Electricity_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utilities Consumption Study\n",
    "# Electricity Consumption Notebook\n",
    "### Sidclay da Silva\n",
    "### July 2020\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "\n",
    "This notebook is part of a study which includes analysis of self water and electricity consumption, and the relation between consumption and the variation of local temperature and relative humidity.\n",
    "\n",
    "A separate document will be developed with the complete analysis.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The source for electricity consumption data was the author monthly bills, which are based on energy company monthly reading from the electricity meter. Data has been collected and stored in a local file in ```CSV``` format. The file contains the following data;\n",
    "\n",
    "| |Column| Data type| Description|\n",
    "|--:|:--|:--|:--|\n",
    "|1|**period**| ```string``` in format *YYYY-MM*| Year and month of the billing reference|\n",
    "|2|**previous_check_date**| ```string``` in format *DD/MM/YYYY*| Date of the previous consumption reading|\n",
    "|3|**current_check_date**| ```string``` in format *DD/MM/YYYY*| Date of the current consumption reading|\n",
    "|4|**previous_mark**| ```integer```| Electricity meter indicator mark of previous consumption reading|\n",
    "|5|**current_mark**| ```integer```| Electricity meter indicator mark of current consumption reading|\n",
    "|6|**consumption_kwh**| ```integer```| Electricity consumption in kilowatt-hour, difference between *current_mark* and *previous_mark*|\n",
    "\n",
    "Read data from ```CSV``` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>previous_check_date</th>\n",
       "      <th>current_check_date</th>\n",
       "      <th>previous_mark</th>\n",
       "      <th>current_mark</th>\n",
       "      <th>consumption_kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>27/05/2020</td>\n",
       "      <td>26/06/2020</td>\n",
       "      <td>16.642</td>\n",
       "      <td>17.002</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>27/05/2020</td>\n",
       "      <td>16.298</td>\n",
       "      <td>16.642</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>27/03/2020</td>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>15.996</td>\n",
       "      <td>16.298</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>27/03/2020</td>\n",
       "      <td>15.726</td>\n",
       "      <td>15.996</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>27/01/2020</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>15.469</td>\n",
       "      <td>15.726</td>\n",
       "      <td>257</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>26/01/2015</td>\n",
       "      <td>19/02/2015</td>\n",
       "      <td>601.000</td>\n",
       "      <td>641.000</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>26/12/2014</td>\n",
       "      <td>26/01/2015</td>\n",
       "      <td>411.000</td>\n",
       "      <td>601.000</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>26/11/2014</td>\n",
       "      <td>26/12/2014</td>\n",
       "      <td>223.000</td>\n",
       "      <td>411.000</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>24/10/2014</td>\n",
       "      <td>26/11/2014</td>\n",
       "      <td>73.000</td>\n",
       "      <td>223.000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2014-10</td>\n",
       "      <td>24/09/2014</td>\n",
       "      <td>24/10/2014</td>\n",
       "      <td>71.000</td>\n",
       "      <td>73.000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     period previous_check_date current_check_date  previous_mark  \\\n",
       "0   2020-06          27/05/2020         26/06/2020         16.642   \n",
       "1   2020-05          29/04/2020         27/05/2020         16.298   \n",
       "2   2020-04          27/03/2020         29/04/2020         15.996   \n",
       "3   2020-03          27/02/2020         27/03/2020         15.726   \n",
       "4   2020-02          27/01/2020         27/02/2020         15.469   \n",
       "..      ...                 ...                ...            ...   \n",
       "64  2015-02          26/01/2015         19/02/2015        601.000   \n",
       "65  2015-01          26/12/2014         26/01/2015        411.000   \n",
       "66  2014-12          26/11/2014         26/12/2014        223.000   \n",
       "67  2014-11          24/10/2014         26/11/2014         73.000   \n",
       "68  2014-10          24/09/2014         24/10/2014         71.000   \n",
       "\n",
       "    current_mark  consumption_kwh  \n",
       "0         17.002              360  \n",
       "1         16.642              344  \n",
       "2         16.298              302  \n",
       "3         15.996              270  \n",
       "4         15.726              257  \n",
       "..           ...              ...  \n",
       "64       641.000               37  \n",
       "65       601.000              193  \n",
       "66       411.000              188  \n",
       "67       223.000              150  \n",
       "68        73.000                2  \n",
       "\n",
       "[69 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read CSV file from local folder\n",
    "df_electr = pd.read_csv('./Data_source/Data_Electricity.csv', sep=';', header=0)\n",
    "df_electr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Entries are sorted in chronological descenting order from **2014-10** to **2020-06**, the dataframe will be sorted in ascending oder.\n",
    "\n",
    "The inicial period **2014-10** will not be considered as the house was unoccupied for almost the whole month.\n",
    "\n",
    "Columns *previous_check_date* and *current_check_date* will be renamed to be shortened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>previous_check</th>\n",
       "      <th>current_check</th>\n",
       "      <th>previous_mark</th>\n",
       "      <th>current_mark</th>\n",
       "      <th>consumption_kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>24/10/2014</td>\n",
       "      <td>26/11/2014</td>\n",
       "      <td>73.000</td>\n",
       "      <td>223.000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>26/11/2014</td>\n",
       "      <td>26/12/2014</td>\n",
       "      <td>223.000</td>\n",
       "      <td>411.000</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>26/12/2014</td>\n",
       "      <td>26/01/2015</td>\n",
       "      <td>411.000</td>\n",
       "      <td>601.000</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>26/01/2015</td>\n",
       "      <td>19/02/2015</td>\n",
       "      <td>601.000</td>\n",
       "      <td>641.000</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>19/02/2015</td>\n",
       "      <td>26/03/2015</td>\n",
       "      <td>641.000</td>\n",
       "      <td>947.000</td>\n",
       "      <td>306</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>27/01/2020</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>15.469</td>\n",
       "      <td>15.726</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>27/03/2020</td>\n",
       "      <td>15.726</td>\n",
       "      <td>15.996</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>27/03/2020</td>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>15.996</td>\n",
       "      <td>16.298</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>27/05/2020</td>\n",
       "      <td>16.298</td>\n",
       "      <td>16.642</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>27/05/2020</td>\n",
       "      <td>26/06/2020</td>\n",
       "      <td>16.642</td>\n",
       "      <td>17.002</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     period previous_check current_check  previous_mark  current_mark  \\\n",
       "0   2014-11     24/10/2014    26/11/2014         73.000       223.000   \n",
       "1   2014-12     26/11/2014    26/12/2014        223.000       411.000   \n",
       "2   2015-01     26/12/2014    26/01/2015        411.000       601.000   \n",
       "3   2015-02     26/01/2015    19/02/2015        601.000       641.000   \n",
       "4   2015-03     19/02/2015    26/03/2015        641.000       947.000   \n",
       "..      ...            ...           ...            ...           ...   \n",
       "63  2020-02     27/01/2020    27/02/2020         15.469        15.726   \n",
       "64  2020-03     27/02/2020    27/03/2020         15.726        15.996   \n",
       "65  2020-04     27/03/2020    29/04/2020         15.996        16.298   \n",
       "66  2020-05     29/04/2020    27/05/2020         16.298        16.642   \n",
       "67  2020-06     27/05/2020    26/06/2020         16.642        17.002   \n",
       "\n",
       "    consumption_kwh  \n",
       "0               150  \n",
       "1               188  \n",
       "2               193  \n",
       "3                37  \n",
       "4               306  \n",
       "..              ...  \n",
       "63              257  \n",
       "64              270  \n",
       "65              302  \n",
       "66              344  \n",
       "67              360  \n",
       "\n",
       "[68 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copy dataframe exlcuding last observation\n",
    "df_electr = df_electr[:-1]\n",
    "\n",
    "# sort dataframe in chronological ascending order\n",
    "df_electr = df_electr.sort_values(by='period', ignore_index=True)\n",
    "\n",
    "# rename columns\n",
    "df_electr = df_electr.rename(columns={'previous_check_date': 'previous_check',\n",
    "                                    'current_check_date': 'current_check'})\n",
    "df_electr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data in columns *previous_check* and *current_check* are in Brazilian standard format __DD/MM/YYYY__, date information is in ```string``` format and stored as ```object``` data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "period              object\n",
       "previous_check      object\n",
       "current_check       object\n",
       "previous_mark      float64\n",
       "current_mark       float64\n",
       "consumption_kwh      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dataframe colunms data types\n",
    "df_electr.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert data in columns *previous_check* and *current_check* to ```datetime``` format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>previous_check</th>\n",
       "      <th>current_check</th>\n",
       "      <th>previous_mark</th>\n",
       "      <th>current_mark</th>\n",
       "      <th>consumption_kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>2014-10-24</td>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>73.000</td>\n",
       "      <td>223.000</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>2014-12-26</td>\n",
       "      <td>223.000</td>\n",
       "      <td>411.000</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2014-12-26</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>411.000</td>\n",
       "      <td>601.000</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>2015-02-19</td>\n",
       "      <td>601.000</td>\n",
       "      <td>641.000</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>2015-02-19</td>\n",
       "      <td>2015-03-26</td>\n",
       "      <td>641.000</td>\n",
       "      <td>947.000</td>\n",
       "      <td>306</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>15.469</td>\n",
       "      <td>15.726</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>15.726</td>\n",
       "      <td>15.996</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>15.996</td>\n",
       "      <td>16.298</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>16.298</td>\n",
       "      <td>16.642</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>16.642</td>\n",
       "      <td>17.002</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     period previous_check current_check  previous_mark  current_mark  \\\n",
       "0   2014-11     2014-10-24    2014-11-26         73.000       223.000   \n",
       "1   2014-12     2014-11-26    2014-12-26        223.000       411.000   \n",
       "2   2015-01     2014-12-26    2015-01-26        411.000       601.000   \n",
       "3   2015-02     2015-01-26    2015-02-19        601.000       641.000   \n",
       "4   2015-03     2015-02-19    2015-03-26        641.000       947.000   \n",
       "..      ...            ...           ...            ...           ...   \n",
       "63  2020-02     2020-01-27    2020-02-27         15.469        15.726   \n",
       "64  2020-03     2020-02-27    2020-03-27         15.726        15.996   \n",
       "65  2020-04     2020-03-27    2020-04-29         15.996        16.298   \n",
       "66  2020-05     2020-04-29    2020-05-27         16.298        16.642   \n",
       "67  2020-06     2020-05-27    2020-06-26         16.642        17.002   \n",
       "\n",
       "    consumption_kwh  \n",
       "0               150  \n",
       "1               188  \n",
       "2               193  \n",
       "3                37  \n",
       "4               306  \n",
       "..              ...  \n",
       "63              257  \n",
       "64              270  \n",
       "65              302  \n",
       "66              344  \n",
       "67              360  \n",
       "\n",
       "[68 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert string date to datetime format\n",
    "df_electr['previous_check'] = pd.to_datetime(df_electr['previous_check'], format='%d/%m/%Y')\n",
    "df_electr['current_check'] = pd.to_datetime(df_electr['current_check'], format='%d/%m/%Y')\n",
    "\n",
    "df_electr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert columns data type to ```datetime```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "period                     object\n",
       "previous_check     datetime64[ns]\n",
       "current_check      datetime64[ns]\n",
       "previous_mark             float64\n",
       "current_mark              float64\n",
       "consumption_kwh             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert columns data type to datetime\n",
    "df_electr = df_electr.astype({'previous_check': 'datetime64', 'current_check': 'datetime64'})\n",
    "\n",
    "df_electr.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column *period* contains the year and month information in ```string``` format **YYYY-MM**. The information will be split into new columns *year* and *month*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>period</th>\n",
       "      <th>previous_check</th>\n",
       "      <th>current_check</th>\n",
       "      <th>previous_mark</th>\n",
       "      <th>current_mark</th>\n",
       "      <th>consumption_kwh</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>2014-10-24</td>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>73.000</td>\n",
       "      <td>223.000</td>\n",
       "      <td>150</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>2014-12-26</td>\n",
       "      <td>223.000</td>\n",
       "      <td>411.000</td>\n",
       "      <td>188</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2014-12-26</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>411.000</td>\n",
       "      <td>601.000</td>\n",
       "      <td>193</td>\n",
       "      <td>2015</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>2015-02-19</td>\n",
       "      <td>601.000</td>\n",
       "      <td>641.000</td>\n",
       "      <td>37</td>\n",
       "      <td>2015</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>2015-02-19</td>\n",
       "      <td>2015-03-26</td>\n",
       "      <td>641.000</td>\n",
       "      <td>947.000</td>\n",
       "      <td>306</td>\n",
       "      <td>2015</td>\n",
       "      <td>03</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>63</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>15.469</td>\n",
       "      <td>15.726</td>\n",
       "      <td>257</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>15.726</td>\n",
       "      <td>15.996</td>\n",
       "      <td>270</td>\n",
       "      <td>2020</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>15.996</td>\n",
       "      <td>16.298</td>\n",
       "      <td>302</td>\n",
       "      <td>2020</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>16.298</td>\n",
       "      <td>16.642</td>\n",
       "      <td>344</td>\n",
       "      <td>2020</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>16.642</td>\n",
       "      <td>17.002</td>\n",
       "      <td>360</td>\n",
       "      <td>2020</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     period previous_check current_check  previous_mark  current_mark  \\\n",
       "0   2014-11     2014-10-24    2014-11-26         73.000       223.000   \n",
       "1   2014-12     2014-11-26    2014-12-26        223.000       411.000   \n",
       "2   2015-01     2014-12-26    2015-01-26        411.000       601.000   \n",
       "3   2015-02     2015-01-26    2015-02-19        601.000       641.000   \n",
       "4   2015-03     2015-02-19    2015-03-26        641.000       947.000   \n",
       "..      ...            ...           ...            ...           ...   \n",
       "63  2020-02     2020-01-27    2020-02-27         15.469        15.726   \n",
       "64  2020-03     2020-02-27    2020-03-27         15.726        15.996   \n",
       "65  2020-04     2020-03-27    2020-04-29         15.996        16.298   \n",
       "66  2020-05     2020-04-29    2020-05-27         16.298        16.642   \n",
       "67  2020-06     2020-05-27    2020-06-26         16.642        17.002   \n",
       "\n",
       "    consumption_kwh  year month  \n",
       "0               150  2014    11  \n",
       "1               188  2014    12  \n",
       "2               193  2015    01  \n",
       "3                37  2015    02  \n",
       "4               306  2015    03  \n",
       "..              ...   ...   ...  \n",
       "63              257  2020    02  \n",
       "64              270  2020    03  \n",
       "65              302  2020    04  \n",
       "66              344  2020    05  \n",
       "67              360  2020    06  \n",
       "\n",
       "[68 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new columns year and month from column period\n",
    "df_electr['year'] = df_electr['period'].str.slice(start=0, stop=4)\n",
    "df_electr['month'] = df_electr['period'].str.slice(start=5, stop=7)\n",
    "\n",
    "df_electr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The checking day varies along the year, and the time in days between the checks is not always the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33, 30, 31, 24, 35, 28, 29, 32])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check differencies in days between checks\n",
    "(df_electr['current_check'] - df_electr['previous_check']).dt.days.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The time difference between checks varies from 24 to 35 days, and it has impact in consumption measure and billing. To estimate a balanced consumption measure three new columns will be created.\n",
    "\n",
    "* **consumption_days**: time in days between readings $$\\left( {current{\\_}check}-{previous{\\_}check} \\right)$$\n",
    "* **consumption_day_kwh**: calculated electricity consumption per day $$\\left( \\frac{consumption{\\_}kwh}{consumption{\\_}days} \\right)$$\n",
    "* **consumption_30d_kwh**: estimated electricity consumption in 30 days $$\\left( {consumption{\\_}day{\\_}kwh} \\times {30} \\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>period</th>\n",
       "      <th>previous_check</th>\n",
       "      <th>current_check</th>\n",
       "      <th>previous_mark</th>\n",
       "      <th>current_mark</th>\n",
       "      <th>consumption_kwh</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>consumption_days</th>\n",
       "      <th>consumption_day_kwh</th>\n",
       "      <th>consumption_30d_kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>2014-10-24</td>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>73.000</td>\n",
       "      <td>223.000</td>\n",
       "      <td>150</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>4.545</td>\n",
       "      <td>136.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>2014-12-26</td>\n",
       "      <td>223.000</td>\n",
       "      <td>411.000</td>\n",
       "      <td>188</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>6.267</td>\n",
       "      <td>188.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2014-12-26</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>411.000</td>\n",
       "      <td>601.000</td>\n",
       "      <td>193</td>\n",
       "      <td>2015</td>\n",
       "      <td>01</td>\n",
       "      <td>31</td>\n",
       "      <td>6.226</td>\n",
       "      <td>186.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>2015-02-19</td>\n",
       "      <td>601.000</td>\n",
       "      <td>641.000</td>\n",
       "      <td>37</td>\n",
       "      <td>2015</td>\n",
       "      <td>02</td>\n",
       "      <td>24</td>\n",
       "      <td>1.542</td>\n",
       "      <td>46.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>2015-02-19</td>\n",
       "      <td>2015-03-26</td>\n",
       "      <td>641.000</td>\n",
       "      <td>947.000</td>\n",
       "      <td>306</td>\n",
       "      <td>2015</td>\n",
       "      <td>03</td>\n",
       "      <td>35</td>\n",
       "      <td>8.743</td>\n",
       "      <td>262.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>15.469</td>\n",
       "      <td>15.726</td>\n",
       "      <td>257</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>31</td>\n",
       "      <td>8.290</td>\n",
       "      <td>248.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>15.726</td>\n",
       "      <td>15.996</td>\n",
       "      <td>270</td>\n",
       "      <td>2020</td>\n",
       "      <td>03</td>\n",
       "      <td>29</td>\n",
       "      <td>9.310</td>\n",
       "      <td>279.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>15.996</td>\n",
       "      <td>16.298</td>\n",
       "      <td>302</td>\n",
       "      <td>2020</td>\n",
       "      <td>04</td>\n",
       "      <td>33</td>\n",
       "      <td>9.152</td>\n",
       "      <td>274.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>16.298</td>\n",
       "      <td>16.642</td>\n",
       "      <td>344</td>\n",
       "      <td>2020</td>\n",
       "      <td>05</td>\n",
       "      <td>28</td>\n",
       "      <td>12.286</td>\n",
       "      <td>368.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>16.642</td>\n",
       "      <td>17.002</td>\n",
       "      <td>360</td>\n",
       "      <td>2020</td>\n",
       "      <td>06</td>\n",
       "      <td>30</td>\n",
       "      <td>12.000</td>\n",
       "      <td>360.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     period previous_check current_check  previous_mark  current_mark  \\\n",
       "0   2014-11     2014-10-24    2014-11-26         73.000       223.000   \n",
       "1   2014-12     2014-11-26    2014-12-26        223.000       411.000   \n",
       "2   2015-01     2014-12-26    2015-01-26        411.000       601.000   \n",
       "3   2015-02     2015-01-26    2015-02-19        601.000       641.000   \n",
       "4   2015-03     2015-02-19    2015-03-26        641.000       947.000   \n",
       "..      ...            ...           ...            ...           ...   \n",
       "63  2020-02     2020-01-27    2020-02-27         15.469        15.726   \n",
       "64  2020-03     2020-02-27    2020-03-27         15.726        15.996   \n",
       "65  2020-04     2020-03-27    2020-04-29         15.996        16.298   \n",
       "66  2020-05     2020-04-29    2020-05-27         16.298        16.642   \n",
       "67  2020-06     2020-05-27    2020-06-26         16.642        17.002   \n",
       "\n",
       "    consumption_kwh  year month  consumption_days  consumption_day_kwh  \\\n",
       "0               150  2014    11                33                4.545   \n",
       "1               188  2014    12                30                6.267   \n",
       "2               193  2015    01                31                6.226   \n",
       "3                37  2015    02                24                1.542   \n",
       "4               306  2015    03                35                8.743   \n",
       "..              ...   ...   ...               ...                  ...   \n",
       "63              257  2020    02                31                8.290   \n",
       "64              270  2020    03                29                9.310   \n",
       "65              302  2020    04                33                9.152   \n",
       "66              344  2020    05                28               12.286   \n",
       "67              360  2020    06                30               12.000   \n",
       "\n",
       "    consumption_30d_kwh  \n",
       "0                136.35  \n",
       "1                188.01  \n",
       "2                186.78  \n",
       "3                 46.26  \n",
       "4                262.29  \n",
       "..                  ...  \n",
       "63               248.70  \n",
       "64               279.30  \n",
       "65               274.56  \n",
       "66               368.58  \n",
       "67               360.00  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new columns\n",
    "df_electr['consumption_days'] = (df_electr['current_check'] - df_electr['previous_check']).dt.days\n",
    "df_electr['consumption_day_kwh'] = round(df_electr['consumption_kwh'] / df_electr['consumption_days'], 3)\n",
    "df_electr['consumption_30d_kwh'] = round(df_electr['consumption_day_kwh'] * 30, 3)\n",
    "\n",
    "df_electr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep only relevant columns and reorder dataframe columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>consumption_kwh</th>\n",
       "      <th>consumption_days</th>\n",
       "      <th>consumption_day_kwh</th>\n",
       "      <th>consumption_30d_kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>150</td>\n",
       "      <td>33</td>\n",
       "      <td>4.545</td>\n",
       "      <td>136.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>188</td>\n",
       "      <td>30</td>\n",
       "      <td>6.267</td>\n",
       "      <td>188.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>01</td>\n",
       "      <td>193</td>\n",
       "      <td>31</td>\n",
       "      <td>6.226</td>\n",
       "      <td>186.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>2015</td>\n",
       "      <td>02</td>\n",
       "      <td>37</td>\n",
       "      <td>24</td>\n",
       "      <td>1.542</td>\n",
       "      <td>46.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>2015</td>\n",
       "      <td>03</td>\n",
       "      <td>306</td>\n",
       "      <td>35</td>\n",
       "      <td>8.743</td>\n",
       "      <td>262.29</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>257</td>\n",
       "      <td>31</td>\n",
       "      <td>8.290</td>\n",
       "      <td>248.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020</td>\n",
       "      <td>03</td>\n",
       "      <td>270</td>\n",
       "      <td>29</td>\n",
       "      <td>9.310</td>\n",
       "      <td>279.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>2020</td>\n",
       "      <td>04</td>\n",
       "      <td>302</td>\n",
       "      <td>33</td>\n",
       "      <td>9.152</td>\n",
       "      <td>274.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>2020</td>\n",
       "      <td>05</td>\n",
       "      <td>344</td>\n",
       "      <td>28</td>\n",
       "      <td>12.286</td>\n",
       "      <td>368.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>2020</td>\n",
       "      <td>06</td>\n",
       "      <td>360</td>\n",
       "      <td>30</td>\n",
       "      <td>12.000</td>\n",
       "      <td>360.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     period  year month  consumption_kwh  consumption_days  \\\n",
       "0   2014-11  2014    11              150                33   \n",
       "1   2014-12  2014    12              188                30   \n",
       "2   2015-01  2015    01              193                31   \n",
       "3   2015-02  2015    02               37                24   \n",
       "4   2015-03  2015    03              306                35   \n",
       "..      ...   ...   ...              ...               ...   \n",
       "63  2020-02  2020    02              257                31   \n",
       "64  2020-03  2020    03              270                29   \n",
       "65  2020-04  2020    04              302                33   \n",
       "66  2020-05  2020    05              344                28   \n",
       "67  2020-06  2020    06              360                30   \n",
       "\n",
       "    consumption_day_kwh  consumption_30d_kwh  \n",
       "0                 4.545               136.35  \n",
       "1                 6.267               188.01  \n",
       "2                 6.226               186.78  \n",
       "3                 1.542                46.26  \n",
       "4                 8.743               262.29  \n",
       "..                  ...                  ...  \n",
       "63                8.290               248.70  \n",
       "64                9.310               279.30  \n",
       "65                9.152               274.56  \n",
       "66               12.286               368.58  \n",
       "67               12.000               360.00  \n",
       "\n",
       "[68 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define relevant and columns order\n",
    "columns = ['period','year','month','consumption_kwh','consumption_days','consumption_day_kwh','consumption_30d_kwh']\n",
    "\n",
    "# overwrite dataframe only with relevant columns\n",
    "df_electr = df_electr[columns]\n",
    "\n",
    "df_electr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check dataframe statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>consumption_kwh</th>\n",
       "      <th>consumption_days</th>\n",
       "      <th>consumption_day_kwh</th>\n",
       "      <th>consumption_30d_kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>248.955882</td>\n",
       "      <td>30.470588</td>\n",
       "      <td>8.149162</td>\n",
       "      <td>244.474853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.058868</td>\n",
       "      <td>1.888461</td>\n",
       "      <td>1.802550</td>\n",
       "      <td>54.076489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.542000</td>\n",
       "      <td>46.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>217.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>7.245750</td>\n",
       "      <td>217.372500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>247.500000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>8.233000</td>\n",
       "      <td>246.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>285.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>9.155750</td>\n",
       "      <td>274.672500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>360.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>12.286000</td>\n",
       "      <td>368.580000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       consumption_kwh  consumption_days  consumption_day_kwh  \\\n",
       "count        68.000000         68.000000            68.000000   \n",
       "mean        248.955882         30.470588             8.149162   \n",
       "std          57.058868          1.888461             1.802550   \n",
       "min          37.000000         24.000000             1.542000   \n",
       "25%         217.000000         29.000000             7.245750   \n",
       "50%         247.500000         30.000000             8.233000   \n",
       "75%         285.500000         32.000000             9.155750   \n",
       "max         360.000000         35.000000            12.286000   \n",
       "\n",
       "       consumption_30d_kwh  \n",
       "count            68.000000  \n",
       "mean            244.474853  \n",
       "std              54.076489  \n",
       "min              46.260000  \n",
       "25%             217.372500  \n",
       "50%             246.990000  \n",
       "75%             274.672500  \n",
       "max             368.580000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dataframe statistics\n",
    "df_electr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a large range between minimum and maximum *consumption_kwh*, interquartile range is also large and standard deviation is quite high, it is a larger number than minimum. It seems that electricity consumption is very irregular, the calculated *consumption_day_kwh* shows the same behaviour. The estimated *consumption_30d_kwh_* is slighly below the *consumption_kwh*, but its minimum is higher.\n",
    "\n",
    "Compare the consumption frequencies using histograms, one for column *consumption_kwh* and another for column *consumption_30d_kwh*. For better comparison, histograms should have the same ```x-ticks``` (bins) and ```y-ticks``` (frequencies).\n",
    "\n",
    "Define bins for histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define bins (x-ticks)\n",
    "cnt, bins = np.histogram(df_electr['consumption_kwh'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define plot style\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "# define axis label size\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=12)\n",
    "\n",
    "# create the plot figure\n",
    "fig = plt.figure(figsize=(18,6))\n",
    "\n",
    "# create consumption histogram (left)\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "ax.hist(df_electr['consumption_kwh'], bins=bins, color='g', alpha=0.6)\n",
    "ax.set_title('Histograma Consumo de Eletricidade\\nHistogram Electricity Consumption\\n' + \\\n",
    "             min(df_electr['year']) + ' - ' + max(df_electr['year']), {'fontsize':13}, fontweight=900)\n",
    "\n",
    "# get y-ticks from histogram\n",
    "h1_locs, h1_labels = plt.yticks()\n",
    "\n",
    "# create 30 days consumption histogram (right)\n",
    "ax = fig.add_subplot(1,2,2)\n",
    "ax.hist(df_electr['consumption_30d_kwh'], bins=bins, color='g', alpha=0.4)\n",
    "ax.set_title('Histograma Consumo de Eletricidade 30 dias\\nHistogram Electricity Consumption 30 days\\n' + \\\n",
    "             min(df_electr['year']) + ' - ' + max(df_electr['year']), {'fontsize':13}, fontweight=900)\n",
    "\n",
    "# get y-ticks from histogram\n",
    "h2_locs, h2_labels = plt.yticks()\n",
    "\n",
    "# take y-ticks with highest frequency\n",
    "if max(h1_locs) > max(h2_locs):\n",
    "    yticks_ = h1_locs\n",
    "else:\n",
    "    yticks_ = h2_locs\n",
    "\n",
    "# loop to define similar settings for plots\n",
    "for a in fig.axes:\n",
    "    # set y-ticks and labels\n",
    "    a.set_yticks(yticks_)\n",
    "    a.set_ylabel('frequência / frequency')\n",
    "    a.set_xlabel('kwh')\n",
    "\n",
    "#save to file\n",
    "plt.savefig('./Images/Electr_Plot_01_Hist_Consump.png', bbox_inches='tight')\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the consumption dynamics along the period with line plots.\n",
    "\n",
    "Calculated the 12 months mean from column *consumption_kwh*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create empty list\n",
    "mean12m = []\n",
    "\n",
    "# calculate mean for last 12 observations of consumption_kwh\n",
    "for i in range(0, df_electr.shape[0]):\n",
    "    if i < 11:\n",
    "        mean12m.append(round(np.mean(df_electr.loc[:i,'consumption_kwh']),3))\n",
    "    else:\n",
    "        mean12m.append(round(np.mean(df_electr.loc[i-11:i,'consumption_kwh']),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define customized x-labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a variable to check for the first observation\n",
    "i = True\n",
    "\n",
    "# define an empty list\n",
    "xlabels_ = []\n",
    "\n",
    "# customize labels having the year only for the first onbservation and for each january\n",
    "for y, m in zip(df_electr['year'], df_electr['month']):\n",
    "    if i == True or m == '01':\n",
    "        xlabels_.append(y+'-'+m)\n",
    "        i = False\n",
    "    else:\n",
    "        xlabels_.append(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the plot figure\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "\n",
    "# plot consumption_kwh, consumption_30d_kwh and mean12m\n",
    "ax.plot(df_electr['period'], df_electr['consumption_kwh'], color='g', alpha=0.9,\n",
    "        label='Consumo\\nConsumption')\n",
    "ax.plot(df_electr['period'], df_electr['consumption_30d_kwh'], color='g', alpha=0.4, \n",
    "        label='Consumo base 30 dias\\nConsumption 30 days basis')\n",
    "ax.plot(df_electr['period'], mean12m, color='r', alpha=0.9, \n",
    "        label='Média 12 meses\\nMean 12 months')\n",
    "\n",
    "# define plot title and axis labels\n",
    "ax.set_title('Consumo Mensal de Eletricidade\\nElectricity Monthly Consumption\\n' \\\n",
    "             + min(df_electr['year']) + ' - ' + max(df_electr['year']), {'fontsize':14}, fontweight=900)\n",
    "ax.set_ylabel('kwh')\n",
    "ax.set_xlabel('período / period')\n",
    "\n",
    "# define legend\n",
    "ax.legend(loc=4, frameon=True, facecolor='white', framealpha=1)\n",
    "\n",
    "# define x-ticks\n",
    "plt.xticks(np.arange(0, len(xlabels_), 1), xlabels_, rotation=90)\n",
    "\n",
    "#save to file\n",
    "plt.savefig('./Images/Electr_Plot_02_Line_ConsumpMonth.png', bbox_inches='tight')\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Electricty consumption has increased from 2014 until middle of 2017, and after a stable period it is in icreasing curve in 2020.\n",
    "\n",
    "Analyse the consumption mean per month with bar plot, comparing *consumption_kwh* and *consumption_30d_kwh*.\n",
    "\n",
    "Create a dataframe grouped by month calculating the mean for required columns, and calculate the overall mean and standard deviation only for the *consumption_khw*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall mean for column consumption_kwh: 249.603\n",
      "Overall std for column consumption_kwh: 34.520\n",
      "\n"
     ]
    },
    {
     "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>month</th>\n",
       "      <th>consumption_kwh</th>\n",
       "      <th>consumption_30d_kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>176.000</td>\n",
       "      <td>177.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>200.833</td>\n",
       "      <td>198.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03</td>\n",
       "      <td>257.833</td>\n",
       "      <td>248.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04</td>\n",
       "      <td>254.333</td>\n",
       "      <td>242.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>269.167</td>\n",
       "      <td>280.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06</td>\n",
       "      <td>306.167</td>\n",
       "      <td>297.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07</td>\n",
       "      <td>276.600</td>\n",
       "      <td>286.146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08</td>\n",
       "      <td>287.200</td>\n",
       "      <td>271.302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09</td>\n",
       "      <td>248.200</td>\n",
       "      <td>241.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>230.400</td>\n",
       "      <td>226.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>255.667</td>\n",
       "      <td>238.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>232.833</td>\n",
       "      <td>233.380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  consumption_kwh  consumption_30d_kwh\n",
       "0     01          176.000              177.760\n",
       "1     02          200.833              198.120\n",
       "2     03          257.833              248.100\n",
       "3     04          254.333              242.830\n",
       "4     05          269.167              280.665\n",
       "5     06          306.167              297.235\n",
       "6     07          276.600              286.146\n",
       "7     08          287.200              271.302\n",
       "8     09          248.200              241.380\n",
       "9     10          230.400              226.356\n",
       "10    11          255.667              238.305\n",
       "11    12          232.833              233.380"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe with mean per month\n",
    "df_mean = df_electr[['month','consumption_kwh','consumption_30d_kwh']].groupby('month').mean().reset_index()\n",
    "\n",
    "# round values to 3 decimal positions\n",
    "df_mean['consumption_kwh'] = df_mean['consumption_kwh'].apply(lambda v: round(v,3))\n",
    "df_mean['consumption_30d_kwh'] = df_mean['consumption_30d_kwh'].apply(lambda v: round(v,3))\n",
    "\n",
    "# calculate overall mean and std for consumption_kwh\n",
    "meanconsum = round(np.mean(df_mean['consumption_kwh']),3)\n",
    "stdconsum = round(np.std(df_mean['consumption_kwh']),3)\n",
    "\n",
    "# show results\n",
    "print('Overall mean for column consumption_kwh: {:.3f}\\nOverall std for column consumption_kwh: {:.3f}\\n'.\n",
    "      format(meanconsum, stdconsum))\n",
    "\n",
    "df_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define a list for months\n",
    "months = ['Jan / Jan','Fev / Feb','Mar / Mar','Abr / Apr','Mai / May','Jun / Jun',\n",
    "          'Jul / Jul','Ago / Aug','Set / Sep','Out / Oct','Nov / Nov','Dez / Dec']\n",
    "\n",
    "# redefine axis label size\n",
    "plt.rc('xtick', labelsize=12)\n",
    "plt.rc('ytick', labelsize=12)\n",
    "\n",
    "# create the plot figure\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "\n",
    "# define x-labels locations and bars width\n",
    "x = np.arange(df_mean.shape[0])\n",
    "width = 0.4\n",
    "\n",
    "# plot mean consumption_kwh, mean consumption_30d_kwh, overall mean and std\n",
    "ax.bar(x - width/2, df_mean['consumption_kwh'], width, color='g', alpha=0.7, \n",
    "       label='Consumo médio anual\\nMean consumption')\n",
    "ax.bar(x + width/2, df_mean['consumption_30d_kwh'], width, color='g', alpha=0.4, \n",
    "       label='Consumo médio base 30 dias\\nMean consumption 30 days basis')\n",
    "ax.plot(df_mean['month'], np.repeat(meanconsum, df_mean.shape[0]), color='r', alpha=0.9, \n",
    "        label='Consumo médio geral\\nOverall mean consumption')\n",
    "ax.fill_between(df_mean['month'], meanconsum-stdconsum, meanconsum+stdconsum, alpha=0.2, \n",
    "                label='Desvio padrão\\nStandard deviation')\n",
    "\n",
    "# define x-ticks\n",
    "plt.xticks(df_mean['month'], months)\n",
    "\n",
    "# define legend\n",
    "ax.legend(loc=4, frameon=True, facecolor='white', framealpha=1)\n",
    "\n",
    "# define plot title and axis labels\n",
    "plt.title('Consumo Médio por Mês\\nMean Consumption per Month\\n' + \n",
    "          min(df_electr['year']) + ' - ' + max(df_electr['year']), {'fontsize':14}, fontweight=900)\n",
    "plt.ylabel('kwh')\n",
    "plt.xlabel('mês / month')\n",
    "\n",
    "#save to file\n",
    "plt.savefig('./Images/Electr_Plot_03_Bar_ConsumpMonthMean.png', bbox_inches='tight')\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further analysis only columns *period*, converted to ```datetime``` format, and *consumption_day_kwh* are required. A new dataframe will be created having only required columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>period</th>\n",
       "      <th>electr_day_kwh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>4.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>6.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>6.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>1.542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>8.743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>8.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>9.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>9.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>12.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>12.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       period  electr_day_kwh\n",
       "0  2014-11-01           4.545\n",
       "1  2014-12-01           6.267\n",
       "2  2015-01-01           6.226\n",
       "3  2015-02-01           1.542\n",
       "4  2015-03-01           8.743\n",
       "..        ...             ...\n",
       "63 2020-02-01           8.290\n",
       "64 2020-03-01           9.310\n",
       "65 2020-04-01           9.152\n",
       "66 2020-05-01          12.286\n",
       "67 2020-06-01          12.000\n",
       "\n",
       "[68 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new dataframe\n",
    "features_electricity = df_electr[['period','consumption_day_kwh']].copy()\n",
    "\n",
    "# convert period to datetime format\n",
    "features_electricity['period'] = pd.to_datetime(features_electricity['period'], format='%Y-%m')\n",
    "\n",
    "# rename column consumption_day_kwh\n",
    "features_electricity = features_electricity.rename(columns={'consumption_day_kwh': 'electr_day_kwh'})\n",
    "\n",
    "features_electricity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export dataframe to local file in ```feather``` format to be retrieved later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export to feather file format\n",
    "features_electricity.to_feather('./Data_source/Features_Electricity.feather')"
   ]
  }
 ],
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