Dos_Palos_Y.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_context(\"notebook\")\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp_main = pd.read_csv(\"DOSPALOS_MAIN.csv\")\n",
    "dp_billing = pd.read_csv(\"DOSPALOS_BILLING.csv\")\n",
    "dp_interval1 = pd.read_excel(\"DOSPALOS_INTERVAL.xlsm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>ANON_SA_ID</th>\n",
       "      <th>SERV_TYPE</th>\n",
       "      <th>KWH</th>\n",
       "      <th>THRM</th>\n",
       "      <th>revn_amt</th>\n",
       "      <th>START_DT</th>\n",
       "      <th>END_DT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>534871</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.86</td>\n",
       "      <td>21OCT2019</td>\n",
       "      <td>19NOV2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>534871</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.76</td>\n",
       "      <td>23AUG2019</td>\n",
       "      <td>20SEP2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>534871</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>19NOV2019</td>\n",
       "      <td>19DEC2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>534871</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.06</td>\n",
       "      <td>20SEP2019</td>\n",
       "      <td>21OCT2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>534871</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>19DEC2019</td>\n",
       "      <td>21JAN2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ANON_SA_ID SERV_TYPE  KWH  THRM  revn_amt   START_DT     END_DT\n",
       "0      534871         G  NaN   0.0      2.86  21OCT2019  19NOV2019\n",
       "1      534871         G  NaN   0.0      2.76  23AUG2019  20SEP2019\n",
       "2      534871         G  NaN   0.0      2.95  19NOV2019  19DEC2019\n",
       "3      534871         G  NaN   0.0      3.06  20SEP2019  21OCT2019\n",
       "4      534871         G  NaN   0.0      3.25  19DEC2019  21JAN2020"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##1) sort only for single family homes 2) look at 12 month period for each home . gas and elec. for each home\n",
    "dp_billing.head()\n",
    "#if start date is certian month, consider as that month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "dp_interval = dp_interval1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_ids = dp_interval[\"ANON_SERVPNT_ID\"].unique()\n",
    "interval_2 = pd.DataFrame()\n",
    "\n",
    "for i in house_ids:\n",
    "    temp = dp_interval.loc[dp_interval[\"ANON_SERVPNT_ID\"] == i]\n",
    "    #start_date = '2018-12-31'\n",
    "    #end_date = '2019-12-31'\n",
    "    #mask = (temp['usg_dt'] > start_date) & (temp['usg_dt'] <= end_date)\n",
    "    #temp = temp.loc[mask]\n",
    "\n",
    "    if len(temp[\"KWH\"]) == 8759:\n",
    "        interval_2[i] = temp.iloc[:,3].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval_2.loc[:,'Total'] = interval_2.sum(axis=1)\n",
    "bottom_row = interval_2.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp2 = dp_interval.loc[dp_interval[\"ANON_SERVPNT_ID\"] == 2210113]\n",
    "# interval_2[\"date\"] = temp2.iloc[:,1].values\n",
    "# interval_2[\"hour\"] = temp2.iloc[:,2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc = 0\n",
    "column = \"date\"\n",
    "value = temp2.iloc[:,1].values\n",
    "interval_2.insert(loc, column, value)\n",
    "loc = 1\n",
    "column = \"hour\"\n",
    "value = temp2.iloc[:,2].values\n",
    "interval_2.insert(loc, column, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval_2.loc['Total',:] = bottom_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval_2.to_csv(r'Dos_Palos_Y_Interval.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb779387940>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function flush_figures at 0x7fb7da87c700> (for post_execute):\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mflush_figures\u001b[0;34m()\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;31m# ignore the tracking, just draw and close all figures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m             \u001b[0;31m# safely show traceback if in IPython, else raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             display(\n\u001b[0m\u001b[1;32m     42\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    311\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-2>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    249\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2209\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2210\u001b[0;31m                 result = print_method(\n\u001b[0m\u001b[1;32m   2211\u001b[0m                     \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2212\u001b[0m                     \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1639\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1641\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m         \"\"\"\n\u001b[0;32m--> 509\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    510\u001b[0m         mpl.image.imsave(\n\u001b[1;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    405\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    406\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   1864\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 **kwargs)\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2745\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2747\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2749\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    582\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bind_draw_path_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdraw_path\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m             \u001b[0mtransform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    585\u001b[0m             \u001b[0mtpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_path_non_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m             \u001b[0maffine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mget_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;34m\"\"\"Return the `~.transforms.Transform` applied to the `Patch`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_patch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArtist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_data_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mget_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_patch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 792\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_patch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    793\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rect_transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36m_update_patch_transform\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    771\u001b[0m         \u001b[0mbbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_extents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    772\u001b[0m         \u001b[0mrot_trans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAffine2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 773\u001b[0;31m         \u001b[0mrot_trans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrotate_deg_around\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mangle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    774\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rect_transform\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBboxTransformTo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    775\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rect_transform\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mrot_trans\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mrotate_deg_around\u001b[0;34m(self, x, y, degrees)\u001b[0m\n\u001b[1;32m   1965\u001b[0m         \u001b[0;31m# Cast to float to avoid wraparound issues with uint8's\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1966\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1967\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrotate_deg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdegrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1968\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1969\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtranslate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mrotate_deg\u001b[0;34m(self, degrees)\u001b[0m\n\u001b[1;32m   1943\u001b[0m         \u001b[0;32mand\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1944\u001b[0m         \"\"\"\n\u001b[0;32m-> 1945\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrotate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mradians\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdegrees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1946\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1947\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrotate_around\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mrotate\u001b[0;34m(self, theta)\u001b[0m\n\u001b[1;32m   1931\u001b[0m         rotate_mtx = np.array([[a, -b, 0.0], [b, a, 0.0], [0.0, 0.0, 1.0]],\n\u001b[1;32m   1932\u001b[0m                               float)\n\u001b[0;32m-> 1933\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mtx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrotate_mtx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mtx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1934\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sns.displot(interval_2, x=\"Total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>510042</th>\n",
       "      <th>1410083</th>\n",
       "      <th>2210113</th>\n",
       "      <th>4010063</th>\n",
       "      <th>17210022</th>\n",
       "      <th>19110013</th>\n",
       "      <th>27210088</th>\n",
       "      <th>31310110</th>\n",
       "      <th>...</th>\n",
       "      <th>245010076</th>\n",
       "      <th>248910068</th>\n",
       "      <th>249410102</th>\n",
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       "      <th>257210059</th>\n",
       "      <th>262810088</th>\n",
       "      <th>264610038</th>\n",
       "      <th>271310066</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12907.92</td>\n",
       "      <td>203.83</td>\n",
       "      <td>1114.73</td>\n",
       "      <td>9293.79</td>\n",
       "      <td>7563.54</td>\n",
       "      <td>1912.85</td>\n",
       "      <td>10299.86</td>\n",
       "      <td>8436.33</td>\n",
       "      <td>...</td>\n",
       "      <td>5568.89</td>\n",
       "      <td>6662.38</td>\n",
       "      <td>2868.33</td>\n",
       "      <td>9028.50</td>\n",
       "      <td>11181.02</td>\n",
       "      <td>-407.74</td>\n",
       "      <td>17486.73</td>\n",
       "      <td>14013.35</td>\n",
       "      <td>5570.36</td>\n",
       "      <td>715072.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5464</th>\n",
       "      <td>2019-08-16 00:00:00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.63</td>\n",
       "      <td>5.54</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4.15</td>\n",
       "      <td>3.84</td>\n",
       "      <td>...</td>\n",
       "      <td>0.43</td>\n",
       "      <td>4.96</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.20</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>5.56</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.66</td>\n",
       "      <td>301.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5442</th>\n",
       "      <td>2019-08-15 00:00:00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.85</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.64</td>\n",
       "      <td>5.33</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3.56</td>\n",
       "      <td>2.76</td>\n",
       "      <td>...</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.07</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.71</td>\n",
       "      <td>5.40</td>\n",
       "      <td>2.83</td>\n",
       "      <td>3.16</td>\n",
       "      <td>293.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5465</th>\n",
       "      <td>2019-08-16 00:00:00</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.64</td>\n",
       "      <td>5.30</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4.51</td>\n",
       "      <td>2.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.43</td>\n",
       "      <td>4.69</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>9.36</td>\n",
       "      <td>5.25</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.21</td>\n",
       "      <td>292.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5440</th>\n",
       "      <td>2019-08-15 00:00:00</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.63</td>\n",
       "      <td>5.61</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.26</td>\n",
       "      <td>4.58</td>\n",
       "      <td>3.54</td>\n",
       "      <td>...</td>\n",
       "      <td>0.41</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.24</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>7.89</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.55</td>\n",
       "      <td>283.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      date  hour    510042  1410083  2210113  4010063  \\\n",
       "Total                  NaN   NaN  12907.92   203.83  1114.73  9293.79   \n",
       "5464   2019-08-16 00:00:00  18.0      4.00     2.40     0.63     5.54   \n",
       "5442   2019-08-15 00:00:00  20.0      6.85     3.42     0.64     5.33   \n",
       "5465   2019-08-16 00:00:00  19.0      3.37     2.85     0.64     5.30   \n",
       "5440   2019-08-15 00:00:00  18.0      3.45     2.40     0.63     5.61   \n",
       "\n",
       "       17210022  19110013  27210088  31310110  ...  245010076  248910068  \\\n",
       "Total   7563.54   1912.85  10299.86   8436.33  ...    5568.89    6662.38   \n",
       "5464       1.72      0.22      4.15      3.84  ...       0.43       4.96   \n",
       "5442       1.63      0.23      3.56      2.76  ...       0.44       2.91   \n",
       "5465       1.73      0.22      4.51      2.72  ...       0.43       4.69   \n",
       "5440       1.57      0.26      4.58      3.54  ...       0.41       3.27   \n",
       "\n",
       "       249410102  254710091  255710074  257210059  262810088  264610038  \\\n",
       "Total    2868.33    9028.50   11181.02    -407.74   17486.73   14013.35   \n",
       "5464        0.20       0.07       1.20      -0.10       5.56       2.44   \n",
       "5442        0.28       0.07       5.00       1.71       5.40       2.83   \n",
       "5465        0.22       0.07       1.08       9.36       5.25       2.09   \n",
       "5440        0.24       1.89       3.24      -2.76       7.89       1.83   \n",
       "\n",
       "       271310066      Total  \n",
       "Total    5570.36  715072.47  \n",
       "5464        0.66     301.42  \n",
       "5442        3.16     293.95  \n",
       "5465        1.21     292.56  \n",
       "5440        0.55     283.60  \n",
       "\n",
       "[5 rows x 87 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_5_peak = interval_2.sort_values(\"Total\", ascending = False).head()\n",
    "top_5_peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total'>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEcCAYAAADUX4MJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAANYElEQVR4nO3db6jW9f3H8ZeeA63Z6HjM4d/+sFktOGeHWTcypYgcFA0WlW5iMNiKZERbNIumg5DJ3LrXjSYLb83qqI3GBkJsljhHjOViRQ37Y3+001bTE1jLOOdcvxujQ/bb3DvznO++8/GAA55zHfm8vnLVk++59Jwpw8PDnQDAfzC16QEAtINgAFAiGACUCAYAJYIBQIlgAFAiGACUNBaMffv2NXX0CWF/s9q8v83bE/ub1uT+xoIxMjLS1NEnhP3NavP+Nm9P7G9ak/t9SQqAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKups4dMWKFRkbG8tDDz3UxPEAHAd3GACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJd1NHPruu+82cSwAn4A7DABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAEsEAoEQwACgRDABKBAOAkkaD8dWvfrXJ4wH+Zxw8eDB33XVXDh06NGFnuMMA+B+wZcuWPPfccxkcHJywMyY9GB+9q3CXAfDJHDx4MDt27Ein08mOHTsm7C6j+1gPDg8P5+233z7qY11dXZk3b96EjAHg49uyZUvGxsaSJGNjYxkcHMzNN998ws855h3Gfffdly9+8YtHvV111VUnfAQAx2/nzp0ZGRlJkoyMjGTnzp0Tcs4x7zBWrVqVFStWHPWxrq6uCRkCwPG59NJL85vf/CYjIyPp7u7OpZdeOiHnHDMYPT096enpmZCDATgxli1blh07diRJpk6dmuXLl0/IOZP+ovcjjzxyzPcB+Hh6e3tz+eWXZ8qUKbn88sszffr0CTnnmHcYALTDsmXL8uqrr07Y3UXScDDcXQCcGL29vVm/fv2EnuEf7gFQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUCJYABQIhgAlAgGACWCAUBJdxOHfvrTn87Y2FgTRwNwnNxhAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUCAYAJYIBQIlgAFAiGACUdDdx6AMPPJDnn3++iaMBOE7uMAAoEQwASgQDgBLBAKBEMAAoEQwASgQDgBLBAKBEMAAoEQwASgQDgBLBAKBEMAAoEQwASgQDgBLBAKBEMAAoEQwASgQDgBLBAKBEMAAoaSwY3d3dTR19QtjfrDbvb/P2xP6mNbl/yvDwcKex0wFojUbuMPbv35/+/v7s37+/ieM/Mfub1eb9bd6e2N+0pvc3EozR0dG8+uqrGR0dbeL4T8z+ZrV5f5u3J/Y3ren9XvQGoEQwACgRDABKGgnG6aefnjvuuCOnn356E8d/YvY3q83727w9sb9pTe/312oBKPElKQBKBAOAkkaC8cILL2Tp0qVZuHBhli5dmhdffHHCz1yzZk36+/vT09OTZ599trRlsh87loMHD+b666/PhRdemEWLFmXlypV56623WnMNK1asyCWXXJIlS5bkyiuvzJ///OfWbP+wH/3oR0c9h9qyv6+vLxdddFEWL16cxYsX57e//W2r9r/33nu57bbb8qUvfSmLFi3Krbfe2pr9r7zyyvif++LFi9PX15ezzz67NfuPMjw83JnstyVLlnQ2btzYGR4e7mzcuLGzZMmSCT9z+/btnWeeeaYzf/78zu9///vSlsl+7Fhv+/bt6/zqV78af/+WW27prFy5sjXX8Morr4z/evPmzZ3+/v7WbP/g7fHHH+9cccUVnXnz5o0/h9qy/6PP+7Y9/2+66abOqlWrOocOHeoMDw939u7d26r9H367+eabOzfeeGMr90/6i95vvvlmFi5cmH379qWrqyujo6M555xzsmfPnpxxxhkTfn5fX18GBwdzwQUXHHNLp9OZ1Mc+7rX/8pe/zKZNm3L//fe37hoefPDBbNy4MVu3bm3N9iNHjuTqq6/Oz372s3zlK1/J4OBgZs6c2Zr9H37ef6Atz//Dhw/nggsuyLPPPpvTTjutdfs/7P33388XvvCFPPzww5k7d27r9k/6tz08cOBA5syZk66uriRJV1dXZs+enf37909KMKpbkkzqYx/n2sfGxrJp06ZceeWVrbqGW265JY899lg6nU62bdvWqu3r16/PsmXLxr+UkLTv+XPjjTem0+nk4osvztq1a1uzf9++fent7c2GDRuya9euTJs2LWvWrMmpp57aiv0ftn379syePTsDAwN56qmnWrffi94ttHr16kybNi033XRT01M+lnvvvTfPPPNM1q5dmx/84AdNzyn7wx/+kD179uRb3/pW01OO2/bt27N79+7xYK9evbrpSWWjo6N5+eWX09/fn8cffzx33313brjhhhw+fLjpaR/bz3/+86xcubLpGcdt0u8w5s6dm9dffz2jo6Pjt0VDQ0OZN2/eZE855pZOpzOpj1WtWbMmL774Yh566KFMnTq1ldfwta99Ld/5zncyZ86cVmzfvXt3nn/++fT39ydJXn/99Vx77bVZv359K/YnGf+cU045Jd/85jfz9a9/PT/84Q9bsX/+/Pnp7u7OddddlyS58MILM2PGjJx66qmt2P+BoaGh7N69Oxs3bkzSzv//TPodxsyZM9PX15dt27YlSbZt25b+/v5J/3LUf9oy2Y9VrFu3Lk899VQ2b96cU045pTXXcPjw4aO+HfP27dszffr0VmxPku9+97v5y1/+kqeffjpPP/105syZk4cffjjXXHNNK/a/8847efvtt5MknU4nv/jFL9LX19eaP/8ZM2ZkyZIleeyxx5L882/6vPnmm/nc5z7Xiv0feOCBB/LlL385vb29Sdrx3+5HNfIvvffu3ZtVq1ZleHg4PT09+elPf5oFCxZM6JmrV6/Or3/96/z1r3/NjBkz0tvbmyeeeOKYWyb7sWN57rnncvHFF+fzn/98PvWpTyVJzjrrrGzevPm//hr+9re/ZcWKFXn33XczderUTJ8+PevWrcvAwMB//fZ/5cMvILdh/8svv5wbbrgho6OjGRsby3nnnZcNGzZk1qxZrdj/wTV8+9vfzqFDh9Ld3Z21a9dm6dKlrdmfJAsXLsyGDRtyxRVXjH+sTfsT3xoEgCIvegNQIhgAlAgGACWCAUCJYABQIhhwgvX09OSll15qegaccILBSWPu3Lnjb9OnT8+sWbPG39+yZcu//D27du066hv2wcls0r81CDTlwIED47/u6+vLvffem8suu6y5QdAy7jA46R05ciR33nlnzj///Jx//vm58847c+TIkbzzzju5/vrrMzQ0NH4nMjQ0lCeffDJLly7NmWeemfPOOy/f+9738v777zd9GTDhBIOT3j333JM//vGP2bVrV373u99lz549+clPfpJp06Zl69atmT17dg4cOJADBw5k9uzZ6erqyvr16/PSSy/l0Ucfzc6dO3P//fc3fRkw4QSDk97WrVuzevXqzJw5M2eccUbuuOOODA4O/tvPHxgYyEUXXZTu7u6cddZZ+cY3vpHdu3dP4mJohtcwOOm98cYbmT9//vj78+fPzxtvvPFvP/+FF17I97///fzpT3/KP/7xj4yMjGRgYGASlkKz3GFw0ps1a1Zee+218ff379+fWbNmJUmmTJny/z7/tttuy4IFC/Lkk0/mtddey9q1a9Pp+B6e/O8TDE561113Xe6555689dZb+fvf/54f//jHWb58eZLks5/9bA4ePDj+8ySSf/58j8985jM57bTTsnfv3mzatKmp6TCpBIOT3u23356BgYFccsklWbRoUfr7+3P77bcnSc4999xce+21GRgYyJlnnpmhoaGsW7cu27Zty7x583LrrbfmmmuuafgKYHL4eRgAlLjDAKBEMAAoEQwASgQDgBLBAKBEMAAoEQwASgQDgBLBAKDk/wBm3ydGF7CiCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot\n",
    "sns.boxplot(x = interval_2['Total'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>510042</th>\n",
       "      <th>1410083</th>\n",
       "      <th>2210113</th>\n",
       "      <th>4010063</th>\n",
       "      <th>17210022</th>\n",
       "      <th>19110013</th>\n",
       "      <th>27210088</th>\n",
       "      <th>31310110</th>\n",
       "      <th>...</th>\n",
       "      <th>245010076</th>\n",
       "      <th>248910068</th>\n",
       "      <th>249410102</th>\n",
       "      <th>254710091</th>\n",
       "      <th>255710074</th>\n",
       "      <th>257210059</th>\n",
       "      <th>262810088</th>\n",
       "      <th>264610038</th>\n",
       "      <th>271310066</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [date, hour, 510042, 1410083, 2210113, 4010063, 17210022, 19110013, 27210088, 31310110, 35210112, 35510054, 38210019, 39210048, 46010049, 49410075, 65610019, 74510037, 76310095, 77110088, 81310050, 83410110, 83610114, 84510007, 95310003, 96110073, 98910029, 100510049, 103410018, 103610055, 107410048, 113010061, 113210022, 114410058, 114410103, 116610087, 117910062, 119510083, 121310032, 124010112, 128210082, 135010112, 136310005, 137510060, 141710079, 144010064, 148010084, 149410111, 152810026, 153710103, 155710076, 158210012, 158310001, 159810040, 163010102, 171310046, 174510091, 177710086, 179210079, 186610046, 189110014, 206110044, 209710007, 210010008, 210310103, 215110041, 215410110, 218010071, 221610015, 222910049, 224510114, 225410046, 227910071, 230010054, 235610031, 237010033, 238710079, 245010076, 248910068, 249410102, 254710091, 255710074, 257210059, 262810088, 264610038, 271310066, Total]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 87 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interval_2.loc[interval_2[\"date\"] == '2019-01-01 00:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "july_1 = interval_2.loc[interval_2[\"date\"] == '2019-07-01 00:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>510042</th>\n",
       "      <th>1410083</th>\n",
       "      <th>2210113</th>\n",
       "      <th>4010063</th>\n",
       "      <th>17210022</th>\n",
       "      <th>19110013</th>\n",
       "      <th>27210088</th>\n",
       "      <th>31310110</th>\n",
       "      <th>...</th>\n",
       "      <th>245010076</th>\n",
       "      <th>248910068</th>\n",
       "      <th>249410102</th>\n",
       "      <th>254710091</th>\n",
       "      <th>255710074</th>\n",
       "      <th>257210059</th>\n",
       "      <th>262810088</th>\n",
       "      <th>264610038</th>\n",
       "      <th>271310066</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [date, hour, 510042, 1410083, 2210113, 4010063, 17210022, 19110013, 27210088, 31310110, 35210112, 35510054, 38210019, 39210048, 46010049, 49410075, 65610019, 74510037, 76310095, 77110088, 81310050, 83410110, 83610114, 84510007, 95310003, 96110073, 98910029, 100510049, 103410018, 103610055, 107410048, 113010061, 113210022, 114410058, 114410103, 116610087, 117910062, 119510083, 121310032, 124010112, 128210082, 135010112, 136310005, 137510060, 141710079, 144010064, 148010084, 149410111, 152810026, 153710103, 155710076, 158210012, 158310001, 159810040, 163010102, 171310046, 174510091, 177710086, 179210079, 186610046, 189110014, 206110044, 209710007, 210010008, 210310103, 215110041, 215410110, 218010071, 221610015, 222910049, 224510114, 225410046, 227910071, 230010054, 235610031, 237010033, 238710079, 245010076, 248910068, 249410102, 254710091, 255710074, 257210059, 262810088, 264610038, 271310066, Total]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 87 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "july_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = dp_interval.merge(dp_main, on = \"ANON_SERVPNT_ID\", how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155.6947488584475"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged)/8760"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.drop(axis = 1, columns = [\"Unnamed: 4\", \"Unnamed: 5\", \"Unnamed: 6\", \"Unnamed: 7\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged[\"ANON_SERVPNT_ID\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_ids = dp_interval[\"ANON_SERVPNT_ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {}\n",
    "for name in house_ids:\n",
    "    d[name] = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_df(original_df, ids):\n",
    "    for i in ids:\n",
    "        d[i] = original_df.loc[original_df[\"ANON_SERVPNT_ID\"] == i]\n",
    "new_df(dp_interval, house_ids)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANON_SERVPNT_ID</th>\n",
       "      <th>usg_dt</th>\n",
       "      <th>INT_HOUR</th>\n",
       "      <th>KWH</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8759</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8760</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>1.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8761</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>1.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8762</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>3.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8763</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>5</td>\n",
       "      <td>7.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17513</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>20</td>\n",
       "      <td>3.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17514</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>21</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17515</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>22</td>\n",
       "      <td>0.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17516</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>23</td>\n",
       "      <td>0.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17517</th>\n",
       "      <td>510042</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>24</td>\n",
       "      <td>0.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8759 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ANON_SERVPNT_ID     usg_dt  INT_HOUR   KWH  Unnamed: 4  Unnamed: 5  \\\n",
       "8759            510042 2019-01-01         1  0.37         NaN         NaN   \n",
       "8760            510042 2019-01-01         2  1.17         NaN         NaN   \n",
       "8761            510042 2019-01-01         3  1.94         NaN         NaN   \n",
       "8762            510042 2019-01-01         4  3.07         NaN         NaN   \n",
       "8763            510042 2019-01-01         5  7.29         NaN         NaN   \n",
       "...                ...        ...       ...   ...         ...         ...   \n",
       "17513           510042 2019-12-31        20  3.24         NaN         NaN   \n",
       "17514           510042 2019-12-31        21  0.61         NaN         NaN   \n",
       "17515           510042 2019-12-31        22  0.52         NaN         NaN   \n",
       "17516           510042 2019-12-31        23  0.51         NaN         NaN   \n",
       "17517           510042 2019-12-31        24  0.98         NaN         NaN   \n",
       "\n",
       "       Unnamed: 6  Unnamed: 7  \n",
       "8759          NaN         NaN  \n",
       "8760          NaN         NaN  \n",
       "8761          NaN         NaN  \n",
       "8762          NaN         NaN  \n",
       "8763          NaN         NaN  \n",
       "...           ...         ...  \n",
       "17513         NaN         NaN  \n",
       "17514         NaN         NaN  \n",
       "17515         NaN         NaN  \n",
       "17516         NaN         NaN  \n",
       "17517         NaN         NaN  \n",
       "\n",
       "[8759 rows x 8 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[house_ids[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       0.0\n",
      "1       0.0\n",
      "2       0.0\n",
      "3       0.0\n",
      "4       0.0\n",
      "       ... \n",
      "8754    0.0\n",
      "8755    0.0\n",
      "8756    0.0\n",
      "8757    0.0\n",
      "8758    0.0\n",
      "Name: 0, Length: 8759, dtype: float64\n",
      "0      NaN\n",
      "1      NaN\n",
      "2      NaN\n",
      "3      NaN\n",
      "4      NaN\n",
      "        ..\n",
      "8754   NaN\n",
      "8755   NaN\n",
      "8756   NaN\n",
      "8757   NaN\n",
      "8758   NaN\n",
      "Name: 1, Length: 8759, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "new_df2 = pd.DataFrame()\n",
    "for i in range(2):\n",
    "    new_df2[i] = d[house_ids[i]][\"KWH\"]\n",
    "    print(new_df2[i])\n",
    "    #print(len(d[house_ids[i]][\"KWH\"])==8759)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df(merged, house_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ANON_SERVPNT_ID</th>\n",
       "      <th>usg_dt</th>\n",
       "      <th>INT_HOUR</th>\n",
       "      <th>KWH</th>\n",
       "      <th>ANON_ACCT_ID</th>\n",
       "      <th>ANON_SA_ID</th>\n",
       "      <th>MultiFam</th>\n",
       "      <th>MasterMeter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1361470</th>\n",
       "      <td>272410042</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>5.27</td>\n",
       "      <td>22815803</td>\n",
       "      <td>9258620883</td>\n",
       "      <td>INDIV</td>\n",
       "      <td>Other</td>\n",
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       "    <tr>\n",
       "      <th>1361471</th>\n",
       "      <td>272410042</td>\n",
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       "      <th>1361472</th>\n",
       "      <td>272410042</td>\n",
       "      <td>2019-01-01</td>\n",
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       "      <td>3.16</td>\n",
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       "      <td>9258620883</td>\n",
       "      <td>INDIV</td>\n",
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       "      <td>272410042</td>\n",
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       "      <td>3.19</td>\n",
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       "      <td>INDIV</td>\n",
       "      <td>Other</td>\n",
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       "      <th>1361474</th>\n",
       "      <td>272410042</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>5</td>\n",
       "      <td>3.12</td>\n",
       "      <td>22815803</td>\n",
       "      <td>9258620883</td>\n",
       "      <td>INDIV</td>\n",
       "      <td>Other</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1363881</th>\n",
       "      <td>272410042</td>\n",
       "      <td>2019-04-11</td>\n",
       "      <td>13</td>\n",
       "      <td>1.21</td>\n",
       "      <td>22815803</td>\n",
       "      <td>9258620883</td>\n",
       "      <td>INDIV</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363882</th>\n",
       "      <td>272410042</td>\n",
       "      <td>2019-04-11</td>\n",
       "      <td>14</td>\n",
       "      <td>1.25</td>\n",
       "      <td>22815803</td>\n",
       "      <td>9258620883</td>\n",
       "      <td>INDIV</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363883</th>\n",
       "      <td>272410042</td>\n",
       "      <td>2019-04-11</td>\n",
       "      <td>15</td>\n",
       "      <td>1.14</td>\n",
       "      <td>22815803</td>\n",
       "      <td>9258620883</td>\n",
       "      <td>INDIV</td>\n",
       "      <td>Other</td>\n",
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       "    <tr>\n",
       "      <th>1363884</th>\n",
       "      <td>272410042</td>\n",
       "      <td>2019-04-11</td>\n",
       "      <td>16</td>\n",
       "      <td>1.15</td>\n",
       "      <td>22815803</td>\n",
       "      <td>9258620883</td>\n",
       "      <td>INDIV</td>\n",
       "      <td>Other</td>\n",
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       "    <tr>\n",
       "      <th>1363885</th>\n",
       "      <td>272410042</td>\n",
       "      <td>2019-04-11</td>\n",
       "      <td>17</td>\n",
       "      <td>1.15</td>\n",
       "      <td>22815803</td>\n",
       "      <td>9258620883</td>\n",
       "      <td>INDIV</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2416 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ANON_SERVPNT_ID     usg_dt  INT_HOUR   KWH  ANON_ACCT_ID  ANON_SA_ID  \\\n",
       "1361470        272410042 2019-01-01         1  5.27      22815803  9258620883   \n",
       "1361471        272410042 2019-01-01         2  3.07      22815803  9258620883   \n",
       "1361472        272410042 2019-01-01         3  3.16      22815803  9258620883   \n",
       "1361473        272410042 2019-01-01         4  3.19      22815803  9258620883   \n",
       "1361474        272410042 2019-01-01         5  3.12      22815803  9258620883   \n",
       "...                  ...        ...       ...   ...           ...         ...   \n",
       "1363881        272410042 2019-04-11        13  1.21      22815803  9258620883   \n",
       "1363882        272410042 2019-04-11        14  1.25      22815803  9258620883   \n",
       "1363883        272410042 2019-04-11        15  1.14      22815803  9258620883   \n",
       "1363884        272410042 2019-04-11        16  1.15      22815803  9258620883   \n",
       "1363885        272410042 2019-04-11        17  1.15      22815803  9258620883   \n",
       "\n",
       "        MultiFam MasterMeter  \n",
       "1361470    INDIV       Other  \n",
       "1361471    INDIV       Other  \n",
       "1361472    INDIV       Other  \n",
       "1361473    INDIV       Other  \n",
       "1361474    INDIV       Other  \n",
       "...          ...         ...  \n",
       "1363881    INDIV       Other  \n",
       "1363882    INDIV       Other  \n",
       "1363883    INDIV       Other  \n",
       "1363884    INDIV       Other  \n",
       "1363885    INDIV       Other  \n",
       "\n",
       "[2416 rows x 8 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[house_ids[120]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = {}\n",
    "for name in house_ids:\n",
    "    e[name] = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_df(original_df, ids):\n",
    "    for i in ids:\n",
    "        e[i] = original_df.loc[original_df[\"ANON_SA_ID\"] == i]\n",
    "#new_df(dp_interval, house_ids)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_ids = merged[\"ANON_SA_ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df(dp_billing, new_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "indiv = dp_main.loc[dp_main[\"MultiFam\"] == \"INDIV\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_indiv = indiv[\"ANON_SA_ID\"].unique()\n",
    "unique_indiv2 = indiv[\"ANON_SERVPNT_ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "indiv_billing = dp_billing.loc[dp_billing[\"ANON_SA_ID\"].isin(unique_indiv)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/teacher/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1596: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/Users/teacher/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1765: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(loc, value)\n"
     ]
    }
   ],
   "source": [
    "for i in indiv_billing.index:\n",
    "    if \"JAN\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"JAN\"\n",
    "    elif \"FEB\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"FEB\"\n",
    "    elif \"MAR\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"MAR\"\n",
    "    elif \"APR\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"APR\"\n",
    "    elif \"MAY\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"MAY\"\n",
    "    elif \"JUN\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"JUN\"\n",
    "    elif \"JUL\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"JUL\"\n",
    "    elif \"AUG\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"AUG\"\n",
    "    elif \"SEP\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"SEP\"\n",
    "    elif \"OCT\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"OCT\"\n",
    "    elif \"NOV\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"NOV\"\n",
    "    elif \"DEC\" in indiv_billing[\"START_DT\"][i]:\n",
    "        indiv_billing.at[i, 'month'] = \"DEC\"   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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></th>\n",
       "      <th>ANON_SA_ID</th>\n",
       "      <th>KWH</th>\n",
       "      <th>THRM</th>\n",
       "      <th>revn_amt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>APR</th>\n",
       "      <td>5.035784e+09</td>\n",
       "      <td>439.412192</td>\n",
       "      <td>18.955899</td>\n",
       "      <td>69.462016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUG</th>\n",
       "      <td>5.034766e+09</td>\n",
       "      <td>783.719836</td>\n",
       "      <td>13.628072</td>\n",
       "      <td>106.048409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEC</th>\n",
       "      <td>5.044169e+09</td>\n",
       "      <td>608.154706</td>\n",
       "      <td>60.300983</td>\n",
       "      <td>104.459770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEB</th>\n",
       "      <td>5.044684e+09</td>\n",
       "      <td>466.451509</td>\n",
       "      <td>42.599374</td>\n",
       "      <td>81.882081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAN</th>\n",
       "      <td>5.042271e+09</td>\n",
       "      <td>580.109181</td>\n",
       "      <td>60.825537</td>\n",
       "      <td>103.635513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JUL</th>\n",
       "      <td>5.019929e+09</td>\n",
       "      <td>933.743719</td>\n",
       "      <td>12.564776</td>\n",
       "      <td>121.347349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JUN</th>\n",
       "      <td>5.083887e+09</td>\n",
       "      <td>814.852918</td>\n",
       "      <td>13.663859</td>\n",
       "      <td>115.213215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAR</th>\n",
       "      <td>5.041234e+09</td>\n",
       "      <td>439.455290</td>\n",
       "      <td>28.531556</td>\n",
       "      <td>72.462848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAY</th>\n",
       "      <td>5.031533e+09</td>\n",
       "      <td>661.883289</td>\n",
       "      <td>15.775501</td>\n",
       "      <td>90.590056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOV</th>\n",
       "      <td>5.052826e+09</td>\n",
       "      <td>602.791524</td>\n",
       "      <td>50.694915</td>\n",
       "      <td>100.023057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCT</th>\n",
       "      <td>5.051158e+09</td>\n",
       "      <td>471.844637</td>\n",
       "      <td>27.269807</td>\n",
       "      <td>70.124683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEP</th>\n",
       "      <td>5.058359e+09</td>\n",
       "      <td>450.451638</td>\n",
       "      <td>16.385140</td>\n",
       "      <td>71.767883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ANON_SA_ID         KWH       THRM    revn_amt\n",
       "month                                                 \n",
       "APR    5.035784e+09  439.412192  18.955899   69.462016\n",
       "AUG    5.034766e+09  783.719836  13.628072  106.048409\n",
       "DEC    5.044169e+09  608.154706  60.300983  104.459770\n",
       "FEB    5.044684e+09  466.451509  42.599374   81.882081\n",
       "JAN    5.042271e+09  580.109181  60.825537  103.635513\n",
       "JUL    5.019929e+09  933.743719  12.564776  121.347349\n",
       "JUN    5.083887e+09  814.852918  13.663859  115.213215\n",
       "MAR    5.041234e+09  439.455290  28.531556   72.462848\n",
       "MAY    5.031533e+09  661.883289  15.775501   90.590056\n",
       "NOV    5.052826e+09  602.791524  50.694915  100.023057\n",
       "OCT    5.051158e+09  471.844637  27.269807   70.124683\n",
       "SEP    5.058359e+09  450.451638  16.385140   71.767883"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indiv_billing.groupby(\"month\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [],
   "source": [
    "jul_houses = indiv_billing.loc[indiv_billing[\"month\"] == \"JUL\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4972"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(jul_houses[\"ANON_SA_ID\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [],
   "source": [
    "indiv_interval = dp_interval.loc[dp_interval[\"ANON_SERVPNT_ID\"].isin(unique_indiv2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_ids = indiv_interval[\"ANON_SERVPNT_ID\"].unique()\n",
    "interval_2 = pd.DataFrame()\n",
    "house_ids2 = []\n",
    "for i in house_ids:\n",
    "    temp = indiv_interval.loc[indiv_interval[\"ANON_SERVPNT_ID\"] == i]\n",
    "    #start_date = '2018-12-31'\n",
    "    #end_date = '2019-12-31'\n",
    "    #mask = (temp['usg_dt'] > start_date) & (temp['usg_dt'] <= end_date)\n",
    "    #temp = temp.loc[mask]\n",
    "\n",
    "    if len(temp[\"KWH\"]) == 8759:\n",
    "        interval_2[i] = temp.iloc[:,3].values\n",
    "        house_ids2.append(i)\n",
    "interval_2.loc[:,'Total'] = interval_2.sum(axis=1)\n",
    "bottom_row = interval_2.sum(axis=0)\n",
    "loc = 0\n",
    "column = \"date\"\n",
    "value = temp2.iloc[:,1].values\n",
    "interval_2.insert(loc, column, value)\n",
    "loc = 1\n",
    "column = \"hour\"\n",
    "value = temp2.iloc[:,2].values\n",
    "interval_2.insert(loc, column, value)\n",
    "interval_2.loc['Total',:] = bottom_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb6ac354640>"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x=bottom_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottom_row2 = bottom_row[0:84]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_df = pd.DataFrame()\n",
    "graph_df[\"ID\"] = house_ids2\n",
    "value = bottom_row2.values\n",
    "graph_df.insert(1, \"Annual Total\", value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8512.767499999984"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(graph_df[\"Annual Total\"].sum())/len(graph_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ID', ylabel='Annual Total'>"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"ID\", y=\"Annual Total\", data=graph_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Annual Total kWh, Individual Houses')"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(graph_df, x=\"Annual Total\")\n",
    "plt.title(\"Annual Total kWh, Individual Houses\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb6ae90d730>"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(jul_houses, x=\"revn_amt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-523-8a7e4d018652>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  indiv_billing_gas[\"THRM (kWh)\"] = indiv_billing_gas[\"THRM\"]/0.034121412\n"
     ]
    }
   ],
   "source": [
    "indiv_billing_gas = indiv_billing.loc[indiv_billing[\"SERV_TYPE\"] == \"G\"]\n",
    "indiv_billing_gas[\"THRM (kWh)\"] = indiv_billing_gas[\"THRM\"]/0.034121412\n",
    "indiv_billing_elec = indiv_billing.loc[indiv_billing[\"SERV_TYPE\"] == \"E\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2132"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(indiv_billing_gas[\"ANON_SA_ID\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1976.25"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(indiv_billing_gas[\"ANON_SA_ID\"])/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='KWH', ylabel='Count'>"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j797dbN26mSuuuAqABx54iFde+ScvvvgyjRs3AWDjxp9JT08vtBXoTzLbWlQq8fvLt6tEiPPhcjlPjoQs+/Oey4oVSxk27BGfsri4O5g//z2eeGIMBw7sZ/DguzEMRWhoKBMnPk/NmjUB6NatB+HhEUyZ8jxZWVlkZWXSuHETJk2aRt265fscXPaTuwDIIIDSy193i/Yd8v5b9pQ7O7nnSq8s95OrTPy1n5ysXSmEECJoSZITQggRtCTJCSGECFqS5IQQQgQtSXJCCCGCliQ5IYQQQUvmyQkhRAUVyK12Dh06yO2338Itt9zGmDHjvOXbt29lyJB7GDt2Ir16xeJyuejXrzc33XQzo0aN8R43Z84s4uM/pVat2gA4nXmYpsnf//4UV13Vpsyv6WwkyQkhRAVls9lZva7st9rp3LF4W+3UqFGD779fh9vtxjRNAL766gtq1rzIe8z69f+jVavWfP31lzz00KOEhYV53+vbN46hQ4d7Xy9YMJ+ZM6fzn/+8W3YXcw7SXSmEEKJQ4eERXHZZCzZu/NlbtmHDejp0uNb7+rPPlhId3YVWrVrz5Zefn/VclmWRlJRE9eo1/BpzftKSE5WGLOklRMl16dKNb775inbtOrB58+9cemlzTi2UlZaWxo8/fs9TT03ANE0WLvyYPn36ej8bH/8p3367mhMn0tFac8MNnXjqqQnlGr+05ESlkX9rHUPBon2S+IQoSqdO0axfvxbLsvjqqy/461+7ed9buXI57dtfQ/Xq1bnxxs7s2PEn27Zt8b7ft28cc+fOZ/bsuURERNC69ZXUqVOnXOOXJCcqNdlTToiiRUREcOmlzfn111/46acffLoqly9fxqZNv9K/fywDB96JYSji4z8tcI7atevw5JPj+de/pnHw4IHyDF+SnBBCiKL99a9d+fe/X6NFi8ux2TxPuY4fP05ychKffLKMhQuXsnDhUqZNe4WVKxPIysoscI4rr7yaG274C2+8UfYDaYoiSU4IIUSR/vKXaLZv38rNN5/uqnz77Vn06hVLaOjp0ZTt2nWgUaOLWblyRaHnGT58BGvXfsvGjb/4O2Qv2WrnAiDbnpTemXV35jY7p8h2O4WTe670ynKrnUDOkytv/tpqR0ZXCiFEBeVJRBUrGV1opLtSCCFE0JIkJ4QQImhJkhNCCBG0JMkJIYQIWpLkRKViO5Ad6BCEEOVIkpyoVIJjwowQorgkyQkhRAVV46IwHI5qZf5/NS4KO+d3Hzp0kE6dOjBt2iSf8u3bt9KpUweWL18KgMvlom/f7kyfPs37esiQu/nkk499Pvfvf7/G+PFjKG8yT04IISqoEJudOT+V/TJYQ9v5dz+5sWMn8thjD3PjjTcRGVmX7du38fnny3nnnfllfi3n4teW3GuvvUbv3r3p3bs306Z5svzatWuJjY0lJiaG6dOne4/dvHkzcXFxdO/enXHjxuFyufwZmhBCiHMo7X5yzZu34Lbb+vPKKy9hWRbTpr3AqFFjqFmzZnlfgv+S3Nq1a/nuu+9YtGgRixcv5vfff2fZsmWMHTuWN954g+XLl7Np0yZWr14NwOjRo5kwYQKff/45WmsWLFjgr9CEEEIU06n95ADvfnJ2u2epsVP7yXXq1Jmbb+7mswPB4MH3s2/fHp577mkaN76E6OibAhG+/5Kcw+HgH//4ByEhIdjtdpo1a8bu3btp3LgxjRo1wmazERsbS0JCAgcOHCAnJ4c2bdoAEBcXR0JCgr9CE0IIUUyl3U/ObrfzxBNPsn79/3j88dGBCt9/Sa558+bepLV7925WrFiBUgqHw+E9JjIykqSkJJKTk33KHQ4HSUlJ/gpNCCFEMZ3PfnJRUfWoWrUaVaue3yLL58PvA0+2b9/O8OHDGTNmDKZpsnv3bu97WmuUUliWhVKqQHlJnO9K1RWdw1Et0CFcsE7VXUTqEdz2PELDQ3zeTzhylAEtGxf20UpN7rnSK03dJScb2GwF2x2GUbL/FhZXYd91JtM0vMd16xbDrFmv0arV5YSFhaCU4sSJdFJSkliyJIGwMM9ozcTEHxk9+nEefXQUVapU8TlHcfjjnvNrkktMTOTRRx9l7Nix9O7dmw0bNpCSkuJ9PyUlhcjISKKionzKU1NTiYyMLNF3yVY7ojBn1l1Wdh6m001udp7PMS4Lqd985J4rvdLWnWVZuFyWT1mey8mQNiPLKjSf8+b/rvzcbs/7LpfF9dd3YtKkZxk69EFcLgutNW+99W9uuSUOmy3Ee66rr25Hw4YXs2LFZ9x6a3+fcxRH/nqr0FvtHDp0iEceeYTp06fTsWNHAK6++mp27drFnj17aNiwIcuWLaNfv340aNCA0NBQEhMTad++PfHx8URHR/srNCGEuCAcD+BWO/Xq1WfhQs9cuIiICL766n/e98aNe4Zx454p9HNvvz2v0HMEit+S3Jw5c8jNzWXKlCnesjvvvJMpU6YwcuRIcnNz6dy5Mz169ADgpZdeYvz48WRkZNC6dWsGDhzor9CEEEJUErIz+AVAuo5KL//O4Ob+bNwNw32OcVlwe2PZHfxMcs+VXlnuDF6Z+GtncFnWSwghRNCSJCcqHdmJQIjKQ5KcqHSCo4NeCFEckuSEEEIErWIlubFjxxYoe/TRR8s8GCGEEKfVuCjcT1vthJ/7y8+wfPlSJk16xvt63LjRDBp0p88xP/30I126dGTnzh0+5Z06dSj19ZeFIqcQTJw4kaSkJBITEzl69Ki33OVysW/fPr8HJ4QQlVmIzcb0xC/K/Lyj2nc790FncezYMbZt20bt2rX57beNXHnl1T7vT578LLNmvePdmifQikxy/fv3Z/v27WzdupXu3bt7y03T9K5LKYQQovJYuXIFbdq0pWnTS1m8+BOfJNe69ZXY7XY++OBdBg68L4BRnlZkkrvyyiu58sorueGGG4iKiiqvmIQQQlRQy5cvZfjwh2na9FLeeutNHnvs/6hevYb3/SeffJr777+XTp0607RpswBG6lGsFU8OHTrE6NGjOX78OGfOHV+6NLDLtQhRErYD2cjASiFKb/v2raSkJHHNNddjs9m47LIWrFixjL/97R7vMVFRUTzwwMPebstAK1aSmzBhAnFxcVx++eUl3h1AiIpCpg4IUXwbN/5MgwYNqVPHgdYa0zT57LMl5OU5ufPO2wDIysoiPv5TnyQH0LdvHKtWfcUHH7wbiNB9FCvJ2Ww2hgwZ4u9YhBBCVBCffbaEli0vJy7udnbs2E7dulEsXPgRr7zyBq1bXwFAZmYGcXG9+emnHwt8/lS3ZaAVawpB8+bN2bp1q79jEUIIUUHce+9gVqxYxj339Gfnzh00bnwJdevW8yY4gCpVqtKnz63Ex39S4PNRUVEMG/ZIeYZcqGIt0HzXXXfx22+/Ub9+fUJDQ73lFemZnCzQLApzZt0tWbfTW37mIs2yQHNBcs+VXlku0FzjonBCbGW/WUyey8XxtIq1vJ2/FmguVu2NGjXqvL5ECCFEyVW0RHQhKlaSu+yyy/wdhxBCCFHmipXkrr/+epRSaK29oysdDgdr1qzxa3BCCCHE+ShWktuyZYv333l5eSxbtoxdu3b5LSghhBCiLJR4F4KQkBDi4uL43//+5494hCgXtv3yrEOIyqBYLbljx455/621ZtOmTaSnp/srJiH8TiaGC1E5lPiZHEDt2rUZN26cXwMTwl8MpbAkywlRKZT4mZwQFzJDKTapVK5QdXAHOhghzqHGRRGE2Mp+y5o8l5vjaVnFOnbnzj8ZOPBOXnhhKjfddDMAI0YMIyUlmfDwCLTWaG0xaND93HxzN5YvX8rMmdOpW9ezqL/b7cLpdPLww48RHX1TmV/LuRQryVmWxZw5c1izZg0ul4u//OUvPPjgg9j8MElRCH9zB+miASL4hNhMXvnxhzI/7+Mdrin2sZ99toQuXboSH/+pN8kBPPnkeNq182yIumPHnzzwwECuu64jAJ06RTNu3DPeY9esWcU//zk5IEmuWANPXn75ZdavX8+gQYMYMmQIP//8M9OmTfN3bEL4hSmLjAtRLC6Xi5UrE3jggYfYtm0LBw7sL/S4Zs0uJTw8nP37C99M+/DhQ1SvXt2foZ5VsZLct99+y7///W+6du1KTEwMb775psyRExckm2Fw1EzGZpy+9Q0Fn+w9HMCohKiY1q79jqioKC6+uDE33ngT8fGfFnrc99+vA+Diiz3Lkn333RoGD76b22/vyy23dGfr1s28+OLL5Rb3mYqV5LTW2O127+uQkBCf10JUZPkTmNaAeznGGQ06GYgiREHLly+ha9fuAN7nbU6nE4CpU19g8OC7GTDgDt57722ee24KERERgKe7cu7c+cyd+wENGzaiUaOLvQmwvBXroVrLli2ZPHky9957L0op3n//fVnqS1xANPH7D3NmJ6XbkmEnQhQlLe0o69evZevWLfz3vx+htebEiXRWr/4a8H0mdzZVqlRl/PhnGTjwb3TocC1XXHFVeYTuo1gtuYkTJ5Kens6dd97J7bffTlpaGk8//fQ5P5eRkUGfPn3Yv9/Tj/vUU08RExND37596du3L1988QUAmzdvJi4uju7duzNu3DhcLtd5XJIQBZ1qqRlKgTyTE+KcEhKW0779tSxatJyFC5fyySfLGDjwPhYvLritTlHq129Av35/49VXX6IYm96UuSJbcnl5eTz99NN07dqVKVOmADBs2DBM06Rq1aK3P9i4cSPjx49n9+7d3rJNmzYxb948IiMjfY4dPXo0L7zwAm3atGHs2LEsWLCAu+++u5SXJMTZ2QyFYYGkOXEhyHO5SzQSsiTnPZcVK5YW2A8uLu4O5s9/jypVSrb9zYABQ/jss3i++CKBmJieJfrs+Soyyc2YMYOMjAzatWvnLXv++ed59tlnmTlzZpFb8CxYsICJEycyZswYALKzszl48CBjx44lKSmJbt26MWLECA4dOkROTg5t2rQBIC4ujhkzZkiSE2XOZhikOD/FtLUo8J4hWU9UQMWdy+YP7733cYGyiy66iK++KnpJx169YunVK9anrGrVqixb9mWZxldcRXZXrlq1ipdffpnatWt7y+rWrcu0adP48suiA540aRIdOpzur01NTeX6669n8uTJLFiwgB9//JGFCxeSnJyMw+HwHudwOEhKSirt9QhRJKe74F+wkuCECF5FtuTsdjthYWEFyqtWrUpISEiJvqhRo0a8/vrr3tcDBgxg8eLFNGvWzLt9D+CznU9JnO/usRWdw1Et0CFcsCIiQjAAZXehLIUyFIapiAgLwTp5jIXUcX5SH6VXmrpLTjaw2Uq8Zn5Q8cc9V2SSMwyDjIyMAs/fMjIySjw4ZOvWrezevZvu3T3DUbXW2Gw2oqKiSElJ8R6Xmppa4JldcRw5koEVpCtZOBzVSEk5EegwLkgORzWysvIwFIQ63WhLY2jQFmTl5HHqlrE0UsdnkHuu9Epbd5Zl4XJZ5z4wiOWvN8NQ592AKfLPhj59+jB+/Hiysk73C2dlZTF+/HhiYmJK9EVaayZPnszx48dxOp18/PHHdOvWjQYNGhAaGkpiYiIA8fHxREdHl+JShBBCCF9FtuQGDRrExIkT+ctf/kLz5s2xLIsdO3YQGxvLI488UtRHC2jZsiXDhg3jrrvuwuVyERMTQ58+fQB46aWXGD9+PBkZGbRu3ZqBAweW/oqEEEKIk5QuxsSFAwcO8Pvvv2MYBldddVWpuhP9TborRWEcjmrM/mkbAKEHcznq+pStYa25IedP8hrH+XRX9ru4XgAjrVjkniu90tbd4cN7iIoKzKogFUFy8j4iIxv5lJVFd2WxVjxp0KABDRo0OK8vEqKiMJXCVAZhB/PIqleyAVRClKdAbrVz6NBB7rorjiZNmgKQm5vDlVdezYMPjiA3N9fnvVNiY2+lX7870Frz8ccfkJCwHPAkq7vvHuhdIqw8yV45olIxlMHFeT+hVA2CtOEvgkiIzeTVDVvL/LyPXVtwrmhh6tRxMHfufMAzrmLWrNcZP/5Jnn76OZ/38ps9+w22bdvKa6/NpmrVqiQnJzFixDBq1KjJNddcV2bXURyVe7yqqFQMpTCUwm25UCdfCyGKRynF0KHD2blzB5mZmWc9LisriwUL5jNq1GjvyPzIyLo8++xkateuU17heklLTgghRLHY7XYaNWrE99+vJTU1hcGDfVemevrp53A687DZbDRs6Pt8rVWr1uUZqpckOVHpGMqUxSuFKDVFaGjoWbsrt27dgt1ecZ51S3elEEKIYnE6nezbt4eOHTud9ZgmTZqQm5vD4cO++zh++eXnLFjwob9DLECSnKiUpCEnRMlYlsWcObO4/PIrMYyzp47Q0DDi4u7g5ZdfJDMzA/CM1Jw16w2aNLmkvML1ku5KUekYClJIp44MPBGiSGc+d7MsN82bt+CZZyaRmZlR6DO5Nm3a8vjjoxk27GHmzn2L4cOHYJo2TNPgwQdHcO2115f7NUiSE5XKqbxmBWDzRiFKKs/lLvZw/5Ke91zq1avPqlXrC32vevXqZ30PwDRNhg4dztChw0sdY1mRJCcqDUMpcoxcDGzgdmMoT6tO5suJiiqQ+8kFC3kmJyoNm6GQBpwQlYskOVEpGAosZwJKhpwIUalIkhOVhmWd/TmEoWDRvsNnfV8IcWGSJCcqLSNfo04GowgRfCTJiaCVv2WmzpgyYCgTt2tFgUQnhAgukuRE0DqzZWYoSOa4z+uj+k9sRsEWnRAieMgUAlFpePYHVpjKJAc3YZbGtt9JXj17oEMTolA1LqpCiK3s2yJ5LovjaWffSSC/5cuX8vPPiQC0bdueXr1ive/NmTMLgKFDh9O/fywzZ86iXr36ZRvweZAkJyqtbFzUktGWogILsRnMXJdW5ucd2fGiMj9nRSXdlaLSUAoMw0ThWbtShpkIEfwkyYmgZSj4cMseAGwHcjGVjY4HqmAYR0GlFGjDybM5IYKPJDkR1NwnB58oFFm4sCw3lrawtIUC/PC4Q4igpc6yqHlRuxIEWsWNTAg/UEphYGAqA5dyk2gu8Y6wlJacEKdt3PgzqakpgGfQlmmaVKtWnYyMEz7HpaUdpVq1aoEIsVgkyYlKw1Cmz/9qNE73uVdjF6Iy+uyzJaxZswqAHTu2U79+Azp0uIYvv1xJdnY2AEeOpLJ27Xe0a3dNACMtmoyuFJWCzTDouK8qyjC4Zn8d1jc4CCgM2VNOVGB5LssvIyHzXNY5j7n33sE8//wEPvnkYxyOSJ57bgrVq1fnzz+3M2zYIAzDQCmDhx4aSdOmzbyfGzDgDp9uzS+++LbM4y8JSXKi0tCWGwyjwBqW0k0pKqqSzGUraxdf3Jj//OfdAuUDBgxhwIAhhX5m4cKl/g6rxKS7UlRqpjLYk/GZzwCU+D3ShSlEsPBrksvIyKBPnz7s378fgLVr1xIbG0tMTAzTp0/3Hrd582bi4uLo3r0748aNw+Vy+TMsIQC8UwhOtey8OxHIQs1CBA2/JbmNGzdy1113sXv3bgBycnIYO3Ysb7zxBsuXL2fTpk2sXr0agNGjRzNhwgQ+//xztNYsWLDAX2EJ4WUaNm9X5an/tbSW7kshgojfktyCBQuYOHEikZGRAPz66680btyYRo0aYbPZiI2NJSEhgQMHDpCTk0ObNm0AiIuLIyEhwV9hCQGAUiYd99fHVAaGMtibsQKb4Zk390d6cqDDE0KUEb8NPJk0aZLP6+TkZBwOh/d1ZGQkSUlJBcodDgdJSUn+CktUQrYD2Rh2O0opFMq7nNeZA1AsffrfbumuFCJolNvoSsuyfIaVaq1RSp21vKRq165aJnFWVA5HxZ1sWVFFpB4BwG63sc2WRoPCDlIKm2liARH2EGx2sIU4WZlsck/riPIMt8KRe670SlN3yckGtkq+BI8/7rlyS3JRUVGkpKR4X6ekpBAZGVmgPDU11dvFWRJHjmRgWcH5F7jDUY2UlBPnPlD4yMrOIyI8BJfTjabgvCClFDYM3G43KIMcVx45LnA7LTIzc0lJqbyjLOWeK73S1p1lWbjyzV+7qGYVbPayT3wup0XasXNPT/jmmy95//25uN1utLbo0aM3d989kBEjhpGSkkx4+Ok/BGvVqsW//vUac+bMIj7+U2rVqg1Abm4OXbp0Zdiwh8/5ffnrzTDUeTdgyi3JXX311ezatYs9e/bQsGFDli1bRr9+/WjQoAGhoaEkJibSvn174uPjiY6OLq+wRCVgKEUt5xrA5uklUAqbEUJUdh6OvD2kR1zKJUd3US1zGYcdfQIdrhBeNrvBui8yyvy8HbudO3GkpCTz2muv8Pbb86hRoyZZWVmMGDGMiy9uDMCTT46nXbsOhX62b984hg4dDkB2djb33NOfq69uy3XXdSy7iyimcktyoaGhTJkyhZEjR5Kbm0vnzp3p0aMHAC+99BLjx48nIyOD1q1bM3DgwPIKS1QSWoMyDJ9nclgWWVY2SoFluQpMEheiMjt27Bgul4ucnBxq1ICIiAjGj3+GkJDQEp0nPDycyy9vzc6dO4IzyX399dfef3fs2JElS5YUOKZly5YsXLjQ36EIAYA6Y1DxmWNMZOaAEKc1b34ZN97YmTvu6Mtll7WgbdsOdOvWg4YNGwEwdeoLPt2VXbrczKBBQwuc5/DhQ/z226/cccfd5Rb7mWRZLxH0DHV6jUqlFLVzsk8u0ny65aaU4ljeTpkjJ8QZ/v73pxg0aCgbNqxnw4Z1DB8+hIkTnweK7q6Mj/+Ub79djdYWhmEwcOAQrrqqTTlGfpokOVHp6JMDlPIPU9IydUAIr7VrvyM7O4ubb46hd+9b6N37FpYsWcSyZfHn/OyZz+QCrXKPVxWVkjo58ATANEzMkxs+GsqUlpwQJ4WFhfHvf7/OoUMHAc8fgdu3b6N58xYBjqxkpCUnKh/DpO3eqijDSdu0E6ypkeV9a3+GZwi3JDtR2bVr14H77nuAMWMe964nfN11HRk8+H6eeGJEgWdyADNnzgpEqEWSJCcqBUN59r4CQBlclJWJVqDdbvTJjkuF4nheAiFGDzafSKYv9QMYsRCe+WzFGe5fmvMWR8+efejZs+C0mtdem33Wz1SUbspTJMmJyuPMR2668B+523JjM2RpL1ExFGfCtiiaPJMTlYcCyD7jpWdxZrvThnJauCzPDuIhBtiku1KIoCBJTlQq3gaaYSNEW9jMUJqd2Ivz5E/BVAaXHVtBiPwyhAgK8lMWQctQvje4At/Fvw2TJsn1QHuex53ilpVPhAgakuREpeBZsjKH2rl5ePOcYYLbjRsIs9IwClnzZNFuSXhCXMgkyYmgZoB3tRNQaKvggBMN3oEopvL9SQTrzhZCVBaS5ETwU6CUDUeO09tdeWab7VQLzlAmLY7vxGZAKbY0FEJUQDKFQAQ9QyncHELraoVuyBvuPoF1MtG5LRc2BaYkOVEB1KpZBdMP+8m5nRZHSzA9Yfnypfz8cyIAbdu2p1evWO97c+Z4JoAPHTqcESOGERlZlwkTni/0/UCQJCeC1qmBJ8oAy3X2bkddYBXL02yG57ncbU1MP0QoRNFMu0HSR2W/n1zdO8t+gvkp33zzJV263MyNN97kt+8oCemuFEHLOLoUG/CH2nXu7sci3j/1XE4GoQhxboMGDeXll6eSnn480KEAkuREENNa0/L3bb6bxgGgCClkAIqhTJ+pBPnJIBQhzu2qq9rQpUtXXnnlpUCHAkiSE0HMlW3gPp57xuhKD8+rkwlLGShlorFk11QhzqGwZ9oAhuGbSoYPf4Q//tjEt9+u8ntM5yJJTgSttodC8k0hKIRhnkxyvq00U8Enew/LbgSi0tq48WdSU1MAT6+IaZpUq1adjIwTPselpR2lWrVqPmVhYWE89dQEXn55KidOpJdbzIWRJCeCVqb7BEfUCeo4fz5jovfprKWUKtB686yK4vn39nR5Bicqr88+W8KaNasA2LFjO/XrN6BDh2v48suVZGd71oA9ciSVtWu/o127awp8/uqr29KlS1fi4z8tz7ALkNGVImhp7fl/Cgi1hZ/1OKVs3mdxSiny3LuxGYU8yhOinLmdll9GQrqLsdXOvfcO5vnnJ/DJJx/jcETy3HNTqF69On/+uZ1hwwZhGJ7tqx56aCRNmzYr9BzDhz/CunXflXX4JaK0Do6f8pEjGUE7MMDhqEZKyolzHyh8/PHJVJS9FVsa7KfbHifabXFqlcqQU+tTWm6Sa+1iZ4SBO8SB03LhsixsZhN+qdqbVtXq4rLAMBQut6Zf08rxd6Hcc6VX2ro7fHgPUVGN/RDRhSE5eR+RkY18ygxDUbv2+SV56a4UQUtrjVKKEMPEcrrOfpzbk/DytMvbe2nlW6Q5WP+AEiLYSZITQUspRbj7MLnqyKkSb3lhztanIYNPhLhwSZITQS1Pu7G0hVKKUMuNAuzuggNKNPnWs1SGLO0lRBCQJCcqjdOPn32bbIYtBKXM/MVCiCAgSU4ELYVx7rxlmDiONPIkOYAzBp2FGBBiSnelEBeygAwVGzBgAEePHsVm83z9c889R2ZmJi+++CK5ubn07NmTUaNGBSI0EURquPLIDPH9O66w53Ha7UahMDE8K5/g6bo0lWeB5jyZLifEBavck5zWmt27d/PNN994k1xOTg49evTg/fffp169egwfPpzVq1fTuXPn8g5PBBGtLQylME620hSq0Odxp7iUE1ObGJbCrfbRMv1z8tJchDS6D9e5pxUJUeZq1YzAtJf9Dhhup5ujx7LOeVxWVhZvvjmTDRvWERYWTpUqVbjvvmF06HAtAGvXfsf7779NVlY2luUmOroLQ4cOZ9eunTz//AQAkpIOEx4eTvXqNbDb7fznP++W+fUUpdyT3M6dOwG47777OHbsGHfccQeXXXYZjRs3plEjzxyJ2NhYEhISJMmJ86KUgeJ0kvMoYssdfXrwieW2sAw3luVme3oyTatEelt08Xvc9G0sW+8I/zPtJsc/3lbm563xt8vOeYzWmiefHEXz5pcxb95/sdvtbNu2hdGjH2fixBfIy8tj+vRpvPzyTC6+uDG5uTlMmPAUc+bM4oEHHmLu3PkATJr0TIE96MpTuSe59PR0OnbsyNNPP43T6WTgwIHcf//9OBwO7zGRkZEkJSWV6LznO2GwonM4qp37IOHjsGnHUAatjh9FEQWcvRWnlInNsJHtziZEhYH2TEQ1tKfPskpEKHYLHI4IwpOzSEiCAVdElN/FBIDcc6VXmrpLTjaw2QoOkzD89FC4sO86U2LijyQlHeaNN2Z7u/kvv/xyhgy5n/fem4PT6WTw4KE0bXrJyfNF8OSTY9m9e7fPuZVSGIY65/eBf+65ck9ybdu2pW3btt7X/fv3Z8aMGbRv395bdmoSb0nIiicivxq5bnLtgHYTYrk46zYDhkmLQ/U5UeVP/qhe9dR6YGhLg9Zol5vs7FxcFrydmIdlaVwWpKQE78M6uedKr7R1Z1kWrkL6xf3137XCvutMv/++iRYtWuF2e34Pp1x1VRveeGMmWls8+ujffc5Tq5aDWrUcPmVaa89vphh9/vnr7YJc8eTHH39k3bp13tdaaxo0aEBKSoq3LCUlhcjIyPIOTQQZjYWpzGKNstSWheX2XRVFKXDqPbQ6voJTf4QG6x9SQhSkcBfyDNvpdKGU53FASEhIAOIqmXJPcidOnGDatGnk5uaSkZHBokWLeOKJJ9i1axd79uzB7XazbNkyoqOjyzs0EYR8OgTO0pBTpo0aeXkole/nkIenJaeDt8UmxNm0bn0FW7b8gcvl+8ffpk2/0rLl5bRs2YotW/7weW/v3j3eAScVRbknuS5dutC5c2duvfVW+vXrR79+/Wjbti1Tpkxh5MiR9OrVi6ZNm9KjR4/yDk0EGYVCk1usYwtbpzx/TpT5cqIyufrqtlxySTNeffVlb6LbsmUz7703h0GDhnL33QN5553/sG/fXsAzEvO116ZTt25UIMMuICDz5B5//HEef/xxn7KOHTuyZMmSQIQjBEop73Y7AAaKPLUXZYEyPPPltp/4iMYRdyI9lqKymDx5GrNnv8GAAXd4N019+unnadeuAwDDhj3MxIlP4XZbuN0uunTpypAhDwQ4al+VY98QUTkpg4uys8iuUbwOC4XCVLZTL7wTw0FacSIw3E53sYb7l+a8xREaGsbIkU8wcuQThb7ftWt3unbtXuQ5xo17pqThlSlJciKoeSaEF+82D7GbtD6WwvYa9cmznEUeayhYtNvNbU1kvpzwn+JM2BZFk7UrRXAzDKJSHMWakqKUAW6LEHcaxhldl0qB3l/w8zLSUoiKT5KcCHqeHcHPTRknW2VKYZ78t4Ei19pFMeaxCiEqIPnpCgFgmNQ62gjDFlrwLRTO7HcIMQ2fZGcozxJfQpSVwkb5Vgb+vG5JckIAGCaqkAafoUwUCsvt5nheNpvTk30GoUiXpSgrNlsImZnplS7Raa3JzEwnPDzML+eXgSdCFIOhDFoc38b2mjJ/U/jHRRc5SEtLISPjWKBDKXc2WwiXXnoJx47llP25y/yMQlQgSqkS/WWsTJvvfDllYuGZCKu1JsQ4PZ3AUMicOVFmTNNGnTr1Ah1GwNjtdqDsk5x0V4qgpQC72138xb4NA8eRRmjDROFJcJceT8HUpmetPsBuQJjNs2O4EKLikyQngpwucqPUAtxuDGXDiRNTmd5Fm0+lSUNBzaSP+OX4XBlxKcQFQH6mohIoQZ+iUlycfhzDDKXp8RRv12WmOoFhKQzl2bEgL9t30dpFu2WUpRAVkSQ5IfLT0CwtBc5oAZ56rmdkwJHcTAw8rbpTz+dklKUQFZMkOSEKozXuQuYUGJxuFxoKtmUkl2tYQoiSkSQnxJkME2WYhJoKw/AdfGwokyZZn+N07fK24jLdSwMUqBCiOCTJCZGPYQvBcbSxJ9GZNmxGCIbyDKe0LDeW9rTwDHVGN6aS53JCVESS5IQ4k2FSO7UB2vIkMtMWwqXpKRiG70/FVAZ2E2zKYMV+t2fOnDyXE6LCkSQnRH5nDDix0Gi3G7vy/FQUYCoTmzLYdjCZK078iVGS0ZtCiHIlSU6Is1CGCYaBNqBp2u/YDBtOYy9u5STctGFDg8vF9hPJ2E7uHi5dlkJULJLkhDgLZZjUTa0PhonWGlOZaG2hlEGLzJ00yfocU9nQaJlKIEQFJUlOiCJotxuFwq6dmOr0Vqpuy4W2PK02uwG1kj/CZpxe7ku24BGiYpAFmkVQ2bn/UyzLRZMtl4Eq5d9whok6mcCUYaJsdpTbxO5OI9cIQ1ngUk6cai82bFyavIJUw02ICT+nJWPsqwv5FoVetNvNbU1kwUshypu05MQFb9vu+QAsO7yLupvzSD9ilMn2AMowQXmmEQAoZdLqWAp2MxTQ6JNTCTzTCjyfcWuNgS4wSVy6MYUIDEly4oK2fc9HaA1v7XwbQylAUfdABin6z/M7sWFSK7UBpmFQNznS06IzQ4hKa0aLE+nYjJDThyoD0+2ZKxeWCVtPJKPRfLZPuiyFCDRJcuKCtPjgDgAsy2JX1nFqHN9GiOHpDsxzp5Ohnef/JW43oMHtQhkmphmOttxorTCUic0IwVQmhjKwK4OQZMXluSsIy9SQjs/UAqOYu/0IIcqWJDlxwVlyaCdhNhtfbn4LpQxshomhTHKTP+BIVgouLCzDjSrtM7lCGLYQ2uypigoJo+HRBoTZq9I0I5VLTxyheeYuLj3xCzp7GqZyk24mEGJAZOoCFiX+DHjWuPxsn1umGAhRzipUklu6dCm9evUiJiaGDz74INDhiArMUIqWO0xvIrMZIVy5pzqgMJRBrawjZf6d2nVyex3Lokl6ClorLLeLPHcKTncOTu0kT+3BstyYCtJyMrGZBksPHMbC86zu1ICUpXvdMgJTiHJQYZJcUlIS06dPZ/78+SxevJiPP/6YP/88z+cqIijZjqzEQIHb01ozT7hxO0HnuTGUQmEABXcQOB/KyDcyUpnUO9IQ0x7qXdT50uOp2Awb4aaNcBuE2AxCsj/icJabqjbYk7uJCPvJa1CaPw4kFUh0kviEKFsVJsmtXbuW66+/npo1axIREUH37t1JSEgIdFiiAvj0wJ/eZ3Db93wEuAkD3EY21TZlkmlLxq5tWKZBqDuPam6LMr21Tw5CUerkgzVbCPWO1EPZQmiY3pQG6ZdSNbwK9Y43okX2Ca50HaKV6yvc1kGap6ej8r4jdu8bxPw5m9/TfuCLXw6z40ASVUxNVbunZbf2R0/PxbasX7A2zi9xiNINKkThKsw8ueTkZBwOh/d1ZGQkv/76a7E/bwT5k/1gv7781h09hAYa5v1KwypXkZybyJo/vyZP51AlNJTQzNVUrVEPE8X1WVdgUyZG1QxAUcXpJNMegt2Z593ZuziUaYJ1empAYc6cCKBOvtYowmtozLyaRNQw6JAZhaUN9jS4hIvTGnIDR6hStQ4uFcZN5j6q5n6PPfJujqhEqh77k5V5d3Fx9Zr8fGw1DaskY5jVWXzov8Rl3skm/QlXtLiNDUdTubZWHe93f7p/KXENY72vG1Y3WH/sCDecccz5qmz3XFmSuiud/PVWFvWotNYVYgLPm2++SW5uLo8//jgACxYsYNOmTTz33HOBDUwIIcQFq8J0V0ZFRZGSkuJ9nZKSQmRkZAAjEkIIcaGrMEnuhhtuYN26dRw9epTs7GxWrlxJdHR0oMMSQghxAaswz+Tq1q3LqFGjGDhwIE6nk/79+3PVVVcFOiwhhBAXsArzTE4IIYQoaxWmu1IIIYQoa5LkhBBCBC1JckIIIYKWJDkhhBBBS5KcEEKIoCVJLsAWLVpEp06d6Nu3L3379mX69OkApKenM2zYMHr27Mk999zjnSifl5fH6NGj6dmzJ7fddhs7dnjWdNRaM3XqVHr06EGvXr1ITEwM2DVVBLKjRUEDBgygd+/e3ntt48aNrF27ltjYWGJiYrz3HsDmzZuJi4uje/fujBs3DtfJHRgOHjzIPffcQ48ePXjooYfIzMwM1OWUi4yMDPr06cP+/fsByqy+zvb7Dhb56+2pp54iJibGe+998cUXQDnVmxYB9dxzz+mlS5cWKH/22Wf1rFmztNZaL1q0SD/22GNaa63feust/fTTT2uttd6wYYO+/fbbtdZar1ixQj/wwAPa7XbrnTt36m7dummn01k+F1HBHD58WHfp0kWnpaXpzMxMHRsbq7dv3x7osALKsizdqVMnn3siOztbd+7cWe/du1c7nU5933336VWrVmmtte7du7f++eeftdZaP/XUU/qDDz7QWms9bNgwvWzZMq211q+99pqeNm1a+V5IOfrll190nz59dOvWrfW+ffvKtL7O9vsOBvnrTWut+/Tpo5OSkgocWx71Ji25APvtt99YtGgRsbGx/P3vf+f48eMArFq1ithYzwK8ffr0Yc2aNTidTlatWsUtt9wCwDXXXMPRo0c5ePAgq1evplevXhiGwSWXXEK9evX4+eefA3ZdgSQ7WhS0c+dOAO677z5uueUW5s2bx6+//krjxo1p1KgRNpuN2NhYEhISOHDgADk5ObRp0waAuLg4EhIScDqd/PDDD3Tv3t2nPFgtWLCAiRMnepcXLMv6OtvvOxjkr7fs7GwOHjzI2LFjiY2NZcaMGViWVW71JkkuwBwOBw8//DBLliyhXr163gWpz9yVwWazUbVqVY4ePVpgtwaHw8Hhw4dJTk72WevzVHllVNiOFklJSQGMKPDS09Pp2LEjr7/+OnPnzuWjjz7i4MGDhdZTYfdYUlISaWlpVK1aFZvN5lMerCZNmkSHDh28r892X5Wmvs72+w4G+estNTWV66+/nsmTJ7NgwQJ+/PFHFi5cWG71VmGW9Qp2K1as4MUXX/Qpa9q0KXPnzvW+vv/+++nWrVuhn9daYxgGWuvT+5qdUW5ZVqHllVFhdXHm68qobdu2tG3b1vu6f//+zJgxg/bt23vLTtXT2eqvsHqsTPV6tnopi/oK5t9ro0aNeP31172vBwwYwOLFi2nWrFm51Ftw1moF1LNnT9asWePzfzNnzvRJclprTNOzA3VkZCSpqakAuFwuMjMzqVmzJnXr1iU5Odn7mdTUVCIjI4mKiiq0vDKSHS0K+vHHH1m3bp33tdaaBg0aFFpP+evv1L1Uq1YtTpw4gdvt9jm+sjjbfVWa+jrb7zsYbd26lc8//9z7WmuNzWYrt3qTJBdAERERvPXWW2zcuBGAefPmeVtynTt3ZvHixQAsX76cDh06YLfb6dy5M/Hx8YDnP1yhoaHUr1+f6Oholi5ditvtZs+ePezevZsrr7wyINcVaLKjRUEnTpxg2rRp5ObmkpGRwaJFi3jiiSfYtWsXe/bswe12s2zZMqKjo2nQoAGhoaHeEbrx8fFER0djt9vp0KEDy5cvB2Dx4sWVql6vvvrqMquvs/2+g5HWmsmTJ3P8+HGcTicff/wx3bp1K796K9GwGVHmfvjhB33rrbfqHj166AcffFCnp6drrbVOS0vTw4cP17169dJ/+9vfvKOUcnJy9JgxY3SvXr30rbfeqjdt2qS19oyemzJliu7Vq5fu1auX/vbbbwN2TRXBkiVLdO/evXVMTIyePXt2oMOpEKZPn6579OihY2Ji9Ny5c7XWWq9du1bHxsbqmJgYPWnSJG1ZltZa682bN+t+/frp7t276yeeeELn5uZqrbXev3+/vvfee3XPnj31fffdp48dOxaw6ykvXbp08f7+yqq+zvb7DiZn1tu8efN0z549dbdu3fQ///lP7zHlUW+yC4EQQoigJd2VQgghgpYkOSGEEEFLkpwQQoigJUlOCCFE0JIkJ4QQImhJkhMigPbv3++zEgl45v9cd911tG7dmnfffddbvmvXLlq0aMG//vUvb9mRI0e44oorOHHiBP/4xz+YM2dOge9o0aJF0CwZJURJSZITogL56KOPmDJlCnPnzmXMmDF8//333ve++eYbunTpwldffeUtW79+Pe3ataNatWqBCFeICk+SnBAVxOzZs5k7dy7z58+nVatWREdH8+OPP2JZFuBJcsOGDSMzM5O9e/cCsG7dOm666aYARi1ExSYLNAtRAUybNo05c+YwYcIEGjZsCMAll1xC9erV2bp1K/Xr12fXrl20adOG6Ohovv76awYPHsy6desYMmSI9zxz585lyZIlgboMISocSXJCBFhWVhbbtm1j9uzZjBo1irZt23L55ZcDEB0dzffff0/t2rW54YYbMAyDLl268MEHH9C1a1eUUjRr1sx7rsGDBzN06FCf87do0aJcr0eIikS6K4UIsLCwMN588006d+7M8OHDGTFiBMeOHQPwdlmuWrXK2y3ZsWNHNm/eLF2VQhSDJDkhAswwDO9K6sOGDePSSy/l//7v/7Asi+uuu47NmzezYcMGbrzxRsCTFFu3bs28efPo3LlzIEMXosKTJCdEBaKUYurUqezYsYNXXnmF8PBwmjRpwiWXXOIzgrJz587s2bOH6667LoDRClHxyS4EQgghgpa05IQQQgQtSXJCCCGCliQ5IYQQQUuSnBBCiKAlSU4IIUTQkiQnhBAiaEmSE0IIEbQkyQkhhAha/w+oreiZ3UewvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=indiv_billing_elec, x=\"KWH\", hue=\"month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [],
   "source": [
    "indiv_billing_1 = indiv_billing_gas.loc[indiv_billing_gas[\"month\"] == \"JAN\"]\n",
    "indiv_billing_1_e = indiv_billing_elec.loc[indiv_billing_elec[\"month\"] == \"JAN\"]\n",
    "\n",
    "indiv_billing_2 = indiv_billing_gas.loc[indiv_billing_gas[\"month\"] == \"FEB\"]\n",
    "indiv_billing_2_e = indiv_billing_elec.loc[indiv_billing_elec[\"month\"] == \"FEB\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(indiv_billing_1, x = indiv_billing_1['THRM'], bins = 80, label = 'Gas')\n",
    "plt.title(\"Jan Individual Accounts (Therms)\")\n",
    "plt.xlabel(\"Therms\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "sns.histplot(indiv_billing_1_e, x = indiv_billing_1_e['KWH'], bins = 80, label = 'Elec')\n",
    "plt.title(\"Jan Individual Accounts (kWh)\")\n",
    "plt.xlabel(\"kWh\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(indiv_billing_2, x = indiv_billing_2['THRM'], bins = 80, label = 'Gas')\n",
    "plt.title(\"Feb Individual Accounts (Therms)\")\n",
    "plt.xlabel(\"Therms\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "sns.histplot(indiv_billing_2_e, x = indiv_billing_2_e['KWH'], bins = 80, label = 'Elec')\n",
    "plt.title(\"Feb Individual Accounts (kWh)\")\n",
    "plt.xlabel(\"kWh\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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