Water_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utilities Consumption Study\n",
    "# Water 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 water consumption data was the author monthly bills, which are based on water company monthly reading from the hydrometer. 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```| Hydrometer indicator mark of previous consumption reading|\n",
    "|5|**current_mark**| ```integer```| Hydrometer indicator mark of current consumption reading|\n",
    "|6|**consumption_kwh**| ```integer``` | Water consumption in cubic meter (m<sup>3</sup>), difference between *current_mark* and *previous_mark*|\n",
    "\n",
    "Read data from ```CSV``` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <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_m3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>29/05/2020</td>\n",
       "      <td>30/06/2020</td>\n",
       "      <td>258</td>\n",
       "      <td>270</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>30/04/2020</td>\n",
       "      <td>29/05/2020</td>\n",
       "      <td>247</td>\n",
       "      <td>258</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>30/04/2020</td>\n",
       "      <td>235</td>\n",
       "      <td>247</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>28/02/2020</td>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>221</td>\n",
       "      <td>235</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>30/01/2020</td>\n",
       "      <td>28/02/2020</td>\n",
       "      <td>215</td>\n",
       "      <td>221</td>\n",
       "      <td>6</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>31/01/2015</td>\n",
       "      <td>04/03/2015</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>30/12/2014</td>\n",
       "      <td>31/01/2015</td>\n",
       "      <td>51</td>\n",
       "      <td>69</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>02/12/2014</td>\n",
       "      <td>30/12/2014</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>30/10/2014</td>\n",
       "      <td>02/12/2014</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2014-10</td>\n",
       "      <td>30/09/2014</td>\n",
       "      <td>30/10/2014</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>1</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          29/05/2020         30/06/2020            258   \n",
       "1   2020-05          30/04/2020         29/05/2020            247   \n",
       "2   2020-04          30/03/2020         30/04/2020            235   \n",
       "3   2020-03          28/02/2020         30/03/2020            221   \n",
       "4   2020-02          30/01/2020         28/02/2020            215   \n",
       "..      ...                 ...                ...            ...   \n",
       "64  2015-02          31/01/2015         04/03/2015             69   \n",
       "65  2015-01          30/12/2014         31/01/2015             51   \n",
       "66  2014-12          02/12/2014         30/12/2014             35   \n",
       "67  2014-11          30/10/2014         02/12/2014             21   \n",
       "68  2014-10          30/09/2014         30/10/2014             20   \n",
       "\n",
       "    current_mark  consumption_m3  \n",
       "0            270              12  \n",
       "1            258              11  \n",
       "2            247              12  \n",
       "3            235              14  \n",
       "4            221               6  \n",
       "..           ...             ...  \n",
       "64            81              12  \n",
       "65            69              18  \n",
       "66            51              16  \n",
       "67            35              14  \n",
       "68            21               1  \n",
       "\n",
       "[69 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read CSV file from local folder\n",
    "df_water = pd.read_csv('./Data_source/Data_Water.csv', sep=';', header=0)\n",
    "df_water"
   ]
  },
  {
   "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_m3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>30/10/2014</td>\n",
       "      <td>02/12/2014</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>02/12/2014</td>\n",
       "      <td>30/12/2014</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>30/12/2014</td>\n",
       "      <td>31/01/2015</td>\n",
       "      <td>51</td>\n",
       "      <td>69</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>31/01/2015</td>\n",
       "      <td>04/03/2015</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>04/03/2015</td>\n",
       "      <td>02/04/2015</td>\n",
       "      <td>81</td>\n",
       "      <td>97</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>30/01/2020</td>\n",
       "      <td>28/02/2020</td>\n",
       "      <td>215</td>\n",
       "      <td>221</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>28/02/2020</td>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>221</td>\n",
       "      <td>235</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>30/04/2020</td>\n",
       "      <td>235</td>\n",
       "      <td>247</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>30/04/2020</td>\n",
       "      <td>29/05/2020</td>\n",
       "      <td>247</td>\n",
       "      <td>258</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>29/05/2020</td>\n",
       "      <td>30/06/2020</td>\n",
       "      <td>258</td>\n",
       "      <td>270</td>\n",
       "      <td>12</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     30/10/2014    02/12/2014             21            35   \n",
       "1   2014-12     02/12/2014    30/12/2014             35            51   \n",
       "2   2015-01     30/12/2014    31/01/2015             51            69   \n",
       "3   2015-02     31/01/2015    04/03/2015             69            81   \n",
       "4   2015-03     04/03/2015    02/04/2015             81            97   \n",
       "..      ...            ...           ...            ...           ...   \n",
       "63  2020-02     30/01/2020    28/02/2020            215           221   \n",
       "64  2020-03     28/02/2020    30/03/2020            221           235   \n",
       "65  2020-04     30/03/2020    30/04/2020            235           247   \n",
       "66  2020-05     30/04/2020    29/05/2020            247           258   \n",
       "67  2020-06     29/05/2020    30/06/2020            258           270   \n",
       "\n",
       "    consumption_m3  \n",
       "0               14  \n",
       "1               16  \n",
       "2               18  \n",
       "3               12  \n",
       "4               16  \n",
       "..             ...  \n",
       "63               6  \n",
       "64              14  \n",
       "65              12  \n",
       "66              11  \n",
       "67              12  \n",
       "\n",
       "[68 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copy dataframe exlcuding last observation\n",
    "df_water = df_water[:-1]\n",
    "\n",
    "# sort dataframe in chronological ascending order\n",
    "df_water = df_water.sort_values(by='period', ignore_index=True)\n",
    "\n",
    "# rename columns\n",
    "df_water = df_water.rename(columns={'previous_check_date': 'previous_check',\n",
    "                                    'current_check_date': 'current_check'})\n",
    "df_water"
   ]
  },
  {
   "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      int64\n",
       "current_mark       int64\n",
       "consumption_m3     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dataframe colunms data types\n",
    "df_water.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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       "  <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_m3</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2014-12-02</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014-12-02</td>\n",
       "      <td>2014-12-30</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2014-12-30</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>51</td>\n",
       "      <td>69</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>2015-04-02</td>\n",
       "      <td>81</td>\n",
       "      <td>97</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <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>2020-01-30</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>215</td>\n",
       "      <td>221</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>221</td>\n",
       "      <td>235</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>235</td>\n",
       "      <td>247</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>247</td>\n",
       "      <td>258</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>258</td>\n",
       "      <td>270</td>\n",
       "      <td>12</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     2014-10-30    2014-12-02             21            35   \n",
       "1   2014-12     2014-12-02    2014-12-30             35            51   \n",
       "2   2015-01     2014-12-30    2015-01-31             51            69   \n",
       "3   2015-02     2015-01-31    2015-03-04             69            81   \n",
       "4   2015-03     2015-03-04    2015-04-02             81            97   \n",
       "..      ...            ...           ...            ...           ...   \n",
       "63  2020-02     2020-01-30    2020-02-28            215           221   \n",
       "64  2020-03     2020-02-28    2020-03-30            221           235   \n",
       "65  2020-04     2020-03-30    2020-04-30            235           247   \n",
       "66  2020-05     2020-04-30    2020-05-29            247           258   \n",
       "67  2020-06     2020-05-29    2020-06-30            258           270   \n",
       "\n",
       "    consumption_m3  \n",
       "0               14  \n",
       "1               16  \n",
       "2               18  \n",
       "3               12  \n",
       "4               16  \n",
       "..             ...  \n",
       "63               6  \n",
       "64              14  \n",
       "65              12  \n",
       "66              11  \n",
       "67              12  \n",
       "\n",
       "[68 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert string date to datetime format\n",
    "df_water['previous_check'] = pd.to_datetime(df_water['previous_check'], format='%d/%m/%Y')\n",
    "df_water['current_check'] = pd.to_datetime(df_water['current_check'], format='%d/%m/%Y')\n",
    "\n",
    "df_water"
   ]
  },
  {
   "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              int64\n",
       "current_mark               int64\n",
       "consumption_m3             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert columns data type to datetime\n",
    "df_water = df_water.astype({'previous_check': 'datetime64', 'current_check': 'datetime64'})\n",
    "\n",
    "df_water.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",
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       "\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_m3</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>2014-10-30</td>\n",
       "      <td>2014-12-02</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014-12-02</td>\n",
       "      <td>2014-12-30</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2014-12-30</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>51</td>\n",
       "      <td>69</td>\n",
       "      <td>18</td>\n",
       "      <td>2015</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>2015-04-02</td>\n",
       "      <td>81</td>\n",
       "      <td>97</td>\n",
       "      <td>16</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>2020-01-30</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>215</td>\n",
       "      <td>221</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>221</td>\n",
       "      <td>235</td>\n",
       "      <td>14</td>\n",
       "      <td>2020</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>235</td>\n",
       "      <td>247</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>247</td>\n",
       "      <td>258</td>\n",
       "      <td>11</td>\n",
       "      <td>2020</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>258</td>\n",
       "      <td>270</td>\n",
       "      <td>12</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     2014-10-30    2014-12-02             21            35   \n",
       "1   2014-12     2014-12-02    2014-12-30             35            51   \n",
       "2   2015-01     2014-12-30    2015-01-31             51            69   \n",
       "3   2015-02     2015-01-31    2015-03-04             69            81   \n",
       "4   2015-03     2015-03-04    2015-04-02             81            97   \n",
       "..      ...            ...           ...            ...           ...   \n",
       "63  2020-02     2020-01-30    2020-02-28            215           221   \n",
       "64  2020-03     2020-02-28    2020-03-30            221           235   \n",
       "65  2020-04     2020-03-30    2020-04-30            235           247   \n",
       "66  2020-05     2020-04-30    2020-05-29            247           258   \n",
       "67  2020-06     2020-05-29    2020-06-30            258           270   \n",
       "\n",
       "    consumption_m3  year month  \n",
       "0               14  2014    11  \n",
       "1               16  2014    12  \n",
       "2               18  2015    01  \n",
       "3               12  2015    02  \n",
       "4               16  2015    03  \n",
       "..             ...   ...   ...  \n",
       "63               6  2020    02  \n",
       "64              14  2020    03  \n",
       "65              12  2020    04  \n",
       "66              11  2020    05  \n",
       "67              12  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_water['year'] = df_water['period'].str.slice(start=0, stop=4)\n",
    "df_water['month'] = df_water['period'].str.slice(start=5, stop=7)\n",
    "\n",
    "df_water"
   ]
  },
  {
   "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, 28, 32, 29, 30, 31, 34])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check differencies in days between checks\n",
    "(df_water['current_check'] - df_water['previous_check']).dt.days.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The time difference between readings varies from 28 to 34 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 checks $$\\left( {current{\\_}check}-{previous{\\_}check} \\right)$$\n",
    "* **consumption_day_m3**: calculated water consumption per day $$\\left( \\frac{consumption{\\_}m3}{consumption{\\_}days} \\right)$$\n",
    "* **consumption_30d_m3**: estimated water consumption in 30 days $$\\left( {consumption{\\_}day{\\_}m3} \\times {30} \\right)$$"
   ]
  },
  {
   "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",
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       "\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_m3</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>consumption_days</th>\n",
       "      <th>consumption_day_m3</th>\n",
       "      <th>consumption_30d_m3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2014-12-02</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>14</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>0.424</td>\n",
       "      <td>12.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014-12-02</td>\n",
       "      <td>2014-12-30</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>16</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>0.571</td>\n",
       "      <td>17.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2014-12-30</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>51</td>\n",
       "      <td>69</td>\n",
       "      <td>18</td>\n",
       "      <td>2015</td>\n",
       "      <td>01</td>\n",
       "      <td>32</td>\n",
       "      <td>0.562</td>\n",
       "      <td>16.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>69</td>\n",
       "      <td>81</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "      <td>02</td>\n",
       "      <td>32</td>\n",
       "      <td>0.375</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>2015-04-02</td>\n",
       "      <td>81</td>\n",
       "      <td>97</td>\n",
       "      <td>16</td>\n",
       "      <td>2015</td>\n",
       "      <td>03</td>\n",
       "      <td>29</td>\n",
       "      <td>0.552</td>\n",
       "      <td>16.56</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>2020-01-30</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>215</td>\n",
       "      <td>221</td>\n",
       "      <td>6</td>\n",
       "      <td>2020</td>\n",
       "      <td>02</td>\n",
       "      <td>29</td>\n",
       "      <td>0.207</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>221</td>\n",
       "      <td>235</td>\n",
       "      <td>14</td>\n",
       "      <td>2020</td>\n",
       "      <td>03</td>\n",
       "      <td>31</td>\n",
       "      <td>0.452</td>\n",
       "      <td>13.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>235</td>\n",
       "      <td>247</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>04</td>\n",
       "      <td>31</td>\n",
       "      <td>0.387</td>\n",
       "      <td>11.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>2020-04-30</td>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>247</td>\n",
       "      <td>258</td>\n",
       "      <td>11</td>\n",
       "      <td>2020</td>\n",
       "      <td>05</td>\n",
       "      <td>29</td>\n",
       "      <td>0.379</td>\n",
       "      <td>11.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>2020-05-29</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>258</td>\n",
       "      <td>270</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "      <td>06</td>\n",
       "      <td>32</td>\n",
       "      <td>0.375</td>\n",
       "      <td>11.25</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     2014-10-30    2014-12-02             21            35   \n",
       "1   2014-12     2014-12-02    2014-12-30             35            51   \n",
       "2   2015-01     2014-12-30    2015-01-31             51            69   \n",
       "3   2015-02     2015-01-31    2015-03-04             69            81   \n",
       "4   2015-03     2015-03-04    2015-04-02             81            97   \n",
       "..      ...            ...           ...            ...           ...   \n",
       "63  2020-02     2020-01-30    2020-02-28            215           221   \n",
       "64  2020-03     2020-02-28    2020-03-30            221           235   \n",
       "65  2020-04     2020-03-30    2020-04-30            235           247   \n",
       "66  2020-05     2020-04-30    2020-05-29            247           258   \n",
       "67  2020-06     2020-05-29    2020-06-30            258           270   \n",
       "\n",
       "    consumption_m3  year month  consumption_days  consumption_day_m3  \\\n",
       "0               14  2014    11                33               0.424   \n",
       "1               16  2014    12                28               0.571   \n",
       "2               18  2015    01                32               0.562   \n",
       "3               12  2015    02                32               0.375   \n",
       "4               16  2015    03                29               0.552   \n",
       "..             ...   ...   ...               ...                 ...   \n",
       "63               6  2020    02                29               0.207   \n",
       "64              14  2020    03                31               0.452   \n",
       "65              12  2020    04                31               0.387   \n",
       "66              11  2020    05                29               0.379   \n",
       "67              12  2020    06                32               0.375   \n",
       "\n",
       "    consumption_30d_m3  \n",
       "0                12.72  \n",
       "1                17.13  \n",
       "2                16.86  \n",
       "3                11.25  \n",
       "4                16.56  \n",
       "..                 ...  \n",
       "63                6.21  \n",
       "64               13.56  \n",
       "65               11.61  \n",
       "66               11.37  \n",
       "67               11.25  \n",
       "\n",
       "[68 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new columns\n",
    "df_water['consumption_days'] = (df_water['current_check'] - df_water['previous_check']).dt.days\n",
    "df_water['consumption_day_m3'] = round(df_water['consumption_m3'] / df_water['consumption_days'], 3)\n",
    "df_water['consumption_30d_m3'] = round(df_water['consumption_day_m3'] * 30, 3)\n",
    "\n",
    "df_water"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hydrometers are replaced each fifth year to avoid reading failures.\n",
    "\n",
    "Check when hydrometer has been changed comparing columns *previous_check* and *current_check*."
   ]
  },
  {
   "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>previous_check</th>\n",
       "      <th>current_check</th>\n",
       "      <th>previous_mark</th>\n",
       "      <th>current_mark</th>\n",
       "      <th>consumption_m3</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>consumption_days</th>\n",
       "      <th>consumption_day_m3</th>\n",
       "      <th>consumption_30d_m3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2018-02</td>\n",
       "      <td>2018-02-02</td>\n",
       "      <td>2018-03-07</td>\n",
       "      <td>434</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2018</td>\n",
       "      <td>02</td>\n",
       "      <td>33</td>\n",
       "      <td>0.303</td>\n",
       "      <td>9.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     period previous_check current_check  previous_mark  current_mark  \\\n",
       "39  2018-02     2018-02-02    2018-03-07            434             3   \n",
       "\n",
       "    consumption_m3  year month  consumption_days  consumption_day_m3  \\\n",
       "39              10  2018    02                33               0.303   \n",
       "\n",
       "    consumption_30d_m3  \n",
       "39                9.09  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check when current_mark is lower than previous_mark\n",
    "df_water[df_water['current_mark'] < df_water['previous_mark']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store when hydrometer has been replaced for reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[39, 10]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# store when hydrometer has been changed [index, consumption_m3]\n",
    "hmrepl = [int(df_water[df_water['current_mark'] < df_water['previous_mark']].index.values),\n",
    "          int(df_water[df_water['current_mark'] < df_water['previous_mark']]['consumption_m3'])]\n",
    "hmrepl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep only relevant columns and reorder dataframe columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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_m3</th>\n",
       "      <th>consumption_days</th>\n",
       "      <th>consumption_day_m3</th>\n",
       "      <th>consumption_30d_m3</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>14</td>\n",
       "      <td>33</td>\n",
       "      <td>0.424</td>\n",
       "      <td>12.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>0.571</td>\n",
       "      <td>17.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01</td>\n",
       "      <td>2015</td>\n",
       "      <td>01</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>0.562</td>\n",
       "      <td>16.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02</td>\n",
       "      <td>2015</td>\n",
       "      <td>02</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>0.375</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03</td>\n",
       "      <td>2015</td>\n",
       "      <td>03</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>0.552</td>\n",
       "      <td>16.56</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>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0.207</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>2020</td>\n",
       "      <td>03</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>0.452</td>\n",
       "      <td>13.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>2020</td>\n",
       "      <td>04</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>0.387</td>\n",
       "      <td>11.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>2020</td>\n",
       "      <td>05</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>0.379</td>\n",
       "      <td>11.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>2020</td>\n",
       "      <td>06</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>0.375</td>\n",
       "      <td>11.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     period  year month  consumption_m3  consumption_days  consumption_day_m3  \\\n",
       "0   2014-11  2014    11              14                33               0.424   \n",
       "1   2014-12  2014    12              16                28               0.571   \n",
       "2   2015-01  2015    01              18                32               0.562   \n",
       "3   2015-02  2015    02              12                32               0.375   \n",
       "4   2015-03  2015    03              16                29               0.552   \n",
       "..      ...   ...   ...             ...               ...                 ...   \n",
       "63  2020-02  2020    02               6                29               0.207   \n",
       "64  2020-03  2020    03              14                31               0.452   \n",
       "65  2020-04  2020    04              12                31               0.387   \n",
       "66  2020-05  2020    05              11                29               0.379   \n",
       "67  2020-06  2020    06              12                32               0.375   \n",
       "\n",
       "    consumption_30d_m3  \n",
       "0                12.72  \n",
       "1                17.13  \n",
       "2                16.86  \n",
       "3                11.25  \n",
       "4                16.56  \n",
       "..                 ...  \n",
       "63                6.21  \n",
       "64               13.56  \n",
       "65               11.61  \n",
       "66               11.37  \n",
       "67               11.25  \n",
       "\n",
       "[68 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define relevant and columns order\n",
    "columns = ['period','year','month','consumption_m3','consumption_days','consumption_day_m3','consumption_30d_m3']\n",
    "\n",
    "# overwrite dataframe only with relevant columns\n",
    "df_water = df_water[columns]\n",
    "\n",
    "df_water"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check dataframe statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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_m3</th>\n",
       "      <th>consumption_days</th>\n",
       "      <th>consumption_day_m3</th>\n",
       "      <th>consumption_30d_m3</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>10.147059</td>\n",
       "      <td>30.441176</td>\n",
       "      <td>0.333029</td>\n",
       "      <td>9.990882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.581592</td>\n",
       "      <td>1.605795</td>\n",
       "      <td>0.082238</td>\n",
       "      <td>2.467126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.297500</td>\n",
       "      <td>8.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.323000</td>\n",
       "      <td>9.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>11.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.571000</td>\n",
       "      <td>17.130000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       consumption_m3  consumption_days  consumption_day_m3  \\\n",
       "count       68.000000         68.000000           68.000000   \n",
       "mean        10.147059         30.441176            0.333029   \n",
       "std          2.581592          1.605795            0.082238   \n",
       "min          4.000000         28.000000            0.125000   \n",
       "25%          9.000000         29.000000            0.297500   \n",
       "50%         10.000000         30.000000            0.323000   \n",
       "75%         11.000000         32.000000            0.375000   \n",
       "max         18.000000         34.000000            0.571000   \n",
       "\n",
       "       consumption_30d_m3  \n",
       "count           68.000000  \n",
       "mean             9.990882  \n",
       "std              2.467126  \n",
       "min              3.750000  \n",
       "25%              8.925000  \n",
       "50%              9.690000  \n",
       "75%             11.250000  \n",
       "max             17.130000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dataframe statistics\n",
    "df_water.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a large range between minimum and maximum *consumption_m3*, but the interquartile range is quite narrow, there should be some outliers on bottom and top.\n",
    "\n",
    "The estimated *consumption_30d_m3* is slighly bellow of *consumption_m3*, because there is no regularity on the checking day. It has an impact on consumption billing since the consumption tariff is done in different ranges, charging more customers who have higher water consumption. The consumption tariff ranges are the following:\n",
    "\n",
    "|TARIFF| RANGE (m<sup>3</sup>)|\n",
    "|:-:|:-:|\n",
    "|T1|>0 and <=5|\n",
    "|T2|>5 and <=10|\n",
    "|T3|>10 and <=15|\n",
    "|T4|>15 and <=20|\n",
    "|T5|>20 and <=40|\n",
    "|T6|>40|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the consumption frequencies using histograms, one for column *consumption_m3* and another for column *consumption_30d_m3*. For better comparison, histograms should have the same ```x-ticks``` (bins), ```y-ticks``` (frequencies) and show tariff ranges.\n",
    "\n",
    "Define tariff ranges object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define tariffs dictionary\n",
    "wtariff = {'T1':[0,5],'T2':[5,10],'T3':[10,15],'T4':[15,20],'T5':[20,40],'T6':[40,99]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define bins for histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define bins (x-ticks)\n",
    "cnt, bins = np.histogram(df_water['consumption_m3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "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_water['consumption_m3'], bins=bins, color='b', alpha=0.6)\n",
    "ax.set_title('Histograma Consumo de Água\\nHistogram Water Consumption\\n' + \\\n",
    "             min(df_water['year']) + ' - ' + max(df_water['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_water['consumption_30d_m3'], bins=bins, color='b', alpha=0.4)\n",
    "ax.set_title('Histograma Consumo de Água 30 dias\\nHistogram Water Consumption 30 days\\n' + \\\n",
    "             min(df_water['year']) + ' - ' + max(df_water['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('m3')\n",
    "    \n",
    "    # loop to include tarrif ranges\n",
    "    # note that not all tariff ranges should be on plot\n",
    "    for t in wtariff:\n",
    "        # add vertical line for tariffs within the consumption range\n",
    "        if (wtariff[t][1] > min(bins)) & (wtariff[t][1] < max(bins)):\n",
    "            a.axvline(x=wtariff[t][1], ymin=min(yticks_), ymax=max(yticks_), color='gray', alpha=0.8, ls='-.')\n",
    "\n",
    "        # add caption for tariffs within the consumption range\n",
    "        if (wtariff[t][0]>=min(bins) and wtariff[t][0]<=max(bins)) | (wtariff[t][1]>=min(bins) and wtariff[t][1]<=max(bins)):\n",
    "            # define the x position\n",
    "            if np.mean([wtariff[t][0],wtariff[t][1]]) < min(bins):\n",
    "                xpos = min(bins)\n",
    "            elif np.mean([wtariff[t][0],wtariff[t][1]]) > max(bins):\n",
    "                xpos = max(bins)\n",
    "            else:\n",
    "                xpos = np.mean([wtariff[t][0],wtariff[t][1]])\n",
    "\n",
    "            # add tariff caption\n",
    "            a.annotate(t, xy=(xpos, max(yticks_)*0.925), xycoords='data', ha='center', color='gray', alpha=1.0, fontsize=12, fontweight=900)\n",
    "\n",
    "#save to file\n",
    "plt.savefig('./Images/Water_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_m3*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create empty list\n",
    "mean12m = []\n",
    "\n",
    "# calculate mean for last 12 observations of consumption_m3\n",
    "for i in range(0, df_water.shape[0]):\n",
    "    if i < 11:\n",
    "        mean12m.append(round(np.mean(df_water.loc[:i,'consumption_m3']),3))\n",
    "    else:\n",
    "        mean12m.append(round(np.mean(df_water.loc[i-11:i,'consumption_m3']),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define customized x-labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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_water['year'], df_water['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": [
    "Define ```y-ticks``` and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get min and max consumption value\n",
    "ymin = int(np.min([df_water['consumption_m3'].min(), df_water['consumption_30d_m3'].min()]))\n",
    "ymax = int(np.max([df_water['consumption_m3'].max(), df_water['consumption_30d_m3'].max()]))\n",
    "\n",
    "# define y labels\n",
    "ylabels_ = np.arange(ymin-1, ymax+1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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_m3, consumption_30d_m3 and mean12m\n",
    "ax.plot(df_water['period'], df_water['consumption_m3'], color='b', alpha=0.9,\n",
    "        label='Consumo\\nConsumption')\n",
    "ax.plot(df_water['period'], df_water['consumption_30d_m3'], color='b', alpha=0.4, \n",
    "        label='Consumo base 30 dias\\nConsumption 30 days basis')\n",
    "ax.plot(df_water['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 Água\\nWater Monthly Consumption\\n' \\\n",
    "             + min(df_water['year']) + ' - ' + max(df_water['year']), {'fontsize':14}, fontweight=900)\n",
    "ax.set_ylabel('m3')\n",
    "ax.set_xlabel('período / period')\n",
    "\n",
    "# define legend\n",
    "ax.legend(loc=1, frameon=True, facecolor='white', framealpha=1)\n",
    "\n",
    "# define y and x-ticks\n",
    "plt.yticks(ylabels_, ylabels_)\n",
    "plt.xticks(np.arange(0, len(xlabels_), 1), xlabels_, rotation=90)\n",
    "\n",
    "# loop to include tariff ranges\n",
    "for t in wtariff:\n",
    "    # add horizontal line for tariffs within the consumption range\n",
    "    if (wtariff[t][1] > min(bins)) & (wtariff[t][1] < max(bins)):\n",
    "        ax.axhline(y=wtariff[t][1], xmin=0, xmax=1, color='gray', alpha=0.8, ls='-.')\n",
    "\n",
    "    # add caption for tariffs within the consumption range\n",
    "    if (wtariff[t][0]>=min(ylabels_) and wtariff[t][0]<=max(ylabels_)) | (wtariff[t][1]>=min(ylabels_) and wtariff[t][1]<=max(ylabels_)):\n",
    "        # define the y position\n",
    "        if np.mean([wtariff[t][0],wtariff[t][1]]) < min(ylabels_):\n",
    "            ypos = min(ylabels_)\n",
    "        elif np.mean([wtariff[t][0],wtariff[t][1]]) > max(ylabels_):\n",
    "            ypos = max(ylabels_)\n",
    "        else:\n",
    "            ypos = np.mean([wtariff[t][0],wtariff[t][1]])\n",
    "\n",
    "        # add tariff caption\n",
    "        ax.annotate(t, xy=(-2, ypos), xycoords='data', ha='left', rotation='vertical', color='gray', fontsize=12, fontweight=900)\n",
    "\n",
    "# add hydrometer replacement with arrow\n",
    "ax.annotate('Troca do hidrometro\\nHydrometer replacement',\n",
    "            xy=(hmrepl[0],hmrepl[1]), xycoords='data',\n",
    "            xytext=(hmrepl[0]-5,hmrepl[1]+3), textcoords='data', color='black', ha='center', fontweight=900,\n",
    "            arrowprops=dict(color='black',arrowstyle='fancy', connectionstyle='arc3'))\n",
    "\n",
    "#save to file\n",
    "plt.savefig('./Images/Water_Plot_02_Line_ConsumpMonth.png', bbox_inches='tight')\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Water consumption has decreased since 2014, achieving the lowest level during the second half of 2019.\n",
    "\n",
    "Analyse the consumption mean per month with bar plot, comparing *consumption_m3* and *consumption_30d_m3*.\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_m3*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall mean for column consumption_m3: 10.125\n",
      "Overall std for column consumption_m3: 0.843\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_m3</th>\n",
       "      <th>consumption_30d_m3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>9.000</td>\n",
       "      <td>8.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>9.500</td>\n",
       "      <td>9.475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03</td>\n",
       "      <td>11.500</td>\n",
       "      <td>11.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04</td>\n",
       "      <td>10.500</td>\n",
       "      <td>10.280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>10.667</td>\n",
       "      <td>10.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06</td>\n",
       "      <td>10.667</td>\n",
       "      <td>10.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07</td>\n",
       "      <td>9.400</td>\n",
       "      <td>8.952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08</td>\n",
       "      <td>9.800</td>\n",
       "      <td>9.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09</td>\n",
       "      <td>8.600</td>\n",
       "      <td>8.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>11.200</td>\n",
       "      <td>10.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>10.167</td>\n",
       "      <td>10.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>10.500</td>\n",
       "      <td>10.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  consumption_m3  consumption_30d_m3\n",
       "0     01           9.000               8.845\n",
       "1     02           9.500               9.475\n",
       "2     03          11.500              11.520\n",
       "3     04          10.500              10.280\n",
       "4     05          10.667              10.690\n",
       "5     06          10.667              10.335\n",
       "6     07           9.400               8.952\n",
       "7     08           9.800               9.858\n",
       "8     09           8.600               8.616\n",
       "9     10          11.200              10.470\n",
       "10    11          10.167              10.005\n",
       "11    12          10.500              10.500"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe with mean per month\n",
    "df_mean = df_water[['month','consumption_m3','consumption_30d_m3']].groupby('month').mean().reset_index()\n",
    "\n",
    "# round values to 3 decimal positions\n",
    "df_mean['consumption_m3'] = df_mean['consumption_m3'].apply(lambda v: round(v,3))\n",
    "df_mean['consumption_30d_m3'] = df_mean['consumption_30d_m3'].apply(lambda v: round(v,3))\n",
    "\n",
    "# calculate overall mean and std for consumption_m3\n",
    "meanconsum = round(np.mean(df_mean['consumption_m3']),3)\n",
    "stdconsum = round(np.std(df_mean['consumption_m3']),3)\n",
    "\n",
    "# show results\n",
    "print('Overall mean for column consumption_m3: {:.3f}\\nOverall std for column consumption_m3: {:.3f}\\n'.\n",
    "      format(meanconsum, stdconsum))\n",
    "\n",
    "df_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 with\n",
    "x = np.arange(df_mean.shape[0])\n",
    "width = 0.4\n",
    "\n",
    "# plot mean consumption_m3, mean consumption_30d_m3, overall mean and std\n",
    "ax.bar(x - width/2, df_mean['consumption_m3'], width, color='b', alpha=0.7, \n",
    "       label='Consumo médio anual\\nMean consumption')\n",
    "ax.bar(x + width/2, df_mean['consumption_30d_m3'], width, color='b', 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_water['year']) + ' - ' + max(df_water['year']), {'fontsize':14}, fontweight=900)\n",
    "plt.ylabel('m3')\n",
    "plt.xlabel('mês / month')\n",
    "\n",
    "#save to file\n",
    "plt.savefig('./Images/Water_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_m3* are required. A new dataframe will be created having only required columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>water_day_m3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>0.424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>0.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>0.562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>0.552</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>0.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>0.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>0.379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       period  water_day_m3\n",
       "0  2014-11-01         0.424\n",
       "1  2014-12-01         0.571\n",
       "2  2015-01-01         0.562\n",
       "3  2015-02-01         0.375\n",
       "4  2015-03-01         0.552\n",
       "..        ...           ...\n",
       "63 2020-02-01         0.207\n",
       "64 2020-03-01         0.452\n",
       "65 2020-04-01         0.387\n",
       "66 2020-05-01         0.379\n",
       "67 2020-06-01         0.375\n",
       "\n",
       "[68 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new dataframe\n",
    "features_water = df_water[['period','consumption_day_m3']].copy()\n",
    "\n",
    "# convert period to datetime format\n",
    "features_water['period'] = pd.to_datetime(features_water['period'], format='%Y-%m')\n",
    "\n",
    "# rename column consumption_day_m3\n",
    "features_water = features_water.rename(columns={'consumption_day_m3': 'water_day_m3'})\n",
    "\n",
    "features_water"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export dataframe to local file in ```feather``` format to be retrieved later on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export to feather file format\n",
    "features_water.to_feather('./Data_source/Features_Water.feather')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}