrrp9azjUMvMnxxSK2Ym0YeDlGE96zBw6fVGFi8axRyP9eLjj1G4/enZuqtahy3Lh5B827euLnTUEJv0kqOVNeG4+cOId5/kPVmud5SzfinWxZ0LJRdVyNuKkC8MgBTvo3E6mExaJmPnr8FFWb9Uf6dOkwekhnNG9QDXyttIiQhVOgAANvCuwUc6oU9fIVKjboie2hU5A3z7vmnMIyAAwDL79YJOHDwjDwGl6JY9Q8W3kAMGjRegRNGoJC9vnUSd2HTkbrxjX1y0XkzYT8TJY78IhbQGYkJ85ahivXIzHDZ6B+33TD0vJw283bd9UDgjziFxBPsRw0JgD9XRzxXu4c/BImDhhNCzDwaqT75AGhh4+eoGhhe1VjeSL+iwY9sW/tTOTM8Y5GWpH81TQMvPxikYT3iWHg5Ri13HL3gaOYGbwGC6a4qzXmumPCzKXIni0r+nd1VD8K33EI67YdwAJ/d8tvkkrO8Av8HpG376l1u+nT2elbffTkeZQu8REyZEivfjZjwWrIDGb0nVpSCZHJZkrIXb1pHxrVqaQeXJMj4Lu1uHPvAQZ2b8kvYTIpyAIpWYCBNyX3TrS67f/5N/W08aKZbz7yDAxZi4NHTiM0aIxGWpAyqmkYeKVW/GKRhPWNYeDlGLXM8cGjJ3Ds6ql2YpBlStEPCWnDfOZgaaAnsmbOpNb0tm9eB44Nv7bsJqmk9C/Hz2JSwHL1ehh960Zpfrve3uqBq96dm+HPG7fgPHgSvId2xdeVyqQSHcuaKbsyfPVFKTWr+/DxE7UERMadjD++VlpmydIpS4CBN2X1h9HazF8WjuVrd6p9JmXGwsvNOdY/lBpqzlutqvrHcP1uvH79GjKLYWdnh9aNa8BjUCfwi0Us64pWPbxw8UoEXr58Bbu0aZEmbRr4evRUuw1wjJpvuXbLfnj6BscKaHtXTce7ObJhYegWLFm9Ha9evUajOpXh3qcd0qaVx654GAqMnDAf4TsPqr/XuqNYYXusmu+tPpYfOyUEsrtI9mxZ0KlVffUfj7gFrv55U60n//3iVbUGWh6cHDHASc2a87WSo0bLAgy8Wu491p0CFKAABShAAQpQwKQAA69JIhagAAUoQAEKUIACFNCyAAOvlnuPdacABShAAQpQgAIUMCnAwGuSiAUoQAEKUIACFKAABbQswMCr5d5j3SlAAQpQgAIUoAAFTAow8JokYgEKUIACFKAABShAAS0LMPBqufdYdwpQgAIUoAAFKEABkwIMvCaJWIACFKAABShAAQpQQMsCDLxa7j3WnQIUoAAFKEABClDApAADr0kiFqAABShAAQpQgAIU0LIAA6+We491pwAFKEABClCAAhQwKcDAa5KIBShAAQpQgAIUoAAFtCzAwKvl3mPdKUABClCAAhSgAAVMCjDwmiRiAQpQgAIUoAAFKEABLQsw8Gq591h3ClCAAhSgAAUoQAGTAgy8JolYgAIUoAAFKEABClBAywIMvFruPdadAhTA0ZPnMXRcENYs8MG7ObKlWJEJM5chW9ZMGNitZYw67tp/FKMnB+PghsBYdY/+O2PlUkqjn7+IQvn6PbBirhdKl/goyav17YwlePL0H0wY2SPJ78UbUIAC2hZg4NV2/7H2KUxgcdg2+AZ+H2+tBnRtgd6dm6rf7/vpBBaFbcOps5chQaHg+++hYZ3K6NquITJlzICzF6+hZfcx8V6rcvlPETx1mPr9L8fPwnnwJDStXxUTR8X8x3/zrp8xZnIwjmydZ5bWN51G4Mr1SH3ZLJkzouiHBdDXuTmqVy6rfi7XdPcJinG9LJkzoXiRD9DPpTmqVCitfnfq3GW07eWNH9bMQJ5cOdTPXr16jdD1u7Bm835cvnYDGTOkR/GiBeHctgFqVSkX7/V1N8ucKYO+Lc/+eYEmnUfAY3An/blmNTIBhaS/CtnnQ+GC+RNwNtDcxRMjBnSA9Fv0w1iQvffgES5fi0T5Mh8jOQKv+G7ccRBtmtRUVTb8syHE2w68/zx/oVxde7VB/RoVEtQvPIkCFEgdAgy8qaOf2cq3JPDo8VNISJHj5u17KoTO8XXFhx/kUz/LkT0bcryTFRKM/WaHon3z2mhcrwqyZs6E336/hKlzw1DQPi8WTR+BNGnS4MatO/qadxk0Ea2+qYEm9auon2XKmBF587yr/n/4t3MRFfUSPxw6ocJltqyZ9eclJPA61KyINk1rqWs8fvwM4TsP4bvQzVg5dyxKFiukAu9ov2BsWjpJfx8pt3bLfixbswNh873x8UcfxBl4XcfOxr6fjqO/Swt8VaEUnj9/gV0Hjqrrj3VzQctvquuvv/Y7n1g9Jy4FC+RVP5c3DBu3H8Sq+d5J3sMd+3+L7h2+Qc0qn1t8r7v3H6FOG1cc2hio3syYG3gTUs7iyhk54dCR05g6Lwxh88aqUoZ/Njz1bQdeuf/KjXuxdPUOrF84Xv2d4UEBClAgLgEGXo4LCiSRQETk36jfbijWLRyvwp/uiLx9Fw7t3OHauw26tHaIcfc/b9xGky6j0N/FEd3aN4rxu7pt3eDStiGcWtSN8fOHj5+iZotB6mPk4ePnol2z2vqwKgUTEnglWLu0axjjPjKTJrNoMtNr7JqNO49Ek3pV0KtTk1iBd+/B4+g3ajoWzRiJCmVLxLj+sjU78dfNv+Hep53ZdW7UcTic2zbUz0DKBRcs34TQdbvw4NETfPFZCXi5dsH7+XKrey1ZtR2h63fjxs07sM+fByMHOulno1v18ELjel/h19/O4+r1SDz957mqi4R/lyGTcPjYWWTIkF4Z+Hr0wvK1uxCyYgtu3bmPvLnfRZc2DWL1ja6B2/YehrRv8cxRsUab4cztT7+ewcDRMxE8ZRhu/X1fv9zBsJzM6k+ZswKXrt5AnlzZUffrChjcoxUiIm+jodNwtUQiR/asePnqFb5q3FfN/o8e0lnvsGPfEVWfDdt/xPyl4ZDxmitndji3aYCOLeupcNtnxFS8fPUamTKmx8gBHeEzbZH+zyvmjlUz/9EPXeCd4tUXISu34uyFqyj0QX5MG9sXRQvbq6Lx1dvOLi28p4Tg6bPn8PXspb9slab94OPeDXW+Lq8+8WhUpzLWbdmPYh/ZY5p3f/XpiLRv3uShscZUEv3V5mUpQAENCjDwarDTWGVtCMQXeCX4zFiwCj+uD0D69OliNcbTNxjn/7iuZlOjH/EFXrne6k0/YE2wD0JWbMWW3T+r8Ks7EivwytKEKhVLY1D3lkYDaYtuo1G7ann07+oYK/BK22S5xNIAD6OdaE6dJQzWajUYW5b5qqUGunA/cdZSBE4YjA8/yI8Js5biesQtLJ89WtVZfjfXzw0lihbCvp9PYIhXIDaETEAh+7xo12cc7tx7iJDpI1QYlpnD6fPDVD/JzGGlb/qooCszvBcvR6B1r7FYHuiJ4kUK4vS5y+jh7o8lszzUsg7Dw3vqIuTJmR39XByNBl5Z4uHUfzzGD+uG2tXKx1jGED3wyqcHDh3cMWpgRzg2qIZLV/9CT3d/dG3fSAXWum1cMXpIF9T4qixO/v4HRk2cDzs7O/XmS47BYwLUMpIGtb5Ek84jETBhEKpVLIPjpy+iu9tkLA30RJmSH6lPIjbuOKSf4TX8s2FjdIH3i8+Kw8vNGbnfzQ63cbORNUsmzPQZqD71MFZvU4FX+ujBwyfwcuuCUsUL451sWVQVug7xhdwzLl9tvFqwlhSgQFILMPAmtTCvn2oF4gu8kwKWq1lE3cfEhkDy8WzgwrU4FD7brMArs14yoyprYP+++0CFwNULfPTBy5zwGP1GsoY3+gyvzBBu3X0YIybMw5JZo1Cu9MdxBt4XL6LUkoZx0xartn1avHCswCvLMooUel+FIWOHbo2wrNc1PDq2rK9mMmUGsu+o6Ti2fb6+SI+h/ihRrCCG9m6rfiYeMmPasHYl9Bg6GWVLFVOBXXf0GjYFn31SRAUlCVNlPy2KkQOc1K+vRdxUM6W69cfRA++xUxfQzdUPm5ZM0s8ey9pkmaWM6xDTsW7OqPh5yXgD75alfqoOHVvWhVOLeqpcfA+tySz25l0/qTc5umP6/FVq9nRZoKcKuHnz5FROslTk4aOnqm82Lp6I7NmyoLrjQMzwGYDPPimKu/cf4r3cb5bGyNHU2QNOjnXQtlntBAde/zF9lLkc8sZh0cqtyspUvc0JvPJpic+wrjEc5e9U5K27mD6uf6p9vWHDKUAB4wIMvBwhFEgiAWOBV4LJ6gXj4ryzBN7ZIetwcGPMp/bjmuE9efYyOvQdh91h0/ShRWb6Chd8H6MGvgluCQm8ETduI106O3W+zNrJTNqQnq3RuvGbh5fiCqRSLn/e3CpQNq77lSpn+NCarGmW9beGgcUQQrdGOK41vNmzZVW7MchMtjwguHf1dP3pDToMg0vbBiqsGR7iJ0sZDA/dg34SNmW5Qtd2b5aSyNKTOq1dsT3UX834Rg+8Em4lVMpShQqfl0SNymXR1KGqWp9teMhMdIMO7vgpfLZaEmF4SKj18F2ATz4uhEePn8VYjxxf4B3rH4Inz55h8ug++sut3/Yjps0LUx7y/zLrL0sW+oyYhg6OdREWvgctG9VQ68lb9xyLQ+GBSGdnpwLx2s37ce/BY6RNmwb3Hz5Wbxg6t3YwGnj/ivxbzdbqDplVr1T+U7VLQ+gcLzVDLIessZ4RvBo7V0yBqXqbE3jrVCuPHk6NYzDOXbIRP/5yMs4lI0n015uXpQAFNCbAwKuxDmN1tSMQX+BdsX43/OeswIH1AWqHAsNDQsGFy3+qmbroR1yBV8qGhe9F9JnQqKhX6iNkCT4SsBISeOt+/YV6eEyOzJkyxpgBlJ/FFUjdvIPUbKlunaiUMwy8Xv4Lceb81Xhnt2U2WUKYOXWWwOs3+3vsWfVf4G3oNEwFtfbN68RylbXFsr5Z1qfGdUjgdahRUb922Vjg1Z0vSwlkXfLWPYcReeuOCnoSjqMf4TsOYfXmH7Bw2og47yuhVtbsStj+8ZdT8B7qop8dtTTw+gYuV2t3ZemAWMinBLVaDlahPWzjXvVApSz12HXgV8yeOOTNso15YQjydVWz23LIJwbNHKqaDLxRL1/hyvUb+jYVyJdHvUky3JbMnMCrq3ecgbdJP/gMe7OG17CPdDeft3QjDhxm4NXOqyNrSoG3L8DA+/bNecdUIhBf4JWP2SW89nNuHmumSoKKPIjl2qu1/mNtHZdh4JWHe2q2HKS2ZKpSoZReVYJI+z7j9MHJnPAYvUsMlzTE1V1xXVO2UZN1vkG+Q+LdlkwCncxAy3pOCTDRD3mYTGYaQ+eMwZbdh01upXbwyCn0GzUjxpIGubbMYHoM6qQuLWtyV4X/oGZ9B3sFIHfOHDFml2XGN997udTMpiWBV4yfPHmm3/f39et/0arHGDRrUC3Wg4iybtn+/Tzo07lZvIHX03eBmtGXZQeTZ4difcgEtQNHfIE3+PvNauZUtyZXLixLGn46egahQW+2spPlGF3aOKigK58myDKMKXNWqvXKstOGvDGQWWqZmZeHzOSQByAlIMssvakZ3rgaE9cuDdEDr6l6T5y1TIV13dIE2WP3y0a99eMlvsArSxqkL2WZBg8KUIACcQkw8HJcUCCJBOILvHI7edBswsylarZRPlKXvW5Pn78C/6AVap/X+f7uSP//JQXxBd41m/ep8rqZ3OjNkJnfPyNvY4G/u342dsOiCTFamj5dOhWqZGZMwpDsESxHQgOvLnTpgpgsg4hrH17ZSUKWAvTq3FTtnSvbqakdA2QPY8/ealcE3QyyYZ11DXgvVw71EXzt1kOwZZmfCnFyyEyrzCJP9+6PEsUKYerclbh4JUKFQNlHVx7WklAk26GdOH1J7Rgxa/xAtbbWVOCt0WKQeiisZaPq2LTzEJas3qHOlRnTP67+ha6uvmqdrjxsFv2QnTomefRE+TLF4w280b94QuokJrLrQHyBV5ZJyHICz0Gd0KxBVZy7eB2yHnlAV0f9cg6ZLT18/CyqViytHm6TNdZfOw5Uyy4CJw5WO4fIWvFte39RD/XJGygJjucuXUOtquXUsoaVG/YgaPF6rA0er8bouq0HYvzZcImGqcBrqt6y84UssZAHCeV+M4NXY+GKrfAf3cfoDK+sp5a9ivnQWhK9mPGyFLABAQZeG+hENiFlChgLvFLjPQePqV0V5CP+qJcvUahAXrUtljxlH9daT8MZ3g59fVC6ZBH9Wt3oCvJkvgQ4+Shbgp3hl0RIWdkmakPIt5i/LByrN+3D1uV+VgdeCVUtuo9RSxvk26/iCrwyGyp79co9r0bcVMsxShX/SG1jptuqLK4vtojePnl4TgKkzGJ2bd9Qv7ZYykhwlpnEp8/+UU/ujxnSBQX+v8xA1kfLdlky8ytLD2QtqHx8L4epwBvw3VoEh25GpXKfqF0gps0PU7OsDx4+Vg+ItW5SM9aMvaxzlaUUP28KinNHDrmv4XZj8glAMxcP9HN2RL48OePdlkyWUgQsXIurf0aqJSeyTle2rNPtRStvKpjOsUAAAAOTSURBVGTPY5m9ld0Y5HDqNx7X/7qFfWtnqj/ff/AYbt6zceLMRdWG4f06qAf95M2YbI0n58m669t37qs3YQXy5Y7xZ/GNfpgKvFLWWL2lz9x95uD0uSvInTO7Wpoi/SVB3qHml3EuaZAx91WTfmr3DcOt7lLmKwNrRQEKJIcAA29yqPOeFKBAogjIF0/IbKvhFm6JcnFeRBMCsmRl8art/OIJTfQWK0mB5BNg4E0+e96ZAhSwUkA+hm/aZaR+z1krL8fTNSYgM8ryhSiygwi/WlhjncfqUuAtCzDwvmVw3o4CFEhcgaMnz8N93Bz1YJZsV8Yj9QjI0ovHT56p5TM8KEABChgTYODl+KAABShAAQpQgAIUsGkBBl6b7l42jgIUoAAFKEABClCAgZdjgAIUoAAFKEABClDApgUYeG26e9k4ClCAAhSgAAUoQAEGXo4BClCAAhSgAAUoQAGbFmDgtenuZeMoQAEKUIACFKAABRh4OQYoQAEKUIACFKAABWxagIHXpruXjaMABShAAQpQgAIUYODlGKAABShAAQpQgAIUsGkBBl6b7l42jgIUoAAFKEABClCAgZdjgAIUoAAFKEABClDApgUYeG26e9k4ClCAAhSgAAUoQAEGXo4BClCAAhSgAAUoQAGbFmDgtenuZeMoQAEKUIACFKAABRh4OQYoQAEKUIACFKAABWxagIHXpruXjaMABShAAQpQgAIUYODlGKAABShAAQpQgAIUsGkBBl6b7l42jgIUoAAFKEABClCAgZdjgAIUoAAFKEABClDApgUYeG26e9k4ClCAAhSgAAUoQAEGXo4BClCAAhSgAAUoQAGbFmDgtenuZeMoQAEKUIACFKAABRh4OQYoQAEKUIACFKAABWxagIHXpruXjaMABShAAQpQgAIUYODlGKAABShAAQpQgAIUsGkBBl6b7l42jgIUoAAFKEABClCAgZdjgAIUoAAFKEABClDApgUYeG26e9k4ClCAAhSgAAUoQAEGXo4BClCAAhSgAAUoQAGbFmDgtenuZeMoQAEKUIACFKAABRh4OQYoQAEKUIACFKAABWxagIHXpruXjaMABShAAQpQgAIUYODlGKAABShAAQpQgAIUsGkBBl6b7l42jgIUoAAFKEABClCAgZdjgAIUoAAFKEABClDApgUYeG26e9k4ClCAAhSgAAUoQAEGXo4BClCAAhSgAAUoQAGbFvgf+g/HIQhT18UAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Does missingness of customers affected depend on the cause category? \n",
    "from scipy import stats\n",
    "df['TOTAL.PRICE(cents / kilowatt-hour)']\n",
    "df.groupby('TOTAL.PRICE(cents / kilowatt-hour)').count()\n",
    "df_ca_missing = df[df['CUSTOMERS.AFFECTED'].isna()]\n",
    "df_ca_not_missing = df[~df['CUSTOMERS.AFFECTED'].isna()]\n",
    "missing_distribution = df_ca_missing['TOTAL.PRICE(cents / kilowatt-hour)']\n",
    "not_missing_distribution = df_ca_not_missing['TOTAL.PRICE(cents / kilowatt-hour)']\n",
    "observed_dataframe = pd.concat([not_missing_distribution, missing_distribution], axis=1, keys=['not_missing_proportion', 'missing_proportion'])\n",
    "specific_df = df[['TOTAL.PRICE(cents / kilowatt-hour)','CUSTOMERS.AFFECTED' ]].copy()\n",
    "specific_df['affected_missing'] = specific_df['CUSTOMERS.AFFECTED'].isna()\n",
    "per_na = df_ca_missing['TOTAL.PRICE(cents / kilowatt-hour)']\n",
    "per_va = df_ca_not_missing['TOTAL.PRICE(cents / kilowatt-hour)']\n",
    "obsersed_stat2 = stats.ks_2samp(per_na, per_va).statistic\n",
    "px.histogram(specific_df, x='TOTAL.PRICE(cents / kilowatt-hour)', color='affected_missing', histnorm='probability', marginal='box',\n",
    "             title=\"total price by Missingness of Population affected\", barmode='overlay', opacity=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cause_one_permutation2(df):\n",
    "    df = df.copy()\n",
    "    df['CUSTOMERS.AFFECTED'] = np.random.permutation(df['CUSTOMERS.AFFECTED'])\n",
    "    df['TOTAL.PRICE(cents / kilowatt-hour)']\n",
    "    df.groupby('TOTAL.PRICE(cents / kilowatt-hour)').count()\n",
    "    df_ca_missing = df[df['CUSTOMERS.AFFECTED'].isna()]\n",
    "    df_ca_not_missing = df[~df['CUSTOMERS.AFFECTED'].isna()]\n",
    "    specific_df = df[['TOTAL.PRICE(cents / kilowatt-hour)','CUSTOMERS.AFFECTED' ]].copy()\n",
    "    specific_df['affected_missing'] = specific_df['CUSTOMERS.AFFECTED'].isna()\n",
    "    per_na = df_ca_missing['TOTAL.PRICE(cents / kilowatt-hour)']\n",
    "    per_va = df_ca_not_missing['TOTAL.PRICE(cents / kilowatt-hour)']\n",
    "    obsersed_stat = stats.ks_2samp(per_na, per_va).statistic\n",
    "    return obsersed_stat\n",
    "result2 = []\n",
    "for i in range(1000):\n",
    "    result2.append(cause_one_permutation2(df))\n",
    "fig2 = px.histogram(pd.DataFrame(result2), x=0, nbins=50, histnorm='probability', \n",
    "                   title='Empirical Distribution of the TVD')\n",
    "fig2.add_vline(x=obsersed_stat2, line_color='red')\n",
    "fig2.update_layout(xaxis_range=[0, 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1148675909814136"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value1 = (np.array(result) > observed_tvd_statistic).mean()\n",
    "p_value2 = (np.array(result2) > obsersed_stat2).mean()\n",
    "observed_tvd_statistic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-31T23:36:28.666489Z",
     "start_time": "2019-10-31T23:36:28.664381Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observed: overall_duration 2625.39837398374 west_duration 1628.331707317073\n",
      "p value for the duration hypothesis test =  0.0003\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "west_duration = df[df['CLIMATE.REGION']=='West']['OUTAGE.DURATION(mins)']\n",
    "overall_duration = df['OUTAGE.DURATION(mins)']\n",
    "print('observed:', 'overall_duration',overall_duration.mean(), 'west_duration',west_duration.mean())\n",
    "\n",
    "duration_simulation_result = []\n",
    "for i in range(10000):\n",
    "    each_duration_simulation = pd.Series(np.random.choice(overall_duration, size=len(west_duration), replace=False)).mean()\n",
    "    duration_simulation_result.append(each_duration_simulation)\n",
    "p_val_hypo_1 = (np.array(duration_simulation_result) < west_duration.mean()).mean()\n",
    "print('p value for the duration hypothesis test = ', p_val_hypo_1)\n",
    "fig_hyp1 = px.histogram(duration_simulation_result)\n",
    "fig_hyp1.add_vline(west_duration.mean(), line_color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis test: people affected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "observed: overall_affected:  143456.22273143905 west_affected:  194579.89393939395\n",
      "p value for the duration hypothesis test =  0.0144\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "west_affected = df[df['CLIMATE.REGION']=='West']['CUSTOMERS.AFFECTED']\n",
    "overall_affected = df['CUSTOMERS.AFFECTED']\n",
    "print('observed:', 'overall_affected: ',overall_affected.mean(), 'west_affected: ',west_affected.mean())\n",
    "\n",
    "affected_simulation_result = []\n",
    "for i in range(10000):\n",
    "    each_affected_simulation = pd.Series(np.random.choice(overall_affected, size=len(west_affected), replace=False)).mean()\n",
    "    affected_simulation_result.append(each_affected_simulation)\n",
    "p_val_hypo_2 = (np.array(affected_simulation_result) > west_affected.mean()).mean()\n",
    "print('p value for the duration hypothesis test = ', p_val_hypo_2)\n",
    "fig_hyp2 = px.histogram(affected_simulation_result)\n",
    "fig_hyp2.add_vline(west_affected.mean(), line_color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}