housing_pro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python project on Ontario Housing (as of the date of Oct 25, 2023)\n",
    "The objective of this project analyze the Ontario Housing dataset. This project mainly uses the Pandas library for extracting data, data processing, data cleaning, and the Seaborn library for visualization.\n",
    "\n",
    "\n",
    "\n",
    "## Resources used in this project\n",
    "- [VScode on Data Science](https://code.visualstudio.com/docs/datascience/data-science-tutorial)\n",
    "- [Ontario Housing Data from Statistics Canada](https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3410013501&pickMembers%5B0%5D=1.13&pickMembers%5B1%5D=4.1&cubeTimeFrame.startMonth=07&cubeTimeFrame.startYear=2013&cubeTimeFrame.endMonth=07&cubeTimeFrame.endYear=2023&referencePeriods=20130701%2C20230701)\n",
    "- [Ontario Population Data from Statistics Canada](https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=1710000901)\n",
    "- [Survey of Household Spending, 2021](https://www150.statcan.gc.ca/n1/pub/62f0026m/62f0026m2023001-eng.htm) - ON average household size: 2.55 {from table G1 in the section of Appendix G}\n",
    "- [Analysis & News used for comparison](https://thenarwhal.ca/ontarios-housing-plan-evidence/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type of unit</th>\n",
       "      <th>Q3 2013</th>\n",
       "      <th>Q4 2013</th>\n",
       "      <th>Q1 2014</th>\n",
       "      <th>Q2 2014</th>\n",
       "      <th>Q3 2014</th>\n",
       "      <th>Q4 2014</th>\n",
       "      <th>Q1 2015</th>\n",
       "      <th>Q2 2015</th>\n",
       "      <th>Q3 2015</th>\n",
       "      <th>...</th>\n",
       "      <th>Q2 2021</th>\n",
       "      <th>Q3 2021</th>\n",
       "      <th>Q4 2021</th>\n",
       "      <th>Q1 2022</th>\n",
       "      <th>Q2 2022</th>\n",
       "      <th>Q3 2022</th>\n",
       "      <th>Q4 2022</th>\n",
       "      <th>Q1 2023</th>\n",
       "      <th>Q2 2023</th>\n",
       "      <th>Q3 2023</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Housing estimates</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Housing starts</th>\n",
       "      <td>Total units</td>\n",
       "      <td>17,532</td>\n",
       "      <td>16,594</td>\n",
       "      <td>10,828</td>\n",
       "      <td>17,039</td>\n",
       "      <td>15,989</td>\n",
       "      <td>15,278</td>\n",
       "      <td>11,382</td>\n",
       "      <td>18,479</td>\n",
       "      <td>21,259</td>\n",
       "      <td>...</td>\n",
       "      <td>25,594</td>\n",
       "      <td>27,765</td>\n",
       "      <td>24,579</td>\n",
       "      <td>17,686</td>\n",
       "      <td>22,460</td>\n",
       "      <td>29,746</td>\n",
       "      <td>26,188</td>\n",
       "      <td>18,490</td>\n",
       "      <td>25,512</td>\n",
       "      <td>24,813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Single-detached</td>\n",
       "      <td>6,705</td>\n",
       "      <td>6,273</td>\n",
       "      <td>3,106</td>\n",
       "      <td>6,801</td>\n",
       "      <td>7,334</td>\n",
       "      <td>6,450</td>\n",
       "      <td>2,567</td>\n",
       "      <td>7,052</td>\n",
       "      <td>7,738</td>\n",
       "      <td>...</td>\n",
       "      <td>8,945</td>\n",
       "      <td>9,350</td>\n",
       "      <td>6,966</td>\n",
       "      <td>4,834</td>\n",
       "      <td>6,543</td>\n",
       "      <td>7,739</td>\n",
       "      <td>6,378</td>\n",
       "      <td>4,021</td>\n",
       "      <td>4,669</td>\n",
       "      <td>4,634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Multiples</td>\n",
       "      <td>10,827</td>\n",
       "      <td>10,321</td>\n",
       "      <td>7,722</td>\n",
       "      <td>10,238</td>\n",
       "      <td>8,655</td>\n",
       "      <td>8,828</td>\n",
       "      <td>8,815</td>\n",
       "      <td>11,427</td>\n",
       "      <td>13,521</td>\n",
       "      <td>...</td>\n",
       "      <td>16,649</td>\n",
       "      <td>18,415</td>\n",
       "      <td>17,613</td>\n",
       "      <td>12,852</td>\n",
       "      <td>15,917</td>\n",
       "      <td>22,007</td>\n",
       "      <td>19,810</td>\n",
       "      <td>14,469</td>\n",
       "      <td>20,843</td>\n",
       "      <td>20,179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Semi-detached</td>\n",
       "      <td>844</td>\n",
       "      <td>754</td>\n",
       "      <td>359</td>\n",
       "      <td>898</td>\n",
       "      <td>854</td>\n",
       "      <td>631</td>\n",
       "      <td>310</td>\n",
       "      <td>522</td>\n",
       "      <td>636</td>\n",
       "      <td>...</td>\n",
       "      <td>929</td>\n",
       "      <td>730</td>\n",
       "      <td>655</td>\n",
       "      <td>558</td>\n",
       "      <td>618</td>\n",
       "      <td>643</td>\n",
       "      <td>453</td>\n",
       "      <td>377</td>\n",
       "      <td>425</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Row</td>\n",
       "      <td>2,850</td>\n",
       "      <td>2,429</td>\n",
       "      <td>1,604</td>\n",
       "      <td>3,103</td>\n",
       "      <td>2,602</td>\n",
       "      <td>2,666</td>\n",
       "      <td>1,044</td>\n",
       "      <td>3,534</td>\n",
       "      <td>2,621</td>\n",
       "      <td>...</td>\n",
       "      <td>3,970</td>\n",
       "      <td>4,333</td>\n",
       "      <td>4,171</td>\n",
       "      <td>2,661</td>\n",
       "      <td>5,047</td>\n",
       "      <td>5,148</td>\n",
       "      <td>4,403</td>\n",
       "      <td>2,670</td>\n",
       "      <td>3,261</td>\n",
       "      <td>4,127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Apartment and other unit type</td>\n",
       "      <td>7,133</td>\n",
       "      <td>7,138</td>\n",
       "      <td>5,759</td>\n",
       "      <td>6,237</td>\n",
       "      <td>5,199</td>\n",
       "      <td>5,531</td>\n",
       "      <td>7,461</td>\n",
       "      <td>7,371</td>\n",
       "      <td>10,264</td>\n",
       "      <td>...</td>\n",
       "      <td>11,750</td>\n",
       "      <td>13,352</td>\n",
       "      <td>12,787</td>\n",
       "      <td>9,633</td>\n",
       "      <td>10,252</td>\n",
       "      <td>16,216</td>\n",
       "      <td>14,954</td>\n",
       "      <td>11,422</td>\n",
       "      <td>17,157</td>\n",
       "      <td>15,558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Housing under construction</th>\n",
       "      <td>Total units</td>\n",
       "      <td>93,600</td>\n",
       "      <td>94,117</td>\n",
       "      <td>92,827</td>\n",
       "      <td>94,582</td>\n",
       "      <td>96,521</td>\n",
       "      <td>96,112</td>\n",
       "      <td>79,695</td>\n",
       "      <td>81,343</td>\n",
       "      <td>87,385</td>\n",
       "      <td>...</td>\n",
       "      <td>136,919</td>\n",
       "      <td>145,712</td>\n",
       "      <td>148,064</td>\n",
       "      <td>147,504</td>\n",
       "      <td>153,536</td>\n",
       "      <td>164,580</td>\n",
       "      <td>171,425</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Single-detached</td>\n",
       "      <td>15,601</td>\n",
       "      <td>15,310</td>\n",
       "      <td>13,268</td>\n",
       "      <td>14,424</td>\n",
       "      <td>15,701</td>\n",
       "      <td>15,300</td>\n",
       "      <td>12,274</td>\n",
       "      <td>14,618</td>\n",
       "      <td>17,025</td>\n",
       "      <td>...</td>\n",
       "      <td>20,457</td>\n",
       "      <td>22,764</td>\n",
       "      <td>22,264</td>\n",
       "      <td>21,014</td>\n",
       "      <td>21,201</td>\n",
       "      <td>23,134</td>\n",
       "      <td>23,282</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Multiples</td>\n",
       "      <td>77,999</td>\n",
       "      <td>78,807</td>\n",
       "      <td>79,559</td>\n",
       "      <td>80,158</td>\n",
       "      <td>80,820</td>\n",
       "      <td>80,812</td>\n",
       "      <td>67,421</td>\n",
       "      <td>66,725</td>\n",
       "      <td>70,360</td>\n",
       "      <td>...</td>\n",
       "      <td>116,462</td>\n",
       "      <td>122,948</td>\n",
       "      <td>125,800</td>\n",
       "      <td>126,490</td>\n",
       "      <td>132,335</td>\n",
       "      <td>141,446</td>\n",
       "      <td>148,143</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Semi-detached</td>\n",
       "      <td>2,629</td>\n",
       "      <td>2,448</td>\n",
       "      <td>2,138</td>\n",
       "      <td>2,125</td>\n",
       "      <td>2,323</td>\n",
       "      <td>2,041</td>\n",
       "      <td>1,666</td>\n",
       "      <td>1,505</td>\n",
       "      <td>1,707</td>\n",
       "      <td>...</td>\n",
       "      <td>2,391</td>\n",
       "      <td>2,504</td>\n",
       "      <td>2,360</td>\n",
       "      <td>2,236</td>\n",
       "      <td>2,217</td>\n",
       "      <td>2,243</td>\n",
       "      <td>2,093</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Row</td>\n",
       "      <td>9,382</td>\n",
       "      <td>8,801</td>\n",
       "      <td>8,326</td>\n",
       "      <td>9,034</td>\n",
       "      <td>9,133</td>\n",
       "      <td>9,144</td>\n",
       "      <td>7,789</td>\n",
       "      <td>9,344</td>\n",
       "      <td>9,885</td>\n",
       "      <td>...</td>\n",
       "      <td>13,860</td>\n",
       "      <td>15,507</td>\n",
       "      <td>15,921</td>\n",
       "      <td>15,979</td>\n",
       "      <td>17,913</td>\n",
       "      <td>20,490</td>\n",
       "      <td>21,694</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Apartment and other unit type</td>\n",
       "      <td>65,988</td>\n",
       "      <td>67,558</td>\n",
       "      <td>69,095</td>\n",
       "      <td>68,999</td>\n",
       "      <td>69,364</td>\n",
       "      <td>69,627</td>\n",
       "      <td>57,966</td>\n",
       "      <td>55,876</td>\n",
       "      <td>58,768</td>\n",
       "      <td>...</td>\n",
       "      <td>100,211</td>\n",
       "      <td>104,937</td>\n",
       "      <td>107,519</td>\n",
       "      <td>108,275</td>\n",
       "      <td>112,205</td>\n",
       "      <td>118,713</td>\n",
       "      <td>124,356</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Housing completions</th>\n",
       "      <td>Total units</td>\n",
       "      <td>13,430</td>\n",
       "      <td>15,987</td>\n",
       "      <td>11,948</td>\n",
       "      <td>15,297</td>\n",
       "      <td>14,266</td>\n",
       "      <td>15,566</td>\n",
       "      <td>27,826</td>\n",
       "      <td>16,674</td>\n",
       "      <td>15,141</td>\n",
       "      <td>...</td>\n",
       "      <td>21,479</td>\n",
       "      <td>18,386</td>\n",
       "      <td>22,154</td>\n",
       "      <td>18,482</td>\n",
       "      <td>16,079</td>\n",
       "      <td>18,607</td>\n",
       "      <td>18,670</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Single-detached</td>\n",
       "      <td>6,260</td>\n",
       "      <td>6,560</td>\n",
       "      <td>5,065</td>\n",
       "      <td>5,633</td>\n",
       "      <td>6,046</td>\n",
       "      <td>6,843</td>\n",
       "      <td>5,440</td>\n",
       "      <td>4,695</td>\n",
       "      <td>5,311</td>\n",
       "      <td>...</td>\n",
       "      <td>6,407</td>\n",
       "      <td>6,998</td>\n",
       "      <td>7,447</td>\n",
       "      <td>6,301</td>\n",
       "      <td>6,333</td>\n",
       "      <td>5,812</td>\n",
       "      <td>6,219</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Multiples</td>\n",
       "      <td>7,170</td>\n",
       "      <td>9,427</td>\n",
       "      <td>6,883</td>\n",
       "      <td>9,664</td>\n",
       "      <td>8,220</td>\n",
       "      <td>8,723</td>\n",
       "      <td>22,386</td>\n",
       "      <td>11,979</td>\n",
       "      <td>9,830</td>\n",
       "      <td>...</td>\n",
       "      <td>15,072</td>\n",
       "      <td>11,388</td>\n",
       "      <td>14,707</td>\n",
       "      <td>12,181</td>\n",
       "      <td>9,746</td>\n",
       "      <td>12,795</td>\n",
       "      <td>12,451</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Semi-detached</td>\n",
       "      <td>941</td>\n",
       "      <td>933</td>\n",
       "      <td>667</td>\n",
       "      <td>913</td>\n",
       "      <td>658</td>\n",
       "      <td>919</td>\n",
       "      <td>672</td>\n",
       "      <td>680</td>\n",
       "      <td>433</td>\n",
       "      <td>...</td>\n",
       "      <td>669</td>\n",
       "      <td>605</td>\n",
       "      <td>794</td>\n",
       "      <td>677</td>\n",
       "      <td>633</td>\n",
       "      <td>593</td>\n",
       "      <td>592</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Row</td>\n",
       "      <td>2,801</td>\n",
       "      <td>3,037</td>\n",
       "      <td>1,978</td>\n",
       "      <td>2,388</td>\n",
       "      <td>2,478</td>\n",
       "      <td>2,608</td>\n",
       "      <td>2,402</td>\n",
       "      <td>1,979</td>\n",
       "      <td>2,051</td>\n",
       "      <td>...</td>\n",
       "      <td>3,553</td>\n",
       "      <td>2,659</td>\n",
       "      <td>3,754</td>\n",
       "      <td>2,546</td>\n",
       "      <td>3,105</td>\n",
       "      <td>2,555</td>\n",
       "      <td>3,218</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>Apartment and other unit type</td>\n",
       "      <td>3,428</td>\n",
       "      <td>5,457</td>\n",
       "      <td>4,238</td>\n",
       "      <td>6,363</td>\n",
       "      <td>5,084</td>\n",
       "      <td>5,196</td>\n",
       "      <td>19,312</td>\n",
       "      <td>9,320</td>\n",
       "      <td>7,346</td>\n",
       "      <td>...</td>\n",
       "      <td>10,850</td>\n",
       "      <td>8,124</td>\n",
       "      <td>10,159</td>\n",
       "      <td>8,958</td>\n",
       "      <td>6,008</td>\n",
       "      <td>9,647</td>\n",
       "      <td>8,641</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Type of unit Q3 2013 Q4 2013  \\\n",
       "Housing estimates                                                           \n",
       "Housing starts                                Total units  17,532  16,594   \n",
       "NaN                                       Single-detached   6,705   6,273   \n",
       "NaN                                             Multiples  10,827  10,321   \n",
       "NaN                                         Semi-detached     844     754   \n",
       "NaN                                                   Row   2,850   2,429   \n",
       "NaN                         Apartment and other unit type   7,133   7,138   \n",
       "Housing under construction                    Total units  93,600  94,117   \n",
       "NaN                                       Single-detached  15,601  15,310   \n",
       "NaN                                             Multiples  77,999  78,807   \n",
       "NaN                                         Semi-detached   2,629   2,448   \n",
       "NaN                                                   Row   9,382   8,801   \n",
       "NaN                         Apartment and other unit type  65,988  67,558   \n",
       "Housing completions                           Total units  13,430  15,987   \n",
       "NaN                                       Single-detached   6,260   6,560   \n",
       "NaN                                             Multiples   7,170   9,427   \n",
       "NaN                                         Semi-detached     941     933   \n",
       "NaN                                                   Row   2,801   3,037   \n",
       "NaN                         Apartment and other unit type   3,428   5,457   \n",
       "\n",
       "                           Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015  \\\n",
       "Housing estimates                                                            \n",
       "Housing starts              10,828  17,039  15,989  15,278  11,382  18,479   \n",
       "NaN                          3,106   6,801   7,334   6,450   2,567   7,052   \n",
       "NaN                          7,722  10,238   8,655   8,828   8,815  11,427   \n",
       "NaN                            359     898     854     631     310     522   \n",
       "NaN                          1,604   3,103   2,602   2,666   1,044   3,534   \n",
       "NaN                          5,759   6,237   5,199   5,531   7,461   7,371   \n",
       "Housing under construction  92,827  94,582  96,521  96,112  79,695  81,343   \n",
       "NaN                         13,268  14,424  15,701  15,300  12,274  14,618   \n",
       "NaN                         79,559  80,158  80,820  80,812  67,421  66,725   \n",
       "NaN                          2,138   2,125   2,323   2,041   1,666   1,505   \n",
       "NaN                          8,326   9,034   9,133   9,144   7,789   9,344   \n",
       "NaN                         69,095  68,999  69,364  69,627  57,966  55,876   \n",
       "Housing completions         11,948  15,297  14,266  15,566  27,826  16,674   \n",
       "NaN                          5,065   5,633   6,046   6,843   5,440   4,695   \n",
       "NaN                          6,883   9,664   8,220   8,723  22,386  11,979   \n",
       "NaN                            667     913     658     919     672     680   \n",
       "NaN                          1,978   2,388   2,478   2,608   2,402   1,979   \n",
       "NaN                          4,238   6,363   5,084   5,196  19,312   9,320   \n",
       "\n",
       "                           Q3 2015  ...  Q2 2021  Q3 2021  Q4 2021  Q1 2022  \\\n",
       "Housing estimates                   ...                                       \n",
       "Housing starts              21,259  ...   25,594   27,765   24,579   17,686   \n",
       "NaN                          7,738  ...    8,945    9,350    6,966    4,834   \n",
       "NaN                         13,521  ...   16,649   18,415   17,613   12,852   \n",
       "NaN                            636  ...      929      730      655      558   \n",
       "NaN                          2,621  ...    3,970    4,333    4,171    2,661   \n",
       "NaN                         10,264  ...   11,750   13,352   12,787    9,633   \n",
       "Housing under construction  87,385  ...  136,919  145,712  148,064  147,504   \n",
       "NaN                         17,025  ...   20,457   22,764   22,264   21,014   \n",
       "NaN                         70,360  ...  116,462  122,948  125,800  126,490   \n",
       "NaN                          1,707  ...    2,391    2,504    2,360    2,236   \n",
       "NaN                          9,885  ...   13,860   15,507   15,921   15,979   \n",
       "NaN                         58,768  ...  100,211  104,937  107,519  108,275   \n",
       "Housing completions         15,141  ...   21,479   18,386   22,154   18,482   \n",
       "NaN                          5,311  ...    6,407    6,998    7,447    6,301   \n",
       "NaN                          9,830  ...   15,072   11,388   14,707   12,181   \n",
       "NaN                            433  ...      669      605      794      677   \n",
       "NaN                          2,051  ...    3,553    2,659    3,754    2,546   \n",
       "NaN                          7,346  ...   10,850    8,124   10,159    8,958   \n",
       "\n",
       "                            Q2 2022  Q3 2022  Q4 2022 Q1 2023 Q2 2023 Q3 2023  \n",
       "Housing estimates                                                              \n",
       "Housing starts               22,460   29,746   26,188  18,490  25,512  24,813  \n",
       "NaN                           6,543    7,739    6,378   4,021   4,669   4,634  \n",
       "NaN                          15,917   22,007   19,810  14,469  20,843  20,179  \n",
       "NaN                             618      643      453     377     425     494  \n",
       "NaN                           5,047    5,148    4,403   2,670   3,261   4,127  \n",
       "NaN                          10,252   16,216   14,954  11,422  17,157  15,558  \n",
       "Housing under construction  153,536  164,580  171,425      ..      ..       0  \n",
       "NaN                          21,201   23,134   23,282      ..      ..       0  \n",
       "NaN                         132,335  141,446  148,143      ..      ..       0  \n",
       "NaN                           2,217    2,243    2,093      ..      ..       0  \n",
       "NaN                          17,913   20,490   21,694      ..      ..       0  \n",
       "NaN                         112,205  118,713  124,356      ..      ..       0  \n",
       "Housing completions          16,079   18,607   18,670      ..      ..       0  \n",
       "NaN                           6,333    5,812    6,219      ..      ..       0  \n",
       "NaN                           9,746   12,795   12,451      ..      ..       0  \n",
       "NaN                             633      593      592      ..      ..       0  \n",
       "NaN                           3,105    2,555    3,218      ..      ..       0  \n",
       "NaN                           6,008    9,647    8,641      ..      ..       0  \n",
       "\n",
       "[18 rows x 42 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = pd.read_csv(\"/Users/nicholas/Desktop/Project/Project_2_python/fixed_ON_housing_1 - Sheet1.csv\")\n",
    "population = pd.read_csv(\"/Users/nicholas/Desktop/Project/Project_2_python/sheet_2_people .csv\")\n",
    "housing.set_index(\"Housing estimates\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing estimates</th>\n",
       "      <th>Type of unit</th>\n",
       "      <th>Q3 2013</th>\n",
       "      <th>Q4 2013</th>\n",
       "      <th>Q1 2014</th>\n",
       "      <th>Q2 2014</th>\n",
       "      <th>Q3 2014</th>\n",
       "      <th>Q4 2014</th>\n",
       "      <th>Q1 2015</th>\n",
       "      <th>Q2 2015</th>\n",
       "      <th>...</th>\n",
       "      <th>Q2 2021</th>\n",
       "      <th>Q3 2021</th>\n",
       "      <th>Q4 2021</th>\n",
       "      <th>Q1 2022</th>\n",
       "      <th>Q2 2022</th>\n",
       "      <th>Q3 2022</th>\n",
       "      <th>Q4 2022</th>\n",
       "      <th>Q1 2023</th>\n",
       "      <th>Q2 2023</th>\n",
       "      <th>Q3 2023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>Total units</td>\n",
       "      <td>17,532</td>\n",
       "      <td>16,594</td>\n",
       "      <td>10,828</td>\n",
       "      <td>17,039</td>\n",
       "      <td>15,989</td>\n",
       "      <td>15,278</td>\n",
       "      <td>11,382</td>\n",
       "      <td>18,479</td>\n",
       "      <td>...</td>\n",
       "      <td>25,594</td>\n",
       "      <td>27,765</td>\n",
       "      <td>24,579</td>\n",
       "      <td>17,686</td>\n",
       "      <td>22,460</td>\n",
       "      <td>29,746</td>\n",
       "      <td>26,188</td>\n",
       "      <td>18,490</td>\n",
       "      <td>25,512</td>\n",
       "      <td>24,813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>Single-detached</td>\n",
       "      <td>6,705</td>\n",
       "      <td>6,273</td>\n",
       "      <td>3,106</td>\n",
       "      <td>6,801</td>\n",
       "      <td>7,334</td>\n",
       "      <td>6,450</td>\n",
       "      <td>2,567</td>\n",
       "      <td>7,052</td>\n",
       "      <td>...</td>\n",
       "      <td>8,945</td>\n",
       "      <td>9,350</td>\n",
       "      <td>6,966</td>\n",
       "      <td>4,834</td>\n",
       "      <td>6,543</td>\n",
       "      <td>7,739</td>\n",
       "      <td>6,378</td>\n",
       "      <td>4,021</td>\n",
       "      <td>4,669</td>\n",
       "      <td>4,634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>10,827</td>\n",
       "      <td>10,321</td>\n",
       "      <td>7,722</td>\n",
       "      <td>10,238</td>\n",
       "      <td>8,655</td>\n",
       "      <td>8,828</td>\n",
       "      <td>8,815</td>\n",
       "      <td>11,427</td>\n",
       "      <td>...</td>\n",
       "      <td>16,649</td>\n",
       "      <td>18,415</td>\n",
       "      <td>17,613</td>\n",
       "      <td>12,852</td>\n",
       "      <td>15,917</td>\n",
       "      <td>22,007</td>\n",
       "      <td>19,810</td>\n",
       "      <td>14,469</td>\n",
       "      <td>20,843</td>\n",
       "      <td>20,179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>Semi-detached</td>\n",
       "      <td>844</td>\n",
       "      <td>754</td>\n",
       "      <td>359</td>\n",
       "      <td>898</td>\n",
       "      <td>854</td>\n",
       "      <td>631</td>\n",
       "      <td>310</td>\n",
       "      <td>522</td>\n",
       "      <td>...</td>\n",
       "      <td>929</td>\n",
       "      <td>730</td>\n",
       "      <td>655</td>\n",
       "      <td>558</td>\n",
       "      <td>618</td>\n",
       "      <td>643</td>\n",
       "      <td>453</td>\n",
       "      <td>377</td>\n",
       "      <td>425</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>Row</td>\n",
       "      <td>2,850</td>\n",
       "      <td>2,429</td>\n",
       "      <td>1,604</td>\n",
       "      <td>3,103</td>\n",
       "      <td>2,602</td>\n",
       "      <td>2,666</td>\n",
       "      <td>1,044</td>\n",
       "      <td>3,534</td>\n",
       "      <td>...</td>\n",
       "      <td>3,970</td>\n",
       "      <td>4,333</td>\n",
       "      <td>4,171</td>\n",
       "      <td>2,661</td>\n",
       "      <td>5,047</td>\n",
       "      <td>5,148</td>\n",
       "      <td>4,403</td>\n",
       "      <td>2,670</td>\n",
       "      <td>3,261</td>\n",
       "      <td>4,127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>Apartment and other unit type</td>\n",
       "      <td>7,133</td>\n",
       "      <td>7,138</td>\n",
       "      <td>5,759</td>\n",
       "      <td>6,237</td>\n",
       "      <td>5,199</td>\n",
       "      <td>5,531</td>\n",
       "      <td>7,461</td>\n",
       "      <td>7,371</td>\n",
       "      <td>...</td>\n",
       "      <td>11,750</td>\n",
       "      <td>13,352</td>\n",
       "      <td>12,787</td>\n",
       "      <td>9,633</td>\n",
       "      <td>10,252</td>\n",
       "      <td>16,216</td>\n",
       "      <td>14,954</td>\n",
       "      <td>11,422</td>\n",
       "      <td>17,157</td>\n",
       "      <td>15,558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>Total units</td>\n",
       "      <td>93,600</td>\n",
       "      <td>94,117</td>\n",
       "      <td>92,827</td>\n",
       "      <td>94,582</td>\n",
       "      <td>96,521</td>\n",
       "      <td>96,112</td>\n",
       "      <td>79,695</td>\n",
       "      <td>81,343</td>\n",
       "      <td>...</td>\n",
       "      <td>136,919</td>\n",
       "      <td>145,712</td>\n",
       "      <td>148,064</td>\n",
       "      <td>147,504</td>\n",
       "      <td>153,536</td>\n",
       "      <td>164,580</td>\n",
       "      <td>171,425</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>Single-detached</td>\n",
       "      <td>15,601</td>\n",
       "      <td>15,310</td>\n",
       "      <td>13,268</td>\n",
       "      <td>14,424</td>\n",
       "      <td>15,701</td>\n",
       "      <td>15,300</td>\n",
       "      <td>12,274</td>\n",
       "      <td>14,618</td>\n",
       "      <td>...</td>\n",
       "      <td>20,457</td>\n",
       "      <td>22,764</td>\n",
       "      <td>22,264</td>\n",
       "      <td>21,014</td>\n",
       "      <td>21,201</td>\n",
       "      <td>23,134</td>\n",
       "      <td>23,282</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>77,999</td>\n",
       "      <td>78,807</td>\n",
       "      <td>79,559</td>\n",
       "      <td>80,158</td>\n",
       "      <td>80,820</td>\n",
       "      <td>80,812</td>\n",
       "      <td>67,421</td>\n",
       "      <td>66,725</td>\n",
       "      <td>...</td>\n",
       "      <td>116,462</td>\n",
       "      <td>122,948</td>\n",
       "      <td>125,800</td>\n",
       "      <td>126,490</td>\n",
       "      <td>132,335</td>\n",
       "      <td>141,446</td>\n",
       "      <td>148,143</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>Semi-detached</td>\n",
       "      <td>2,629</td>\n",
       "      <td>2,448</td>\n",
       "      <td>2,138</td>\n",
       "      <td>2,125</td>\n",
       "      <td>2,323</td>\n",
       "      <td>2,041</td>\n",
       "      <td>1,666</td>\n",
       "      <td>1,505</td>\n",
       "      <td>...</td>\n",
       "      <td>2,391</td>\n",
       "      <td>2,504</td>\n",
       "      <td>2,360</td>\n",
       "      <td>2,236</td>\n",
       "      <td>2,217</td>\n",
       "      <td>2,243</td>\n",
       "      <td>2,093</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>Row</td>\n",
       "      <td>9,382</td>\n",
       "      <td>8,801</td>\n",
       "      <td>8,326</td>\n",
       "      <td>9,034</td>\n",
       "      <td>9,133</td>\n",
       "      <td>9,144</td>\n",
       "      <td>7,789</td>\n",
       "      <td>9,344</td>\n",
       "      <td>...</td>\n",
       "      <td>13,860</td>\n",
       "      <td>15,507</td>\n",
       "      <td>15,921</td>\n",
       "      <td>15,979</td>\n",
       "      <td>17,913</td>\n",
       "      <td>20,490</td>\n",
       "      <td>21,694</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>Apartment and other unit type</td>\n",
       "      <td>65,988</td>\n",
       "      <td>67,558</td>\n",
       "      <td>69,095</td>\n",
       "      <td>68,999</td>\n",
       "      <td>69,364</td>\n",
       "      <td>69,627</td>\n",
       "      <td>57,966</td>\n",
       "      <td>55,876</td>\n",
       "      <td>...</td>\n",
       "      <td>100,211</td>\n",
       "      <td>104,937</td>\n",
       "      <td>107,519</td>\n",
       "      <td>108,275</td>\n",
       "      <td>112,205</td>\n",
       "      <td>118,713</td>\n",
       "      <td>124,356</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>Total units</td>\n",
       "      <td>13,430</td>\n",
       "      <td>15,987</td>\n",
       "      <td>11,948</td>\n",
       "      <td>15,297</td>\n",
       "      <td>14,266</td>\n",
       "      <td>15,566</td>\n",
       "      <td>27,826</td>\n",
       "      <td>16,674</td>\n",
       "      <td>...</td>\n",
       "      <td>21,479</td>\n",
       "      <td>18,386</td>\n",
       "      <td>22,154</td>\n",
       "      <td>18,482</td>\n",
       "      <td>16,079</td>\n",
       "      <td>18,607</td>\n",
       "      <td>18,670</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>Single-detached</td>\n",
       "      <td>6,260</td>\n",
       "      <td>6,560</td>\n",
       "      <td>5,065</td>\n",
       "      <td>5,633</td>\n",
       "      <td>6,046</td>\n",
       "      <td>6,843</td>\n",
       "      <td>5,440</td>\n",
       "      <td>4,695</td>\n",
       "      <td>...</td>\n",
       "      <td>6,407</td>\n",
       "      <td>6,998</td>\n",
       "      <td>7,447</td>\n",
       "      <td>6,301</td>\n",
       "      <td>6,333</td>\n",
       "      <td>5,812</td>\n",
       "      <td>6,219</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>Multiples</td>\n",
       "      <td>7,170</td>\n",
       "      <td>9,427</td>\n",
       "      <td>6,883</td>\n",
       "      <td>9,664</td>\n",
       "      <td>8,220</td>\n",
       "      <td>8,723</td>\n",
       "      <td>22,386</td>\n",
       "      <td>11,979</td>\n",
       "      <td>...</td>\n",
       "      <td>15,072</td>\n",
       "      <td>11,388</td>\n",
       "      <td>14,707</td>\n",
       "      <td>12,181</td>\n",
       "      <td>9,746</td>\n",
       "      <td>12,795</td>\n",
       "      <td>12,451</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>Semi-detached</td>\n",
       "      <td>941</td>\n",
       "      <td>933</td>\n",
       "      <td>667</td>\n",
       "      <td>913</td>\n",
       "      <td>658</td>\n",
       "      <td>919</td>\n",
       "      <td>672</td>\n",
       "      <td>680</td>\n",
       "      <td>...</td>\n",
       "      <td>669</td>\n",
       "      <td>605</td>\n",
       "      <td>794</td>\n",
       "      <td>677</td>\n",
       "      <td>633</td>\n",
       "      <td>593</td>\n",
       "      <td>592</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>Row</td>\n",
       "      <td>2,801</td>\n",
       "      <td>3,037</td>\n",
       "      <td>1,978</td>\n",
       "      <td>2,388</td>\n",
       "      <td>2,478</td>\n",
       "      <td>2,608</td>\n",
       "      <td>2,402</td>\n",
       "      <td>1,979</td>\n",
       "      <td>...</td>\n",
       "      <td>3,553</td>\n",
       "      <td>2,659</td>\n",
       "      <td>3,754</td>\n",
       "      <td>2,546</td>\n",
       "      <td>3,105</td>\n",
       "      <td>2,555</td>\n",
       "      <td>3,218</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>Apartment and other unit type</td>\n",
       "      <td>3,428</td>\n",
       "      <td>5,457</td>\n",
       "      <td>4,238</td>\n",
       "      <td>6,363</td>\n",
       "      <td>5,084</td>\n",
       "      <td>5,196</td>\n",
       "      <td>19,312</td>\n",
       "      <td>9,320</td>\n",
       "      <td>...</td>\n",
       "      <td>10,850</td>\n",
       "      <td>8,124</td>\n",
       "      <td>10,159</td>\n",
       "      <td>8,958</td>\n",
       "      <td>6,008</td>\n",
       "      <td>9,647</td>\n",
       "      <td>8,641</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Housing estimates                   Type of unit Q3 2013 Q4 2013  \\\n",
       "0               Housing starts                    Total units  17,532  16,594   \n",
       "1               Housing starts                Single-detached   6,705   6,273   \n",
       "2               Housing starts                      Multiples  10,827  10,321   \n",
       "3               Housing starts                  Semi-detached     844     754   \n",
       "4               Housing starts                            Row   2,850   2,429   \n",
       "5               Housing starts  Apartment and other unit type   7,133   7,138   \n",
       "6   Housing under construction                    Total units  93,600  94,117   \n",
       "7   Housing under construction                Single-detached  15,601  15,310   \n",
       "8   Housing under construction                      Multiples  77,999  78,807   \n",
       "9   Housing under construction                  Semi-detached   2,629   2,448   \n",
       "10  Housing under construction                            Row   9,382   8,801   \n",
       "11  Housing under construction  Apartment and other unit type  65,988  67,558   \n",
       "12         Housing completions                    Total units  13,430  15,987   \n",
       "13         Housing completions                Single-detached   6,260   6,560   \n",
       "14         Housing completions                      Multiples   7,170   9,427   \n",
       "15         Housing completions                  Semi-detached     941     933   \n",
       "16         Housing completions                            Row   2,801   3,037   \n",
       "17         Housing completions  Apartment and other unit type   3,428   5,457   \n",
       "\n",
       "   Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015  ...  Q2 2021  Q3 2021  \\\n",
       "0   10,828  17,039  15,989  15,278  11,382  18,479  ...   25,594   27,765   \n",
       "1    3,106   6,801   7,334   6,450   2,567   7,052  ...    8,945    9,350   \n",
       "2    7,722  10,238   8,655   8,828   8,815  11,427  ...   16,649   18,415   \n",
       "3      359     898     854     631     310     522  ...      929      730   \n",
       "4    1,604   3,103   2,602   2,666   1,044   3,534  ...    3,970    4,333   \n",
       "5    5,759   6,237   5,199   5,531   7,461   7,371  ...   11,750   13,352   \n",
       "6   92,827  94,582  96,521  96,112  79,695  81,343  ...  136,919  145,712   \n",
       "7   13,268  14,424  15,701  15,300  12,274  14,618  ...   20,457   22,764   \n",
       "8   79,559  80,158  80,820  80,812  67,421  66,725  ...  116,462  122,948   \n",
       "9    2,138   2,125   2,323   2,041   1,666   1,505  ...    2,391    2,504   \n",
       "10   8,326   9,034   9,133   9,144   7,789   9,344  ...   13,860   15,507   \n",
       "11  69,095  68,999  69,364  69,627  57,966  55,876  ...  100,211  104,937   \n",
       "12  11,948  15,297  14,266  15,566  27,826  16,674  ...   21,479   18,386   \n",
       "13   5,065   5,633   6,046   6,843   5,440   4,695  ...    6,407    6,998   \n",
       "14   6,883   9,664   8,220   8,723  22,386  11,979  ...   15,072   11,388   \n",
       "15     667     913     658     919     672     680  ...      669      605   \n",
       "16   1,978   2,388   2,478   2,608   2,402   1,979  ...    3,553    2,659   \n",
       "17   4,238   6,363   5,084   5,196  19,312   9,320  ...   10,850    8,124   \n",
       "\n",
       "    Q4 2021  Q1 2022  Q2 2022  Q3 2022  Q4 2022 Q1 2023 Q2 2023 Q3 2023  \n",
       "0    24,579   17,686   22,460   29,746   26,188  18,490  25,512  24,813  \n",
       "1     6,966    4,834    6,543    7,739    6,378   4,021   4,669   4,634  \n",
       "2    17,613   12,852   15,917   22,007   19,810  14,469  20,843  20,179  \n",
       "3       655      558      618      643      453     377     425     494  \n",
       "4     4,171    2,661    5,047    5,148    4,403   2,670   3,261   4,127  \n",
       "5    12,787    9,633   10,252   16,216   14,954  11,422  17,157  15,558  \n",
       "6   148,064  147,504  153,536  164,580  171,425      ..      ..       0  \n",
       "7    22,264   21,014   21,201   23,134   23,282      ..      ..       0  \n",
       "8   125,800  126,490  132,335  141,446  148,143      ..      ..       0  \n",
       "9     2,360    2,236    2,217    2,243    2,093      ..      ..       0  \n",
       "10   15,921   15,979   17,913   20,490   21,694      ..      ..       0  \n",
       "11  107,519  108,275  112,205  118,713  124,356      ..      ..       0  \n",
       "12   22,154   18,482   16,079   18,607   18,670      ..      ..       0  \n",
       "13    7,447    6,301    6,333    5,812    6,219      ..      ..       0  \n",
       "14   14,707   12,181    9,746   12,795   12,451      ..      ..       0  \n",
       "15      794      677      633      593      592      ..      ..       0  \n",
       "16    3,754    2,546    3,105    2,555    3,218      ..      ..       0  \n",
       "17   10,159    8,958    6,008    9,647    8,641      ..      ..       0  \n",
       "\n",
       "[18 rows x 43 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"Housing estimates\"][1:6] = \"Housing starts\"\n",
    "housing[\"Housing estimates\"][7:12] = \"Housing under construction\"\n",
    "housing[\"Housing estimates\"][13:] = \"Housing completions\"\n",
    "housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Housing estimates</th>\n",
       "      <th>Q3 2013</th>\n",
       "      <th>Q4 2013</th>\n",
       "      <th>Q1 2014</th>\n",
       "      <th>Q2 2014</th>\n",
       "      <th>Q3 2014</th>\n",
       "      <th>Q4 2014</th>\n",
       "      <th>Q1 2015</th>\n",
       "      <th>Q2 2015</th>\n",
       "      <th>Q3 2015</th>\n",
       "      <th>...</th>\n",
       "      <th>Q2 2021</th>\n",
       "      <th>Q3 2021</th>\n",
       "      <th>Q4 2021</th>\n",
       "      <th>Q1 2022</th>\n",
       "      <th>Q2 2022</th>\n",
       "      <th>Q3 2022</th>\n",
       "      <th>Q4 2022</th>\n",
       "      <th>Q1 2023</th>\n",
       "      <th>Q2 2023</th>\n",
       "      <th>Q3 2023</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type of unit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total units</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>17,532</td>\n",
       "      <td>16,594</td>\n",
       "      <td>10,828</td>\n",
       "      <td>17,039</td>\n",
       "      <td>15,989</td>\n",
       "      <td>15,278</td>\n",
       "      <td>11,382</td>\n",
       "      <td>18,479</td>\n",
       "      <td>21,259</td>\n",
       "      <td>...</td>\n",
       "      <td>25,594</td>\n",
       "      <td>27,765</td>\n",
       "      <td>24,579</td>\n",
       "      <td>17,686</td>\n",
       "      <td>22,460</td>\n",
       "      <td>29,746</td>\n",
       "      <td>26,188</td>\n",
       "      <td>18,490</td>\n",
       "      <td>25,512</td>\n",
       "      <td>24,813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single-detached</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>6,705</td>\n",
       "      <td>6,273</td>\n",
       "      <td>3,106</td>\n",
       "      <td>6,801</td>\n",
       "      <td>7,334</td>\n",
       "      <td>6,450</td>\n",
       "      <td>2,567</td>\n",
       "      <td>7,052</td>\n",
       "      <td>7,738</td>\n",
       "      <td>...</td>\n",
       "      <td>8,945</td>\n",
       "      <td>9,350</td>\n",
       "      <td>6,966</td>\n",
       "      <td>4,834</td>\n",
       "      <td>6,543</td>\n",
       "      <td>7,739</td>\n",
       "      <td>6,378</td>\n",
       "      <td>4,021</td>\n",
       "      <td>4,669</td>\n",
       "      <td>4,634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiples</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>10,827</td>\n",
       "      <td>10,321</td>\n",
       "      <td>7,722</td>\n",
       "      <td>10,238</td>\n",
       "      <td>8,655</td>\n",
       "      <td>8,828</td>\n",
       "      <td>8,815</td>\n",
       "      <td>11,427</td>\n",
       "      <td>13,521</td>\n",
       "      <td>...</td>\n",
       "      <td>16,649</td>\n",
       "      <td>18,415</td>\n",
       "      <td>17,613</td>\n",
       "      <td>12,852</td>\n",
       "      <td>15,917</td>\n",
       "      <td>22,007</td>\n",
       "      <td>19,810</td>\n",
       "      <td>14,469</td>\n",
       "      <td>20,843</td>\n",
       "      <td>20,179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semi-detached</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>844</td>\n",
       "      <td>754</td>\n",
       "      <td>359</td>\n",
       "      <td>898</td>\n",
       "      <td>854</td>\n",
       "      <td>631</td>\n",
       "      <td>310</td>\n",
       "      <td>522</td>\n",
       "      <td>636</td>\n",
       "      <td>...</td>\n",
       "      <td>929</td>\n",
       "      <td>730</td>\n",
       "      <td>655</td>\n",
       "      <td>558</td>\n",
       "      <td>618</td>\n",
       "      <td>643</td>\n",
       "      <td>453</td>\n",
       "      <td>377</td>\n",
       "      <td>425</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>2,850</td>\n",
       "      <td>2,429</td>\n",
       "      <td>1,604</td>\n",
       "      <td>3,103</td>\n",
       "      <td>2,602</td>\n",
       "      <td>2,666</td>\n",
       "      <td>1,044</td>\n",
       "      <td>3,534</td>\n",
       "      <td>2,621</td>\n",
       "      <td>...</td>\n",
       "      <td>3,970</td>\n",
       "      <td>4,333</td>\n",
       "      <td>4,171</td>\n",
       "      <td>2,661</td>\n",
       "      <td>5,047</td>\n",
       "      <td>5,148</td>\n",
       "      <td>4,403</td>\n",
       "      <td>2,670</td>\n",
       "      <td>3,261</td>\n",
       "      <td>4,127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apartment and other unit type</th>\n",
       "      <td>Housing starts</td>\n",
       "      <td>7,133</td>\n",
       "      <td>7,138</td>\n",
       "      <td>5,759</td>\n",
       "      <td>6,237</td>\n",
       "      <td>5,199</td>\n",
       "      <td>5,531</td>\n",
       "      <td>7,461</td>\n",
       "      <td>7,371</td>\n",
       "      <td>10,264</td>\n",
       "      <td>...</td>\n",
       "      <td>11,750</td>\n",
       "      <td>13,352</td>\n",
       "      <td>12,787</td>\n",
       "      <td>9,633</td>\n",
       "      <td>10,252</td>\n",
       "      <td>16,216</td>\n",
       "      <td>14,954</td>\n",
       "      <td>11,422</td>\n",
       "      <td>17,157</td>\n",
       "      <td>15,558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total units</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>93,600</td>\n",
       "      <td>94,117</td>\n",
       "      <td>92,827</td>\n",
       "      <td>94,582</td>\n",
       "      <td>96,521</td>\n",
       "      <td>96,112</td>\n",
       "      <td>79,695</td>\n",
       "      <td>81,343</td>\n",
       "      <td>87,385</td>\n",
       "      <td>...</td>\n",
       "      <td>136,919</td>\n",
       "      <td>145,712</td>\n",
       "      <td>148,064</td>\n",
       "      <td>147,504</td>\n",
       "      <td>153,536</td>\n",
       "      <td>164,580</td>\n",
       "      <td>171,425</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single-detached</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>15,601</td>\n",
       "      <td>15,310</td>\n",
       "      <td>13,268</td>\n",
       "      <td>14,424</td>\n",
       "      <td>15,701</td>\n",
       "      <td>15,300</td>\n",
       "      <td>12,274</td>\n",
       "      <td>14,618</td>\n",
       "      <td>17,025</td>\n",
       "      <td>...</td>\n",
       "      <td>20,457</td>\n",
       "      <td>22,764</td>\n",
       "      <td>22,264</td>\n",
       "      <td>21,014</td>\n",
       "      <td>21,201</td>\n",
       "      <td>23,134</td>\n",
       "      <td>23,282</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiples</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>77,999</td>\n",
       "      <td>78,807</td>\n",
       "      <td>79,559</td>\n",
       "      <td>80,158</td>\n",
       "      <td>80,820</td>\n",
       "      <td>80,812</td>\n",
       "      <td>67,421</td>\n",
       "      <td>66,725</td>\n",
       "      <td>70,360</td>\n",
       "      <td>...</td>\n",
       "      <td>116,462</td>\n",
       "      <td>122,948</td>\n",
       "      <td>125,800</td>\n",
       "      <td>126,490</td>\n",
       "      <td>132,335</td>\n",
       "      <td>141,446</td>\n",
       "      <td>148,143</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semi-detached</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>2,629</td>\n",
       "      <td>2,448</td>\n",
       "      <td>2,138</td>\n",
       "      <td>2,125</td>\n",
       "      <td>2,323</td>\n",
       "      <td>2,041</td>\n",
       "      <td>1,666</td>\n",
       "      <td>1,505</td>\n",
       "      <td>1,707</td>\n",
       "      <td>...</td>\n",
       "      <td>2,391</td>\n",
       "      <td>2,504</td>\n",
       "      <td>2,360</td>\n",
       "      <td>2,236</td>\n",
       "      <td>2,217</td>\n",
       "      <td>2,243</td>\n",
       "      <td>2,093</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>9,382</td>\n",
       "      <td>8,801</td>\n",
       "      <td>8,326</td>\n",
       "      <td>9,034</td>\n",
       "      <td>9,133</td>\n",
       "      <td>9,144</td>\n",
       "      <td>7,789</td>\n",
       "      <td>9,344</td>\n",
       "      <td>9,885</td>\n",
       "      <td>...</td>\n",
       "      <td>13,860</td>\n",
       "      <td>15,507</td>\n",
       "      <td>15,921</td>\n",
       "      <td>15,979</td>\n",
       "      <td>17,913</td>\n",
       "      <td>20,490</td>\n",
       "      <td>21,694</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apartment and other unit type</th>\n",
       "      <td>Housing under construction</td>\n",
       "      <td>65,988</td>\n",
       "      <td>67,558</td>\n",
       "      <td>69,095</td>\n",
       "      <td>68,999</td>\n",
       "      <td>69,364</td>\n",
       "      <td>69,627</td>\n",
       "      <td>57,966</td>\n",
       "      <td>55,876</td>\n",
       "      <td>58,768</td>\n",
       "      <td>...</td>\n",
       "      <td>100,211</td>\n",
       "      <td>104,937</td>\n",
       "      <td>107,519</td>\n",
       "      <td>108,275</td>\n",
       "      <td>112,205</td>\n",
       "      <td>118,713</td>\n",
       "      <td>124,356</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total units</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>13,430</td>\n",
       "      <td>15,987</td>\n",
       "      <td>11,948</td>\n",
       "      <td>15,297</td>\n",
       "      <td>14,266</td>\n",
       "      <td>15,566</td>\n",
       "      <td>27,826</td>\n",
       "      <td>16,674</td>\n",
       "      <td>15,141</td>\n",
       "      <td>...</td>\n",
       "      <td>21,479</td>\n",
       "      <td>18,386</td>\n",
       "      <td>22,154</td>\n",
       "      <td>18,482</td>\n",
       "      <td>16,079</td>\n",
       "      <td>18,607</td>\n",
       "      <td>18,670</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single-detached</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>6,260</td>\n",
       "      <td>6,560</td>\n",
       "      <td>5,065</td>\n",
       "      <td>5,633</td>\n",
       "      <td>6,046</td>\n",
       "      <td>6,843</td>\n",
       "      <td>5,440</td>\n",
       "      <td>4,695</td>\n",
       "      <td>5,311</td>\n",
       "      <td>...</td>\n",
       "      <td>6,407</td>\n",
       "      <td>6,998</td>\n",
       "      <td>7,447</td>\n",
       "      <td>6,301</td>\n",
       "      <td>6,333</td>\n",
       "      <td>5,812</td>\n",
       "      <td>6,219</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multiples</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>7,170</td>\n",
       "      <td>9,427</td>\n",
       "      <td>6,883</td>\n",
       "      <td>9,664</td>\n",
       "      <td>8,220</td>\n",
       "      <td>8,723</td>\n",
       "      <td>22,386</td>\n",
       "      <td>11,979</td>\n",
       "      <td>9,830</td>\n",
       "      <td>...</td>\n",
       "      <td>15,072</td>\n",
       "      <td>11,388</td>\n",
       "      <td>14,707</td>\n",
       "      <td>12,181</td>\n",
       "      <td>9,746</td>\n",
       "      <td>12,795</td>\n",
       "      <td>12,451</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semi-detached</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>941</td>\n",
       "      <td>933</td>\n",
       "      <td>667</td>\n",
       "      <td>913</td>\n",
       "      <td>658</td>\n",
       "      <td>919</td>\n",
       "      <td>672</td>\n",
       "      <td>680</td>\n",
       "      <td>433</td>\n",
       "      <td>...</td>\n",
       "      <td>669</td>\n",
       "      <td>605</td>\n",
       "      <td>794</td>\n",
       "      <td>677</td>\n",
       "      <td>633</td>\n",
       "      <td>593</td>\n",
       "      <td>592</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>2,801</td>\n",
       "      <td>3,037</td>\n",
       "      <td>1,978</td>\n",
       "      <td>2,388</td>\n",
       "      <td>2,478</td>\n",
       "      <td>2,608</td>\n",
       "      <td>2,402</td>\n",
       "      <td>1,979</td>\n",
       "      <td>2,051</td>\n",
       "      <td>...</td>\n",
       "      <td>3,553</td>\n",
       "      <td>2,659</td>\n",
       "      <td>3,754</td>\n",
       "      <td>2,546</td>\n",
       "      <td>3,105</td>\n",
       "      <td>2,555</td>\n",
       "      <td>3,218</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apartment and other unit type</th>\n",
       "      <td>Housing completions</td>\n",
       "      <td>3,428</td>\n",
       "      <td>5,457</td>\n",
       "      <td>4,238</td>\n",
       "      <td>6,363</td>\n",
       "      <td>5,084</td>\n",
       "      <td>5,196</td>\n",
       "      <td>19,312</td>\n",
       "      <td>9,320</td>\n",
       "      <td>7,346</td>\n",
       "      <td>...</td>\n",
       "      <td>10,850</td>\n",
       "      <td>8,124</td>\n",
       "      <td>10,159</td>\n",
       "      <td>8,958</td>\n",
       "      <td>6,008</td>\n",
       "      <td>9,647</td>\n",
       "      <td>8,641</td>\n",
       "      <td>..</td>\n",
       "      <td>..</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Housing estimates Q3 2013 Q4 2013  \\\n",
       "Type of unit                                                                \n",
       "Total units                                Housing starts  17,532  16,594   \n",
       "Single-detached                            Housing starts   6,705   6,273   \n",
       "Multiples                                  Housing starts  10,827  10,321   \n",
       "Semi-detached                              Housing starts     844     754   \n",
       "Row                                        Housing starts   2,850   2,429   \n",
       "Apartment and other unit type              Housing starts   7,133   7,138   \n",
       "Total units                    Housing under construction  93,600  94,117   \n",
       "Single-detached                Housing under construction  15,601  15,310   \n",
       "Multiples                      Housing under construction  77,999  78,807   \n",
       "Semi-detached                  Housing under construction   2,629   2,448   \n",
       "Row                            Housing under construction   9,382   8,801   \n",
       "Apartment and other unit type  Housing under construction  65,988  67,558   \n",
       "Total units                           Housing completions  13,430  15,987   \n",
       "Single-detached                       Housing completions   6,260   6,560   \n",
       "Multiples                             Housing completions   7,170   9,427   \n",
       "Semi-detached                         Housing completions     941     933   \n",
       "Row                                   Housing completions   2,801   3,037   \n",
       "Apartment and other unit type         Housing completions   3,428   5,457   \n",
       "\n",
       "                              Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015  \\\n",
       "Type of unit                                                                    \n",
       "Total units                    10,828  17,039  15,989  15,278  11,382  18,479   \n",
       "Single-detached                 3,106   6,801   7,334   6,450   2,567   7,052   \n",
       "Multiples                       7,722  10,238   8,655   8,828   8,815  11,427   \n",
       "Semi-detached                     359     898     854     631     310     522   \n",
       "Row                             1,604   3,103   2,602   2,666   1,044   3,534   \n",
       "Apartment and other unit type   5,759   6,237   5,199   5,531   7,461   7,371   \n",
       "Total units                    92,827  94,582  96,521  96,112  79,695  81,343   \n",
       "Single-detached                13,268  14,424  15,701  15,300  12,274  14,618   \n",
       "Multiples                      79,559  80,158  80,820  80,812  67,421  66,725   \n",
       "Semi-detached                   2,138   2,125   2,323   2,041   1,666   1,505   \n",
       "Row                             8,326   9,034   9,133   9,144   7,789   9,344   \n",
       "Apartment and other unit type  69,095  68,999  69,364  69,627  57,966  55,876   \n",
       "Total units                    11,948  15,297  14,266  15,566  27,826  16,674   \n",
       "Single-detached                 5,065   5,633   6,046   6,843   5,440   4,695   \n",
       "Multiples                       6,883   9,664   8,220   8,723  22,386  11,979   \n",
       "Semi-detached                     667     913     658     919     672     680   \n",
       "Row                             1,978   2,388   2,478   2,608   2,402   1,979   \n",
       "Apartment and other unit type   4,238   6,363   5,084   5,196  19,312   9,320   \n",
       "\n",
       "                              Q3 2015  ...  Q2 2021  Q3 2021  Q4 2021  \\\n",
       "Type of unit                           ...                              \n",
       "Total units                    21,259  ...   25,594   27,765   24,579   \n",
       "Single-detached                 7,738  ...    8,945    9,350    6,966   \n",
       "Multiples                      13,521  ...   16,649   18,415   17,613   \n",
       "Semi-detached                     636  ...      929      730      655   \n",
       "Row                             2,621  ...    3,970    4,333    4,171   \n",
       "Apartment and other unit type  10,264  ...   11,750   13,352   12,787   \n",
       "Total units                    87,385  ...  136,919  145,712  148,064   \n",
       "Single-detached                17,025  ...   20,457   22,764   22,264   \n",
       "Multiples                      70,360  ...  116,462  122,948  125,800   \n",
       "Semi-detached                   1,707  ...    2,391    2,504    2,360   \n",
       "Row                             9,885  ...   13,860   15,507   15,921   \n",
       "Apartment and other unit type  58,768  ...  100,211  104,937  107,519   \n",
       "Total units                    15,141  ...   21,479   18,386   22,154   \n",
       "Single-detached                 5,311  ...    6,407    6,998    7,447   \n",
       "Multiples                       9,830  ...   15,072   11,388   14,707   \n",
       "Semi-detached                     433  ...      669      605      794   \n",
       "Row                             2,051  ...    3,553    2,659    3,754   \n",
       "Apartment and other unit type   7,346  ...   10,850    8,124   10,159   \n",
       "\n",
       "                               Q1 2022  Q2 2022  Q3 2022  Q4 2022 Q1 2023  \\\n",
       "Type of unit                                                                \n",
       "Total units                     17,686   22,460   29,746   26,188  18,490   \n",
       "Single-detached                  4,834    6,543    7,739    6,378   4,021   \n",
       "Multiples                       12,852   15,917   22,007   19,810  14,469   \n",
       "Semi-detached                      558      618      643      453     377   \n",
       "Row                              2,661    5,047    5,148    4,403   2,670   \n",
       "Apartment and other unit type    9,633   10,252   16,216   14,954  11,422   \n",
       "Total units                    147,504  153,536  164,580  171,425      ..   \n",
       "Single-detached                 21,014   21,201   23,134   23,282      ..   \n",
       "Multiples                      126,490  132,335  141,446  148,143      ..   \n",
       "Semi-detached                    2,236    2,217    2,243    2,093      ..   \n",
       "Row                             15,979   17,913   20,490   21,694      ..   \n",
       "Apartment and other unit type  108,275  112,205  118,713  124,356      ..   \n",
       "Total units                     18,482   16,079   18,607   18,670      ..   \n",
       "Single-detached                  6,301    6,333    5,812    6,219      ..   \n",
       "Multiples                       12,181    9,746   12,795   12,451      ..   \n",
       "Semi-detached                      677      633      593      592      ..   \n",
       "Row                              2,546    3,105    2,555    3,218      ..   \n",
       "Apartment and other unit type    8,958    6,008    9,647    8,641      ..   \n",
       "\n",
       "                              Q2 2023 Q3 2023  \n",
       "Type of unit                                   \n",
       "Total units                    25,512  24,813  \n",
       "Single-detached                 4,669   4,634  \n",
       "Multiples                      20,843  20,179  \n",
       "Semi-detached                     425     494  \n",
       "Row                             3,261   4,127  \n",
       "Apartment and other unit type  17,157  15,558  \n",
       "Total units                        ..       0  \n",
       "Single-detached                    ..       0  \n",
       "Multiples                          ..       0  \n",
       "Semi-detached                      ..       0  \n",
       "Row                                ..       0  \n",
       "Apartment and other unit type      ..       0  \n",
       "Total units                        ..       0  \n",
       "Single-detached                    ..       0  \n",
       "Multiples                          ..       0  \n",
       "Semi-detached                      ..       0  \n",
       "Row                                ..       0  \n",
       "Apartment and other unit type      ..       0  \n",
       "\n",
       "[18 rows x 42 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.set_index(\"Type of unit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Housing estimates\n",
       "Housing completions           6\n",
       "Housing starts                6\n",
       "Housing under construction    6\n",
       "Name: Type of unit, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.groupby([\"Housing estimates\"])[\"Type of unit\"].count()  ##check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_start = housing.iloc[:6, 1:]\n",
    "housing_under_construction = housing.iloc[6:12, 1:]\n",
    "housing_completions = housing.iloc[12:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_s = housing_start.set_index(\"Type of unit\").transpose()\n",
    "housing_s = housing_s.reset_index()\n",
    "housing_s = housing_s.rename(columns={\"index\": \"Quarter\"})\n",
    "\n",
    "housing_uc = housing_under_construction.set_index(\"Type of unit\").transpose()\n",
    "housing_uc = housing_uc.reset_index()\n",
    "housing_uc = housing_uc.rename(columns={\"index\": \"Quarter\"})\n",
    "\n",
    "housing_c = housing_completions.set_index(\"Type of unit\").transpose()\n",
    "housing_c = housing_c.reset_index()\n",
    "housing_c = housing_c.rename(columns={\"index\": \"Quarter\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index(['Total units', 'Single-detached', 'Multiples', 'Semi-detached', 'Row',\n",
    "       'Apartment and other unit type'],\n",
    "      dtype='object', name='Type of unit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Type of unit</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Total units</th>\n",
       "      <th>Single-detached</th>\n",
       "      <th>Multiples</th>\n",
       "      <th>Semi-detached</th>\n",
       "      <th>Row</th>\n",
       "      <th>Apartment and other unit type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3 2013</td>\n",
       "      <td>17,532</td>\n",
       "      <td>6,705</td>\n",
       "      <td>10,827</td>\n",
       "      <td>844</td>\n",
       "      <td>2,850</td>\n",
       "      <td>7,133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q4 2013</td>\n",
       "      <td>16,594</td>\n",
       "      <td>6,273</td>\n",
       "      <td>10,321</td>\n",
       "      <td>754</td>\n",
       "      <td>2,429</td>\n",
       "      <td>7,138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q1 2014</td>\n",
       "      <td>10,828</td>\n",
       "      <td>3,106</td>\n",
       "      <td>7,722</td>\n",
       "      <td>359</td>\n",
       "      <td>1,604</td>\n",
       "      <td>5,759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q2 2014</td>\n",
       "      <td>17,039</td>\n",
       "      <td>6,801</td>\n",
       "      <td>10,238</td>\n",
       "      <td>898</td>\n",
       "      <td>3,103</td>\n",
       "      <td>6,237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q3 2014</td>\n",
       "      <td>15,989</td>\n",
       "      <td>7,334</td>\n",
       "      <td>8,655</td>\n",
       "      <td>854</td>\n",
       "      <td>2,602</td>\n",
       "      <td>5,199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q4 2014</td>\n",
       "      <td>15,278</td>\n",
       "      <td>6,450</td>\n",
       "      <td>8,828</td>\n",
       "      <td>631</td>\n",
       "      <td>2,666</td>\n",
       "      <td>5,531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q1 2015</td>\n",
       "      <td>11,382</td>\n",
       "      <td>2,567</td>\n",
       "      <td>8,815</td>\n",
       "      <td>310</td>\n",
       "      <td>1,044</td>\n",
       "      <td>7,461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q2 2015</td>\n",
       "      <td>18,479</td>\n",
       "      <td>7,052</td>\n",
       "      <td>11,427</td>\n",
       "      <td>522</td>\n",
       "      <td>3,534</td>\n",
       "      <td>7,371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Q3 2015</td>\n",
       "      <td>21,259</td>\n",
       "      <td>7,738</td>\n",
       "      <td>13,521</td>\n",
       "      <td>636</td>\n",
       "      <td>2,621</td>\n",
       "      <td>10,264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Q4 2015</td>\n",
       "      <td>19,036</td>\n",
       "      <td>7,596</td>\n",
       "      <td>11,440</td>\n",
       "      <td>622</td>\n",
       "      <td>3,240</td>\n",
       "      <td>7,578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Q1 2016</td>\n",
       "      <td>14,789</td>\n",
       "      <td>4,389</td>\n",
       "      <td>10,400</td>\n",
       "      <td>318</td>\n",
       "      <td>1,816</td>\n",
       "      <td>8,266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Q2 2016</td>\n",
       "      <td>20,200</td>\n",
       "      <td>8,132</td>\n",
       "      <td>12,068</td>\n",
       "      <td>765</td>\n",
       "      <td>3,001</td>\n",
       "      <td>8,302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Q3 2016</td>\n",
       "      <td>20,151</td>\n",
       "      <td>8,704</td>\n",
       "      <td>11,447</td>\n",
       "      <td>743</td>\n",
       "      <td>3,372</td>\n",
       "      <td>7,332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Q4 2016</td>\n",
       "      <td>19,812</td>\n",
       "      <td>8,854</td>\n",
       "      <td>10,958</td>\n",
       "      <td>549</td>\n",
       "      <td>3,487</td>\n",
       "      <td>6,922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Q1 2017</td>\n",
       "      <td>17,555</td>\n",
       "      <td>5,516</td>\n",
       "      <td>12,039</td>\n",
       "      <td>529</td>\n",
       "      <td>3,021</td>\n",
       "      <td>8,489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Q2 2017</td>\n",
       "      <td>18,423</td>\n",
       "      <td>7,957</td>\n",
       "      <td>10,466</td>\n",
       "      <td>733</td>\n",
       "      <td>3,426</td>\n",
       "      <td>6,307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Q3 2017</td>\n",
       "      <td>23,248</td>\n",
       "      <td>8,792</td>\n",
       "      <td>14,456</td>\n",
       "      <td>959</td>\n",
       "      <td>4,746</td>\n",
       "      <td>8,751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Q4 2017</td>\n",
       "      <td>19,897</td>\n",
       "      <td>7,448</td>\n",
       "      <td>12,449</td>\n",
       "      <td>767</td>\n",
       "      <td>4,235</td>\n",
       "      <td>7,447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Q1 2018</td>\n",
       "      <td>18,143</td>\n",
       "      <td>4,530</td>\n",
       "      <td>13,613</td>\n",
       "      <td>568</td>\n",
       "      <td>1,721</td>\n",
       "      <td>11,324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Q2 2018</td>\n",
       "      <td>20,052</td>\n",
       "      <td>6,779</td>\n",
       "      <td>13,273</td>\n",
       "      <td>598</td>\n",
       "      <td>3,168</td>\n",
       "      <td>9,507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Q3 2018</td>\n",
       "      <td>18,822</td>\n",
       "      <td>6,521</td>\n",
       "      <td>12,301</td>\n",
       "      <td>741</td>\n",
       "      <td>3,000</td>\n",
       "      <td>8,560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Q4 2018</td>\n",
       "      <td>21,725</td>\n",
       "      <td>5,956</td>\n",
       "      <td>15,769</td>\n",
       "      <td>577</td>\n",
       "      <td>3,930</td>\n",
       "      <td>11,262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Q1 2019</td>\n",
       "      <td>13,044</td>\n",
       "      <td>2,786</td>\n",
       "      <td>10,258</td>\n",
       "      <td>266</td>\n",
       "      <td>2,414</td>\n",
       "      <td>7,578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Q2 2019</td>\n",
       "      <td>17,455</td>\n",
       "      <td>5,108</td>\n",
       "      <td>12,347</td>\n",
       "      <td>454</td>\n",
       "      <td>3,586</td>\n",
       "      <td>8,307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Q3 2019</td>\n",
       "      <td>21,265</td>\n",
       "      <td>6,129</td>\n",
       "      <td>15,136</td>\n",
       "      <td>557</td>\n",
       "      <td>4,058</td>\n",
       "      <td>10,521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Q4 2019</td>\n",
       "      <td>17,221</td>\n",
       "      <td>5,264</td>\n",
       "      <td>11,957</td>\n",
       "      <td>538</td>\n",
       "      <td>3,527</td>\n",
       "      <td>7,892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Q1 2020</td>\n",
       "      <td>14,594</td>\n",
       "      <td>3,614</td>\n",
       "      <td>10,980</td>\n",
       "      <td>336</td>\n",
       "      <td>2,531</td>\n",
       "      <td>8,113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Q2 2020</td>\n",
       "      <td>19,842</td>\n",
       "      <td>5,112</td>\n",
       "      <td>14,730</td>\n",
       "      <td>614</td>\n",
       "      <td>2,974</td>\n",
       "      <td>11,142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Q3 2020</td>\n",
       "      <td>25,160</td>\n",
       "      <td>6,670</td>\n",
       "      <td>18,490</td>\n",
       "      <td>706</td>\n",
       "      <td>3,747</td>\n",
       "      <td>14,037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Q4 2020</td>\n",
       "      <td>21,709</td>\n",
       "      <td>7,286</td>\n",
       "      <td>14,423</td>\n",
       "      <td>1,068</td>\n",
       "      <td>3,549</td>\n",
       "      <td>9,806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Q1 2021</td>\n",
       "      <td>21,628</td>\n",
       "      <td>6,092</td>\n",
       "      <td>15,536</td>\n",
       "      <td>579</td>\n",
       "      <td>2,736</td>\n",
       "      <td>12,221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Q2 2021</td>\n",
       "      <td>25,594</td>\n",
       "      <td>8,945</td>\n",
       "      <td>16,649</td>\n",
       "      <td>929</td>\n",
       "      <td>3,970</td>\n",
       "      <td>11,750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Q3 2021</td>\n",
       "      <td>27,765</td>\n",
       "      <td>9,350</td>\n",
       "      <td>18,415</td>\n",
       "      <td>730</td>\n",
       "      <td>4,333</td>\n",
       "      <td>13,352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Q4 2021</td>\n",
       "      <td>24,579</td>\n",
       "      <td>6,966</td>\n",
       "      <td>17,613</td>\n",
       "      <td>655</td>\n",
       "      <td>4,171</td>\n",
       "      <td>12,787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Q1 2022</td>\n",
       "      <td>17,686</td>\n",
       "      <td>4,834</td>\n",
       "      <td>12,852</td>\n",
       "      <td>558</td>\n",
       "      <td>2,661</td>\n",
       "      <td>9,633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Q2 2022</td>\n",
       "      <td>22,460</td>\n",
       "      <td>6,543</td>\n",
       "      <td>15,917</td>\n",
       "      <td>618</td>\n",
       "      <td>5,047</td>\n",
       "      <td>10,252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Q3 2022</td>\n",
       "      <td>29,746</td>\n",
       "      <td>7,739</td>\n",
       "      <td>22,007</td>\n",
       "      <td>643</td>\n",
       "      <td>5,148</td>\n",
       "      <td>16,216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Q4 2022</td>\n",
       "      <td>26,188</td>\n",
       "      <td>6,378</td>\n",
       "      <td>19,810</td>\n",
       "      <td>453</td>\n",
       "      <td>4,403</td>\n",
       "      <td>14,954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Q1 2023</td>\n",
       "      <td>18,490</td>\n",
       "      <td>4,021</td>\n",
       "      <td>14,469</td>\n",
       "      <td>377</td>\n",
       "      <td>2,670</td>\n",
       "      <td>11,422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Q2 2023</td>\n",
       "      <td>25,512</td>\n",
       "      <td>4,669</td>\n",
       "      <td>20,843</td>\n",
       "      <td>425</td>\n",
       "      <td>3,261</td>\n",
       "      <td>17,157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Q3 2023</td>\n",
       "      <td>24,813</td>\n",
       "      <td>4,634</td>\n",
       "      <td>20,179</td>\n",
       "      <td>494</td>\n",
       "      <td>4,127</td>\n",
       "      <td>15,558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Type of unit  Quarter Total units Single-detached Multiples Semi-detached  \\\n",
       "0             Q3 2013      17,532           6,705    10,827           844   \n",
       "1             Q4 2013      16,594           6,273    10,321           754   \n",
       "2             Q1 2014      10,828           3,106     7,722           359   \n",
       "3             Q2 2014      17,039           6,801    10,238           898   \n",
       "4             Q3 2014      15,989           7,334     8,655           854   \n",
       "5             Q4 2014      15,278           6,450     8,828           631   \n",
       "6             Q1 2015      11,382           2,567     8,815           310   \n",
       "7             Q2 2015      18,479           7,052    11,427           522   \n",
       "8             Q3 2015      21,259           7,738    13,521           636   \n",
       "9             Q4 2015      19,036           7,596    11,440           622   \n",
       "10            Q1 2016      14,789           4,389    10,400           318   \n",
       "11            Q2 2016      20,200           8,132    12,068           765   \n",
       "12            Q3 2016      20,151           8,704    11,447           743   \n",
       "13            Q4 2016      19,812           8,854    10,958           549   \n",
       "14            Q1 2017      17,555           5,516    12,039           529   \n",
       "15            Q2 2017      18,423           7,957    10,466           733   \n",
       "16            Q3 2017      23,248           8,792    14,456           959   \n",
       "17            Q4 2017      19,897           7,448    12,449           767   \n",
       "18            Q1 2018      18,143           4,530    13,613           568   \n",
       "19            Q2 2018      20,052           6,779    13,273           598   \n",
       "20            Q3 2018      18,822           6,521    12,301           741   \n",
       "21            Q4 2018      21,725           5,956    15,769           577   \n",
       "22            Q1 2019      13,044           2,786    10,258           266   \n",
       "23            Q2 2019      17,455           5,108    12,347           454   \n",
       "24            Q3 2019      21,265           6,129    15,136           557   \n",
       "25            Q4 2019      17,221           5,264    11,957           538   \n",
       "26            Q1 2020      14,594           3,614    10,980           336   \n",
       "27            Q2 2020      19,842           5,112    14,730           614   \n",
       "28            Q3 2020      25,160           6,670    18,490           706   \n",
       "29            Q4 2020      21,709           7,286    14,423         1,068   \n",
       "30            Q1 2021      21,628           6,092    15,536           579   \n",
       "31            Q2 2021      25,594           8,945    16,649           929   \n",
       "32            Q3 2021      27,765           9,350    18,415           730   \n",
       "33            Q4 2021      24,579           6,966    17,613           655   \n",
       "34            Q1 2022      17,686           4,834    12,852           558   \n",
       "35            Q2 2022      22,460           6,543    15,917           618   \n",
       "36            Q3 2022      29,746           7,739    22,007           643   \n",
       "37            Q4 2022      26,188           6,378    19,810           453   \n",
       "38            Q1 2023      18,490           4,021    14,469           377   \n",
       "39            Q2 2023      25,512           4,669    20,843           425   \n",
       "40            Q3 2023      24,813           4,634    20,179           494   \n",
       "\n",
       "Type of unit    Row Apartment and other unit type  \n",
       "0             2,850                         7,133  \n",
       "1             2,429                         7,138  \n",
       "2             1,604                         5,759  \n",
       "3             3,103                         6,237  \n",
       "4             2,602                         5,199  \n",
       "5             2,666                         5,531  \n",
       "6             1,044                         7,461  \n",
       "7             3,534                         7,371  \n",
       "8             2,621                        10,264  \n",
       "9             3,240                         7,578  \n",
       "10            1,816                         8,266  \n",
       "11            3,001                         8,302  \n",
       "12            3,372                         7,332  \n",
       "13            3,487                         6,922  \n",
       "14            3,021                         8,489  \n",
       "15            3,426                         6,307  \n",
       "16            4,746                         8,751  \n",
       "17            4,235                         7,447  \n",
       "18            1,721                        11,324  \n",
       "19            3,168                         9,507  \n",
       "20            3,000                         8,560  \n",
       "21            3,930                        11,262  \n",
       "22            2,414                         7,578  \n",
       "23            3,586                         8,307  \n",
       "24            4,058                        10,521  \n",
       "25            3,527                         7,892  \n",
       "26            2,531                         8,113  \n",
       "27            2,974                        11,142  \n",
       "28            3,747                        14,037  \n",
       "29            3,549                         9,806  \n",
       "30            2,736                        12,221  \n",
       "31            3,970                        11,750  \n",
       "32            4,333                        13,352  \n",
       "33            4,171                        12,787  \n",
       "34            2,661                         9,633  \n",
       "35            5,047                        10,252  \n",
       "36            5,148                        16,216  \n",
       "37            4,403                        14,954  \n",
       "38            2,670                        11,422  \n",
       "39            3,261                        17,157  \n",
       "40            4,127                        15,558  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_s_copy = housing_s\n",
    "housing_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 7 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   Quarter                        41 non-null     object\n",
      " 1   Total units                    41 non-null     object\n",
      " 2   Single-detached                41 non-null     object\n",
      " 3   Multiples                      41 non-null     object\n",
      " 4   Semi-detached                  41 non-null     object\n",
      " 5   Row                            41 non-null     object\n",
      " 6   Apartment and other unit type  41 non-null     object\n",
      "dtypes: object(7)\n",
      "memory usage: 2.4+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Quarter', 'Total units', 'Single-detached', 'Multiples',\n",
       "       'Semi-detached', 'Row', 'Apartment and other unit type'],\n",
       "      dtype='object', name='Type of unit')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_s_copy.info()\n",
    "housing_s_copy.columns    ### Dtype = object creates diffculties for plotting; should contain int dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in housing_s_copy.iterrows():\n",
    "    housing_s_copy.at[index, \"Total units\"] = row[\"Total units\"].replace(',', '')\n",
    "    housing_s_copy.at[index, \"Single-detached\"] = row[\"Single-detached\"].replace(',', '')\n",
    "    housing_s_copy.at[index, \"Multiples\"] = row[\"Multiples\"].replace(',', '')\n",
    "    housing_s_copy.at[index, \"Semi-detached\"] = row[\"Semi-detached\"].replace(',', '')\n",
    "    housing_s_copy.at[index, \"Row\"] = row[\"Row\"].replace(',', '')\n",
    "    housing_s_copy.at[index, \"Apartment and other unit type\"] = row[\"Apartment and other unit type\"].replace(',', '')\n",
    "\n",
    "for index, row in housing_uc.iterrows():\n",
    "    housing_uc.at[index, \"Total units\"] = row[\"Total units\"].replace(',', '')\n",
    "    housing_uc.at[index, \"Single-detached\"] = row[\"Single-detached\"].replace(',', '')\n",
    "    housing_uc.at[index, \"Multiples\"] = row[\"Multiples\"].replace(',', '')\n",
    "    housing_uc.at[index, \"Semi-detached\"] = row[\"Semi-detached\"].replace(',', '')\n",
    "    housing_uc.at[index, \"Row\"] = row[\"Row\"].replace(',', '')\n",
    "    housing_uc.at[index, \"Apartment and other unit type\"] = row[\"Apartment and other unit type\"].replace(',', '')\n",
    "\n",
    "for index, row in housing_c.iterrows():\n",
    "    housing_c.at[index, \"Total units\"] = row[\"Total units\"].replace(',', '')\n",
    "    housing_c.at[index, \"Single-detached\"] = row[\"Single-detached\"].replace(',', '')\n",
    "    housing_c.at[index, \"Multiples\"] = row[\"Multiples\"].replace(',', '')\n",
    "    housing_c.at[index, \"Semi-detached\"] = row[\"Semi-detached\"].replace(',', '')\n",
    "    housing_c.at[index, \"Row\"] = row[\"Row\"].replace(',', '')\n",
    "    housing_c.at[index, \"Apartment and other unit type\"] = row[\"Apartment and other unit type\"].replace(',', '')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Type of unit</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Total units</th>\n",
       "      <th>Single-detached</th>\n",
       "      <th>Multiples</th>\n",
       "      <th>Semi-detached</th>\n",
       "      <th>Row</th>\n",
       "      <th>Apartment and other unit type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3 2013</td>\n",
       "      <td>93600</td>\n",
       "      <td>15601</td>\n",
       "      <td>77999</td>\n",
       "      <td>2629</td>\n",
       "      <td>9382</td>\n",
       "      <td>65988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q4 2013</td>\n",
       "      <td>94117</td>\n",
       "      <td>15310</td>\n",
       "      <td>78807</td>\n",
       "      <td>2448</td>\n",
       "      <td>8801</td>\n",
       "      <td>67558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q1 2014</td>\n",
       "      <td>92827</td>\n",
       "      <td>13268</td>\n",
       "      <td>79559</td>\n",
       "      <td>2138</td>\n",
       "      <td>8326</td>\n",
       "      <td>69095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q2 2014</td>\n",
       "      <td>94582</td>\n",
       "      <td>14424</td>\n",
       "      <td>80158</td>\n",
       "      <td>2125</td>\n",
       "      <td>9034</td>\n",
       "      <td>68999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q3 2014</td>\n",
       "      <td>96521</td>\n",
       "      <td>15701</td>\n",
       "      <td>80820</td>\n",
       "      <td>2323</td>\n",
       "      <td>9133</td>\n",
       "      <td>69364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q4 2014</td>\n",
       "      <td>96112</td>\n",
       "      <td>15300</td>\n",
       "      <td>80812</td>\n",
       "      <td>2041</td>\n",
       "      <td>9144</td>\n",
       "      <td>69627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q1 2015</td>\n",
       "      <td>79695</td>\n",
       "      <td>12274</td>\n",
       "      <td>67421</td>\n",
       "      <td>1666</td>\n",
       "      <td>7789</td>\n",
       "      <td>57966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q2 2015</td>\n",
       "      <td>81343</td>\n",
       "      <td>14618</td>\n",
       "      <td>66725</td>\n",
       "      <td>1505</td>\n",
       "      <td>9344</td>\n",
       "      <td>55876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Q3 2015</td>\n",
       "      <td>87385</td>\n",
       "      <td>17025</td>\n",
       "      <td>70360</td>\n",
       "      <td>1707</td>\n",
       "      <td>9885</td>\n",
       "      <td>58768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Q4 2015</td>\n",
       "      <td>91625</td>\n",
       "      <td>18310</td>\n",
       "      <td>73315</td>\n",
       "      <td>1853</td>\n",
       "      <td>11141</td>\n",
       "      <td>60321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Q1 2016</td>\n",
       "      <td>90278</td>\n",
       "      <td>16691</td>\n",
       "      <td>73587</td>\n",
       "      <td>1635</td>\n",
       "      <td>10122</td>\n",
       "      <td>61830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Q2 2016</td>\n",
       "      <td>96428</td>\n",
       "      <td>18445</td>\n",
       "      <td>77983</td>\n",
       "      <td>1883</td>\n",
       "      <td>10642</td>\n",
       "      <td>65458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Q3 2016</td>\n",
       "      <td>98140</td>\n",
       "      <td>20957</td>\n",
       "      <td>77183</td>\n",
       "      <td>2090</td>\n",
       "      <td>11334</td>\n",
       "      <td>63759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Q4 2016</td>\n",
       "      <td>100199</td>\n",
       "      <td>22481</td>\n",
       "      <td>77718</td>\n",
       "      <td>2003</td>\n",
       "      <td>12601</td>\n",
       "      <td>63114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Q1 2017</td>\n",
       "      <td>98974</td>\n",
       "      <td>20953</td>\n",
       "      <td>78021</td>\n",
       "      <td>1858</td>\n",
       "      <td>12733</td>\n",
       "      <td>63430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Q2 2017</td>\n",
       "      <td>100898</td>\n",
       "      <td>21320</td>\n",
       "      <td>79578</td>\n",
       "      <td>2073</td>\n",
       "      <td>13135</td>\n",
       "      <td>64370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Q3 2017</td>\n",
       "      <td>104860</td>\n",
       "      <td>21750</td>\n",
       "      <td>83110</td>\n",
       "      <td>2238</td>\n",
       "      <td>14117</td>\n",
       "      <td>66755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Q4 2017</td>\n",
       "      <td>107048</td>\n",
       "      <td>21339</td>\n",
       "      <td>85709</td>\n",
       "      <td>2301</td>\n",
       "      <td>15613</td>\n",
       "      <td>67795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Q1 2018</td>\n",
       "      <td>106829</td>\n",
       "      <td>19385</td>\n",
       "      <td>87444</td>\n",
       "      <td>2224</td>\n",
       "      <td>14192</td>\n",
       "      <td>71028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Q2 2018</td>\n",
       "      <td>106701</td>\n",
       "      <td>18857</td>\n",
       "      <td>87844</td>\n",
       "      <td>2124</td>\n",
       "      <td>12654</td>\n",
       "      <td>73066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Q3 2018</td>\n",
       "      <td>106311</td>\n",
       "      <td>18648</td>\n",
       "      <td>87663</td>\n",
       "      <td>2132</td>\n",
       "      <td>12053</td>\n",
       "      <td>73478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Q4 2018</td>\n",
       "      <td>109011</td>\n",
       "      <td>17948</td>\n",
       "      <td>91063</td>\n",
       "      <td>1987</td>\n",
       "      <td>12681</td>\n",
       "      <td>76395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Q1 2019</td>\n",
       "      <td>108420</td>\n",
       "      <td>15255</td>\n",
       "      <td>93165</td>\n",
       "      <td>1664</td>\n",
       "      <td>12353</td>\n",
       "      <td>79148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Q2 2019</td>\n",
       "      <td>110671</td>\n",
       "      <td>14411</td>\n",
       "      <td>96260</td>\n",
       "      <td>1588</td>\n",
       "      <td>13030</td>\n",
       "      <td>81642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Q3 2019</td>\n",
       "      <td>115874</td>\n",
       "      <td>15359</td>\n",
       "      <td>100515</td>\n",
       "      <td>1589</td>\n",
       "      <td>14205</td>\n",
       "      <td>84721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Q4 2019</td>\n",
       "      <td>118576</td>\n",
       "      <td>15452</td>\n",
       "      <td>103124</td>\n",
       "      <td>1578</td>\n",
       "      <td>14401</td>\n",
       "      <td>87145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Q1 2020</td>\n",
       "      <td>116951</td>\n",
       "      <td>14824</td>\n",
       "      <td>102127</td>\n",
       "      <td>1435</td>\n",
       "      <td>13346</td>\n",
       "      <td>87346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Q2 2020</td>\n",
       "      <td>120858</td>\n",
       "      <td>15069</td>\n",
       "      <td>105789</td>\n",
       "      <td>1604</td>\n",
       "      <td>13178</td>\n",
       "      <td>91007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Q3 2020</td>\n",
       "      <td>126826</td>\n",
       "      <td>16428</td>\n",
       "      <td>110398</td>\n",
       "      <td>1823</td>\n",
       "      <td>13673</td>\n",
       "      <td>94902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Q4 2020</td>\n",
       "      <td>131493</td>\n",
       "      <td>17864</td>\n",
       "      <td>113629</td>\n",
       "      <td>2129</td>\n",
       "      <td>13725</td>\n",
       "      <td>97775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Q1 2021</td>\n",
       "      <td>132762</td>\n",
       "      <td>17944</td>\n",
       "      <td>114818</td>\n",
       "      <td>2131</td>\n",
       "      <td>13532</td>\n",
       "      <td>99155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Q2 2021</td>\n",
       "      <td>136919</td>\n",
       "      <td>20457</td>\n",
       "      <td>116462</td>\n",
       "      <td>2391</td>\n",
       "      <td>13860</td>\n",
       "      <td>100211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Q3 2021</td>\n",
       "      <td>145712</td>\n",
       "      <td>22764</td>\n",
       "      <td>122948</td>\n",
       "      <td>2504</td>\n",
       "      <td>15507</td>\n",
       "      <td>104937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Q4 2021</td>\n",
       "      <td>148064</td>\n",
       "      <td>22264</td>\n",
       "      <td>125800</td>\n",
       "      <td>2360</td>\n",
       "      <td>15921</td>\n",
       "      <td>107519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Q1 2022</td>\n",
       "      <td>147504</td>\n",
       "      <td>21014</td>\n",
       "      <td>126490</td>\n",
       "      <td>2236</td>\n",
       "      <td>15979</td>\n",
       "      <td>108275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Q2 2022</td>\n",
       "      <td>153536</td>\n",
       "      <td>21201</td>\n",
       "      <td>132335</td>\n",
       "      <td>2217</td>\n",
       "      <td>17913</td>\n",
       "      <td>112205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Q3 2022</td>\n",
       "      <td>164580</td>\n",
       "      <td>23134</td>\n",
       "      <td>141446</td>\n",
       "      <td>2243</td>\n",
       "      <td>20490</td>\n",
       "      <td>118713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Q4 2022</td>\n",
       "      <td>171425</td>\n",
       "      <td>23282</td>\n",
       "      <td>148143</td>\n",
       "      <td>2093</td>\n",
       "      <td>21694</td>\n",
       "      <td>124356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Q1 2023</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>39</th>\n",
       "      <td>Q2 2023</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>40</th>\n",
       "      <td>Q3 2023</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Type of unit  Quarter Total units Single-detached Multiples Semi-detached  \\\n",
       "0             Q3 2013       93600           15601     77999          2629   \n",
       "1             Q4 2013       94117           15310     78807          2448   \n",
       "2             Q1 2014       92827           13268     79559          2138   \n",
       "3             Q2 2014       94582           14424     80158          2125   \n",
       "4             Q3 2014       96521           15701     80820          2323   \n",
       "5             Q4 2014       96112           15300     80812          2041   \n",
       "6             Q1 2015       79695           12274     67421          1666   \n",
       "7             Q2 2015       81343           14618     66725          1505   \n",
       "8             Q3 2015       87385           17025     70360          1707   \n",
       "9             Q4 2015       91625           18310     73315          1853   \n",
       "10            Q1 2016       90278           16691     73587          1635   \n",
       "11            Q2 2016       96428           18445     77983          1883   \n",
       "12            Q3 2016       98140           20957     77183          2090   \n",
       "13            Q4 2016      100199           22481     77718          2003   \n",
       "14            Q1 2017       98974           20953     78021          1858   \n",
       "15            Q2 2017      100898           21320     79578          2073   \n",
       "16            Q3 2017      104860           21750     83110          2238   \n",
       "17            Q4 2017      107048           21339     85709          2301   \n",
       "18            Q1 2018      106829           19385     87444          2224   \n",
       "19            Q2 2018      106701           18857     87844          2124   \n",
       "20            Q3 2018      106311           18648     87663          2132   \n",
       "21            Q4 2018      109011           17948     91063          1987   \n",
       "22            Q1 2019      108420           15255     93165          1664   \n",
       "23            Q2 2019      110671           14411     96260          1588   \n",
       "24            Q3 2019      115874           15359    100515          1589   \n",
       "25            Q4 2019      118576           15452    103124          1578   \n",
       "26            Q1 2020      116951           14824    102127          1435   \n",
       "27            Q2 2020      120858           15069    105789          1604   \n",
       "28            Q3 2020      126826           16428    110398          1823   \n",
       "29            Q4 2020      131493           17864    113629          2129   \n",
       "30            Q1 2021      132762           17944    114818          2131   \n",
       "31            Q2 2021      136919           20457    116462          2391   \n",
       "32            Q3 2021      145712           22764    122948          2504   \n",
       "33            Q4 2021      148064           22264    125800          2360   \n",
       "34            Q1 2022      147504           21014    126490          2236   \n",
       "35            Q2 2022      153536           21201    132335          2217   \n",
       "36            Q3 2022      164580           23134    141446          2243   \n",
       "37            Q4 2022      171425           23282    148143          2093   \n",
       "38            Q1 2023          ..              ..        ..            ..   \n",
       "39            Q2 2023          ..              ..        ..            ..   \n",
       "40            Q3 2023           0               0         0             0   \n",
       "\n",
       "Type of unit    Row Apartment and other unit type  \n",
       "0              9382                         65988  \n",
       "1              8801                         67558  \n",
       "2              8326                         69095  \n",
       "3              9034                         68999  \n",
       "4              9133                         69364  \n",
       "5              9144                         69627  \n",
       "6              7789                         57966  \n",
       "7              9344                         55876  \n",
       "8              9885                         58768  \n",
       "9             11141                         60321  \n",
       "10            10122                         61830  \n",
       "11            10642                         65458  \n",
       "12            11334                         63759  \n",
       "13            12601                         63114  \n",
       "14            12733                         63430  \n",
       "15            13135                         64370  \n",
       "16            14117                         66755  \n",
       "17            15613                         67795  \n",
       "18            14192                         71028  \n",
       "19            12654                         73066  \n",
       "20            12053                         73478  \n",
       "21            12681                         76395  \n",
       "22            12353                         79148  \n",
       "23            13030                         81642  \n",
       "24            14205                         84721  \n",
       "25            14401                         87145  \n",
       "26            13346                         87346  \n",
       "27            13178                         91007  \n",
       "28            13673                         94902  \n",
       "29            13725                         97775  \n",
       "30            13532                         99155  \n",
       "31            13860                        100211  \n",
       "32            15507                        104937  \n",
       "33            15921                        107519  \n",
       "34            15979                        108275  \n",
       "35            17913                        112205  \n",
       "36            20490                        118713  \n",
       "37            21694                        124356  \n",
       "38               ..                            ..  \n",
       "39               ..                            ..  \n",
       "40                0                             0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_uc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_uc = housing_uc[0:38]\n",
    "housing_c = housing_c[0:38]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/2m/tcmnc8kn17365qn34w_vgxmr0000gn/T/ipykernel_92985/3509841642.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  housing_uc[\"Total units\"] = housing_uc[\"Total units\"].astype(int)\n",
      "/var/folders/2m/tcmnc8kn17365qn34w_vgxmr0000gn/T/ipykernel_92985/3509841642.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  housing_uc[\"Single-detached\"] = housing_uc[\"Single-detached\"].astype(int)\n",
      "/var/folders/2m/tcmnc8kn17365qn34w_vgxmr0000gn/T/ipykernel_92985/3509841642.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  housing_uc[\"Multiples\"] = housing_uc[\"Multiples\"].astype(int)\n",
      "/var/folders/2m/tcmnc8kn17365qn34w_vgxmr0000gn/T/ipykernel_92985/3509841642.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  housing_uc[\"Semi-detached\"] = housing_uc[\"Semi-detached\"].astype(int)\n",
      "/var/folders/2m/tcmnc8kn17365qn34w_vgxmr0000gn/T/ipykernel_92985/3509841642.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  housing_uc[\"Row\"] = housing_uc[\"Row\"].astype(int)\n",
      "/var/folders/2m/tcmnc8kn17365qn34w_vgxmr0000gn/T/ipykernel_92985/3509841642.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  housing_uc[\"Apartment and other unit type\"] = housing_uc[\"Apartment and other unit type\"].astype(int)\n"
     ]
    }
   ],
   "source": [
    "housing_s_copy[\"Total units\"] = housing_s_copy[\"Total units\"].astype(int)\n",
    "housing_s_copy[\"Single-detached\"] = housing_s_copy[\"Single-detached\"].astype(int)\n",
    "housing_s_copy[\"Multiples\"] = housing_s_copy[\"Multiples\"].astype(int)\n",
    "housing_s_copy[\"Semi-detached\"] = housing_s_copy[\"Semi-detached\"].astype(int)\n",
    "housing_s_copy[\"Row\"] = housing_s_copy[\"Row\"].astype(int)\n",
    "housing_s_copy[\"Apartment and other unit type\"] = housing_s_copy[\"Apartment and other unit type\"].astype(int)\n",
    "\n",
    "housing_uc[\"Total units\"] = housing_uc[\"Total units\"].astype(int)\n",
    "housing_uc[\"Single-detached\"] = housing_uc[\"Single-detached\"].astype(int)\n",
    "housing_uc[\"Multiples\"] = housing_uc[\"Multiples\"].astype(int)\n",
    "housing_uc[\"Semi-detached\"] = housing_uc[\"Semi-detached\"].astype(int)\n",
    "housing_uc[\"Row\"] = housing_uc[\"Row\"].astype(int)\n",
    "housing_uc[\"Apartment and other unit type\"] = housing_uc[\"Apartment and other unit type\"].astype(int)\n",
    "\n",
    "housing_c[\"Total units\"] = housing_c[\"Total units\"].astype(int)\n",
    "housing_c[\"Single-detached\"] = housing_c[\"Single-detached\"].astype(int)\n",
    "housing_c[\"Multiples\"] = housing_c[\"Multiples\"].astype(int)\n",
    "housing_c[\"Semi-detached\"] = housing_c[\"Semi-detached\"].astype(int)\n",
    "housing_c[\"Row\"] = housing_c[\"Row\"].astype(int)\n",
    "housing_c[\"Apartment and other unit type\"] = housing_c[\"Apartment and other unit type\"].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/2m/tcmnc8kn17365qn34w_vgxmr0000gn/T/ipykernel_92985/3279338308.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  housing_uc[\"Difference_total_units\"] = housing_uc[\"Total units\"].diff()\n"
     ]
    }
   ],
   "source": [
    "housing_s_copy[\"Difference_total_units\"] = housing_s_copy[\"Total units\"].diff()\n",
    "housing_uc[\"Difference_total_units\"] = housing_uc[\"Total units\"].diff()\n",
    "housing_c[\"Difference_total_units\"] = housing_c[\"Total units\"].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Type of unit</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Total units</th>\n",
       "      <th>Single-detached</th>\n",
       "      <th>Multiples</th>\n",
       "      <th>Semi-detached</th>\n",
       "      <th>Row</th>\n",
       "      <th>Apartment and other unit type</th>\n",
       "      <th>Difference_total_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3 2013</td>\n",
       "      <td>93600</td>\n",
       "      <td>15601</td>\n",
       "      <td>77999</td>\n",
       "      <td>2629</td>\n",
       "      <td>9382</td>\n",
       "      <td>65988</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q4 2013</td>\n",
       "      <td>94117</td>\n",
       "      <td>15310</td>\n",
       "      <td>78807</td>\n",
       "      <td>2448</td>\n",
       "      <td>8801</td>\n",
       "      <td>67558</td>\n",
       "      <td>517.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q1 2014</td>\n",
       "      <td>92827</td>\n",
       "      <td>13268</td>\n",
       "      <td>79559</td>\n",
       "      <td>2138</td>\n",
       "      <td>8326</td>\n",
       "      <td>69095</td>\n",
       "      <td>-1290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q2 2014</td>\n",
       "      <td>94582</td>\n",
       "      <td>14424</td>\n",
       "      <td>80158</td>\n",
       "      <td>2125</td>\n",
       "      <td>9034</td>\n",
       "      <td>68999</td>\n",
       "      <td>1755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q3 2014</td>\n",
       "      <td>96521</td>\n",
       "      <td>15701</td>\n",
       "      <td>80820</td>\n",
       "      <td>2323</td>\n",
       "      <td>9133</td>\n",
       "      <td>69364</td>\n",
       "      <td>1939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q4 2014</td>\n",
       "      <td>96112</td>\n",
       "      <td>15300</td>\n",
       "      <td>80812</td>\n",
       "      <td>2041</td>\n",
       "      <td>9144</td>\n",
       "      <td>69627</td>\n",
       "      <td>-409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q1 2015</td>\n",
       "      <td>79695</td>\n",
       "      <td>12274</td>\n",
       "      <td>67421</td>\n",
       "      <td>1666</td>\n",
       "      <td>7789</td>\n",
       "      <td>57966</td>\n",
       "      <td>-16417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q2 2015</td>\n",
       "      <td>81343</td>\n",
       "      <td>14618</td>\n",
       "      <td>66725</td>\n",
       "      <td>1505</td>\n",
       "      <td>9344</td>\n",
       "      <td>55876</td>\n",
       "      <td>1648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Q3 2015</td>\n",
       "      <td>87385</td>\n",
       "      <td>17025</td>\n",
       "      <td>70360</td>\n",
       "      <td>1707</td>\n",
       "      <td>9885</td>\n",
       "      <td>58768</td>\n",
       "      <td>6042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Q4 2015</td>\n",
       "      <td>91625</td>\n",
       "      <td>18310</td>\n",
       "      <td>73315</td>\n",
       "      <td>1853</td>\n",
       "      <td>11141</td>\n",
       "      <td>60321</td>\n",
       "      <td>4240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Q1 2016</td>\n",
       "      <td>90278</td>\n",
       "      <td>16691</td>\n",
       "      <td>73587</td>\n",
       "      <td>1635</td>\n",
       "      <td>10122</td>\n",
       "      <td>61830</td>\n",
       "      <td>-1347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Q2 2016</td>\n",
       "      <td>96428</td>\n",
       "      <td>18445</td>\n",
       "      <td>77983</td>\n",
       "      <td>1883</td>\n",
       "      <td>10642</td>\n",
       "      <td>65458</td>\n",
       "      <td>6150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Q3 2016</td>\n",
       "      <td>98140</td>\n",
       "      <td>20957</td>\n",
       "      <td>77183</td>\n",
       "      <td>2090</td>\n",
       "      <td>11334</td>\n",
       "      <td>63759</td>\n",
       "      <td>1712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Q4 2016</td>\n",
       "      <td>100199</td>\n",
       "      <td>22481</td>\n",
       "      <td>77718</td>\n",
       "      <td>2003</td>\n",
       "      <td>12601</td>\n",
       "      <td>63114</td>\n",
       "      <td>2059.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Q1 2017</td>\n",
       "      <td>98974</td>\n",
       "      <td>20953</td>\n",
       "      <td>78021</td>\n",
       "      <td>1858</td>\n",
       "      <td>12733</td>\n",
       "      <td>63430</td>\n",
       "      <td>-1225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Q2 2017</td>\n",
       "      <td>100898</td>\n",
       "      <td>21320</td>\n",
       "      <td>79578</td>\n",
       "      <td>2073</td>\n",
       "      <td>13135</td>\n",
       "      <td>64370</td>\n",
       "      <td>1924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Q3 2017</td>\n",
       "      <td>104860</td>\n",
       "      <td>21750</td>\n",
       "      <td>83110</td>\n",
       "      <td>2238</td>\n",
       "      <td>14117</td>\n",
       "      <td>66755</td>\n",
       "      <td>3962.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Q4 2017</td>\n",
       "      <td>107048</td>\n",
       "      <td>21339</td>\n",
       "      <td>85709</td>\n",
       "      <td>2301</td>\n",
       "      <td>15613</td>\n",
       "      <td>67795</td>\n",
       "      <td>2188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Q1 2018</td>\n",
       "      <td>106829</td>\n",
       "      <td>19385</td>\n",
       "      <td>87444</td>\n",
       "      <td>2224</td>\n",
       "      <td>14192</td>\n",
       "      <td>71028</td>\n",
       "      <td>-219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Q2 2018</td>\n",
       "      <td>106701</td>\n",
       "      <td>18857</td>\n",
       "      <td>87844</td>\n",
       "      <td>2124</td>\n",
       "      <td>12654</td>\n",
       "      <td>73066</td>\n",
       "      <td>-128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Q3 2018</td>\n",
       "      <td>106311</td>\n",
       "      <td>18648</td>\n",
       "      <td>87663</td>\n",
       "      <td>2132</td>\n",
       "      <td>12053</td>\n",
       "      <td>73478</td>\n",
       "      <td>-390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Q4 2018</td>\n",
       "      <td>109011</td>\n",
       "      <td>17948</td>\n",
       "      <td>91063</td>\n",
       "      <td>1987</td>\n",
       "      <td>12681</td>\n",
       "      <td>76395</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Q1 2019</td>\n",
       "      <td>108420</td>\n",
       "      <td>15255</td>\n",
       "      <td>93165</td>\n",
       "      <td>1664</td>\n",
       "      <td>12353</td>\n",
       "      <td>79148</td>\n",
       "      <td>-591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Q2 2019</td>\n",
       "      <td>110671</td>\n",
       "      <td>14411</td>\n",
       "      <td>96260</td>\n",
       "      <td>1588</td>\n",
       "      <td>13030</td>\n",
       "      <td>81642</td>\n",
       "      <td>2251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Q3 2019</td>\n",
       "      <td>115874</td>\n",
       "      <td>15359</td>\n",
       "      <td>100515</td>\n",
       "      <td>1589</td>\n",
       "      <td>14205</td>\n",
       "      <td>84721</td>\n",
       "      <td>5203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Q4 2019</td>\n",
       "      <td>118576</td>\n",
       "      <td>15452</td>\n",
       "      <td>103124</td>\n",
       "      <td>1578</td>\n",
       "      <td>14401</td>\n",
       "      <td>87145</td>\n",
       "      <td>2702.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Q1 2020</td>\n",
       "      <td>116951</td>\n",
       "      <td>14824</td>\n",
       "      <td>102127</td>\n",
       "      <td>1435</td>\n",
       "      <td>13346</td>\n",
       "      <td>87346</td>\n",
       "      <td>-1625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Q2 2020</td>\n",
       "      <td>120858</td>\n",
       "      <td>15069</td>\n",
       "      <td>105789</td>\n",
       "      <td>1604</td>\n",
       "      <td>13178</td>\n",
       "      <td>91007</td>\n",
       "      <td>3907.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Q3 2020</td>\n",
       "      <td>126826</td>\n",
       "      <td>16428</td>\n",
       "      <td>110398</td>\n",
       "      <td>1823</td>\n",
       "      <td>13673</td>\n",
       "      <td>94902</td>\n",
       "      <td>5968.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Q4 2020</td>\n",
       "      <td>131493</td>\n",
       "      <td>17864</td>\n",
       "      <td>113629</td>\n",
       "      <td>2129</td>\n",
       "      <td>13725</td>\n",
       "      <td>97775</td>\n",
       "      <td>4667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Q1 2021</td>\n",
       "      <td>132762</td>\n",
       "      <td>17944</td>\n",
       "      <td>114818</td>\n",
       "      <td>2131</td>\n",
       "      <td>13532</td>\n",
       "      <td>99155</td>\n",
       "      <td>1269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Q2 2021</td>\n",
       "      <td>136919</td>\n",
       "      <td>20457</td>\n",
       "      <td>116462</td>\n",
       "      <td>2391</td>\n",
       "      <td>13860</td>\n",
       "      <td>100211</td>\n",
       "      <td>4157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Q3 2021</td>\n",
       "      <td>145712</td>\n",
       "      <td>22764</td>\n",
       "      <td>122948</td>\n",
       "      <td>2504</td>\n",
       "      <td>15507</td>\n",
       "      <td>104937</td>\n",
       "      <td>8793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Q4 2021</td>\n",
       "      <td>148064</td>\n",
       "      <td>22264</td>\n",
       "      <td>125800</td>\n",
       "      <td>2360</td>\n",
       "      <td>15921</td>\n",
       "      <td>107519</td>\n",
       "      <td>2352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Q1 2022</td>\n",
       "      <td>147504</td>\n",
       "      <td>21014</td>\n",
       "      <td>126490</td>\n",
       "      <td>2236</td>\n",
       "      <td>15979</td>\n",
       "      <td>108275</td>\n",
       "      <td>-560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Q2 2022</td>\n",
       "      <td>153536</td>\n",
       "      <td>21201</td>\n",
       "      <td>132335</td>\n",
       "      <td>2217</td>\n",
       "      <td>17913</td>\n",
       "      <td>112205</td>\n",
       "      <td>6032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Q3 2022</td>\n",
       "      <td>164580</td>\n",
       "      <td>23134</td>\n",
       "      <td>141446</td>\n",
       "      <td>2243</td>\n",
       "      <td>20490</td>\n",
       "      <td>118713</td>\n",
       "      <td>11044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Q4 2022</td>\n",
       "      <td>171425</td>\n",
       "      <td>23282</td>\n",
       "      <td>148143</td>\n",
       "      <td>2093</td>\n",
       "      <td>21694</td>\n",
       "      <td>124356</td>\n",
       "      <td>6845.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Type of unit  Quarter  Total units  Single-detached  Multiples  Semi-detached  \\\n",
       "0             Q3 2013        93600            15601      77999           2629   \n",
       "1             Q4 2013        94117            15310      78807           2448   \n",
       "2             Q1 2014        92827            13268      79559           2138   \n",
       "3             Q2 2014        94582            14424      80158           2125   \n",
       "4             Q3 2014        96521            15701      80820           2323   \n",
       "5             Q4 2014        96112            15300      80812           2041   \n",
       "6             Q1 2015        79695            12274      67421           1666   \n",
       "7             Q2 2015        81343            14618      66725           1505   \n",
       "8             Q3 2015        87385            17025      70360           1707   \n",
       "9             Q4 2015        91625            18310      73315           1853   \n",
       "10            Q1 2016        90278            16691      73587           1635   \n",
       "11            Q2 2016        96428            18445      77983           1883   \n",
       "12            Q3 2016        98140            20957      77183           2090   \n",
       "13            Q4 2016       100199            22481      77718           2003   \n",
       "14            Q1 2017        98974            20953      78021           1858   \n",
       "15            Q2 2017       100898            21320      79578           2073   \n",
       "16            Q3 2017       104860            21750      83110           2238   \n",
       "17            Q4 2017       107048            21339      85709           2301   \n",
       "18            Q1 2018       106829            19385      87444           2224   \n",
       "19            Q2 2018       106701            18857      87844           2124   \n",
       "20            Q3 2018       106311            18648      87663           2132   \n",
       "21            Q4 2018       109011            17948      91063           1987   \n",
       "22            Q1 2019       108420            15255      93165           1664   \n",
       "23            Q2 2019       110671            14411      96260           1588   \n",
       "24            Q3 2019       115874            15359     100515           1589   \n",
       "25            Q4 2019       118576            15452     103124           1578   \n",
       "26            Q1 2020       116951            14824     102127           1435   \n",
       "27            Q2 2020       120858            15069     105789           1604   \n",
       "28            Q3 2020       126826            16428     110398           1823   \n",
       "29            Q4 2020       131493            17864     113629           2129   \n",
       "30            Q1 2021       132762            17944     114818           2131   \n",
       "31            Q2 2021       136919            20457     116462           2391   \n",
       "32            Q3 2021       145712            22764     122948           2504   \n",
       "33            Q4 2021       148064            22264     125800           2360   \n",
       "34            Q1 2022       147504            21014     126490           2236   \n",
       "35            Q2 2022       153536            21201     132335           2217   \n",
       "36            Q3 2022       164580            23134     141446           2243   \n",
       "37            Q4 2022       171425            23282     148143           2093   \n",
       "\n",
       "Type of unit    Row  Apartment and other unit type  Difference_total_units  \n",
       "0              9382                          65988                     NaN  \n",
       "1              8801                          67558                   517.0  \n",
       "2              8326                          69095                 -1290.0  \n",
       "3              9034                          68999                  1755.0  \n",
       "4              9133                          69364                  1939.0  \n",
       "5              9144                          69627                  -409.0  \n",
       "6              7789                          57966                -16417.0  \n",
       "7              9344                          55876                  1648.0  \n",
       "8              9885                          58768                  6042.0  \n",
       "9             11141                          60321                  4240.0  \n",
       "10            10122                          61830                 -1347.0  \n",
       "11            10642                          65458                  6150.0  \n",
       "12            11334                          63759                  1712.0  \n",
       "13            12601                          63114                  2059.0  \n",
       "14            12733                          63430                 -1225.0  \n",
       "15            13135                          64370                  1924.0  \n",
       "16            14117                          66755                  3962.0  \n",
       "17            15613                          67795                  2188.0  \n",
       "18            14192                          71028                  -219.0  \n",
       "19            12654                          73066                  -128.0  \n",
       "20            12053                          73478                  -390.0  \n",
       "21            12681                          76395                  2700.0  \n",
       "22            12353                          79148                  -591.0  \n",
       "23            13030                          81642                  2251.0  \n",
       "24            14205                          84721                  5203.0  \n",
       "25            14401                          87145                  2702.0  \n",
       "26            13346                          87346                 -1625.0  \n",
       "27            13178                          91007                  3907.0  \n",
       "28            13673                          94902                  5968.0  \n",
       "29            13725                          97775                  4667.0  \n",
       "30            13532                          99155                  1269.0  \n",
       "31            13860                         100211                  4157.0  \n",
       "32            15507                         104937                  8793.0  \n",
       "33            15921                         107519                  2352.0  \n",
       "34            15979                         108275                  -560.0  \n",
       "35            17913                         112205                  6032.0  \n",
       "36            20490                         118713                 11044.0  \n",
       "37            21694                         124356                  6845.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_uc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type of unit\n",
       "Quarter                           object\n",
       "Total units                        int64\n",
       "Single-detached                    int64\n",
       "Multiples                          int64\n",
       "Semi-detached                      int64\n",
       "Row                                int64\n",
       "Apartment and other unit type      int64\n",
       "Difference_total_units           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_s_copy.astype({'Total units': 'int64'}).dtypes    #Check the dtype of values before plotting, otherwise, graphs will be plotted incorrectly\n",
    "                                                          #Change the dtype of data, for example, from str to int for better data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs_copy = housing_s_copy.set_index(\"Quarter\")\n",
    "huc_copy = housing_uc.set_index(\"Quarter\")\n",
    "hc_copy = housing_c.set_index(\"Quarter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_hs = hs_copy[26:38]\n",
    "covid_huc = huc_copy[26:]\n",
    "covid_hc = hc_copy[26:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization with Matplotlib and Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(40, 15))\n",
    "\n",
    "sns.barplot(data=hs_copy, x=\"Quarter\", y=\"Total units\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e1DNlZU4cpeN7+oW1uWnGotz4yOIsenF92+1D+3TNCXsNzQl7DsnOIie2gvrGDbl7QX2mza/Pbxe+kHWbmpMkQ3p3zVePH5dDRg5s5wnZXtQ3bsiFN87K1Pn1SZI9d+ydyz+8R3YZ0KOdJ6MjeOz5VfnMzx7JMyvWpaws+dRBu+bcI0Zst8FA0T39wtpccMPMtt1e9t6pT7554u7ZbaA/77A1CUE6KCEIALBN/fu/J5/+dOv7P/lJcuqp7TvPa5kxIzn00KShofXXW25JuqrfAQAAAABgW3uifnW+O+2J/PqxJflT/5FDRw3M2Yfulj137PO2H3/56o25a+6yTJlTl/ufeCFNLS+frrJj3245alxtJo2tzZ479HY1cTqUVes25ZaZS3PjjEWZ8dyqttt7dK7MMeMH5YS9huQ9O/f1ecs2s7GpOXfPX56v3DI3i1e1BkkfmjA4Fx07Jv16dG7n6Xi3KpVK+fVjS3LRr+akYf3mVFWU57wjR+ST79slFb7+8QprNzblyzfPyXUPLUqS7DG0OlecsqdQskCamlvyX79/Opff+Xg2NrWka6eKfP6okfnbiTv78w7bgBCkgxKCAADbzE03JSeemJRKyde/nlx4YXtP9Of94Q/JEUcka9YkRx2V/PKXSWf/QAkAAAAAANvCgrrV+fdpC3PrrKVtO3YcPromnz5st+w+tPdWec6GdZszbcGyTJldl3sfX54Nm1va1gb27JxJY2tz1LjavHdYX1eQpl1sbm7JvQuW54YZizJ1Xn02Nbd+jpaXJe8bPiAn7j00R4yuSdeqinaelO3Z2o1N+dZdj+e/f/90WkpJn26d8qVjx+T4PYfYZYl31AtrNuZLv5yd22fXJUnGDemVyz88ISNre7bzZHRkt85cmgtvnJnGDU3pVlWRSz44Nh/ee6ivTx3c/LrGnP+LmXlsUUOS5MDd+ufSE8Znh77d2nky2H4IQTooIQgAsE1Mn966u8aGDcnf/31y1VVJR/9G+r77WiOQ9euTD30ouf76pFOn9p4KAAAAAADeteYtbcy/T1uY22bVtd02aWxNzj50eMYNqd5mc6zb1JT7Hl+eKbPrMnVefVZvbGpb692tUw4fXZOjxtbmwOH906WTk+7ZekqlUmYvbswNMxbl5seWZMXaTW1ro2p75sS9huZDEwZnYK8u7TglvNpjz6/K+TfMzPy61UmS9w3vn68f76Rd3hlTZi/NF26anRVrN6WyvCxnHzo8/3jIrkJN3pAlq9bn3J8/mgeeXpkkOWb8oHz9+PGp7uZ8kI5mU1NLvnf3E/n+PU9kc3MpPbtU5kvHjMmH9xHvwLYmBOmghCAAwFa3cGEycWKyYkVy7LGtO4NUVrb3VG/Mb37TOvPGjclf/VXy058WZ3YAAAAAACiI2YsbcuXUhblz7rIkrdeSmjxuUM46dLeMHtS+5zJsamrJ/U++kDvm1OXOOcu2OBG/W1VFDhk1MJPG1uaQkQPSs4sTCHln1DVsyC8fXZwbZyzK48vWtN3ev0dVPjRhSE7ca2jGDHaeDx3b5uaWXHPfU7li6sJsampJ104V+eyRI/Kx/XdOpRP2eQtWrduUi389J796dEmS1iDu3z68xzaNRXl3aG4p5T/uezLfuvPxNLWUMqi6S7598oTst0u/9h6NP3ns+VX5/C9mZsGy1qDwiDE1+epx41IjfoV2IQTpoIQgAMBWVV+f7L9/8uSTyT77JPfck3Tv3t5TvTm33ZYcd1yyeXPyN3+T/OhHSbl/mAQAAAAAgLdr5qJVuXLqwvxmXn2S1gDk2N0H5+xDd8uImp7tPN2rNbeU8tAzKzNlTl3umF2XJQ0b2taqKspz4PD+OWpsbQ4fU5O+3avacVKKaN2mptw5Z1lumLEov3vihbx09lRVZXmOHFOTE/camvcN7+8EegrnqeVrcuGNs9quvj9+SHW+ceL4jB3s5H3euGnzl+WCG2alfvXGlJclnzp413z6sOHpXGlnLt66x55flc/87JE8s2JdysqSTx20a849YoTdZdrR+k3N+fZvHs9//vaptJSSft2rcskHx+bY3QfZBQTakRCkgxKCAABbzbp1ySGHJH/8YzJsWDJ9elJT095TvTU33ZR8+MNJc3NyxhnJ1Ve3/jQKAAAAAAB40x557sVcOXVh7l6wPElSXpZ8cI/BOevQ4dltYI92nu6NKZVKmbW4IVNm12XK7Lo89cLatrXysmTfYf1y1LjaHDm2JoOqu7bjpHRkLS2l/OHpFblxxuLcPmtp1m5qblt7z859csJeQzN5/KBUd7XbDMXW0lLKdQ89n6/dNi+rNzSlorwsZ7x/l3zmsOHp0smJ/Px5jRs25ys3z831Dy9Kkuw6oHsu/6sJmbBD7/YdjHeNtRub8uWb5+S6h1o/x/YYWp0rTtkzO/cv2EVO3wX+8NSKXHDDzDyzYl2S5LgJg3PRB8YKrKEDEIJ0UEIQAGCraG5OTjwx+dWvkr59k/vvT0aObO+p3p6f/Sw59dSkpSX59KeT73xHDAIAAAAAAG/Cw8+uzHd+szC/XfhCkqSivCzHMtwtjQAAzUZJREFUTRiSMw/ZNbsMKEYA8lpKpVKeqF/TGoXMqcucJY1brE/YoXeOGlebo8bWOqmQJMmTy9fkxhmL8stHlmTxqvVtt+/Yt1tO2GtIjt9zSHbq53OFd5/6xg25+NdzcvvsuiTJzv265esnjM/+u/Zv58noiH67cHnO/8XMLGnYkLKy5O8OHJbPHjlSPMRWcevMpbnwxplp3NCUblUVueSDY/PhvYfahWIbWL1hc75x+/z89IHnkiS1vbrk6yeMy6GjCnqxWXgXEoJ0UEIQAOAdVyolZ5+dfO97SefOydSpyQEHtPdU74wf/zj52Mda3//855NvfEMMAgDANrGpqSW3zFyS//r903l2xbocsGv/HDm2JoeOGpje3VwNCwAA6NgeeGpFrpy2ML9/YkWSpLK8LCfsNST/ePBu78ow4vmV63LHnNadQh5+7sW88iyYUbU9M2lsbY4aV5tRtT2dXLgdeXHtptwyc0lumLE4jz6/qu32np0rc+weg3LCXkOzz059fE6wXbhjTl0u+tXsLGvcmCQ55T075MKjR6e6m91vaN2h4dLb5+Unf2g9KXynft3ybx/eI+/ZuW87T8a73ZJV63Puzx/NA0+vTJIcM35Qvn78eF+btqK7F9TnCzfOypKGDUmSj7x3x1w4eVR6dfExh45ECNJBCUEAgHfcv/5rayRRVpZcd11y0kntPdE76+qrk099qvX9iy9OLrmkXccBAODdrWHd5lz7x+fyo/ufbvvB+CtVlJflvTv3zZFja3LEmJoM7dOtHaYEAAB4tVKplOlPrcgVv1nYdjJdZXlZPrzP0Pzjwbtlh77bx/cv9Y0bcufcZbljTl3uf3JFmltePiVmp37dctTY2kwaV5sJQ3unvFwA8G6zqakl9yyoz40zFmfq/GXZ3Nz637+ivCzvH94/J+w1NEeMqXF1e7ZLjRs255uvuAJ8/x6d8y8fGpujx9UKorZjDzy1Iv/0i5l5buW6JMlpE3fK+UePSreqynaejO1Fc0spV9/7ZL591+NpaillcHWXfOvkCdlvl37tPdq7yotrN+Urt8zNjY8sTtK6K9o3TrRDFHRUQpAOSggCALyjfvaz5CMfaX3/W99Kzj23fefZWr7znZdf26WXJhdc0K7jAADw7vPsirX5798/k+seej7rNjUnSQb07JyP7b9z9tulb+5dsDx3zl2W+XWrt7jfmEG92qKQMYN6+aE5AACwzZVKpfz+iRW5curC/PGZ1gCkU0VZ/mqfHfKpg3fdrgP2Ves2Zeq8+kyZU5f7Hl+ejU0tbWs1vTq37hQytjbvHdY3lRXl7Tgpb0epVMrMRQ25ccai/PqxJXlx3ea2tTGDeuWEvYbkgxMGZ2DPLu04JXQcf3x6ZS64cWaeWr42SXL46Jp85bixGVTdtZ0nY1tav6k5/3rHgvz3/U+nVEqG9O6ay07aPQfs5qRw2sdjz6/KZ372SJ5ZsS5lZck/Hrxrzjl8RDr5f7S37bZZS3PRr2bnhTWbUl6WfOKAYfnskSPTtUoYCx2VEKSDEoIAAO+Ye+9Njjwy2bQp+cxnWmOJd7NvfCO58MLW97/97eScc9p1HAAA3h0efnZlfnDf07ljbl1e+lfSUbU983fv2yUf2GNQOldu+YOQ51asy51z63Ln3GV56JmVecWFZTOkd9e2KOS9OzuJCAAA2LpKpVLufXx5rpy6MDOeW5Ukqaosz0fes0P+/qBdM7i3E3pfae3Gptz7+PJMmV2XafPrs2ZjU9ta726dcsTomhw1rjYH7NbfbhEFsWTV+vzy0cW5ccbiPFG/pu32AT0757gJg3PCXkMzepBzc+C1bNjcnO/f/US+f8+TaWoppUfnypx/9Kic+t4d7Za0HZjx3Iv53HWP5akXWmOgU96zQ75wzOj07NKpnSdje7d2Y1O+fPOcXPfQoiTJHkOrc8Upe2bn/t3bebJiqm/ckIt+NSdT5tQlSYYP7JHLTto9e+7Yp50nA16PEKSDEoIAAO+IuXOTAw5IVq1KTjghue66pGI7+KHEJZckX/5y6/tXXZX8wz+06zgAABRTU3NL7pizLP/5u6fyyJ9OlkqSg0YMyCfft0sO2K3fG9rZY+XaTZk6b1numrss9y1cng2bX76ybO9unXLoyIE5cmxN3j9iQLpVVW6NlwIAAGyHSqVS7l5QnyumPpHHnl+VJOlcWZ6/3nfH/MNBu6aml10PXs/Gpubc/8SKTJldlzvn1m2xg0T3qoocMmpgjhpXm4NHDkyPzr6f60jWbmzKHXPqcsOMRbn/yRVtF3XoXFmeI8fW5sS9huTA3fq7OAO8QfPrGnPBDbPy6J/+Ptlnpz75xonjs9vAnu07GFvFxqbmfPuuhbnmvifTUmrdHesbJ+6eQ0YObO/RYAu3zlyaC2+cmcYNTelWVZFLPjg2H957qB2536BSqZRfPLwoX7llbho3NKWyvCz/ePCuOfPQ3V518SugYxKCdFBCEADgbVu6NNlvv+S555L9909+85uk63ZyVa9SKbngguSyy1p//9//nXzsY+06EgAAxbFmY1N+/uDz+e/fP51FL65PklRVlOf4PYfk9PcNy4iat/4D7vWbmvPbhctz19xl+c28ZVucRNS5sjwH7tY/R46tyWGja9K/R+e3/VoAAIDtT6lUyl1zl+XKaQsze3FjkqRLp/J8dN+dcsZBu2RgTwHIW9HU3JIHn3kxd8ypy5TZdalr3NC2VlVZnvcP759JY2tz+Oia9Ole1Y6Tbr+aW0r5w1MrcsOMRZkyuy7rNjW3rb13WN+cuNeQHD1+UHq5kj28Jc0tpfzP9Gdy2R0Lsm5Tc6oqynPWobvlHw7aNVWVoqp3g41Nzfn1o0ty9b1P5snlrbuAnLDnkFz8gbGp7uZrJx3TklXrc+7PH80DT69MkhwzflC+fvx4n7OvY9GL6/LPN83OfY8vT5KMH1Kdy07a3S5pUDBCkA5KCAIAvC2rVycHHZQ88kgyfHhy//1J//7tPdW2VSol55yTXHllUl6e/PSnySmntPdUAAB0YEtWrc+P7n8m//vAc1m9sSlJ0rd7VT663075m/12yoCe72yY0dTckoeffTF3zV2WO+cuy3Mr17WtlZUle+/YJ0eOrckRY2ozzJb2AADA62hpKeXOuXW5YuoTmbe0NQDpVlWRv5m4Uz75vl3E5u+glpZSZi5uyJTZdZkye2meWfHy93MV5WXZb5e+OWpsbY4cW2vnlW3gifrVuXHG4tz0yOIsbXg50NmpX7ecsOfQnLDXkOzQt1s7TgjvLotXrc8Xb5qVuxe0njw8oqZHLj1h9+y9U592noy3asWajfnpA8/l/01/Ni+s2Zgk6d+jKl8/fnyOHFvbztPB62tuKeXqe5/Mt+96PE0tpQyu7pJvnTwh++3Sr71H63BaWkr5nz88m29Omd8a9VWW57wjRuTvDhxmpzQoICFIByUEAQDess2bkw9+MJkyJRkwIJk+Pdl11/aeqn2USsk//ENyzTVJRUVy/fXJ8ce391QAAHQwsxc35Ae/fSq3zlyappbWfwLdZUD3nH7gsJy419B06bT1t0AvlUp5fNma3DmnLnfOXZZZixu2WB8+sEeOGFOTI8fWZvch1Skvt7U9AADQqqWllNtn1+Xfpy3M/LrVSZLuVRU5bf+d83fv2yV97U6xVb30/dyU2XWZMqeuLcJ5yZ479s7R42ozaWxtduon8n+nrFy7KTc/tiQ3zliUxxa9/D10ry6VOXaPwTlxryHZa8c+KSvz/TPbsaVLk+uuS268MencOTnppOSEE96RCwiWSqX8+rEl+Zeb52bF2k0pK0tOm7hzPjdpZHp0rnwHhmdbWLhsdf7r90/nxhmLs7GpJUlS26tLTtt/5/z1e3e0owKF89jzq/KZnz2SZ1asS1lZ8o8H75pzDh+RTgKHJMmTy9fkghtm5sFnXkySvGfnPvnmibtnlwE92nky4K0SgnRQQhAA4C0plZJPfjL54Q+Trl2Te+5J3vve9p6qfbW0JB//ePL//l/SqVPyy18mkye391QAALSzlpZSps2vzw9++1TblvFJMnGXfvm79w3LISMHtmtssWTV+vxm3rLcNXdZpj+5oi1QSZKaXp1z+OjWKGTiLv1SVemHWAAAsD1qbinl1llL8+9TF2Zh/ZokSc/OlfnYATvn9AOHpXc3AUh7eHbF2twxpy5TZtdlxnOrtlgbVdszR42rzVHjajOypqdI4U3a2NScu+cvz40zFuXuBfXZ3Nz6vXJFeVkOHjEgJ+w1NIeNHrhNLugAHdaqVa3hx7XXJnff3fqz0leqqEiOOCI5+eTkuOOS3r3f1tO9uHZTvnrrvNwwY1GSZHB1l3z1+HE5dFTN23pctp5SqZTfLnwhP/zd07n38eVtt+8+tDqnHzgsk8cPctI8hbZ2Y1O+fPOcXPdQ69elPYZW54pT9szO2/Gu203NLfnBb5/Ot3/zeDY1taR7VUXOP3pUPrrvTi46BQUnBOmghCAAwFvyla8kF12UlJe3Bg8f+EB7T9QxNDUlH/1o8vOft17t5pZbksMPb++pAABoB+s3NeeGGYvyX797Ok+9sDZJUllelmN3H5S/e98uGTekup0nfLWG9Ztzz4L63Dl3We6ZX5+1m5rb1np2rsxBIwfkyLG1OXjkgPTq4ip9wPZnU1OLKA6A7UpTc0tunrkk/z7tiTy1vPX7ml5dKvOJA4fl4/sPc/XuDqSuYUPumtu6U8gfnlqZ5ldE/jv365ZJ42pz1Nja7DG0t5Pw/oxSqZTHFjXkhocX5eaZS7Jq3ea2tbGDe+XEvYbmgxMGp3+Pzu04JbSzdetaf/75v/+b3HZbsmnTy2v77Zd85CPJ+vWtPyt95JGX16qqkqOOao1CPvCBpGfPtzzCbxcuzz/fNCvPr1yfJPnAHoNz8QfG+LPZgWzY3JxfPbo4P/zd03l8WWtAWlaWHDmmJn/3vv/P3n2Hx1We+f9/j3rvvVd32bJsS+6AKcam2MYGDIEEEkgCgQ1sfgnJZjdlv1myIVuygSVlQwIEMAaDbcAGTDNY7lVyt3rvZdSnnt8fj2ZGY0m2bMtWu1/XNdeMzpwZPZLHmjPPeT73ncLcROmiJMaXbfk1/OTdfNp6zPh4uPKLO6dz95y4Cfc6P1XdxjPv5Nu7kC+dFM6za2YQF+wzwiMTQgwHCYKMUhIEEUIIIcQle+UVeOghdfvFF+Gxx0Z0OKOOyQT33KMCMt7e8NFHsHTpSI9KCCGEEEJcI/XtPfx9bxmv7SujpXfRiL+XG/fnJPDQwiSiA71HeIRDYzBb2FvUxI5TqltIQ7vBfp+7q475KaHcMi2Sm6dFERXoNYIjFUKIq6e9x8S+4mZyCxrYVdhISWMnjy5J4UfLJ+MmVUuFEEKMY2aLlS3HqvnfLwop6Q22B3q788jiZL6xKEmC4aNcS6eRT0/X8fHJWr4qaMRodlTp93Bzwc1Fh4tOh04HLjodLjrQnXettjvv4/y1uu28/wUe76Ku4fzn67O/i+3x/b+fjvP3udAYbWMZ4HvoBv4ebT1mPsivtgeeACL8PVkzO5Y1WbFMiZL1NGICM5ng009V548tW6Cjw3HfjBkq/LF+PaSkOD/u3DkVCNm4EU6edGz38oLbblOhkNtuA59LXyDcZTTzu08L+MuuYqwaBPm488+3TWNtVuyEW3g9mjS0G3htn5oXbepUISFfD1funhvPw4uSSAyduF0SxPhX3drN0xuP2buC35YRzbNrMiZEcNpgtvDC54X8YWcRZqtGoLc7/3K7/E0WYryRIMgoJUEQIYQQQlySTz6BlStV54tnnoF///eRHtHoZDCoFscffQR+fur3Nn/+SI9KCCGEEEJcRWdr23kpt5gtR6sxWtQim/gQb765KJm758bj5+k2wiO8fFarRl5lKztO1bHjZC1FfRbGgGp5f/O0SG6ZHkV6hJ+c3BFCjFlmi5W8ylZ2FTSSW9DI0YpWp2raNkvSw3j+vtkE+XiMwCiFEEKIq8dksbL5SBUvfFFIeXMXAME+7jy6NIUH5yfiLwGQMafDYGbn2Xo+OlHLF+d1fhT9ebm7sHx6FGuz4liUFobrRO2eYrVCQQHs3w+urnDzzRARMdKjEteS1Qq7d6vOH2+9BU1NjvuSklT44777ICNjaM938qQjFHLunGO7ry/ceacKhdx6K3heWleP45V6nnknn1M1bQAsTgvj39bMkMDBNXamto2XdpWw9ZhjXjQ2yJuHFiZxz7x4Ar3l+EFMDBarxh+/LOK/PzmH2aoRE+jFf92byfyU0JEe2lVzpLyFZzblU1CvQoK3To/iX1dPJ8JfikcJMd5IEGSUkiCIEEIIIYYsLw+WLIH2djWx99pr4CLVLwfV3Q233w6ffw6Bgeo6K2ukRyWEEEIIIYaRpmnsKmjkL7klfHWuwb49KyGIR5ekcMv0qHG5aKSooYNPejuFHClvoe9sblKojz0UkpUQPC5/fiHE+KFpGiWNneQWNrKroJF9RU20G8xO+ySG+rA4LYwl6WF0GS38dPMJuk0WEkJ8+PPX50h1aCGEEOOC0Wxl0+FKXtxZSGVLNwChvh58e2kKD8xPxHcMB9uFg8Fsob5NdXu0ahpWTV1rfW5brbZtvdect49VXTs9ps/+juccyj5971f7a+d9ff5z2r7/gI9nCPsM8JyapqHT6ViQGsqKGVETM/DU3KxCH/v3w7596rq11XmfuXNhxQpVMG7ePBUQEeOLpqnzwW+8AW++CRUVjvsiIuCee+D++1Xxu8stAqJpcOyYIxRSWuq4LyAA1qxRoZCbbgL3of1fNFms/GVXCb/79BwGsxUvdxf+8eZJfHNRsnRyvIqsVo0vCxp4aVcJuYWN9u2Z8UE8siSZW6dHye9fTFh5Fa18/82jlDZ1odPB49en8tRNk3AfR/8nuoxm/nPHOf66uwRNgzA/T/7fqumsyIge6aEJIa4SCYKMUhIEEUIIIcSQVFSoSb3qarj+etXp4hIrskxInZ2qek1uLoSEwM6dQ6+MI4ZNj8nC4bIW9hQ1sqeoiRNVeuKCfZgZF0hGbCCz4oOYHhOAj4eczBRCCCHE0BjMFt47Vs1LuSWcqW0HwEUHt86I4luLU5iTGDzCI7x2GtoNfHa6jh2n6sgtbMRottrvC/X14MapEdwyLYrF6WF4ucsiESHEyGvuNLK7UHX8yC1spKq12+n+QG93FqWFsjgtnCXpYcSH+Djdf7qmjW///RAVzd34eLjyH3fPYqWc5BbiokwWK1+da2DrsWo+OVVHiK8Hd2XFsm5OnFRsFmIEGcwW3jpUyR++KKRa3wOoRVzfvS6F+3MSZM5UiPHIZIL8fEfoY98+1f3jfF5eqsBZdzccPep8X2goLF+ugiHLl0N4+LUZu7g6CgtV54833oAzZxzbAwLgrrtUgcBly8BtmN8TNA0OHlShk7fegqoqx30hIep733uvOjc9hO9d2tjJT949zt5i1b1kRmwA/37XTGbEBg7vuCe4bqOFd49W8tfcEnvXYBcdrJgRzTcXJ0+oeVEhLqTTYOaX75/krUOVgOqq/T/rZ5MUNvY//+4pbOTH7x63dxC8KyuWn90+TTrnCjHOSRBklJIgiBBCCCEuqrVVdQI5cQKmTVNtgIOCRnpUY0dbm2qZfeCAqpbz5ZcwZcpIj2pcM1ms5FW0sqeoiT1FjRwpa7W3IR6Miw4mRfozMy6QmXFBzIwLZEpUAB5u46cqhxBCCCGuXEunkdf3l/HK3jIa2lUVVR8PV+6ZG883FyWTEOpzkWcY3zoNZr4618COU3V8drqOth5HVX1vd1eWTgrj5mlR3DglgmBfOSkkhLg2ekwWDpW2sKuwgdyCRk5Wtznd7+6qY05iMEvSw1mcFsaM2MCLdjNq6TTyxIYj7C5UC4y+d0MqP7h5Mi7SBUkIJ1arxuHyFrYcrWL78RpaukwD7pedFMK6OXGsnBmNn3QdEOKa6DFZePNAOX/8spjaNhUAifD35LvXpXJ/ToKEuIUYLzQNKiudO30cOgQ9Pf33TU9XReFyctT1zJmOjgw1NapI3Pbt8MknoNc7HqfTqQ4hK1eqYMjcueAi51ZGvepq1ZFjwwYVxrDx9ITbb1edP1auVIGga8FqhT171Jjefhvq6hz3RUTAunUqFLJ48QVfX5qm8fahSn617RRtPWZcXXQ8siSZp26chLeHvLddifq2Hl7dW8br+8vsx/X+nm7cOy+ebyxM6ldEQQihbMuv4Sfv5tPWY8bHw5Vf3Dmdu+fEobvczkojqK3HxK+3n2bDAdUxKibQi3+7K4MbJkeM8MiEENeCBEFGKQmCCCGEEOKCjEbV0eKLLyA6Wk0SJySM9KjGnpYWVSnn2DGIiYGvvoLU1JEe1bhhsWqcqm6zd/w4WNpMl9HitE9kgCeLUsNYkBrK7IRgKlu6OF6pJ69ST35lK/W9Czn78nB1YWq0vz0YMis+iNRwv4suCBJCCCHE+FPS2MlLucVsOlxJj0kFTKMCvHhoURL3ZScQ6O0+wiMcfUwWKwdLmtlxqo4dJ2vt1YUBXF10zEsK5uZpUdwyLVJOFAshhpXVqnG6ts3e8eNASTMGs3NxgClR/ixOC2NxehjZySGXVe3cbLHy7x+e4S+5JQAsmxLB79ZnEuAl7wlCnKltY+uxat47Vu3UdSfMz5M7ZkVz+8wYqlu72XS4kl0FDVh7zwx7u7uyYkYU6+bEMT8lVMJVQlwF3UYLbxwo509fFtnnRKMCvHj8hlTumRsvARAhxrrOTjh82NHpY/9+teD/fEFBKvBhC31kZ6suH0NhMqnn3r4dPvwQ8vKc7w8PV11CVq6EW24Z+vOKq6+lBd55R3X+2LlTBYUAXF3hpptU54/VqyFwhDtoWCyqsN7GjWq8TU2O+2Ji4J57VCgkJ0cFkQZQ397DL98/xbb8GgASQ3349ZoMFqaFXYufYFw5Wa3npdwS3s+rxmRRr5m4YG8eXpTMPXPj8JfPwEJcVHVrN09vPMb+kmYAbsuI5tk1GQT6jJ3/P5+druOnm0/YQ+QPzE/gmVunyN8AISYQCYKMUhIEEUIIIcSgNA2+/nV47TXw84NduyAzc6RHNXY1NqrWxSdPqjDNV19BYuJIj2pM0jSNwvoOe8ePfcXN6LudK0qG+HqwICWUBamhLEwNJTnM94JVNWr1PeRVtvaGQ1rJr9T3e05QFb9nxASqziHxQcyKCyQhxGdMVuwQQgghxIVpmsaBkmb+b1cJn52ps58bnx4TwKNLUliZES3dw4ZI0zROVrex41Qdn5yq43SNczX+qdEB3DwtklumRTI9JkCOrYQQl6xG382ugkZyCxrZU9RIY4fR6f4If08Wp4exJD2MRWlhRPgPX1XbzUcr+fE7xzGYraSE+fLnr88lLcJv2J5fiLGisqWL9/JU+ONMbbt9u5+nG8unR7F6dgwLUkJxc3U+fqrV9/Du0Uo2Ha6kuKHTvj02yJu1WbGsnRNHYqjvNfs5hBivuoxmXt9Xzp++KqaxQwVAYoO8eez6VO6eG4enmwRAhBhzrFY4d8459HH8uFpE35erq+ruYQt9zJ+vun8MV9eOqirnbiHtjuMAXFzU912xQgVDZs+WbiHXWlcXvP++Cn98+KEK8tgsXKg6f9x9t+q6MRqZTPD55/Dmm7B5s3M3msREFQpZv169tgaYT/rkVB3/ssWxcPmeuXH808qpBPlIp9oLsVo1Pj9Tz0u5JewtdgRx5iYG863Fydw8LbLfcb0Q4sIsVo0/flnEf39yDrNVIybQi/+6N5P5KaM7MNncaeSX759k6zEVLE0K9eE3a2eSM8rHLYQYfhIEGaUkCCKEEEKIQf30p/Dss2qCeNs2Vb1HXJnaWrjuOjUxn5qqqtnExo70qEY9TdOoaO62d/zYU9RkP1lp4+/pRk5KCAtSw1iYGsrkSP8rqhqpaRrlzV3kVeo5XtlKXqWeE1X6fp1GAAK93VUwJC6QmXFBzIoLIirwGrXKFkIIIcSwM1msbD9ew0u5JeRXOk4u3zglgkeWpDA/JUSCCleoormrNxRSy4GSZnsVcFCL0W6eFsnN0yLJTg7BXU4qCyEG0GEws6+oidzCRnYVNFDUZ/E4qBB/TnIIi9PDWZIeRnqE31X92328Us93/n6Ian0Pfp5u/O7eTG6aFnnVvp8Qo0VLp5Ftx2vYeqyKg6Ut9u3urjpumBzBqsxYbpwaMaQOA5qmcbSilU2HK3k/r5r2HrP9vuykENbNiWPlzGj8PC+9g48QE1mnwcyre8v4v13FNHeqoGRcsDffuyGNtVlxEm4XYixpalJhD1voY/9+50XxNjExjsDH/PmQlQW+1yhUaTTCnj0qcLB9O5w44Xx/RIQKhaxYobqFBAdfm3FNNCYT7NgBGzbAli2qU4zNzJmq88f69ZCUNFIjvDwGg/q5Nm6ErVuho8NxX1qa6hJy770wY4ZTKKS9x8RzH53l7/vKAAjz8+AXd07ntoxomeM7T5fRzDuHK/nr7lJKGtXrxtVFx8qMaL61OJnM+KCRHaAQ40BeRSvff/MopU1d6HTw+PWpPHXTpFE3D65pGu/n1/CL907S3GnERQePLknh6ZsnSRdBISaocRUE+fWvf827777LmTNn8Pb2ZuHChfzmN79h8uTJ9n16enr4wQ9+wJtvvonBYGD58uW8+OKLREY6Jv7Ly8t57LHH+OKLL/Dz8+Mb3/gGv/71r3Fzc0xg7ty5k3/8x3/k5MmTxMfH88///M889NBDTuP53//9X377299SW1vLrFmzeP7558nOzh7SzyJBECGEEEIM6M9/hu98R93+61/h4YdHdjzjSWUlLF0KJSUwZYpqvRwpi0POV6vvYW9xI3sKVfCjqrXb6X4vdxfmJYX0dvwIY0ZMwFWvPGOxahQ1dJBX0crxKj15lXpOV7dhtFj77Rvh72kPhtiuQ3ylupAQQggxmrX1mNh4oIK/7S6hWq+qBHq6ubB2ThzfXJQs1d2vkpZOI5+fqWfHqVq+OtdIt8kRvA3wcuPGqSoUct2kcHxl4acQE5bZYiWvUk9uQSO5hQ0cLW/F3CdF5qKDjLgglqSFsTg9jKyE4Gu+sLWxw8Djrx3hQGkzAP948ySeuCHtiooUCDEadRnNfHq6nq1Hq/jyXIP9/6JOBznJIazKjGXljGgCfdwv+3v0mCzsOFXHpsOV7CposHdm83Z3ZcWMKNbNjWN+cqj8/xLiAtp7TLy6t4y/7CqmpUtVf08I8eGJG9JYkxU76haaCSHOYzRCfr4j9LFvHxQW9t/P2xvmznV0+8jJgbi4az/ewVRUqFDIhx/Cp586L9x3cYEFC1SnkBUrIDNzwI4OYoisVsjNVZ0/Nm1SwSGb5GTV+eO++2D69JEb43Dq7lZho40b4YMP1Nc2U6eqoMu990KftXyHSpv58bvHKaxXr8Obpkbwr6tmEBPkfa1HP+rU6nt4ZW8pb+wvR9+tjhv8vdy4PzuBry9MIlZ+R0IMq06DmV++f5K3DlUCMCsukP9ZP5uksNHRDbOurYefbj7Bp6frAJgS5c9v1s5kloTBhJjQxlUQ5NZbb2X9+vXMmzcPs9nMP/3TP3HixAlOnTqFb2+K/rHHHmPbtm28/PLLBAYG8sQTT+Di4sLu3bsBsFgsZGZmEhUVxW9/+1tqamr4+te/zqOPPsqzzz4LQElJCTNmzOC73/0ujzzyCJ999hlPPfUU27ZtY3lvRe6NGzfy9a9/nT/+8Y/k5OTwu9/9jrfffpuzZ88SMYS2fRIEEUIIIUQ/27bBnXeqCcOf/xx+8YuRHtH4U1qqwiAVFZCRAV98AaETu3Vmc6eRfcVN9q4fxedVc3Vz0TE7Icje8WN2QhCebiNfacJotnK2tp28ylaOV+rJq2yloL4Di7X/R5r4EG9mxjqCIRlxgVLFUoxpBrMFfbcJHTp0OtABOp0OFx3oUBsG2m47l6fr/dpFp+7X2bbJyT4hxDVW2dLF33aXsvFgBR0GVXk6zM+DB+cn8cD8BEL9PEd4hBNHj8lCbkEjO07V8tnpepp6KxYDeLi5sDgtjJunRXLj1Agi/KUDmxDjmaZplDZ1kVvQwK6CRvYWNzl1BwC1mHVxehhL0sJYmBp2RYvOh4vRbOVX207x6l5VafbW6VH8xz2z5LOfGPNMFiu5hY28d6yaj0/WOnVMnRYdwOrZMdwxK4bowOFfIFaj72bz0So2Ha50mi+KDfJm7Zw41mbFkhg6OhbLCDEa6LtNvLKnlJdyS+wLOZPDfHnihjRWZcZc9WI6QojLoGnqfFHf0Mfhw6r7wfkmT3YOfWRkgPvIHwcPidGoggrbt6tgyKlTzvdHRTm6hdx8MwQFjcgwxxRNg6NHVeePN99UxehsIiNVEOL++yE7e3yHbDo6VBjkzTfVa8vomE9i1ixHp5CUFAxmCy9+UcSLOwsxWTR8PVx5ZsUUHshJnJAh4+OVel7KLeaD/Bp7wDsx1IeHFyZx99x4KcoixFW2Lb+Gn7ybT1uPGR8PV35x53TunhM3YudKNU3jrUMV/Grbadp7zLi76vjeDWk8fn2adBIUQoyvIMj5GhoaiIiI4Msvv2Tp0qXo9XrCw8N54403WLduHQBnzpxh6tSp7N27l/nz5/Phhx9y++23U11dbe8S8sc//pFnnnmGhoYGPDw8eOaZZ9i2bRsn+rRKXL9+Pa2trXz00UcA5OTkMG/ePF544QUArFYr8fHxPPnkk/z4xz++6NglCCKEEEIIJ4cOwXXXQVeX6gLy0kvje2JwJBUUqN91TY1qy/3ZZxNqUru9x8SBkmb2FKmOH6dr2pzud9HBjNhAe8ePeUnB+HiMjcnGbqOFk9WqY0h+b0CkuLGz3346HaSG+zEzNlCFQ+KDmBYdIK1Uxain7zbx19wS/rq7pN9ivOGk04FL34CILVxC7/Y+IRMdgG7g7brzgybnBVd0uv7bXXSO79X3sS6974nOz+0YJ73XLjrwdHPF090FTzcXddvNBU93F7zs23u3ubng6e7ab78Bb/fZ181FJ6EZIa7QsYpW/m9XMR+dqLWHONMj/HhkSTKrMmPlPXmEWawaR8pb2HGylh2n6ihr6rLfp9PB7PggbpkexS3TIkkJl24t4tJZrRqNHQYCvN3l//so0dJpZHdRI7kFjewqaOzXGTLQ252FqaG94Y9wEkJ9RmikF/fmgXJ+tvUkRouVSZF+/PnBuaOmqqMQQ6VpGkfKW9l6rIpt+TVOAc34EG9WzYplVWYM6ZH+12w8Ryta2XS4kvfzqp0+j2Ynh7BuThwrM6IleCUmLH2XiZd2l/C3PvM1qeG+PLksndtnRksARIjRpKNDnY+zhT727YPa2v77BQc7hz6ysyEk5NqP92opK1OL9rdvV+fIuhyf+3F1hYULHd1CZs6U85V9nTunwh8bNsDZs47tgYGwdq3q/HH99eA2AY+L9HrYulV1CtmxA8x9zmHMm6cCIffcwzmPIH78Tj5HylsBmJMYzL/flXHNjm1HksWq8enpOl7KLeFASbN9e3ZyCN9anMxNUyNxnYChGCFGSnVrN09vPMb+3v+Pt2VE8+yajGte8KSiuYufvHuc3MJGQHUpeW7dLCZHjf+/i0KIoRnXQZDCwkLS09M5fvw4M2bM4PPPP+fGG2+kpaWFoD6L+RITE3nqqad4+umn+dnPfsZ7773HsWPH7PeXlJSQkpLCkSNHmD17NkuXLiUrK4vf/e539n3+9re/8dRTT6HX6zEajfj4+LBp0yZWr15t3+cb3/gGra2tbN26td9YDQYDhj5VA9ra2oiPj5cgiBBCCCGgpERNJtfXwy23qMopY6WK0Fh1+rQKgzQ0qN/9jh3gPz4/SHcbLRwua7F3/Dhepe/XNWNypH9v8COUnOTQUVHNdbjou02cqNLbO4fkV+r7LWoC1flkcpQ/M+NsnUMCmRTpj7ucqBWjgL7bxN92l/BSrmNBge3c29j6FD8+9A2beA0SOnEKkFxsH6f9L/68ciJIjFUWq8Ynp+p4KbeYg6Ut9u1L0sP41uJkrpsULiGrUUjTNArqO9hxspZPTtWRV6l3uj813Jdbpkdx87RIMmID5dhJ9NPaZeRMbTtnato4W9fOmdp2ztW209lb0T7E14PoQC+iA72JDfIiOsib6EAvYoK8iQnyJtLfUxZPXgUGs4XDpS3sKlThjxPVeqfjSndXHXMSg1mSHs6itDAyYgPH1DHI4bIWHnvtMPXtBgK83Hj+/iyumxQ+0sMS4qIK6trZeqyarXlVVDQ75i5CfT24fWY0d2bGkpUQNKLHTD0mCx+frGXT4UpyCxvtfzu83V1ZkRHFujlxzE8OnZBVncXE09Jp5KXcEl7eU2rvcDgp0o8nl6WzMiN6TL13CjEuWa1w5oxz6OPECbW9Lzc3FXawhT7mz4f09IkTfjAY4KuvHMGQvuEGgNhYuPVWFQy56SaYiOubqqpUuOGNN1THGBsvL7jjDtX5Y8UK8JTOtnZNTbB5s/q9ff658/+7hQux3nsv7yTP5xcHm+k0WuyV7x+7PhVPt/FXMKLTYObtQxX8bU+pveCKm4uO22dG863FKWTEBY7wCIWYuCxWjT9+WcR/f3IOs1UjJtCL/7o3k/kpodfke7+6t5TnPjpLt8mCp5sL/98tk/nm4mT5LCGEcDJugyBWq5U777yT1tZWcnNzAXjjjTd4+OGHnQIXANnZ2dxwww385je/4dvf/jZlZWV8/PHH9vu7urrw9fVl+/btrFixgkmTJvHwww/zk5/8xL7P9u3bue222+jq6qKlpYXY2Fj27NnDggUL7Pv86Ec/4ssvv2T//v39xvuLX/yCX/7yl/22SxBECCGEmOCammDRIjWxOmuWmmyVY4NrIz8fbrgBmpth6VI1ye0zequaDpXRbCW/srW340cjR8paMVqcT2wkhfqwIDWMhamhzE8JJdx/Yk1ON7QbOF7VSl6FnuNVqntIY4ex336ebi5MjwlgZlwQs+IDyYgNIiXMVxYziGumrcfE33JLeSm3mLbeAMjkSH++f1M6t06Psr8WNU1D00Cz3QasvdvU/aChvrb23q9pwCDbNXWH0/Nofb9Pn8f1/Z7O4zjvuS82lj7PZ7UOcSzqYU7bLVYNo8WKwWTBYLb2XiwYTH1um629X/fZp3f/HpMF4wCPO//v6Ehyc9Gd181kCGETNxfiQ3yYFR/E9JiAMdPpSYwPXUYzbx+q5K+7S+wnOt1dddw5K5ZHliQzNVqOfceSWn0Pn5yuY8fJWvYVN2GyOKaT3V11JIX6kh7pR1q4H2mR/qRH+JEc5itdHyYAg9lCUX0nZ+vaOFOjAh9na9upbeu5oud10UGEvxfRQV7EBHoTE6RCI45rb0J9PeQY/SI0TeN0TTu5hQ3sKmjkYGkzPSbn45vJkf4sTg9jcXoYOckhY/54oa6th+++dpij5a246OBHt07hO0tTJHQoRp0afTfvHatm67FqTvXp2urr4cry6VHcmRnDorSwURm2rNF38+6RKt45XOnUkTU2yJu1c+JYlxU3qjsICXG5GjsM/GVXCX/fW2oPt06J8ucfbnSerxFCXGONjc6hjwMHoK2t/35xcSrsYQt+ZGWNi3NDw6a4WJ0v+/BDtYC/u09hLTc3WLxYhR5WroTp08dvYKa5GTZtUp0/vvzSUZHJ1VUV9bvvPli9etwWmRtWdXXwzjsqFLJrl+N3qdNhWLSEjSkL+e/ADFp8AkmL8OM3azOYkzg+OvBUtXbzyp5SNhwotxf5CvR252s5CXx9QRJRgV4jPEIhhE1eRSvff/MopU1d6HTw+PWpPHXTpKv2Wbywvp1n3jnO4TJVNCsnOYTfrJ0pXW2FEAMat0GQxx57jA8//JDc3Fzi4uKA0R0EkY4gQgghhOinp0dVz9m9G+Lj1cR0TMxIj2piOXwYli1TJwNuugnef19V8BlDLFaNU9Vt9o4fB0ub6eo9AWkTFeDFwrRQFqaGsSA1lNgg7xEa7eikaRrV+h7yK1rJq9RzvKqV/Eq9fVK2L39PN2bEBjIzPpBZcUFkxAYSF+wti4nEsGrrMfHy7lL+sssRAJkU6cf3b5zEihmyoGAkWO0Bk74BEgs9gwVMBgmaDCWY0mPq/zizdfima1x0MCnSn8z4IHvQTTogiauhrq2HV/aU8vr+cvTdJkCd6HxgvjrRGRkwto65RH9tPSZ2nm1gx8lavjzbQLuh/7ETqL87CSE+pEX4kxbhR3qEH+mRfqSG++HrObYXmk9EmqZR1drN2VoV9lCBjzaKGzoHfb+KC/ZmSpQ/k6P8mRIVwJQof5LCfOk0mKlu7aFG3011azfV+h5qeq+rW7upa+txChsNxsPVhahArz6dRPqHRQK83CbcMXutvoddBQ3kFjayu7CxX/g93N+TJWkq+LE4LYyIcfh32WC28LMtJ9l4qAKAO2bF8NzamXh7SDhNjKzWLiMfnqhly9EqDpQ229fCubnouH5yOKsyY7lpauSYea1qmsaR8lY2Ha7kg7xqp2OC7OQQ1s2JY2VGNH7yvi/GuLq2Hv78VTGv7y+zByqnRQfwDzemc8u0SJmvEeJaMhohL88R+ti/H4qK+u/n4wNz5zpCHzk5qsOFGJqeHhWCsHULKShwvj8+XoVCVqyAG28c+6GIzk547z3V+ePjj8Fkcty3eLHq/LFuHYRLt8HLVlWlAjYbN8LevfbNVldX9idn8m76InZMXsiq66fzw+WT8fdyH8HBXr5jFa38ZVcxH56oxdI7V5Ic5ss3FyezNit2zBdeEGK86jSY+eX7J3nrUCUAs+IC+Z/1s4c1nGGyWPnzV8X8z6cFGC1W/Dzd+MnKKdw3L0E+TwghBjUugyBPPPEEW7du5auvviI5Odm+/fPPP+fGG2+kpaWFoKAg+/bExESeeuopnn76aX72s5/x3nvvcezYMfv9JSUlpKSkcOTIEWbPns3SpUvJysrid7/7nX2fv/3tbzz11FPo9XqMRiM+Pj5s2rSJ1atX2/f5xje+QWtrK1u3br3oz3Ap/zBCCCGEGIesVli/Ht5+GwIDVRhk+vSRHtXEtGePqt7T2Qm33QbvvgseHiM9qkFpmkZBfQd7ClXwY19xk32huE2IrwcLUkNZmKrCH0mhPhNu0dOVslo1Sps6ya/Uk1fZyvFKPSeq9f2q5gKE+nqQEReoFlTHBZIRF0iE//hbRCWuvnZbACS3xL5oOj3Cj+/flM7KGdEyATiBmS3WPkGUwTqbOAImjjCJ2tZtslBY38Gxilbq2w39nt/TzYUZsSrgNqs36JYo7x3iMp2qbuMvucW8n1dtX8CdFOrDtxYns3ZOnJzoHKesVo1qfTeF9R0U1ndQUNdBYUMHBXXt/Y5V+4oN8iYtws8pIJIW7k+gz9g80T/etPWYVOCjps3e4eNsbfugoZ8ALzemRAWowEe0P1Oi/JkU4Yd/axOcOaMuZ8+q65IS1Q0zOhqiotSl7+2oKKwRkTRaXOwBkarWbmr0tuCICos0dBgYylkNXw9XooO8VVikNxxi6zJiux4rC64H02kws6+4iV0FjeQWNlJY3+F0v7e7KzkpISxOC2NJejiTIv0mxHu9pmm8tq+MX75/CrNVY1p0AH96cA7xIVL1WVxbPSYLn56uY+uxanaerXcKumUnhbBqdgwrZ0QT7Dt656SGosdk4eOTtWw6XEluYaP9b7SPhysrZkSzbk4cOckh8vlWjCmVLV386ctiNh6qwGhWc4Mz4wJ5clk6N02NmBDvp0KMKE2DsjJHt4/9++HIETD0n+NiyhRH6GP+fJgxQ3WxEMOjsNDRLeSLL1RQxMbdHZYsUZ1CVqyAqVPHRrcQo1GFPjZsgK1boavLcV9mpur8sX49JCSM2BDHrbIyeOstFQo5fNi+2ejixlfJs8nNupHr/r9vcUN22ggOcujMFis7TtXxUm6JvcI/wMLUUL61OJkbJkfIMbAQY8S2/Bp+8m4+bT1mfDxc+cWd07l7TtwVH/efqNLzo0359m6gN0wO59/WZBAjRTyFEBcxroIgmqbx5JNPsnnzZnbu3El6errT/Xq9nvDwcDZs2MDatWsBOHv2LFOmTGHv3r3Mnz+fDz/8kNtvv52amhoiIiIA+POf/8wPf/hD6uvr8fT05JlnnmH79u0cP37c/tz3338/zc3NfPTRRwDk5OSQnZ3N888/D4DVaiUhIYEnnniCH//4xxf9WSQIIoQQQkxwP/gB/Nd/qYnRjz+GG24Y6RFNbF9+qSamu7vhrrvUpOMoOTmgaRrlzV3sKWpiT1ETe4v6V3L193QjJ6U3+JEWyqQIf5lMvArMFisF9R3kV/Z2DqnUc6a2bcAqxdGBXsy0h0NU5xBZ0CgG095j4pU9pfzfLkcAJC3Cj+/fmM7KjGhc5f+zGEa1+h7yKlvJq2glr3LwDkiB3u7MjAt06hwiITcxGKtV48uCBv6yq5jdhU327dlJITyyJJkbp0bK37IJStM0GtoNKhxS30FBfbs9LHL+MW1f4f6eKhjSGxJJi/AnPdKPUF8PWWh3FZgsVoobOjlT6wh8nKlpo1rfM+D+7q46UsP9nDp8TA72ILqxCp0t6NE3+NHWdvmDCwzsHxLpc9sUFkG9XzBVrr7UtBtUWKTVERap0XfT0mW6+PcBgn3c+3USsd2ODvQiKtBrVHXQMlus5FfpyS1oJLegkSPlLU5dWXQ6mBkb2NvxI5ysxCA83cZ22OVK7C9u4vHXj9DUaSTYx53/vT+LhWlhIz0sMc6ZLVb2FDWx5VgVO07W0dEnSDclyp9VmbHcmRkzbju31ui7efdIFe8crqS4sdO+PS7Ym7VZcazNiiMhVEJZYvQqa+rkxS+KeOdIpf09dm5iME/emM7S9DA5LhXiamlvh0OHHKGPffugrq7/fiEhzqGPefMgOPjaj3ei6u6GnTtVp5Dt26G42Pn+xER13m3lSli2DHyHr5r6FbNa4auvVOePTZugxbFon9RUFf647z6YNm3kxjjRFBaqc7MbN0KftXoGV3dOz15MyhPfImDd6tH1OurV3mNi48EKXt5TSmVLN6DmTe6cFcs3FycxPSZwhEcohLgc1a3dPL3xGPtLmgG4LSOaZ9dkXNb5/h6Thd9/VsCfvirGYtUI8nHn53dMY3VmrHymEEIMybgKgjz++OO88cYbbN26lcmTJ9u3BwYG4u2tJkkfe+wxtm/fzssvv0xAQABPPvkkAHv27AHAYrGQmZlJTEwMzz33HLW1tTz44IM88sgjPPvss4DqEDJjxgy+973v8c1vfpPPP/+cf/iHf2Dbtm0sX74cgI0bN/KNb3yDP/3pT2RnZ/O73/2Ot956izNnzhAZGXnRn0WCIEIIIcQE9vvfw/e/r26//rpqJSxG3o4dcMcdqvrPfffB3/8OriOzSKZW38Oeosbe4EcTVa3dTvd7ubswLymEBamhLEoNY3pMAG6jaEHSRNJjsnCmtl2FQyr0HK9qpaC+Y8CqxEmhPsyMC2JmXCCz4oOYHhMgVdEnuA6DuTcAUkxr7wLF1HBfvn/TJG6TAIi4RqxWjZKmTvvfsWMVrZyqabNXOe0rJtCrNxSigiEZsYH4e0nIbSLrMVnYcrSKv+SW2CvPu7roWDEjikeXpDArPmhkByhGtZZOY2/XEOeASM0g4QOAIB/33nCIv1MXkagALzlpNgSaplHb1sOZ2nbO1LRztjf4UdTQMWC4GdTfftXhQwU+prkbSWqowL2owDnwUVysFtMMxMUFUlJUdd4pU2DyZPV1ZyfU1EBtrbqcf3ugCr+DcXWFyMh+XUWIjsYQFk6jbzA13kGUeQZS0QM1rT1U61WHkerWbrqMlot+C50OIvw9+4dFAr2I7r0O8/O8aqF8TdMoa+piV2EjuQUN7Clq6hfmjA/xZnFaOEvSw1iYGkqQz9juLDDcqlu7+c7fD3O8So+ri46frpzKw4uS5O+HGFaapnGsopWtx6r5IL+Gxg7H37LYIG9WZcawKjOWyVH+IzjKa0vTNI6Ut7LpcCUf5FU7dZbKSQ5h3Zw4VmZE4+spcyRidCis7+DFLwrZmleNpTcAsjA1lCeXpTM/JUTeN4QYTmaz+jyxf78j9HHyZP/PFm5uqjuDLfQxf75asC//H0cHTYOCAtUpZPt2VXyt7+c5Dw+47joVDFmxQn0mvNb/dpqmOsm88Qa8+SZUVzvui46Ge+9V5wbnzZPX1Ug7fRrTGxtoe/k1QitL7JvNXt64rroT3b33qteR18gWLapo7uLlPaVsPFhhD3wH+7jzwPxEHpyfSESAFFUSYqyzWDX++GUR//3JOcxWjZhAL/7r3kzmp4QO+TkOlzXzo035FDWo4gi3ZUTzizunE+7vebWGLYQYh8ZVEGSwSZW//e1vPPTQQwD09PTwgx/8gA0bNmAwGFi+fDkvvvgiUVFR9v3Lysp47LHH2LlzJ76+vnzjG9/g3//933HrU/V5586dPP3005w6dYq4uDj+5V/+xf49bF544QV++9vfUltbS2ZmJr///e/JyckZ0s8iQRAhhBBignr3XVi3Tk04/vrXMIROYuIa+uADWLNGnXx46CF46SW1aOkqa+owsK+4mT1FjewtanKqkgiqcszs+GAWpKquH5kJE7uS62jXaTBzokpPfqWevMpWjlfpKWvq6refiw7SI/yZGRfI/JRQVmRESTBkghgoAJIS7sv3b0zn9pkxEgARI85otnK2tp1jla3k93YOGSjkptNBarifvXPIrLggpkT7y3vUBNDUYeDv+8r4+94ymjpVVwc/TzfWz4vnoUVJxAVLZWdx+dp7TBQ1dFJQ5wiHFNR3UNHSNWDYFtTrL7VPBxHVTcSf2GDvCfu+2mEwq84etW2916rLR9sAXaBA/Q5Vhw9/poZ5k2FqIa25Et+SQtXVwxb4aGoa8PEA+Ps7wh59Qx9paeB5iSc3NQ30+oFDIud/3dBwac/t7+/UWUSLjMQQFklzQAj1vkFUeQZS4h5AMd5UtRmp0fdQq+/BaBkk6NKHu6uOqMDekIgtIGILi/QGSAK93Ye8gLS1y8juwiZyCxvYVdBory5qE+DlxqK0MBanh7EkLVwq6w9Bj8nCT949zuajVQDclRXLs2sy8HKX4xdxZYoaOth6rJr3jlVR2mcOINjHndtmRrM6M5ashOAJ38G122hhx6laNh2uJLew0f7e7uPhyooZ0aybE0dOcsiE/z2JkXGmto3nPy9k+/Ea+2vz+snhPLksjTmJISM7OCHGg7Y2yM+HY8cgL09dnzgBPQMUA0hIcA59zJ4N3uOzg9a41NkJX3zhCIaUljrfn5ysOoWsWAE33AA+V/FzzNmzsGGDCoAUFDi2BwWp87X33adCKiNUGE5cgKZR+OluDj/3J+Yf+pTE1lrHff7+sHq1CvDcfLMKG12TIWkcKW/hpdwSPjpRi60pZ2q4L99anMKa2bF4e8hrSYjxJq+ile+/eZTSpi50Onj8+lSeumnSBTsHdxrM/Pbjs7yytxRNUx2w/9+qGdw6I2rQxwghxGDGVRBkPJEgiBBCCDEB7dkDN96oJrUfewz+93+lqsxo9M47auLQYrlq/07tPSYOlDSzp6iJ3YWNnKltd7rfRQcZsYEsSFVVXOcmBUtAYIxr7TKSX6lXFfcr9Ryv1FPb5nyCy9/TjTszY7gvO4EZsdIqejzqNJh5ZW8p//dVMS22AEiYL/9wYzp3zJIAiBjdOnpDbnkVreRXqs4h53esArUAdlp0gL1zSGZ8IClhfrKQa5worO/gpdwS3j1SiaG3a0xskDcPL0ri3nnx47tDjMUCp06pReexsRAfr046i2um22ihuLE3GFJnC4i0U9rUZa/UfD5PNxdSw1XXkDTbdYQ/iaE+FzxRN5aYLVZKmzo5XdPuCHzUtvULDNi4uuhICfNlcpQ/s/x1zOqsJbW5kpCKYnRnz6pFMgUFYDIN/k0TE1XA4/zQR1TUyHzGNZmgvn7wziJ9b3cP/HsZkIsLRERAdDRaZBQ9YeG0BYbS5B9CrU8wlR4BFLsFcFbnQ0mPC/XtPQzyUnTi7e5KTJAXMUHeRPcGRGKDvInu7TJS395DbkEjuYWNHK/SOwWg3F11ZCUEsyQ9jMXp4WTEBsox5GXQNI2Xckt4dvtprBrMjAvkTw/OITpQFheKS1PX1sP7edVsPVbN8Sq9fbu3uyu3TI9kVWYMS9LDx817znCrbu1m89EqNh2upKRPUZS4YG/WZsWxbk4c8SEScBNX3/FKPb//vIBPTtXZt90yLZInlqUxMy5o5AYmxFilaVBRoYIefUMfxcUD7+/rC3PnOkIfOTkqtC3GB01TnzNtoZCvvgKj0XG/pydcf70jGJKefuXfs7JSdf3YsEF1AbHx9oY774T774flyy+9YIEYEWaLlb/mFrPj1W3ccvxLbj+bS0xbn4IQwcGqyN/69SpY5Db851PNFisfnqjlL7kl5FW02rcvSQ/jm4uTuS49XOa/hRjnOg1mfvHeSd4+XAnArPgg/ufeTJLCfPvtm1vQyI/fzbfPz949J45/vm0agT7j+PyJEOKqkiDIKCVBECGEEGKCOXcOFi5U1VNvvx02b74qE1FimLzxBjzwgJqgfuop+K//uqIFTd1GC4fLWthT1MieoiaOV+n7LVabEuXf2/EjjOzkEAK9ZSJgvKtr6+ldTN3CB/k1Tl1DZsQGsH5eAqsyY8b3otoJotNg5tW9Zfz5qyJ7ACQ5zJd/uDGNO2fFyuI9MWY1dhjIr2zlWEVv0K2i1f4a78vP042M2EBmxQcxK05dRwd6DbkauhhZmqaxt7iJv+wq4fMz9fbts+ICeWRJCitmROE2Hhc3VlbCgQOwf7+6HDqkKlr2FRCgAiG2S1xc/9u+/U8EieFlNFspa+qkwBYQaeigoK6d4sZOjOaBOzi4u+pICvW1B0TSIv1Jj/AjOcx31HYF0DSNhnYDp2vbOVvb1tvho53Cho5Bf87IAE+mRPiR49rBzN7AR0RVKa7nejt81NYO+DhALZCxhT36hj7S08fu61rToL19aF1G6usZtAXNQHx90aKiMIZH0hkcRmtACA3+IdR4BVHmHkCRqy9ndH4U4YPF5dJeY5Mi/VicFs6SdPVZ0ddT5hKGS25BI09sOEJrl4kwPw/+8MAc5iVJxXdxYfpuEx+fqGXLsSr2FjfZ/1S4uuhYmh7G6tmx3DQ1Uv6vXgJbZeVNhyv5IK+GdoOje1VOcgjr5sSxMiNafqdi2B0ua+b5zwvZeVYtJtXpYGVGNE/ckMbUaDl/f0l6eiA3Fz7+GHbvVpX2J092vkRHS2Gs8chggNOn+4c+WlsH3j8uDjIzYdYsdZ2ZCSkp16QzuxglOjrg888dwZDycuf709JUIGTFChUQGWonmKYm2LRJnd/btcvxec7NDW65RYU/Vq0CP79h/XHEtVPW1Mk/bT7OnoIGZled5RsV+1h5Jhf3ekeQk/BwWLtWFfxbsuSKO73ou01sPFjOy7tLqdar4m4eri6snh3DNxcnMyVKjhfEGKRpYLVe/rWbG4SFjfRPMWK25dfwk3fzaesx4+vhyi/unM66OXHodDr03Sb+bdsp3jqkwiKxQd78+q4Mlk4KH+FRCyHGOgmCjFISBBFCCCEmkPp6WLBAVTuaN0+1Qx6rC2cmkr/+Fb71LXX7Jz+Bf/u3IZ+oMpqt5FW2sqewiT1FjRwtb8VocV6clRzm2xv8CGV+SihhflJ5aCKzWjX2FTex4WAFH5+otb9evN1duX1mNOuz48lKCJZF02NMl9EWACmmuVNVOUsO8+XJZWncOStmfC6cFhOapmlUNHeT1xsKyats5URVG90mS799w/w8yYwPZFZcEDN7AyJBPh5Xf5AWi1qM0NysLmYzTJumKscJJ0azlW3Hq/nLrhJOVrcB6lDo5qmRPLIkhXlJ4+h9qb1dBT36Bj+qq/vv5+cHCQlqsXhLy9CeOzjYOSAyUGBkqIsaxCWxWDUqmrtUQKS+ncL6Dvuly9j/7xKoznwJIT6kRfg7dRFJDfe7potPu4xmztY6d/g4W9s+YNgOwMfDlYwgNxZZm5jVUUtKUyWRNWV4FJ5TRQku1AUjOrp/Z4/Jk9XrcyIvxjKboaFhaF1Gzg+JXYCm02ENC6M7NIKOoDCaA0Ko9wmi0iuIUjd/Cl38MPn7MysxlMykULJSwggL9FGLVwa6uLgMvH28/H2+Biqau3j01UOcqW3HzUXHL+6czgPzE0d6WGKU6TFZ+OJMPVuPVfP52XqnAN7cxGBWZcawMiOaUJnbuWLdRgs7TtWy6XAluYWN9jWcPh6urMyIZt2cOLKTQqTisrhsmqaxr7iZ5z8vYE9RE6CCXKtmxfD4DamkRUjXvyGxVfj/+GN12bnz4p3X/P37h0MmT1ZBYx/p/jMmNDU5gh620MepU+rY+Xxubmq+pW/oY9YsCA29tmMWo5umqSDR9u0qGLJrl3N3Si8v1eHB1i0kNdX58R0dsHWr6vzx8cfOr8WlS+G++2Ddugm9YHm80TSNTYcr+dW20+i7TbhpFn4R3ML64r24bX4HGhsdO0dHw913q1DI/PmXNMdR1tTJ33aX8tahCvscUqivBw/MT+SB+YmE+8tx/7jU2gp79qhw65kz6lzClYYmNG14nmO4rodjafDcuXDw4JU/zxhW3drN0xuPsb+kGYDbMqJZPiOKX31wivp2AzodfGNBEj9cPlkKGgghhoUEQUYpCYIIIYQQE0RnJyxbphaVJSfD3r0QGTnSoxJD9eKL8L3vqdv/+q/wL/9ywd2bO408u/002/Jr+i16jQ70YmFqGAtTQ1mQGkpMkCz4EwNr6TTy7tEq3jxQTkF9h337pEg/7p2XwF2zYwn2vQaLpcVl6zKa+XtvAKSpNwCSFOrDk8vSWZUpARAxsZgtVgrqO+ydQ/IqWjlb196vMxao/ycz44LsnUOmxwTi7TFI1TaTSS3EtwU6Bro0NfXfNlhFyvh4x6IE2wKFCVCRUtM02g1mmjuMNHcZ7deVzV28daiS2jZV6c7b3ZW758bx8KJkkgdodT6mmM1w8qQj8HHggPr6/GlRV1fIyIDsbMjJUZcpUxyVBDs6VNeQigrHte1i+7qtbWhjCg29cFeRuDjwlBPMw8Vq1ahp66GgzhEOUd1E2mnrGWABU6/YIG/SIvxIj/BT15F+pIX7E+hz+d3bLFaNsqbO3rCHo9NHeXPXgOdlXdCY49HDYnMjM3sDHxHVJXgVF6I7v4pqX+7uaoHdQIEPmZu+ch0dQ+syUlenTrxfCzrdpYdHLna5nMcN52N8fODWW6/KIq4uo5kfbspnW34NAPdlJ/CLO6fh6TY6OwSJa8PSW7Bhy9EqPjpR69SlYlKkH6syY7lzVgzxIbJ4+Wqpbu1m89EqNh2upKTREbqLD/FmbVYca7Pi5PcvhkzTNL4qaOSFzws4WKpC3W4uOtZmxfH4Dakkho7xzznXQmsrfPaZI/xx/vFndDQsXw433ghdXSoocuaMui4pufBxUEKCczc62yUuTgKuI8FqhaKi/qGPysqB9w8KcnT3sM2pTJ0qn2PFpWtvV39nbMGQ819zkyapQMjs2fDRRyoE0jeENnu26vxx771qPkWMWw3tBv71g1O8n6cKuSSE+PDsHVNZXHkc3nwT3n3XeR44Lk6FgxYsgIULYeZMFVjrQ9M0Dpa28FJuMTtO1dnnZSZF+vGtxcmsyowdtV1kxWUqL1ehD9vlxInhCUqMZzodzJkz4YMgoOYM/vhlEf/9yTnMfc53pYT78pu1M6XjrBBiWEkQZJSSIIgQQggxAVgscNdd8N57EBKiKkhMnjzSoxKX6r/+C37wA3X7uefghz8ccLftx2v42dYTNHaoRd+hvh69HT9U+CMx1Gf8VM0W14SmaRwpb2HDgQo+yK+mx6ROlnq4uXDr9CjWZ8ezICVUXlejSLfRwt/3lfKnLx0BkMTeAMhqCYAIYddttHCqRs+xCj0nS+opLaikrbqeoJ52grvbCepuJ7CnnRBDB4kYiKebCHMXgd3teLXr0TU3D32B/WD8/dXxmab1X7xi4+enggB9AyIZGaO6s5vZYqWly0Rzp5GmTgMtnSaaOw00dRpp6TSq6y4jTR1GmntvmyyDTweG+3vy0MIkvpaTcG06tgw3TVOBjL6dPg4fVouSzpeQ4Ah8ZGdDVtaV/1u3tQ0eErFdhtpJICJi4JCI7RITAx5j8N9oFNE0jYYOA4V1KhhS2KeTiO0YfyDh/p6k9wmI2LqJhPp6OB2nNXYYOFPj6O5xpradgvp2+zFeXx5mE7MNDSy0Bz4qCK8uxbu4EF17++A/RFiYY/Fc38BHUlK/BQ5iBFgsqjrphbqM1NSoYInFcuHLRD2V4+mpqvo++aT6Oz2MNE3jD18W8duPz6JpMCcxmD98LYuIAK9h/T5idNM0jeNVerYeq+b9vGrq2w32+2ICvbgjM4bVmbFMifKXz+LXkG1+5O1DlXyQX0NHn1DO/JQQ1s2JZ8WMKKm0KgakaRqfna7n+c8LyKvUA+Dh6sK98+L57vWpxEqxnsFZLKpzoi34sX+/2mbj6QlLlqjwx/LlMGPG4KENg0EFC86edb6cOXPhbos+Pmrhty0YYjvWnTRJfWYXV66rC44fdw595OcP/lk1JaV/6CM+XgI7YvhpmlqU/eGH6pKbO3D3mfR0Ff647z45BzsBfXa6jn/ecoIavSpmszYrjn++bSrB7sAnn8DGjbBliwoZ9eXjA/PmwYIFmHNy+CQghRdPtnG8Sm/f5bpJ4TyyJJnFaWFy7D8eWK2qGJAt9LFrl5qbPV96OixerIJlXl7q/c3FRa51OnmvH0ReRSvff/MoFS3dfHtpCt+/MV1CY0KIYSdBkFFKgiBCCCHEOKdp8MQTqqOEp6eqYLNo0UiPSlyuZ5+Fn/5U3f7979Wik14N7QZ+/t4Jth+vBVRlmF+tzmBeUrBMDIph09ZjYuuxat48UM7Jasfi56RQH+6dl8DaObFE+MsCpZHSbbTw+v4y/vhlkX2haEKID08uS2PN7FgJgIiJobv70rtzNDcPfRH8IKyBQehCQ9CFhKhgx2CX0FDH7eBgVR3fprVVLXLIy3MsfDhxQi1UOZ9Op04G2RY72AIisbHDfiJE0zS6TRZ7aMPWsaOlSwU67F08Oh0Xfbfpsr6Xj4crwT4ehPp5EOLrQYiPBwvTwrhjVvTYqobe1qaqkdk6fezfrxZWny8gQJ3s7Rv8iIq69uPVNNDrBw+J2L7uW91yMDqd6jw4UEjE9nVMjIQBLlNLp5HChg4K6hzhkML6DvtCg4EE+biTHuGHh5sLZ2vb+4dJNI2Q7jam6qtYYGwgo7OO5MYKwqtK8KosRzdYxWRXV7X46vzOHpMnX5VOCWKU0jRHKMRqvXhwpO9lrO5fWqrep20WLlTzLmvXDmsQ7ouz9fzDhqO095iJDPDkTw/OJTM+aNieX4xOJY2dbD1WxXvHqinu03kiyMedlRnRrJoVw7ykEFxcZJ5npHUbLXx8spZNhyvZXdRoz8X5eLiyMiOadXPiyJZ/K4HqAvfRyVqe/7yQ0zVqLs3L3YWv5STy7aUpRErQb2CVlbBjhwp+fPqp+tze15QpjuDHddephbRXQtNUUPb8gMjZsyo4MtCib5vYWOfuIbagSELCuO/ueVk0TX0+tnX3sIU+CgoG7tbi5aXCPX1DHzNnSkdBMXL0eke3kPx8FUS77z5VmV7OxU1oHQYz//HxWV7ZW4qmqUJ9P79zOnfMjFbnaXt61KL/vXsdF72+3/OUBkWTFzcFFixg9j0rSLh+vsyjjWU9PWqe2Bb82L27/7+7q6sqMrF4sbosWqTmV4W4RCaLlfYeMyG+UqhJCHF1SBBklJIgiBBCCDHOPfccPPOMmnx8+221MEGMbf/yL/CrX6nbf/4z2iOP8F5eNb947yQtXSZcXXQ8fn0qTyxLG1sLJsWYc7xSz4aD5bx3rNpeBdPNRceNUyNYn53A0vRwXGXBwzXhCIAU09ihFozHh3jz5LJ01syOxV0CIGKs0TRVCXIoAY7z9+kZfDH0Rel0KpzRJ7ChhYTQ5RtAjas3JVYvzpncOdHtSq2rD63e/rR6+dHm5YfFxZVgH3dmxgUxKy6QWfFBzIwLItzf88p+F2YznDvnWCBhWyRRVzfw/iEhzsGQzEyYOtVpYarVqtHarTp0NHc6X/ft2NHc57bBPMhC8AvQ6SDI250QXw9CfT0J9nUnxNeTUF8Pgn09nK5Dei9jskKVyaQCO7ZOHwcOwOnT/avku7mpxSrZ2Y7gx+TJY2dhkKap/2fnB0XOvz1QcOl8Li4q8DJQSMR2OzpanQQVQ9LeY6KooZOCOkc4pKC+g4qWLvtL0c1iJl5fR1pzJXO668horyG5qZKwqlI89BeofhwQ4Bz2sF1SU6X7i5iYNE39vX/+eTXPYuoNP0ZFwXe+oy7R0cPyrUoaO3n01UMU1nfg4erCr9bM4J658cPy3GL0qG/v4YO8GrYeq7J3CQC1UPymqZGszoxl6aRwPNzGyDHDBFTV2s3mI5VsOlxJaZOj41t8iDdrs+JYmxVHfMgVLlIXY47ZYuWD/Bpe+KKQwvoOAHw9XHlwQRKPLEkmzO8KPyuON93d8NVXjq4fp0453x8YCDfd5Ah/JCRcu7GZTFBcPHBIpKFh8Md5eakCDueHRCZPVj/PRGA2q99T39BHXh7U1w+8f0SEI/Bhm9uYNEkWQAshxpTDZS38+J18Cnrf/5dNieD/rZ7Rr/tXcV0bH7z1BfUf72RGxSmyqs4wqWmAjtG+vqqQzMKFsGABzJ8vBThGs6Ym2LPHEfw4dAiM5xVl8fVV/5ZLlqjgR3a2dBgTQggxJkgQZJSSIIgQQggxjm3YoNoQA/z3f8NTT43ocMQw0TT40Y/gP/4DTafjpUd+zq9C5gIwLTqA59bNZEbsBDmRJEaFLqOZD/JrePNAOUfKW+3bY4O8uXtuHPfMjSfmvAluMTx6TBZe31/OH78soqG9TwDkhnTWZEkAZMzQNOeL1XrxbZf69Wh5DqsVOjqGFvA4/+TIpXB1HVpHjvMvgYFDWpRvtWoUN3aQV6Enr7KVvEo9p6vbMFr6hyVig7yZFR/IrDgVDMmIC8TPcxgWMNTV2RdRWI4ew3osD7eCs+gsln67ml3dqIxK4lx0KsdDEzkanMDJ8GRafC7teMHDzcUptGG/+HgQ4td77au6eQT7eBDk4zH+AoGaBmVlzp0+jhwZuFNGUpKjy0dOjqrq5j3O3w9tVXQv1FmkstKxaPpCXF1V55CBQiK225GRIx+k0TT18xiNl399JY+9yHNYjUYsPUYsBgPubXpczYP87nU6SEx0dPXoG/iIjJSqpkIMprYW/vxn+OMfoaZGbXNzg7vvVl1CFiy44v8/7T0m/vGtPD45pUKgDy1M4qe3TZVj/TGuvcfERydqeS+vmt2FjVh7z4q6uuhYnBbGqswYbpkeNTzHjeKa0TSNw2UtbDpcyQf5NfaiGQALUkJZNyeOFRlR+HjIv+t4ZrJY2Xy0ihe/KLQHg/y93Hh4UTLfXJREkI8EaQF1HH3qlCP48dVXzoUdXFzUoldb8CM7e3SGAZqbVfGGM2ecAyKFhRee14iKGjggkpQ0On/OodDrVXeEvqGPwbqcurion7dvl9PMzJHpkCmEEFeB0WzlDzuL+N8vCjFarPh6uPLD5ZN5cEES+0ua+GtuCZ+dqbcX75gaHcC3FidzR6I3nocPOTqG7NunOg+fLz1dfd60XWbMkIIqI0HTVNdQW+gjN7d/mBXU+5ut28fixep9b6y+3wshhJjQJAgySkkQRAghhBinvvwSbrlFnWx46ikVBBHjhma1Unjft0h/62UsOheeXvUj0p/4Jt+9PlUWg4gRdba2nTcPlvPukSr03WqhoYsOrpsUzvrsBJZNiZDX6DDoMVl4Y385f+gTAIkL9ubJZWnclRUnv+ORotfD1q2wcaNq7202Dy0cIQbn7n7h4MZg4Q5//2u+aNlgtnCmpp38ylaO9QZEiho6+v0T63SQFu7HrPggdYkLZEpUgL3Cs6ZptPWYae40Dtqpo6W3W4ftdqdRhT88zUbSG8uZWl/MtPoSptWXMLW+hABD54BjrvMPoyQ2lcrESTSmTqV90nSsqamEBHj3D3v4euDj4Ypuoi0Gb22Fgwedgx8DVS4NDHTu9DFvnlo8L/qzWtXvcLCgSEUFVFXBAKGmftzcIDbWOSQSF6dOfF+rQIbZfPFxjiY+Ps5BD9vt9HR1nxDi8hiNsHmz6hKye7dje1aWCoSsX39FYUCrVeP3nxfwu08LAMhJDuHFr2URKtXkxxSD2cLOsw28d6yaT0/XOXVcm50QxOrMWFZmRF95RzkxKnQbLXx8spZNhyvZXdRo/1zg6+HKyoxo1s2JY15SCC7jLTg9gRnMFt4+VMkfdhZR1aqC4sE+7jyyJIUHFyQS4OU+wiMcBZqb4dNPVfBjxw51/N9XbKwj+HHTTerz/VhlNqsiArZgSN+gSG3t4I/z8FCd92zH6n0vo+X3oWlQXq6CHn1DHyUlA+/v5+fctXTWLLVgWT5/CCEmgML6dn78znEOlalOrME+7rR0OYp03Dglgm8tTmZBaujA865Wq+o8vGePIxxy5kz//fz81NykLRgyf76aMxfDy2KB48dV4GPXLnVdXd1/vylTnIMfKSlSZEUIIcS4IEGQUUqCIOJS9JgsPPHGEUJ9PQn18yDMr/918His+CmEEGPNqVOwaJFauLZ2Lbz11shXyhXDpqq1m3969zhfna3j2Y9e4L78HWhubug2bYJVq0Z6eEIA6rjx45O1bDhQzr7iZvv2cH9P7p4Tx73z4kkM9R3BEY5NPSYLGw6U84edRdT3BkBigxwBENtCcnENdXTABx/Am2/CRx8NXOXwWtLp1MXFxXF7KF9fzmMu5zl1OhXSuFiwwxbu8PEZ0ydI2ntMHK/Sk1+pJ6+ilbyKVqr1Pf3283B1IS7Em/YeMy2dRszWS58Wc3fVEewzULcOdxLaG4ivKCCy5CzB507ic/okrqWDLNDw8YGMDOdFGhkZ6t9tvDMa1Ym8/fsdl7Nn++/n5qZ+L32DH+npcrw9nCwW1fVmoJCI7XZ1tToZPhq5u6uLh4e62G5f6vVwPyYwUAVl5LUqxNV19Ci88AK88YajsnloKDz6KDz2GCQkXPZT7zhZy9Mbj9FptBAb5M2fHpwjHUFHOatVY39JM1uPVbH9eA1tPY4AYWq4L6szY1mVGUtCqCyGHc+qWrvZfKSSTYcr7R0iABJCfFibFcddWbHEh8hrYKzqNqq5mj99VURdm5oTCPPz5NtLk/laTiK+E7mzj9msAvW2rh8HDzofw3t5wdKljvDHtGljeg5gyPR61UXk/JBIQYFzV5TzhYU5gtx9AyIpKeqY/2owGNT5pr6hj7w8de5pIPHxzh0+Zs1S45PPIEKICcxq1Xj9QDm/+fAMHQYzXu4urJsTx8OLkkkN97v0J2xuVvOWtmDI/v3Q3t5/v0mTVChk4UJ1PW2adA25VF1d6ljG1u1jz57+v2s3N5g71xH6WLgQwsNHZrxCCCHEVSZBkFFKgiDiUlS2dLH4N19ccB+dDkJ8HOGQUD9PQn09CPdX16F+noT1CY9IC2whhBhm1dVqMqe8XE00fPrpFVWdFKOHpmm8caCcX29XE4Uebi78YFkqj/71X3F5/XW1uGvrVrj11pEeqhBOSho72Xiwgk2HK2jsMNq3L0wNZX12AsunR+LpJpPPF9JjsvDmgXJePC8A8sSyNNZKAOTa6+6G7dtV548PPlBf20yZAvfeq4J5gYFXJ3Ax2LaJsFhiHKhv7yG/t2NIXm9AxNZBqS8/TzdCfD0I9vUgtF+4o/far/e2nwf+nm6X1q2jrQ3y8x2LOPLyVAii7+u5r9RUxyIO24KO+Pix+7rTNFWttG+njyNHBg5zpaSosIct+DF7tlqwJEaW2Qw1Nf1DIlVV6v7hDFdcymPd3Mbu/wshxPBqaoK//AVefFHN0YA6hlu1Cp58Eq6//rL+XhTUtfPoq4coberCy92F36ydyarM2OEdu7hiTR0G3j5cyRv7yylvdiz8jwrw4s7MGO6cFcP0mICJ121tgtM0jUNlLWw6VMm24zV0GBzBoAUpoaybE8eKjCg5bzZGdBrMvLavjP/bVWyf74oK8OK716WwPjsBL/cJOtdVXu4Ifnz2Wf/AwPTpqpP58uUqBCLnLhysVvX7swVE+gZFbJ9zBuLmpj63DhQSCQsb+vFGY6Oju4ct9HH69MBdEN3d1YJi2/xAZibMnCnV54UQ4gJq9T3sKWrkhskRBPt6DN8TWywqtGcLhuzdO3BxG39/NbfZt2tIcPDwjWM8aGhQXT5twY/Dh/u/D/r7qzUYtuBHdrZ0uRJCCDFhSBBklJIgiLgU+m4T2/JraOow0NhhoLHTSFOHgaYOI40dBqcWhkPl7e5KmL8Hob7OARFb15FwP08VJvHzkG4jQghxMe3t6uTJsWOqyseePTLxPk5UNHfxzDv57ClqAiArIYjn1s0iLcJPTUDdfz+8/bZalLhtGyxbNsIjFqI/k8XKZ6fr2HCggq8KGrB96gv2ceeurDjuy44nLWICVJy/BD0mCxsPVvDizkJ7VcmYQC++tyyNu+fESwDkWjIY1CKGjRvhvfdUJxCb1FQV/rj3XtU5QRZziUugaRrlzV1UtXQT6ONOqK8nQT7uI7NoyGJRFUhtCz9sAZGB2tsDBAU5V/mcNUst6vH0vIaDHqKWFkfgwxb+aGzsv19wsHOnj3nzpIKbEEKIK2OxwPvvqy4hn33m2D59OjzxBDzwAPhdWhVYfbeJ7795lJ1nGwD49tIUfrR8Mm6u8vlgJNkW+b++r4ztx2sxWlTVe38vN27LiGZVZizZySFyjkMA0GU08/HJWjYdrmRPUZN9jsTXw5XlM6JYkh5GdnIosUGySH60aesx8cruUl7aXUJr73nRuGBvHr8+jbVzYidesZOuLvjyS0f448wZ5/uDg+Hmm1Xw45ZbVIc6cek6Opy7iPS9dHUN/rjgYOdgiC0s4ubWP/QxWNgkONi5y0dmJkydqoLwQgghRqemJjUHumePCoYcOOB8TsNmyhRHMMTWNWSidHHSNCgqcoQ+cnMHDtDExMCSJY7gR0aGdFYRQggxYUkQZJSSIIgYTmaLleYuI43tRpo6HQGRxo7ewEin+rqpw0hDhwGj2XrxJ+3DRQchvo6QiC00EtbbZaTv9jA/T7w95OBbjF4ljZ3sL24iyMedcH8vIvw9iQjwnHgnCcTwMZngjjvUyZaICDWpk5Iy0qMSV8hq1Xh1bym/+egs3SYLXu4u/HD5FB5amOS8cMBkgnXr1OJkHx/1Oli8eOQGLsRFVLZ08dahSt4+VEGNvse+fW5iMOuzE7gtI3pCH8sZzL0BkC+KqG1Tv5+YQC8evyGNu+fGyfHCtWIyqcV6GzfC5s2g1zvuS0hwhD+ysiT8Ica3hgZHKORiVUFdXdWCkL6dQ2bNUsen14rBoMbYN/hRUNB/P3d31d2jb/AjLU3+PwshhLh6Tp1SgZBXX4XOTrUtMBAefhi+9z31PjREFqvGf+44y4s7iwBYkh7G8/fNJshHFmVea+09JrYcreL1/eWcqW23b58ZF8gDOYncMStmQn++FRdX2dLF5iNVbDpSSVmT84LuuGBvspNDmJ8cSnZyCImhPtJJZoS0dBr56+4SXt5TSnuP+iyUHObL49ensnp2LO4TJYynaXDihCP4sWuXc2dFFxdVYXz5cnWZO1cWS15NVqsKcAwUECkvh0tddtO3G6gt9BEXJ5+ThRBirLNY1Pt3364hA82XBgY6dw3JyVHFgMYDs1nNbfcNftTV9d9v+nRH6GPxYkhMlPdBIYQQopcEQUYpCYKIkaJpGp1GC43tBpo6bWERW1BEdRtR96kQyeV0G/HxcHUERno7jjgCJJ6E+Xqoaz8PgqTbiLhG9N0mfv9ZAa/sKcVs7f92F+jtbg+FRPQGRML9PYkI6A2L9N7285T28KIPTYNHHoG//lWFAHbuVNWLxZhW0tjJjzblcbC0BYCc5BB+s3YmSWG+Az/AYIBVq9QJOH9/+PRTtbBRiFHMYtX48lw9Gw5U8PmZeiy9743+nm6smh3D+nkJzIgNHOFRXjsGs4W3Dlbw4s4ie0AmujcAco8EQK4Ni0W9j27cCO++qypn2cTEwN13q/DH/Pky+S8mNoNBLWY9PyDS0jLw/lFRzp1DMjMhPV1VIb0StsptfTt9HD0KRmP/fdPS1MlLW/AjM3N0di8RQggx/un18PLLKhRSWOjYvmIFPPmkWjg7xCqsH+RX88O38+k2WUgI8eHPX5/DlCg513MtnKzW8/r+crYcraLLaAHAy92FVbNi+dr8BGbGBY3sAMWYo2kaB0tb+ORULQdKmjlR3WafJ7GJDPAkuzcUMj85hLQIPwmGXGWNHQb+b1cxr+0to7P3/3p6hB9PLEvj9pkxE+PcYlMTfPKJmnfesaN/18iEBEfw48Ybx8+C0bGuu1st8j1zpn9IxGRSFc37hj4yMkDWiwghxMTR2Aj79jmCIQcOOAoW9DVtmnPXkClTxkbXkI4ONV+cm6uCq/v29f/5PDzUmgpb6GPhQggJGZnxCiGEEGOABEFGKQmCiLHCZLHS0mlUgZFOg72ziK3bSGOHLTRyZd1GbF1GnLuLqK/D/D0J7d1HKniJS2Wxarx5sJz/3HGO5k61KGl2QhAA9W0GGtoNGC1Df936eLj2BkO8CA/wJNzPOTxiux3s4y4ngiaCf/1X+PnP1aTL1q1w++0jPSJxBSxWjb/mlvAfO85iMFvx8XDlJyum8LWcRFwudmKxqwtuu00tYg4Kgi++UCdxhBgD6tt6ePtwJRsPVlDe7KiAmREbyPrseO6cFYO/l/sIjvDqMZgtvHWokhe/KLQHQKICvPjeDancMy9eAiBXm9WqTgZs3AibNkF9veO+iAjVcenee9WJgLFwgkOIkaJpUFnpCIXYAiJ9F7j25eUFM2Y4B0RmzlSV5wbT1OTc6ePAAWhu7r9faKgj8JGdrS6hocPwQwohhBDDyGpVC2qffx62b3dsT0tTHUIefvjC74u9TlW38e2/H6KypRsfD1f+8+5ZrMiIvooDn7h6TBa25dfw2v4yjpa32renRfjxtZwE7sqKI9B7fH5uFddeh8HM4bIWDpQ0sb+4mbzKVkwW51PoIb4eZCeFkJ0cQk5KCFOiAiZGMOEaqNX38KevithwoJwekzp3My06gCeXpbF8etTF52nHMrNZLZa0df04dMi5s4S3N1x/vSP8MXmyFMsYSzRNXWSOSwghRF9mMxw/7tw1pKio/35BQf27hgzhc+tVV1sLu3c7un0cPaoKf/UVGAiLFjmCH/PmqTlqIYQQQgyJBEFGKQmCiPFI0zQ6DGaaekMjDe3quskeGjHagyONHQZaL7PbSN/QSLi/BzGB3tw9N56oQPmgIJztLWriXz84xemaNkCdGPyX26dx3aRw+z6apqHvNlHfbqC+zUB9e0+/243tBurbDXQYzEP+3u6uOsL9PAnv21HE36s3KOK4HerrgdtEaV0+3rz8sloYAPCHP8B3vzuiwxFXpqCunR9uyudYRSsAS9LDeHZNBvEhPkN/ko4OdQJuzx5wdYWkJFVxOy3NcZ2WBsnJ4C6LE8ToY7Vq7C1uYsOBcnacrLMHJb3dXbljVjTrsxOYHR80LoKORrOVtw5V8OIXhVT3BkAiAzz53g1p3CsBkKtL09Qi8o0b4e23oarKcV9ICKxdq8If11135R0LhJjoOjrUScS+AZH8fBVgHUhysiMYMmOGqjZrC34MdPLRwwOyshzBj5wcSEmRhUhCCCHGlsJCePFF1e1Vr1fbfH3h619XoZDp0y/48JZOI09sOMLuQtXR7okb0vjHmyeN74XK11BJYyev7ytj05FK+/kEd1cdy6dH8cD8RHKSQ8bFZ1QxuvWYLBwpb+FASTMHSpo5Ut5iDyjY+Hu5MS8phJxkFQ6ZERuIu8z7X5LKli7+sLOItw9V2uekZsUH8Q/L0lg2JWL8/l8vLXUEPz77DNranO/PyHAEPxYvlkWTQgghxERQX+/cNeTgwf5zujqd+rzat2vIpElXN3CoaXDunCP0kZs7cDGihARH6GPxYjVOCUIKIYQQl02CIKOUBEGEcO42ogIiBntnEVt4pKnTSGO7gcZO4wW7jXi6ufDwomQeuy6VQB9ZXDvRVTR38ez203x4ohaAAC83nr55Eg/MT7yiky+dBjMNvaGQ+vae3rCIut3QJzzScgkhJ50OQn09CPfvExgZoMNIuL8nXu6yKHXU2LFDdX8wm+HHP4Zf/3qkRyQuk9li5U9fFfM/nxZgtFjx93Tjp7dN5d558Zd3clGvhzvvhK++GnwfV1dITBw8JOLhcfk/kBDDpLnTyLtHKtlwoJyiBkfL5smR/tw7L567smIJ8hl7r1Wj2crbhyt48Ysiqlq7ARUAefx6FQCR99qrRNPgyBEV/njrLSgrc9wXEABr1qjwx003SVBOiKvNYlGhDlvXEFtApLLy4o+dNMnR6SMnRwVG5LhFCCHEeNHRAa+9Bi+8ACdPOrYvWwZPPAF33DFoUNlssfLvH57hL7kl6iFTIvjd+kwCxmlnxavNbLHy6ek6XttXTm5ho317bJA39+ckcM/ceML9PUdwhGKiM5qtHK9qZV+xCoYcKm2m0+hc9djHw5U5icFkJ4WQkxLKzLhAmXMYRGljJy/uLOTdI1WYrWqpQnZSCE/emMbitLDxFwDp7FRdpW3hj3PnnO8PDYWbb1bBj1tugZiYERmmEEIIIUYRk8nRNWTPHnVdUtJ/v+BgmD/fEQzJzlbnYC6X0ag6fPQNfjQ2Ou+j06ngqi30sWiRCoIIIYQQYthIEGSUkiCIEJemb7eRxt7uIrbgyFfnGjhU1gKoBf+P35DGQwuTZFJ9Auo0mHlxZyH/t6sEo9mKiw6+lpPI0zdPIsT32i1QMpqtNHQYqG9THUVs4ZGG88IjjR1GLNahv/UGeLkR0bfDSO/t8D4dRsL9PfH3dBt/J0dGk7w8WLIE2tvh/vvh73+XChZj1OmaNn64KY8TVarK2g2Tw3n2rgyiA72v7Ik1TVXQLiyEggLn68LCwatwg3otDRYSSUmRxZbimtM0jUNlLWw4UM724zX2ipcebi6smBHF+nkJzE8ZhgqsXV3qxPfZs86X6mqYOtWx6Dg7+7JOgBvNVt45UskLnxfaAyAR/p48fn0q67MT5LjxatA0dWLCFv7oWxXKz0+F5u69Vy1s8JRFXEKMuKYmRygkL08tgI2MdHT6mDdPnUgUQgghxjtNUwt0X3gBtmwBa29xooQEePxx+Na3ICxswIduPlrJj985jsFsJSXMlz9/fS5pEX7XbOhjXY2+mzcPVPDmwXLq2gyAWlN0w+QIvpaTwPWTI3CVTitiFDJbrJyqaeNASTP7ips5WNqMvtu5WJSHmwuz44N6O4aEkpUYhI/HxO6CWVjfzgufF/JeXjW20ySL08J4Ylka81NCR3Zww0nTVGdGW/AjN1ctqrRxdVULNW1dP7Ky1DYhhBBCiAupq3N0DLF1Denpcd5Hp1Odn8/vGjLYOb22NtWJZNcudcyyfz90dzvv4+mpztUtXqzWTCxYAEFBV+VHFEIIIYQiQZBRSoIgQgwfTdP4/Ew9z310lrN17QBEBXjx1E3prJsTh5u03x73rFaNzUer+M1HZ6hvVycJF6WF8i+3T2NK1Oj9G2uxajR3GlV3kXYDDb0dRer7dBdRgRIDhgt0xDmft7trbzjEuaNI3/BIhL8nwT4euMjJ00tTUaGqaFRXw/XXw0cfyeLVMchotvK/XxTyv18UYrZqBHq78/M7prFmduzVD1FpGtTUDB4S6ewc/LEuLmrhSd9wiO12Soq8FsVVp+828d6xKjYcqOBUTZt9e3KYL/fOi2dtVtyFq7Jarari/Plhj7Nnobx86AOJjXWEQrKzYe5c8PcfcFeTxco7hyt54YtCKlvUZHV4bwDkPgmAXB1nzqjwx8aNcPq0Y7u3N9x+uwp/rFypvhZCCCGEEGI0Ky+HP/wB/u//VGgSwMsL7rsPnnwSZs/u95DjlXq+/fdD1Oh78PN043f3ZnLTtMhrPPCxw2rVyC1s5LV9ZXx2pt5eNCfMz4N75sZzX3YC8SE+IzxKIS6N1apxrr6d/b0dQ/aXNNHYYXTax81FR0ZcIDnJoeQkhzAnKXh0dBHS61VRh/x8x/WpU6ozto+P4+Ltfdlfl3RqbDhRz6dlHXS5etLt7knOjDi+c/M05iSFjPRvYHg0NMAnn6jgx44dUFvrfH9SkiP4sWwZBAaOyDCFEEIIMY6YTKrAT99wSGlp//1CQhyhkPnz1WddW7ePvDxHMYS++y9a5Oj4MWeOnJMWQgghrjEJgoxSEgQR4gppmmp1mJsLZWVw661Ysuaw5Vg1//XJOXul55RwX360fDLLp0dJh4Rx6kh5C798/xR5Fa0AJIT48M+3TeXmaZHj5t9c0zTaesz9Ooo43e4NkrQbzEN+XjcXnT0gEt7bUSTYx51Ab8clwNv5a7+J3G2ktVVNbpw8CdOnq78/Ut1izDleqeeHm/I4U6uCg7dMi+RXq2cQEeA1wiNDvbfV1g4cEikouHBIRKe7cEjEaxT8fGLc0DSN41V6Nhyo4L1jVXQaLYB6X7l5WiRfmxbCAksTrgW9HT7OnFHXBQUX7ogTHAxTpsDkyY5LdLRaeHDggLqcONF/Elqng2nTnLqGmKZO493jdTz/uXMA5LHrUrk/RwIgw66oyBH+yM93bPfwgBUrVPjjjjtUJxAhhBBCCCHGmu5udaz7/PNw5Ihj+8KFKhBy111OHTwb2g187/UjHChtBuAfb57EEzekSUGWPpo7jbx9qII3DpRT1uT4nJiTHMLX5idy6/QoPNykwJMYHzRNo7ixU4VCipvYX9JMjd65WrOLDqbFBJCdFEpOSgjZSSEEX80u52az6szaN/CRn39phTqGm07nHCC5grDJBb/29h7+Dt8mk1psaev6ceSImuu18fGBG25whD/S0wevxC2EEEIIMVxqapyDIYcOgcFw4cckJztCH4sXq/N2w33sJIQQQohLIkGQUUqCIEJcIotFLfzLzVVtCHftUhX5+5o2DR56CMP6+3itzMQLnxfQ0qXab2fGB/HMrVNYkDqO2klPcLX6Hn7z0Rk2H60CwNfDlSdvTOfhRUl4uk3cxZ3dRotTV5EG223bpa2HhnYDTZ3Giz/ZAFx09AuH9Pvay/lr28Xfy23snvA2GNRC1i++gJgYNVGSkDDSoxKXoMdk4fefFfCnr4qxWDVCfD345Z3TuX1m9NgIN2maavE7WEiko2Pwx+p0EB/vHA7pGxKRqvziclgsUFZGz4lTnN55kNqD+QSWF5PSXElUR/Pgj3Nzg9RU57CH7RIWdvGT4B0d6mT6gQOqJfWBAwMuUuhx9+R4RCp50ekUpcwgc+0trFq9EC8Ptyv8wYVdWRm89ZZaEHf4sGO7mxvccosKf6xaJVUthRBCCCHE+KFpak7ohRfg7bfVQmpQAfbvfEddoqIA1Y30V9tO8ereMgBunR7Ff9wzCz/PifuZRNM0jpS38Nq+crYdr8HY2wHZ39ONtXPi+FpOAumRA3d7FGI80TSNypZu9pc0c6BEBUP6BqJsJkf6k50cQnZyCDnJIZdXyMY2p3h+4OP06cEXAcbHQ0YGzJypLjNmqCBDV5e6dHc7bg/h69bmNqqrmzC2deBlMuBlNhKMCT+rCdfuLhWiuNa8vIYnXFJbq4Ifn33Wf3521ixH8GPRIqmcLYQQQoiRZzTCsWOOYMjBgxAQAEuWqNDHokUQGzvSoxRCCCHEeSQIMkpJEESIizAY1IcOW/Bj927Vkrovd3eYO1edaNu+HXp6Kyi5uMCtt9J9/wP8OXA6fzpQTVdvperrJoXzo1snMz1GFqSNVT0mC//3VTEv7iyi22RBp4N1WXH88NbJRPhLxfuhMlmsNHYY+nUY0Xeb+l3aeq8NZuvFn/gCdDp1YjfQZ+CwyPmBkr7bArzccHMdoUoTmgYPPgivv64qme/aBZmZIzMWcVmOlLfwo035FNark3G3z4zml3dOJ9RvnJx80zSor3cOh9huFxRAe/vgj9XpIC5u4JBIaqqERITqhnT2rPPlzBn1GrtA1aBGn0CKQuIoCY1FN3kKk5bOIWNZNm5pqeoYbjjV1mLet49z739O5649TC4/Q4BhgA464eGqa0jfS0jI8I5lvKuuVgveNm5UJwlsXFxg2TIV/lizBkIlfC2EEEIIIca5mhr485/hj39UC4FBfdZZt051CZk/H3Q63jxQzs+2nsRosTIp0o8/PziXpDDfkR37NdZhMLP5aBWv7yuzd2gFyIgN5IH5CdwxKwYfCe0PD5NJnUe50KWtDfz9VXGQ5GR1iYqSKr8jrFbfw/6SJtU1pKTZPo/ZV3KYLzm2YEhKKLFB583bdXXBqVP9Qx+NjQN/Uz8/FfKwBT4yMtQlOPiKfx5N09hb1MTznxeyt7gJAFcXHaszY3n8hlRSw/t0DDWbLzlccllf9/QMMtphEhamCmMsX66ue4OBQgghhBBCCCGEEFdCgiCjlARBhDiPXg979jiCHwcO9F9c6OcHCxeqNPqSJWrxnm2Bql6vFqW9/LIKjdgEB9O99h5emXw9/9Hkj7n3r9yqzBh+cPNkEkJ9rsmPJ66cpmlsP17Ls9tPU9XaDcCcxGB+fsc0ZsYFjezgJogek8UeCukfFjEPGB6xXbpNliv+/n6ebn3CIW79O5D4DB4ocR9KiMRshqoqVeG8vFxdl5Wpk2e7d6sK59u2qZM4YkzoMVn4zx1neSm3BKsGYX6e/Gr1DG6dMYFOwmkaNDQMHhJpa7vw4y8UEvGR99Bxw2yG4uL+gY+zZ1XIaDAeHuo1cV5nj56UND6qMrDhQDn7SxzdQSL8Pbl7bhz3zk0YtmMws8XK5qNVvPBFob16ZriPGz9McWW1sRKPI4fUceWxYwNXmExLU8eUOTnqOjNTVYUUDvX1sGmTCn/s2qX+roAKki1dqsIfa9dCRMTIjlMIIYQQQoiRYDTCO++oLiF79ji2Z2WpQMj69Ryu6+ax1w5T324gwMuN5+/P4rpJ4SM35mvkdE0br+0rY8vRKjp7CzV5urlw56wYHpifyKz4oJEd4GhjMFw8xGELcgx2X3f35X1vLy9ISnIEQ/qGRJKTIShoOH9SMQSNHQYOlTazr7iZAyXNnK5tc3wc16zE6etZ1FnFdYZaZjSVEVlWgHtJETrrAMWcXFzU/I2ty4ftOilp2ANAmqbx5bkGnv+8kMNlLQC4u+pYNyeOx65LG9lzclarIyAyXEETLy9VGGP5cpg9WwJVQgghhBBCCCGEGHYSBBmlJAgiJryaGrWQbNcuFf7Iz1eTsH1FRDhCH0uWqIlptyFUBjt3Dl59FV55BSor7ZuNk6ewPWs5/xYyhwa/ENxdddyfncATy9IJ9x8nVeHHqZPVen75/ikO9C4mjQ704icrp3LHzGh0Ot2lP6HZPLTXkhg2RrNVBUR6BgiLdJ0XKukxoe822+/vMJiv+Pv7eLgS7mIhvaeZ5K5G4tobidHXE9lSR2hTDYH11fg01uFiuUBg5W9/g4ceuuKxiGvjQEkzz7yTT0mj6gpw1+xY/uX2aQT7eozwyEYRTVMVAfuGQ/qGRM7vxHW+2FjncIjtdmoq+E6s6qZjRmPjwGGPwkL13jiYmJh+YQ8mT4bERHB1veC3LG7oYOPBCjYdrqSp02jfvigtlPXzErhleiSebhd+joGYLVa2HKvm+c8L7AGQUF8PvnNdCg/MT+xfTbanB/LyVChk/351XVDQ/4nd3WHWLOdwyKRJE+9EflMTvPuuCn988YXzcfrChSr8sW6dem0IIYQQQgghlCNH4PnnYcMGR5Gj0FB49FEaHniYR3c2cKyiFRcd/OjWKXxnacrlzW2OYj0mC9uP1/DavjKOlLfat6eE+/JATiJrs+II9BnmLpEjTdPUZ85LDW2cf7lA181L5ucHgYGOS0CA8+2WFigpUZfy8v7nZs4XHOwcDOkbFElMlIIKV1NLCxw/TvfhYzTvPQTH8wkpOYe3YeDQT7t/EB2TpuE1J5PA7Dm4zJoJ06Zd9YIumqbxyak6XviikPxKNafo4ebCffPi+c51qcSc371ECCGEEEIIIYQQQgyJBEFGKQmCiAlF09RCu77Bj6Ki/vulpjoHP9LSVKXhy2WxqIVrL7+sqrL1tn3WXFzIn57Dn5OX8ml6Dq4+3jyyJIVHlyTj7zXOTkKNcY0dBv5zx1nePFiBpoGXuwvfWZrKd69LxdvjEhaNWq1q0efWrbBli1r0GhQE0dGqPfeFroODr+x1KK6Y2WKlrcc8QBeSPtddRkyNTXhUVeBTW4V/XTXBDTWEN9cS29ZAbFs9YV0XWdQOGF3cqA4IpyownKqACKoDwqkLjqR+UgZTb1nE6tmxpEX4XfR5xMjpNJj57cdneWVvKZoGkQGePLsmgxunRo700MYWTVMLwQcLibS2XvjxMTEXDonI39Wrx2hUx1lnzvQPfDQ3D/44b28VeDg/7DFpklokcqXDMlv59HQdGw6Uk1vYaK9iGeLrwV2zY1mfnTCkv69mi5WtvQGQ0t4ASIivB99ZmsKDCwYIgFxIczMcPKhCIbaASEND//0CA2HePBUKsQVEosZhZyG9Xh0nbdwIn3ziHA6aO1eFP+65BxISRmyIQgghhBBCjAmNjfCXv8CLL0JFhdrm4oJl1Sr+MnMlv+6OAp2OO2bF8NzamZc2zzlKlTZ28saBct4+VEFLl+rG6OaiY/n0KL42P4EFKaGjM/Siaaqq/6UENga6DNSB8nL5+zuHOC71EhBw0aINTkwm9TotKVHdQm0BEdvXA31OPl9s7OBBkZiYSxvPRGUyqbmb48dV4bT8fHXb9jfkPJqHB52pkyiLTeVQYDw73aM4EZpIg2+Qfd4t2Med7OQQspNDyUkOYWp0AK4uw/v/0GLV+OhELc9/XsCZ2nYAvN1deWB+Ao8uSSEiQEJCQgghhBBCCCGEEFdCgiCjlARBxLhmNquKy32DH/X1zvvodKrasi30sXixWnR/tej18PbbKhSye7d9c4e3H5unLGXTjBupSJvB95al88D8hMuqTC2Gj9Fs5ZU9pfz+swLae7tB3Dkrhh+vmDL0ylE9PfD55yr88d57UFt7eYPx8FCLPS8WGImMVPuKq8NqVZ2EyspUlbqysv6Xjo6LPo3Z14+u6DjaI2NoCY2iITSK2qBIqgIjKPMLo9w9gFaDxR4wsQ5wZDQzLpA1s2O5Y1YMYX7STWg02VPYyDPv5lPRrCri3Ts3nn+6bSqB3hLyG3aDhUQKCy8cNgB18t/HRwVCfHyuzu3x3vVJ06CubuDuHiUlKgw7mISEgbt7xMVds64XFc1dvH2ogrcOVVLb1mPfPi8pmPXzEliZEd1vIZTZYuW9vGqe/7zQ3uknxNeDby9N4cH5ifh6DsO/uaap95O+XUMOH4buAapsxsc7dw2ZM0dVXB1r2tvh/fdV+OOjj1SQyGbWLEf4IzV15MYohBBCCCHEWGU2q+Pt559XBYt6taRM5j8n3cw7U68nOTGCPz04h/iQq1ut/2owW6x8erqe1/eXsaug0b49JtCL+7ITuHde/LVdBG40QlWVWjhfUaHmEltbLxzgaGu7cIfMS6HT9e+8cakhDn//0Rea6OiA0tL+QRHb7c7OCz/e3R2SkgYOiSQnQ0jIxCoYomnqtXl+4OPUqcEDRQkJMHOmumRkqOv0dPW77dVjsnCsopX9xc0cKG3icFkLPSbnTi/+nm7MTQomJyWU7OQQMmIDcXe9vLkgs8XK+/nVvPB5IUUN6jXg5+nG1xck8q3FyYTKvLkQQgghhBBCCCHEsJAgyCglQRAxrnR3q8VytuDH3r39F2R7eqpFcrbgx4IF6sTGSCgogFdeUZfKSsfm0Hg2ZdzIvgUr+PrahayeHTvs1ZHEhWmaxudn6vnVttP2RZ4ZsYH87I5pzEsKufgTtLTA9u2qmvVHHzm/DgMCYOVKWL1avQb1enXCpbZ28OuLLWY+X2joxQMjUVHqtT+RTm4NhcGgTtAOFvSoqBhaZb2ICEhMVJeEBMdt2yUoaMi/e6tVo8NoRt9l4lhFK1uOVvHluQbMvekQVxcdS9PDWD07llumRY2L6o1jVXuPiV9/eIY39pcDEBvkza/vymDppPARHtkE1dw8eEikqenajMHd3REKuRqBEx+faxOa6OlRv7uzZ/t3+GhrG/xxfn4Dhz3S09XPMUqYLVa+PNfAhgMVfHG2Hkvv31d/LzdWZ8ayPjueKVEBvJdXxfOfFVLce2wQ7OPOt5em8vUFwxQAuRCTCU6edA6HnDwJ5390dnGB6dOdwyHTp4/OUFJXF2zbpsIf27bZu+YBMHUqrF+vAiCTJ4/cGIUQQgghhBhvTp6EF16AV19Vx+RAu5cvG2fcxHsLV/HjJ+9gYVrYCA9yaGr1Pbx5sJw3D1TYw/06HVw3KZwHchK5YUrE8M+rm81qztYW8qioUHP7fb+uq+v/WW2oXFwuL7jR9+Lnd80KLIwamqY64AzWTaS8/OJBm4CAwbuJJCWpOZixqrNT/d8/P/Qx2PyYv78j6GG7njFDzWlfIqPZyvEqPQdKmtlf0sSh0hY6DM7/Ft7ursxJDCY7OYSc5BBmxQfh5X7hOW6j2crmo5W8uLOIst5OrQFebnxzcTIPL0wm0EcK8gghhBBCCCGEEEIMJwmCjFISBBFjWnOz6qphC34cPtx/gXZgICxa5Ah+zJ2rwiCjicWiKrG9/DLaO++g610EZ9G58FXybPYsvp2FTz/M9bMSRmfb+nGmoK6df/3glL16XJifJz+6dTLrsuJwudCJw/Jy1fVj61b48kvnE0uxsbBqlbpcf/2ld+wwGNQJxIsFRmprhxZSsPHyGlpgJDJydC4gvRxtbRfu5lFbe/ETta6uqmL9YCGPhATwHmLHmMvU1GHg/bxqNh+rJq+i1b7d18OVW2dEc1dWLPNTQiVEdg3tPFvPP717nGq9+hv+wPwEfrxiKn5Xe3G4uDzt7erS1aUunZ0Xvn2x+8/f91p+nPHyGt5wSWtr/+4eZWWD/0w6nVoUMVDgIzp6zAUO69p6ePtQBRsPVdi7+gAE+bjT2mWy3/720hS+viBpZP+Pt7er498DBxwBkT7hYjsfH9UpJDvbERBJSBiZfxuDQYVkN25UndL6VmxNS1PBj3vvVQtMxthrRwghhBBCiDGltVV1rX7hBSgqAsCKji9T52B87Hvc8tSD6EZbRwhUwZTdRY28tq+MT087gvyhvh7cPTeer+UkXH5XE6tVzcGeH+zoe6mpuXD3SxtPTzV/GB8PMTEQHDz0EId8Fhp+ZrPq0jJQSKSkZGhdvKOiBg+KxMWNjvlzq1X9TOcHPgoLB57XcXGBSZOcAx8zZ6o57qv0OjRbrJyuaWd/SRMHSpo5UNpsn2+x8XBzITM+iJzkEHKSQ8lKDMLHQ/1+e0wW3j5cyR93FlHVquZtQnw9eGRJMg/OT8TfSwIgQgghhBBCCCGEEFeDBEFGKQmCiDGlvBxycx3Bj5Mn++8TE+MIfSxZohaRjaXqV3o9vP02lr/9Ddc9exybPX3Zl3MLCf/4GFPvvElOBl0FrV1GfvdpAX/fV4bFquHh6sI3FyfzvRtSBz55oGnqJMqWLSr8ceSI8/3Tp6uuH6tXq8WX1+LfzGpV3UguFhipqVGvtaHS6SAszBEMuVBoxN9/5F6fmgb19RcOerS2Xvx5vL0HD3kkJqq/M6PhxF6vooYOth6tYvOxKqdFy1EBXqzKjGFNVixTouQ9/mrRd5n4f9tOsemwWnydEOLDb9bOZEFq6AiPTIwYTVOL3a9GyMR2u7v74uMYbkFBA4c90tJUGGWcsS0uevNABTtO1WKyaAT5uPPokhS+sXCEAyAXUl0NBw86uoYcPDhw15aICOeuIfPmqYVJV4PRCJ9+qsIfW7Y4jycx0RH+mD1bjnGFEEIIIYS41qxW+OgjLM8/j+tHH9k3N0QnEPT/PYX7t745ch21+2jpNLLpcCWv7y+jtLf6P0B2Ughfm5/ArTOi8HS7QHBF01T3g8ECHhUVKigwlCI7bm6q8E98vCPscf4lPFw+34wl3d1QWuocFOl7+0LdUEG9JhISBg+KXI3XQ3Nz/8DHiRPOBRf6Cg+HWbOcAx9Tp171gkYXY7VqFNR3sL+kif0lzewvbqaxw+C0j5uLjhmxgcyIDeCTU3XUtan7w/09+c7SFO7PSbAHRYQQQgghhBBCCCHE1SFBkFFKgiBi1LJa4fRp5+BHeXn//aZMgcWLHcGPpKTxc4KloICev/wV419fJqDRUZGqOiYZ928+TPhj31IL0sUVMVusbDhQzn9+cs5eeermaZH8dOVUksJ8z9vZrLrQbNmiLqWljvtcXFT3GVvnj7S0a/UjXJ7ubkcXkQsFRurqhlblzsbHZ2hdRiIiVGeNS2E2q4p8gwU9ysuht6POBYWEXDjoERY2Jv+OaJrG4bIW3j1axbb8GvTdjhPXU6L8uSsrljtnxRIVOP4WbI+UT0/V8U+bj1PfbkCng4cWJvHD5ZPlxKO4+qxW9ffuaoRMfHzU8dX5gY8JvIilqcPAyeo2ZicEjb3Kklar6upi6xhy4ADk5Tl3LrOZNMm5a8isWZffSc9sVh3vNm6Ed99VQVWb2Fi4+25Yv159rwn6uhJCCCGEEGK00c6d4+RPf03i+2/hb1BhC6uvLy5f/zo88QRMm3Ztx6NpHClv5fV9ZXxwvAaj2QqAv6cbd2XFcn9OIpOj/FXIQ6+/cMijsnJo84Y6nZq/HCjcYbtERl76vKYYuzRNfaYdrJtIWZkqgHAhvr6Dh0SSk1UnmMEYjXDmTP/QR1XVwPt7eqoiVX0DHxkZ6nU7BmiaRkljJwdKmnuDIU32Dsw2MYFefPf6VO6ZG4+Xu/xfFEIIIYQQQgghhLgWJAgySkkQRIwaRqPqqGALfuTmqopGfbm6QlaWI/ixeLFalDjeWSw0ffAR5f/5IlP2foa3WVU7supcMN54E16PfgvuvHNcVuO+2nYXNvKv75/ibF07AJMi/fjZ7dNZnB7m2KmzE3bsUF0/3n/f+XXp5QW33KK6ftx++/h8PVqt0Nh48cBIbS20tw/9eV1c1O9roKBIRIQ6eXt+0KOqSo3nQnQ6FZAaLOSRkKC6loxzBrOFL840sPloJZ+fqcdkUYdWOh0sSg1jzexYls+IGr0V7Ue5lk4jv3j/JFuPVQOQEubLc+tmMjcpZIRHJoQQQ9DTA0ePqlCILSBSVNR/Pw8PyMx0DoekpQ3ebc9iUcfwGzfCpk3Q0OC4LzIS1q1TnT8WLRpbHfuEEEIIIYSYYPYcK+WLf/5P7t73HpOa+hRnuvFGFQi5446rGoToMJjZeqyK1/aVc7qmDR9jN9FtjSzw6OSOEAuz6cCjtto56NHRMbQnj4i4cMgjOhrcx1gBADGyLBbVnXOgkEhJyeCBjb7Cw52DIf7+cPKkCnycPj1wMQdQhdHOD3ykp4+qbtbDobKli/3FzRyv0jM12p81s+PwcJN5BSGEEEIIIYQQQohrSYIgo5QEQcSI6eiAvXsdwY99+1SHgr68vWHBAkfwY/78C1dGmgCKC6vY/es/MuXjd5lXdcq+3RoUhMt998FDD8G8eVJZ+SJKGzv5t+2n+eRUHQBBPu784OZJ3JedgJuri1q4+P77quvHJ584V4sLDVWhj9Wr4eabVTUvoXR0qA4iFwuM1NdfPNAxGA8PdVJ2oJBHYiLExal9hF1rl5Ftx2vYcrSKg6WOiuxe7i4snx7F6tmxLEkLU699cVHbj9fws60naOww4qKDR5em8PRNk6T6nBBibGtqgoMHHV1DDhxQQdDzBQWpY82cHBUOmTdPLXDZuBHeflu919uEhsLatSr8cd11UjFXCCGEEEKIMaS8qYtvv3qQ4AO7efjIB9xUuB8X23xeYiI89hg88og67r8S3d2qW0dlJdXHz3Fy/wlazhYT1lJPdHsjMe2NBPYMMeQREjJ4wCMuTl0ut+uhEJerp0cVORooJFJS4txBczABAf0DHzNmQGDg1R+/EEIIIYQQQgghhBBIEGTUkiCIuGbq6527fRw9qiol9RUaqkIftuBHVpZU3xrE0fIWXnnlE1I+fJe1Jz4ntr1PxeWpU1Ug5IEHVGcEYdfeY+KFLwr5W24pRosVVxcdD85P5Kmb0gmqLlddP7ZsgT17nIMKyckq+LFqlapiPc4qal1zZrNaXDpYUKSuTp3cGijoERkpVcSvQHlTF1uOVbHlaBXFjZ327WF+HtwxK4a7ZscxIzYAnYTJ+mnsMPCzrSfYfrwWUB2Enls3i8z4oJEdmBBCXA2aphak9O0acuSIczh2IIGBsGYNrF8Py5bJsbwQQgghhBBjWJfRzA/fzmfb8Rpi9fX8W20u1321BZ2tY7KXF9x/v+oSMnt2/ycwmVQ3hL6dO86/DBRAH0hAwMDhjr63pWCPGItaW/t3E2lrU+d5bOGPhAQp/iWEEEIIIYQQQgghRpQEQUYpCYKIq8K2cGzXLkfw4+zZ/vslJqrAhy34MWWKLPC+BJqm8eW5Bp7bfpqg/bmsO/EZK8/twctkUDu4uMDy5SoUcued6sTcBGW1amw6XMlzH5+lsUP9fpamhvCreAMJuz5RAZCTJ50flJWlwh+rV6vqWnKiRYwjmqaRV6lny9Eq3s+rpqnTaL8vNdyXu7LiWJUZQ1ywzwiOcnTQNI338qr5xXsnaeky4eqi4/HrU3liWRqeblLdXggxgZhMcPy4czjk9Gm12GrVKtX545ZbpMKuEEIIIYQQ44imafzhyyJ++/FZNA0WRPvwJ/cCAv7yR1XsyWbRIpg71znkUVenzhVcRJe7JzX+4dQEhOESH0/8zEnEzZyELiHBEfSQ81dCCCGEEEIIIYQQQggxYiQIMkpJEEQMC4sFTpxwDn5UV/ffb8YM5+BHfPy1H+s4ZLVqvJ9fzX/uOEdzTSMrz+TywJkvmFl63LFTUBDcd58KhcybN6FCDYdKm/nl+6c4XqXH3WJidWsB3+84SeyuT9FVVTl2dHOD669XCxnvvFNV2RJiAjBZrOwqaODdI1V8cqoOg9nRDSc7OYQ1s2NZmRFNoPfEq+pe19bDTzef4NPTdQBMjQ7gt+tmMiM2cIRHJoQQo0Rnp+r64eEx0iMRQgghhBBCXEVfnK3nHzYcpb3HTGSAJ396YA6ZFafg+efhnXdUB+CBeHjYO3dY4+Io9Q7hqx5vvur2oiYgjGr/cHwiw7gvJ5F758UTGTBxixkJIYQQQgghhBBCCCHEaCVBkFFKgiDishgMcPCgI/Sxezfo9c77uLurCmC24MeiRRASMjLjnSCMZisbDpTz/OcFNHYYSWqu4rvlu1md/xleNX0CD1OmqEDIgw9CTMyIjfdqq2rt5t8/PMMXBwq5vvgQK4v2c1PpYTw6Oxw7+fnBihWq68eKFRAcPGLjFWI0aO8x8eGJWrYcrWJvcZO9aKOHmws3TY1gdWYs10+OwMNtfHdv0jTVRej/fXCKth4z7q46nlyWzmPXp+LuOr5/diGEEEIIIYQQQoiBlDR28uirhyis78DD1YVfrZnBPXPjVVGoV16BlhZHBw/bJSyMug4jbx6o4M2D5dToewBVp2hpejhfy0lg2ZQI3GS+RQghhBBCCCGEEEIIIUYtCYKMUhIEEZfkzBn49rfhwAEVBunLzw8WLnQEP7KzwcdnZMY5wXUazPxlVwl//qqITqMFnWblUXMpj5XmEvzh+9DdrXZ0cYFbblGhkFWrwGt8VFvrNlr4+zt7qP77Rm44s5cFZfl4WPtUpIuKUh0/Vq+GZcvA03PExirEaFbd2s17edVsPlLF2bp2+/YgH3dunxnNmtlxZCUEoRtnHYaqW7v5ybvH+fJcAwAz4wJ5bt1MpkTJcZIQQgghhBBCCCEmtvYeE09vzLN3T31oYRI/vW1qv8IZVqvGnqImXttXxien67BY1Wm/EF8P7p4bx9eyE0kIlfMHQgghhBBCCCGEEEIIMRZIEGSUkiCIuCRNTRAWpm5HRKjQhy34MWsWuLmN7PiEk6YOAy98Ucjr+8oxWqwArE3158edxwl/503VzcUmKAjWr1ehkOxsVZJtLNE0tJMnOfOn19C2bGFa5Vnn+6dMUWGX1avVz+ciFeaEGCpN0zhV08aWo1VsPVZNfbsjCJgY6sPqzFjWzI4lKcx3BEd55TRNY8OBCp7dfpoOgxkPNxeevmkSjy5JlqqUQgghhBBCCCGEEL2sVo3/+ayA//msAICc5BBe/FoWoX6etHYZ2XS4ktf3l1PS2Gl/zLykYL6Wk8iKjCg83VxHauhCCCGEEEIIIYQQQgghLoMEQUYpCYKIS/b22yr0kZ4+9sICE1RFcxf//ck5Nh+rQtPA1UXHvfPi+UGSC6HvvgmvvAIVFY4HTJmiAiEPPACxsSM27ouyWGDvXti6FcOmd/EsLbbfZdXp0M/MIui+u9GtXg2TJ4/cOIUYRyxWjT1FjWw+UsVHJ2vpMlrs981OCGLN7FhunxlDiK/HCI7y0lU0d/HMO/nsKWoCICshiOfWzSItwm+ERyaEEEIIIYQQQggxOn18spZ/3HiMTqOF2CBvspND2Ha8BqNZFSXy83RjzexYvjY/QTqtCiGEEEIIIYQQQgghxBgmQZBRSoIgQkwcp2va+I+Pz/LZmXoAvNxd+OaiZL6zJJnAfbnw8svwzjvQ3a0e4OICt9yiQiGrVoGX14iN3a67Gz79FLZsgfffh4YG+10GV3f2Js/GescdLHz6YbziR3GIRYhxoMtoZsfJOjYfrWJXQQPW3qM3Nxcd108OZ83sOG6cGoGX++it8mi1avx9Xxm/+egMXUYLXu4u/HD5FB5amISri4QdhRBCCCGEEEIIIS6koK6dR189RGlTl33b1OgAHpifwKrMWPw8pYu4EEIIIYQQQgghhBBCjHUSBBmlJAgixMRzoKSZf//wNEfKWwEI9Hbnezek8vUFSXh1d8KmTSoUsmuX40FBQbB+vQqFZGdf224wTU3wwQewdSt8/DF0OU4q6r38+Cx1Hp+k5RC4+naeWjOHqMBREFgRYoKpb+/hvWPVbDlWxYmqNvt2f083VmZEsyYrluykEFxGUbiipLGTZzblc6C0GYDs5BCeWzuTpDDfER6ZEEIIIYQQQgghxNih7zbxb9tOAbA+O4HZ8UHopJu4EEIIIYQQQgghhBBCjBsSBBmlJAgixMSkaRqfnq7nuY/OUFDfAUB0oBdP3zSJu7JicXN1gcJCePVVeOUVKC93PHjKFBUIeeABiL1KXTdKSlTwY+tWFUixWOx3dUfHsj0lm03xczkYN53pSWH8/I5pZCUEX52xCCEuSUFdO5uPVrHlaBXV+h779tggb1ZlxrBmdizpkf4jNj6LVeOvuSX8x46zGMxWfDxc+cmKKXwtJ3FUBVWEEEIIIYQQQgghhBBCCCGEEEIIIYQQQoiRJkGQUUqCIEJMbBarxrtHKvnvT87ZF2ynRfjxw+WTuWVapKrcZrXCzp2qS8imTdDdrR7s4gK33KJCIatWgdcVdOLQNDh6VAU/tmyB/Hzn+2fNovGmFfyXzxTeMISCTkeEvyfP3DqFNbNjZfG2EKOQ1apxoLSZzUeq2H68hnaD2X7fjNgAVmfGcmdmDBH+166LT0FdOz/clM+xilYAFqeF8eu7MogP8blmYxBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcYKCYKMUhIEEUIA9Jgs/H1vGf+7s5DWLhMAWQlBPHPrFHJSQh07trWpMMjLL6tOHTaBgbB+vQqF5OSAbgjBDJMJvvrK0fmjb9cRV1dYsgRWr0Z/0638R4GJ1/eXYdXAw82FR5ck8/j1afh6ug3Lzy+EuLp6TBY+O13P5qOV7DzbgNmqDvVcdLAkPZw1s2O5ZXokPh5X5/+02WLlT18V8z+fFmC0WPH3dOOnt03l3nnxKvAmhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIfiQIMkpJEEQI0Vdbj4k/f1nMS7kldJssANwwOZwf3TqFqdHn/Y0oLIRXX4VXXnEOcUyerAIhDz4IsbHOj2lvh48/Vl0/tm2D1lbHfT4+sHw5rF4Nt92GKSiY1/aV8btPC9B3q3DKihlR/NPKqVK9X4gxrLnTyAf51Ww+WsXR8lb7dh8PV26dHsWarFgWpobhOkydfk7XtPGjTfkcr9IDcP3kcJ5dk0FMkPewPL8QQgghhBBCCCGEEEIIIYQQQgghhBBCCDFeSRBklJIgiBBiIPVtPfz+8wI2HKjAYtXQ6WB1Ziz/ePOk/iEMqxV27lRdQjZtgu5utd3FBW6+Gb7xDejoUOGPzz4Dg8H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      "text/plain": [
       "<Figure size 4000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40, 15))\n",
    "\n",
    "ax1 = sns.lineplot(data=hs_copy, x=\"Quarter\", y=\"Total units\", label=\"starts\")\n",
    "sns.lineplot(data=huc_copy, x=\"Quarter\", y=\"Total units\", color=\"green\", label=\"uc\")\n",
    "sns.lineplot(data=hc_copy, x=\"Quarter\", y=\"Total units\", color=\"red\", label=\"c\")\n",
    "\n",
    "ax1.legend(loc=\"upper left\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "##its rather messy: zoom in to period after covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "ax1 = sns.barplot(data=covid_hs, x=\"Quarter\", y=\"Total units\", label=\"Constuction Starts\",alpha=0.30)\n",
    "ax2 = sns.barplot(data=covid_huc, x=\"Quarter\", y=\"Total units\", color=\"green\", label=\"Under Construction\", alpha=0.25)\n",
    "ax3 = sns.barplot(data=covid_hc, x=\"Quarter\", y=\"Total units\", color=\"red\", label=\"Completed\", alpha=0.35)\n",
    "\n",
    "ax1.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Quarter</th>\n",
       "      <th>Persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3 2013</td>\n",
       "      <td>13,511,902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q4 2013</td>\n",
       "      <td>13,559,499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q1 2014</td>\n",
       "      <td>13,563,311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q2 2014</td>\n",
       "      <td>13,583,220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q3 2014</td>\n",
       "      <td>13,617,763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q4 2014</td>\n",
       "      <td>13,661,282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q1 2015</td>\n",
       "      <td>13,657,423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q2 2015</td>\n",
       "      <td>13,669,290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Q3 2015</td>\n",
       "      <td>13,709,293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Q4 2015</td>\n",
       "      <td>13,759,762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Q1 2016</td>\n",
       "      <td>13,774,364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Q2 2016</td>\n",
       "      <td>13,816,652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Q3 2016</td>\n",
       "      <td>13,876,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Q4 2016</td>\n",
       "      <td>13,948,180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Q1 2017</td>\n",
       "      <td>13,975,516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Q2 2017</td>\n",
       "      <td>14,012,209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Q3 2017</td>\n",
       "      <td>14,078,499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Q4 2017</td>\n",
       "      <td>14,161,084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Q1 2018</td>\n",
       "      <td>14,199,811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Q2 2018</td>\n",
       "      <td>14,251,136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Q3 2018</td>\n",
       "      <td>14,326,746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Q4 2018</td>\n",
       "      <td>14,413,055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Q1 2019</td>\n",
       "      <td>14,449,986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Q2 2019</td>\n",
       "      <td>14,493,612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Q3 2019</td>\n",
       "      <td>14,573,565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Q4 2019</td>\n",
       "      <td>14,666,727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Q1 2020</td>\n",
       "      <td>14,718,155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Q2 2020</td>\n",
       "      <td>14,752,374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Q3 2020</td>\n",
       "      <td>14,761,811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Q4 2020</td>\n",
       "      <td>14,757,582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Q1 2021</td>\n",
       "      <td>14,772,726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Q2 2021</td>\n",
       "      <td>14,808,093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Q3 2021</td>\n",
       "      <td>14,842,488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Q4 2021</td>\n",
       "      <td>14,942,272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Q1 2022</td>\n",
       "      <td>14,999,441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Q2 2022</td>\n",
       "      <td>15,046,211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Q3 2022</td>\n",
       "      <td>15,145,006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Q4 2022</td>\n",
       "      <td>15,283,961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Q1 2023</td>\n",
       "      <td>15,378,179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Q2 2023</td>\n",
       "      <td>15,457,075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Q3 2023</td>\n",
       "      <td>15,608,369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Quarter     Persons\n",
       "0   Q3 2013  13,511,902\n",
       "1   Q4 2013  13,559,499\n",
       "2   Q1 2014  13,563,311\n",
       "3   Q2 2014  13,583,220\n",
       "4   Q3 2014  13,617,763\n",
       "5   Q4 2014  13,661,282\n",
       "6   Q1 2015  13,657,423\n",
       "7   Q2 2015  13,669,290\n",
       "8   Q3 2015  13,709,293\n",
       "9   Q4 2015  13,759,762\n",
       "10  Q1 2016  13,774,364\n",
       "11  Q2 2016  13,816,652\n",
       "12  Q3 2016  13,876,500\n",
       "13  Q4 2016  13,948,180\n",
       "14  Q1 2017  13,975,516\n",
       "15  Q2 2017  14,012,209\n",
       "16  Q3 2017  14,078,499\n",
       "17  Q4 2017  14,161,084\n",
       "18  Q1 2018  14,199,811\n",
       "19  Q2 2018  14,251,136\n",
       "20  Q3 2018  14,326,746\n",
       "21  Q4 2018  14,413,055\n",
       "22  Q1 2019  14,449,986\n",
       "23  Q2 2019  14,493,612\n",
       "24  Q3 2019  14,573,565\n",
       "25  Q4 2019  14,666,727\n",
       "26  Q1 2020  14,718,155\n",
       "27  Q2 2020  14,752,374\n",
       "28  Q3 2020  14,761,811\n",
       "29  Q4 2020  14,757,582\n",
       "30  Q1 2021  14,772,726\n",
       "31  Q2 2021  14,808,093\n",
       "32  Q3 2021  14,842,488\n",
       "33  Q4 2021  14,942,272\n",
       "34  Q1 2022  14,999,441\n",
       "35  Q2 2022  15,046,211\n",
       "36  Q3 2022  15,145,006\n",
       "37  Q4 2022  15,283,961\n",
       "38  Q1 2023  15,378,179\n",
       "39  Q2 2023  15,457,075\n",
       "40  Q3 2023  15,608,369"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Quarter  41 non-null     object\n",
      " 1   Persons  41 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 784.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "population.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in population.iterrows():\n",
    "    population.at[index, \"Persons\"] = row[\"Persons\"].replace(',', '')\n",
    "\n",
    "population[\"Persons\"] = population[\"Persons\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "population[\"difference\"] = population[\"Persons\"].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Quarter</th>\n",
       "      <th>Persons</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q3 2013</td>\n",
       "      <td>13511902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q4 2013</td>\n",
       "      <td>13559499</td>\n",
       "      <td>47597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q1 2014</td>\n",
       "      <td>13563311</td>\n",
       "      <td>3812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q2 2014</td>\n",
       "      <td>13583220</td>\n",
       "      <td>19909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q3 2014</td>\n",
       "      <td>13617763</td>\n",
       "      <td>34543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q4 2014</td>\n",
       "      <td>13661282</td>\n",
       "      <td>43519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q1 2015</td>\n",
       "      <td>13657423</td>\n",
       "      <td>-3859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q2 2015</td>\n",
       "      <td>13669290</td>\n",
       "      <td>11867.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Q3 2015</td>\n",
       "      <td>13709293</td>\n",
       "      <td>40003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Q4 2015</td>\n",
       "      <td>13759762</td>\n",
       "      <td>50469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Q1 2016</td>\n",
       "      <td>13774364</td>\n",
       "      <td>14602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Q2 2016</td>\n",
       "      <td>13816652</td>\n",
       "      <td>42288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Q3 2016</td>\n",
       "      <td>13876500</td>\n",
       "      <td>59848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Q4 2016</td>\n",
       "      <td>13948180</td>\n",
       "      <td>71680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Q1 2017</td>\n",
       "      <td>13975516</td>\n",
       "      <td>27336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Q2 2017</td>\n",
       "      <td>14012209</td>\n",
       "      <td>36693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Q3 2017</td>\n",
       "      <td>14078499</td>\n",
       "      <td>66290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Q4 2017</td>\n",
       "      <td>14161084</td>\n",
       "      <td>82585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Q1 2018</td>\n",
       "      <td>14199811</td>\n",
       "      <td>38727.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Q2 2018</td>\n",
       "      <td>14251136</td>\n",
       "      <td>51325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Q3 2018</td>\n",
       "      <td>14326746</td>\n",
       "      <td>75610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Q4 2018</td>\n",
       "      <td>14413055</td>\n",
       "      <td>86309.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Q1 2019</td>\n",
       "      <td>14449986</td>\n",
       "      <td>36931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Q2 2019</td>\n",
       "      <td>14493612</td>\n",
       "      <td>43626.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Q3 2019</td>\n",
       "      <td>14573565</td>\n",
       "      <td>79953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Q4 2019</td>\n",
       "      <td>14666727</td>\n",
       "      <td>93162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Q1 2020</td>\n",
       "      <td>14718155</td>\n",
       "      <td>51428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Q2 2020</td>\n",
       "      <td>14752374</td>\n",
       "      <td>34219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Q3 2020</td>\n",
       "      <td>14761811</td>\n",
       "      <td>9437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Q4 2020</td>\n",
       "      <td>14757582</td>\n",
       "      <td>-4229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Q1 2021</td>\n",
       "      <td>14772726</td>\n",
       "      <td>15144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Q2 2021</td>\n",
       "      <td>14808093</td>\n",
       "      <td>35367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Q3 2021</td>\n",
       "      <td>14842488</td>\n",
       "      <td>34395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Q4 2021</td>\n",
       "      <td>14942272</td>\n",
       "      <td>99784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Q1 2022</td>\n",
       "      <td>14999441</td>\n",
       "      <td>57169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Q2 2022</td>\n",
       "      <td>15046211</td>\n",
       "      <td>46770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Q3 2022</td>\n",
       "      <td>15145006</td>\n",
       "      <td>98795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Q4 2022</td>\n",
       "      <td>15283961</td>\n",
       "      <td>138955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Q1 2023</td>\n",
       "      <td>15378179</td>\n",
       "      <td>94218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Q2 2023</td>\n",
       "      <td>15457075</td>\n",
       "      <td>78896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Q3 2023</td>\n",
       "      <td>15608369</td>\n",
       "      <td>151294.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Quarter   Persons  difference\n",
       "0   Q3 2013  13511902         NaN\n",
       "1   Q4 2013  13559499     47597.0\n",
       "2   Q1 2014  13563311      3812.0\n",
       "3   Q2 2014  13583220     19909.0\n",
       "4   Q3 2014  13617763     34543.0\n",
       "5   Q4 2014  13661282     43519.0\n",
       "6   Q1 2015  13657423     -3859.0\n",
       "7   Q2 2015  13669290     11867.0\n",
       "8   Q3 2015  13709293     40003.0\n",
       "9   Q4 2015  13759762     50469.0\n",
       "10  Q1 2016  13774364     14602.0\n",
       "11  Q2 2016  13816652     42288.0\n",
       "12  Q3 2016  13876500     59848.0\n",
       "13  Q4 2016  13948180     71680.0\n",
       "14  Q1 2017  13975516     27336.0\n",
       "15  Q2 2017  14012209     36693.0\n",
       "16  Q3 2017  14078499     66290.0\n",
       "17  Q4 2017  14161084     82585.0\n",
       "18  Q1 2018  14199811     38727.0\n",
       "19  Q2 2018  14251136     51325.0\n",
       "20  Q3 2018  14326746     75610.0\n",
       "21  Q4 2018  14413055     86309.0\n",
       "22  Q1 2019  14449986     36931.0\n",
       "23  Q2 2019  14493612     43626.0\n",
       "24  Q3 2019  14573565     79953.0\n",
       "25  Q4 2019  14666727     93162.0\n",
       "26  Q1 2020  14718155     51428.0\n",
       "27  Q2 2020  14752374     34219.0\n",
       "28  Q3 2020  14761811      9437.0\n",
       "29  Q4 2020  14757582     -4229.0\n",
       "30  Q1 2021  14772726     15144.0\n",
       "31  Q2 2021  14808093     35367.0\n",
       "32  Q3 2021  14842488     34395.0\n",
       "33  Q4 2021  14942272     99784.0\n",
       "34  Q1 2022  14999441     57169.0\n",
       "35  Q2 2022  15046211     46770.0\n",
       "36  Q3 2022  15145006     98795.0\n",
       "37  Q4 2022  15283961    138955.0\n",
       "38  Q1 2023  15378179     94218.0\n",
       "39  Q2 2023  15457075     78896.0\n",
       "40  Q3 2023  15608369    151294.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Step with the population dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "ax1 = sns.barplot(data=covid_hs, x=\"Quarter\", y=\"Total units\", label=\"Constuction Starts\",alpha=0.30)\n",
    "ax2 = sns.barplot(data=covid_huc, x=\"Quarter\", y=\"Total units\", color=\"green\", label=\"Under Construction\", alpha=0.25)\n",
    "ax3 = sns.barplot(data=covid_hc, x=\"Quarter\", y=\"Total units\", color=\"red\", label=\"Completed\", alpha=0.35)\n",
    "\n",
    "ax1.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Persons</th>\n",
       "      <th>difference</th>\n",
       "      <th>Amount_housing_needed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Q1 2020</td>\n",
       "      <td>14718155</td>\n",
       "      <td>51428.0</td>\n",
       "      <td>20167.843137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Q2 2020</td>\n",
       "      <td>14752374</td>\n",
       "      <td>34219.0</td>\n",
       "      <td>13419.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Q3 2020</td>\n",
       "      <td>14761811</td>\n",
       "      <td>9437.0</td>\n",
       "      <td>3700.784314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Q4 2020</td>\n",
       "      <td>14757582</td>\n",
       "      <td>-4229.0</td>\n",
       "      <td>-1658.431373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Q1 2021</td>\n",
       "      <td>14772726</td>\n",
       "      <td>15144.0</td>\n",
       "      <td>5938.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Q2 2021</td>\n",
       "      <td>14808093</td>\n",
       "      <td>35367.0</td>\n",
       "      <td>13869.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Q3 2021</td>\n",
       "      <td>14842488</td>\n",
       "      <td>34395.0</td>\n",
       "      <td>13488.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Q4 2021</td>\n",
       "      <td>14942272</td>\n",
       "      <td>99784.0</td>\n",
       "      <td>39130.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Q1 2022</td>\n",
       "      <td>14999441</td>\n",
       "      <td>57169.0</td>\n",
       "      <td>22419.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Q2 2022</td>\n",
       "      <td>15046211</td>\n",
       "      <td>46770.0</td>\n",
       "      <td>18341.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Q3 2022</td>\n",
       "      <td>15145006</td>\n",
       "      <td>98795.0</td>\n",
       "      <td>38743.137255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Q4 2022</td>\n",
       "      <td>15283961</td>\n",
       "      <td>138955.0</td>\n",
       "      <td>54492.156863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Quarter   Persons  difference  Amount_housing_needed\n",
       "26  Q1 2020  14718155     51428.0           20167.843137\n",
       "27  Q2 2020  14752374     34219.0           13419.215686\n",
       "28  Q3 2020  14761811      9437.0            3700.784314\n",
       "29  Q4 2020  14757582     -4229.0           -1658.431373\n",
       "30  Q1 2021  14772726     15144.0            5938.823529\n",
       "31  Q2 2021  14808093     35367.0           13869.411765\n",
       "32  Q3 2021  14842488     34395.0           13488.235294\n",
       "33  Q4 2021  14942272     99784.0           39130.980392\n",
       "34  Q1 2022  14999441     57169.0           22419.215686\n",
       "35  Q2 2022  15046211     46770.0           18341.176471\n",
       "36  Q3 2022  15145006     98795.0           38743.137255\n",
       "37  Q4 2022  15283961    138955.0           54492.156863"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_household = 2.55\n",
    "population[\"Amount_housing_needed\"] = population[\"difference\"] / average_household\n",
    "\n",
    "population_test = population[26:38]\n",
    "population_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3500x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(35, 20))\n",
    "\n",
    "ax_1 = sns.scatterplot(data=hs_copy, x=\"Quarter\", y=\"Difference_total_units\", label=\"Constuction Starts\")\n",
    "ax_2 = sns.scatterplot(data=hc_copy, x=\"Quarter\", y=\"Difference_total_units\", color=\"red\", label=\"Completed\").set(ylim=(-10000,65000))\n",
    "ax_3 = sns.lineplot(data=population, x=\"Quarter\", y=\"Amount_housing_needed\", color=\"green\", label=\"Needed\").set(ylim=(-10000,65000))\n",
    "\n",
    "ax_1.legend(loc=\"upper left\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Year</th>\n",
       "      <th>Housing_starts</th>\n",
       "      <th>Housing_finished</th>\n",
       "      <th>Population_growth</th>\n",
       "      <th>Amount_of_housing_needed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>59134</td>\n",
       "      <td>57077</td>\n",
       "      <td>101783.0</td>\n",
       "      <td>39914.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>70156</td>\n",
       "      <td>74347</td>\n",
       "      <td>98480.0</td>\n",
       "      <td>38619.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>74952</td>\n",
       "      <td>65926</td>\n",
       "      <td>188418.0</td>\n",
       "      <td>73889.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>79123</td>\n",
       "      <td>71597</td>\n",
       "      <td>212904.0</td>\n",
       "      <td>83491.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>78742</td>\n",
       "      <td>74374</td>\n",
       "      <td>251971.0</td>\n",
       "      <td>98812.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>68985</td>\n",
       "      <td>58855</td>\n",
       "      <td>253672.0</td>\n",
       "      <td>99479.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>81305</td>\n",
       "      <td>67916</td>\n",
       "      <td>90855.0</td>\n",
       "      <td>35629.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021</td>\n",
       "      <td>99566</td>\n",
       "      <td>81158</td>\n",
       "      <td>184690.0</td>\n",
       "      <td>72427.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022</td>\n",
       "      <td>96080</td>\n",
       "      <td>71838</td>\n",
       "      <td>341689.0</td>\n",
       "      <td>133995.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Housing_starts  Housing_finished  Population_growth  \\\n",
       "0  2014           59134             57077           101783.0   \n",
       "1  2015           70156             74347            98480.0   \n",
       "2  2016           74952             65926           188418.0   \n",
       "3  2017           79123             71597           212904.0   \n",
       "4  2018           78742             74374           251971.0   \n",
       "5  2019           68985             58855           253672.0   \n",
       "6  2020           81305             67916            90855.0   \n",
       "7  2021           99566             81158           184690.0   \n",
       "8  2022           96080             71838           341689.0   \n",
       "\n",
       "   Amount_of_housing_needed  \n",
       "0                   39914.9  \n",
       "1                   38619.6  \n",
       "2                   73889.4  \n",
       "3                   83491.8  \n",
       "4                   98812.2  \n",
       "5                   99479.2  \n",
       "6                   35629.4  \n",
       "7                   72427.5  \n",
       "8                  133995.7  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_stat = []\n",
    "\n",
    "for year in range(2014, 2023):\n",
    "    annual_total = housing_s_copy[housing_s_copy[\"Quarter\"].str.contains(str(year))][\"Total units\"].sum()   ###find year in quarter and use panda fuction to sum it up & hard to do with 2 for-loops\n",
    "    annual_total_4 = housing_c[housing_c[\"Quarter\"].str.contains(str(year))][\"Total units\"].sum()\n",
    "    annual_total_2 = population[housing_s_copy[\"Quarter\"].str.contains(str(year))][\"difference\"].sum()\n",
    "    annual_total_3 = round(annual_total_2 / average_household, 1)\n",
    "    #print(f'The annual number of housing starts in {year} is {annual_total}, the growth of population is {annual_total_2}, and the amount of housing needed is {annual_total_3}')\n",
    "\n",
    "    annual_stat.append([year, annual_total, annual_total_4, annual_total_2, annual_total_3])\n",
    "\n",
    "annual_df = pd.DataFrame(annual_stat, columns=[\"Year\", \"Housing_starts\", \"Housing_finished\", \"Population_growth\", \"Amount_of_housing_needed\"])\n",
    "annual_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                          2018.50\n",
       "Housing_starts               80445.50\n",
       "Housing_finished             69971.00\n",
       "Population_growth           197085.00\n",
       "Amount_of_housing_needed     77288.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_df[2:8].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "a1 = sns.scatterplot(data=annual_df, x=\"Year\", y=\"Housing_starts\", label=\"Constuction Starts\")\n",
    "a2 = sns.scatterplot(data=annual_df, x=\"Year\", y=\"Housing_finished\", color=\"red\", label=\"Completed\")\n",
    "a3 = sns.lineplot(data=annual_df, x=\"Year\", y=\"Amount_of_housing_needed\", color=\"green\", label=\"Needed\")\n",
    "\n",
    "a1.legend(loc=\"upper left\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}