├── InsightReport.docx
├── Consumer Price Index.xlsx
├── HPI-brisbane-sydney-melbourne.csv
├── README.md
└── House Price Index and Local economy.ipynb


/InsightReport.docx:
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https://raw.githubusercontent.com/Abinaya-Krishnan/Australian-House_Price_Index_Analysis-using-Python/HEAD/InsightReport.docx


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/Consumer Price Index.xlsx:
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https://raw.githubusercontent.com/Abinaya-Krishnan/Australian-House_Price_Index_Analysis-using-Python/HEAD/Consumer Price Index.xlsx


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/HPI-brisbane-sydney-melbourne.csv:
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https://raw.githubusercontent.com/Abinaya-Krishnan/Australian-House_Price_Index_Analysis-using-Python/HEAD/HPI-brisbane-sydney-melbourne.csv


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/README.md:
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1 | The two excel files 'Consumer Price Index' and 'HPI-brisbane-sydney-melbourne' are required input files for the analysis
2 | The .ipynb file contains the analysis code 
3 | 


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/House Price Index and Local economy.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Analysis of Australian House Price Index "
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "Analysis focus\n",
  15 |     "\n",
  16 |     "i) What do housing-market indicators say about the economic conditions in different geographical locations ? \n",
  17 |     "ii) how can they be used to provide forecasts of future economic conditions?"
  18 |    ]
  19 |   },
  20 |   {
  21 |    "cell_type": "markdown",
  22 |    "metadata": {},
  23 |    "source": [
  24 |     "DESCRIPTION \n",
  25 |     "\n",
  26 |     "Housing markets generally describes the supply and demands for houses in a specific geographic location. Housing market trends can be analysed using various indicators like House Price Index, construction, local sales and so on. This question specifically focuses on housing market and its correlation with economic condition of different geographical location. It also mentions about how efficient the analysis made on housing markets can be used in future economic prediction.\n",
  27 |     "Considering House Price Index(HPI) as a key indicator of housing market, analysing how a fall/growth of a HPI creates a impact on economic condition. Finally, providing valuable insights for business stakeholders like real estate owners, government and property buyers who may be interested in this analysis.\n",
  28 |     "For eg: real estate owners or property buyers who wants to sell or buy property, to identify the right time to buy or sell.DATA SOURCES "
  29 |    ]
  30 |   },
  31 |   {
  32 |    "cell_type": "markdown",
  33 |    "metadata": {},
  34 |    "source": [
  35 |     "DATA SOURCES \n",
  36 |     "\n",
  37 |     "Two different set of sources are utilised in this analysis. The first set of data is derived from Queensland Government site (https://www.qgso.qld.gov.au/statistics/theme/economy/prices-indexes/housing) which describes about the house price index of Sydney, Brisbane and Melbourne from the year 2002 to 2019. The second set of data is derived from economy.id community (https://economy.id.com.au/sydney/consumer-price-index), which describes about the Consumer Price Index(CPI) of Sydney and whole Australia. "
  38 |    ]
  39 |   },
  40 |   {
  41 |    "cell_type": "code",
  42 |    "execution_count": 1,
  43 |    "metadata": {},
  44 |    "outputs": [],
  45 |    "source": [
  46 |     "#library imports\n",
  47 |     "import pandas as pd                # used for data manipulation and data analysis\n",
  48 |     "import matplotlib.pyplot as plt    # used for visualisation\n",
  49 |     "\n",
  50 |     "#code to obtain HPI data \n",
  51 |     "file_path = \"HPI-brisbane-sydney-melbourne.csv\"\n",
  52 |     "import chardet                                 #To overcome unicode-decode error\n",
  53 |     "with open(file_path, 'rb') as rawdata:\n",
  54 |     "    result = chardet.detect(rawdata.read(100000))\n",
  55 |     "result\n",
  56 |     "\n",
  57 |     "data1 = pd.read_csv(file_path, encoding = 'Windows-1252')            #HPI data\n",
  58 |     "\n",
  59 |     "#code to obtain CPI data\n",
  60 |     "file_path = \"Consumer Price Index.xlsx\"\n",
  61 |     "import chardet\n",
  62 |     "with open(file_path, 'rb') as rawdata:\n",
  63 |     "    result = chardet.detect(rawdata.read(100000))\n",
  64 |     "result\n",
  65 |     "\n",
  66 |     "data2 = pd.read_excel(file_path, encoding = 'Windows-1252')          #CPI data"
  67 |    ]
  68 |   },
  69 |   {
  70 |    "cell_type": "markdown",
  71 |    "metadata": {},
  72 |    "source": [
  73 |     "ANALYSIS for HPI DATA"
  74 |    ]
  75 |   },
  76 |   {
  77 |    "cell_type": "code",
  78 |    "execution_count": 2,
  79 |    "metadata": {
  80 |     "collapsed": true
  81 |    },
  82 |    "outputs": [
  83 |     {
  84 |      "data": {
  85 |       "text/html": [
  86 |        "<div>\n",
  87 |        "<style scoped>\n",
  88 |        "    .dataframe tbody tr th:only-of-type {\n",
  89 |        "        vertical-align: middle;\n",
  90 |        "    }\n",
  91 |        "\n",
  92 |        "    .dataframe tbody tr th {\n",
  93 |        "        vertical-align: top;\n",
  94 |        "    }\n",
  95 |        "\n",
  96 |        "    .dataframe thead th {\n",
  97 |        "        text-align: right;\n",
  98 |        "    }\n",
  99 |        "</style>\n",
 100 |        "<table border=\"1\" class=\"dataframe\">\n",
 101 |        "  <thead>\n",
 102 |        "    <tr style=\"text-align: right;\">\n",
 103 |        "      <th></th>\n",
 104 |        "      <th>House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2018–19</th>\n",
 105 |        "      <th>Unnamed: 1</th>\n",
 106 |        "      <th>Unnamed: 2</th>\n",
 107 |        "      <th>Unnamed: 3</th>\n",
 108 |        "      <th>Unnamed: 4</th>\n",
 109 |        "      <th>Unnamed: 5</th>\n",
 110 |        "      <th>Unnamed: 6</th>\n",
 111 |        "    </tr>\n",
 112 |        "  </thead>\n",
 113 |        "  <tbody>\n",
 114 |        "    <tr>\n",
 115 |        "      <td>0</td>\n",
 116 |        "      <td>NaN</td>\n",
 117 |        "      <td>NaN</td>\n",
 118 |        "      <td>NaN</td>\n",
 119 |        "      <td>NaN</td>\n",
 120 |        "      <td>NaN</td>\n",
 121 |        "      <td>NaN</td>\n",
 122 |        "      <td>NaN</td>\n",
 123 |        "    </tr>\n",
 124 |        "    <tr>\n",
 125 |        "      <td>1</td>\n",
 126 |        "      <td>Financial year (c)</td>\n",
 127 |        "      <td>Capital city</td>\n",
 128 |        "      <td>NaN</td>\n",
 129 |        "      <td>NaN</td>\n",
 130 |        "      <td>NaN</td>\n",
 131 |        "      <td>NaN</td>\n",
 132 |        "      <td>NaN</td>\n",
 133 |        "    </tr>\n",
 134 |        "    <tr>\n",
 135 |        "      <td>2</td>\n",
 136 |        "      <td>NaN</td>\n",
 137 |        "      <td>Brisbane</td>\n",
 138 |        "      <td>NaN</td>\n",
 139 |        "      <td>Sydney</td>\n",
 140 |        "      <td>NaN</td>\n",
 141 |        "      <td>Melbourne</td>\n",
 142 |        "      <td>NaN</td>\n",
 143 |        "    </tr>\n",
 144 |        "    <tr>\n",
 145 |        "      <td>3</td>\n",
 146 |        "      <td>NaN</td>\n",
 147 |        "      <td>Index</td>\n",
 148 |        "      <td>Annual % change</td>\n",
 149 |        "      <td>Index</td>\n",
 150 |        "      <td>Annual % change</td>\n",
 151 |        "      <td>Index</td>\n",
 152 |        "      <td>Annual % change</td>\n",
 153 |        "    </tr>\n",
 154 |        "    <tr>\n",
 155 |        "      <td>4</td>\n",
 156 |        "      <td>2002–03</td>\n",
 157 |        "      <td>52.6</td>\n",
 158 |        "      <td>n.a.</td>\n",
 159 |        "      <td>78.2</td>\n",
 160 |        "      <td>n.a.</td>\n",
 161 |        "      <td>54.1</td>\n",
 162 |        "      <td>n.a.</td>\n",
 163 |        "    </tr>\n",
 164 |        "    <tr>\n",
 165 |        "      <td>5</td>\n",
 166 |        "      <td>2003–04</td>\n",
 167 |        "      <td>69.7</td>\n",
 168 |        "      <td>32.5</td>\n",
 169 |        "      <td>87.5</td>\n",
 170 |        "      <td>11.9</td>\n",
 171 |        "      <td>60.1</td>\n",
 172 |        "      <td>11.1</td>\n",
 173 |        "    </tr>\n",
 174 |        "    <tr>\n",
 175 |        "      <td>6</td>\n",
 176 |        "      <td>2004–05</td>\n",
 177 |        "      <td>72.6</td>\n",
 178 |        "      <td>4.2</td>\n",
 179 |        "      <td>84.1</td>\n",
 180 |        "      <td>–3.9</td>\n",
 181 |        "      <td>61.2</td>\n",
 182 |        "      <td>1.8</td>\n",
 183 |        "    </tr>\n",
 184 |        "    <tr>\n",
 185 |        "      <td>7</td>\n",
 186 |        "      <td>2005–06</td>\n",
 187 |        "      <td>75.4</td>\n",
 188 |        "      <td>3.9</td>\n",
 189 |        "      <td>81.6</td>\n",
 190 |        "      <td>–3.0</td>\n",
 191 |        "      <td>63.9</td>\n",
 192 |        "      <td>4.4</td>\n",
 193 |        "    </tr>\n",
 194 |        "    <tr>\n",
 195 |        "      <td>8</td>\n",
 196 |        "      <td>2006–07</td>\n",
 197 |        "      <td>83.1</td>\n",
 198 |        "      <td>10.2</td>\n",
 199 |        "      <td>83.6</td>\n",
 200 |        "      <td>2.5</td>\n",
 201 |        "      <td>70.4</td>\n",
 202 |        "      <td>10.2</td>\n",
 203 |        "    </tr>\n",
 204 |        "    <tr>\n",
 205 |        "      <td>9</td>\n",
 206 |        "      <td>2007–08</td>\n",
 207 |        "      <td>98.8</td>\n",
 208 |        "      <td>18.9</td>\n",
 209 |        "      <td>89.1</td>\n",
 210 |        "      <td>6.6</td>\n",
 211 |        "      <td>84.1</td>\n",
 212 |        "      <td>19.5</td>\n",
 213 |        "    </tr>\n",
 214 |        "    <tr>\n",
 215 |        "      <td>10</td>\n",
 216 |        "      <td>2008–09</td>\n",
 217 |        "      <td>97.4</td>\n",
 218 |        "      <td>–1.4</td>\n",
 219 |        "      <td>85.8</td>\n",
 220 |        "      <td>–3.7</td>\n",
 221 |        "      <td>83.5</td>\n",
 222 |        "      <td>–0.7</td>\n",
 223 |        "    </tr>\n",
 224 |        "    <tr>\n",
 225 |        "      <td>11</td>\n",
 226 |        "      <td>2009–10</td>\n",
 227 |        "      <td>105.7</td>\n",
 228 |        "      <td>8.5</td>\n",
 229 |        "      <td>97.8</td>\n",
 230 |        "      <td>14.0</td>\n",
 231 |        "      <td>100.2</td>\n",
 232 |        "      <td>20.0</td>\n",
 233 |        "    </tr>\n",
 234 |        "    <tr>\n",
 235 |        "      <td>12</td>\n",
 236 |        "      <td>2010–11</td>\n",
 237 |        "      <td>104.6</td>\n",
 238 |        "      <td>–1.0</td>\n",
 239 |        "      <td>102.2</td>\n",
 240 |        "      <td>4.5</td>\n",
 241 |        "      <td>104.8</td>\n",
 242 |        "      <td>4.6</td>\n",
 243 |        "    </tr>\n",
 244 |        "    <tr>\n",
 245 |        "      <td>13</td>\n",
 246 |        "      <td>2011–12</td>\n",
 247 |        "      <td>100.0</td>\n",
 248 |        "      <td>–4.4</td>\n",
 249 |        "      <td>100.0</td>\n",
 250 |        "      <td>–2.2</td>\n",
 251 |        "      <td>100.0</td>\n",
 252 |        "      <td>–4.6</td>\n",
 253 |        "    </tr>\n",
 254 |        "    <tr>\n",
 255 |        "      <td>14</td>\n",
 256 |        "      <td>2012–13</td>\n",
 257 |        "      <td>101.8</td>\n",
 258 |        "      <td>1.8</td>\n",
 259 |        "      <td>104.4</td>\n",
 260 |        "      <td>4.4</td>\n",
 261 |        "      <td>100.5</td>\n",
 262 |        "      <td>0.5</td>\n",
 263 |        "    </tr>\n",
 264 |        "    <tr>\n",
 265 |        "      <td>15</td>\n",
 266 |        "      <td>2013–14</td>\n",
 267 |        "      <td>108.0</td>\n",
 268 |        "      <td>6.1</td>\n",
 269 |        "      <td>120.4</td>\n",
 270 |        "      <td>15.3</td>\n",
 271 |        "      <td>110.3</td>\n",
 272 |        "      <td>9.8</td>\n",
 273 |        "    </tr>\n",
 274 |        "    <tr>\n",
 275 |        "      <td>16</td>\n",
 276 |        "      <td>2014–15</td>\n",
 277 |        "      <td>113.2</td>\n",
 278 |        "      <td>4.8</td>\n",
 279 |        "      <td>140.0</td>\n",
 280 |        "      <td>16.3</td>\n",
 281 |        "      <td>118.1</td>\n",
 282 |        "      <td>7.1</td>\n",
 283 |        "    </tr>\n",
 284 |        "    <tr>\n",
 285 |        "      <td>17</td>\n",
 286 |        "      <td>2015–16</td>\n",
 287 |        "      <td>118.4</td>\n",
 288 |        "      <td>4.6</td>\n",
 289 |        "      <td>157.3</td>\n",
 290 |        "      <td>12.4</td>\n",
 291 |        "      <td>131.2</td>\n",
 292 |        "      <td>11.1</td>\n",
 293 |        "    </tr>\n",
 294 |        "    <tr>\n",
 295 |        "      <td>18</td>\n",
 296 |        "      <td>2016–17</td>\n",
 297 |        "      <td>123.2</td>\n",
 298 |        "      <td>4.1</td>\n",
 299 |        "      <td>175.4</td>\n",
 300 |        "      <td>11.5</td>\n",
 301 |        "      <td>148.9</td>\n",
 302 |        "      <td>13.5</td>\n",
 303 |        "    </tr>\n",
 304 |        "    <tr>\n",
 305 |        "      <td>19</td>\n",
 306 |        "      <td>2017–18</td>\n",
 307 |        "      <td>126.6</td>\n",
 308 |        "      <td>2.8</td>\n",
 309 |        "      <td>178.8</td>\n",
 310 |        "      <td>1.9</td>\n",
 311 |        "      <td>162.1</td>\n",
 312 |        "      <td>8.9</td>\n",
 313 |        "    </tr>\n",
 314 |        "    <tr>\n",
 315 |        "      <td>20</td>\n",
 316 |        "      <td>2018–19</td>\n",
 317 |        "      <td>126.1</td>\n",
 318 |        "      <td>–0.4</td>\n",
 319 |        "      <td>163.5</td>\n",
 320 |        "      <td>–8.6</td>\n",
 321 |        "      <td>149.7</td>\n",
 322 |        "      <td>–7.6</td>\n",
 323 |        "    </tr>\n",
 324 |        "    <tr>\n",
 325 |        "      <td>21</td>\n",
 326 |        "      <td>NaN</td>\n",
 327 |        "      <td>NaN</td>\n",
 328 |        "      <td>NaN</td>\n",
 329 |        "      <td>NaN</td>\n",
 330 |        "      <td>NaN</td>\n",
 331 |        "      <td>NaN</td>\n",
 332 |        "      <td>NaN</td>\n",
 333 |        "    </tr>\n",
 334 |        "    <tr>\n",
 335 |        "      <td>22</td>\n",
 336 |        "      <td>n.a. = not available.</td>\n",
 337 |        "      <td>NaN</td>\n",
 338 |        "      <td>NaN</td>\n",
 339 |        "      <td>NaN</td>\n",
 340 |        "      <td>NaN</td>\n",
 341 |        "      <td>NaN</td>\n",
 342 |        "      <td>NaN</td>\n",
 343 |        "    </tr>\n",
 344 |        "    <tr>\n",
 345 |        "      <td>23</td>\n",
 346 |        "      <td>(a) Established houses.</td>\n",
 347 |        "      <td>NaN</td>\n",
 348 |        "      <td>NaN</td>\n",
 349 |        "      <td>NaN</td>\n",
 350 |        "      <td>NaN</td>\n",
 351 |        "      <td>NaN</td>\n",
 352 |        "      <td>NaN</td>\n",
 353 |        "    </tr>\n",
 354 |        "    <tr>\n",
 355 |        "      <td>24</td>\n",
 356 |        "      <td>(b) Base of each index: 2011–12 = 100.</td>\n",
 357 |        "      <td>NaN</td>\n",
 358 |        "      <td>NaN</td>\n",
 359 |        "      <td>NaN</td>\n",
 360 |        "      <td>NaN</td>\n",
 361 |        "      <td>NaN</td>\n",
 362 |        "      <td>NaN</td>\n",
 363 |        "    </tr>\n",
 364 |        "    <tr>\n",
 365 |        "      <td>25</td>\n",
 366 |        "      <td>(c) Average four quarters.</td>\n",
 367 |        "      <td>NaN</td>\n",
 368 |        "      <td>NaN</td>\n",
 369 |        "      <td>NaN</td>\n",
 370 |        "      <td>NaN</td>\n",
 371 |        "      <td>NaN</td>\n",
 372 |        "      <td>NaN</td>\n",
 373 |        "    </tr>\n",
 374 |        "    <tr>\n",
 375 |        "      <td>26</td>\n",
 376 |        "      <td></td>\n",
 377 |        "      <td>NaN</td>\n",
 378 |        "      <td>NaN</td>\n",
 379 |        "      <td>NaN</td>\n",
 380 |        "      <td>NaN</td>\n",
 381 |        "      <td>NaN</td>\n",
 382 |        "      <td>NaN</td>\n",
 383 |        "    </tr>\n",
 384 |        "    <tr>\n",
 385 |        "      <td>27</td>\n",
 386 |        "      <td>Source: ABS 6416.0, Residential Property Price...</td>\n",
 387 |        "      <td>NaN</td>\n",
 388 |        "      <td>NaN</td>\n",
 389 |        "      <td>NaN</td>\n",
 390 |        "      <td>NaN</td>\n",
 391 |        "      <td>NaN</td>\n",
 392 |        "      <td>NaN</td>\n",
 393 |        "    </tr>\n",
 394 |        "    <tr>\n",
 395 |        "      <td>28</td>\n",
 396 |        "      <td>NaN</td>\n",
 397 |        "      <td>NaN</td>\n",
 398 |        "      <td>NaN</td>\n",
 399 |        "      <td>NaN</td>\n",
 400 |        "      <td>NaN</td>\n",
 401 |        "      <td>NaN</td>\n",
 402 |        "      <td>NaN</td>\n",
 403 |        "    </tr>\n",
 404 |        "    <tr>\n",
 405 |        "      <td>29</td>\n",
 406 |        "      <td>NaN</td>\n",
 407 |        "      <td>NaN</td>\n",
 408 |        "      <td>NaN</td>\n",
 409 |        "      <td>NaN</td>\n",
 410 |        "      <td>NaN</td>\n",
 411 |        "      <td>NaN</td>\n",
 412 |        "      <td>NaN</td>\n",
 413 |        "    </tr>\n",
 414 |        "    <tr>\n",
 415 |        "      <td>30</td>\n",
 416 |        "      <td>NaN</td>\n",
 417 |        "      <td>NaN</td>\n",
 418 |        "      <td>NaN</td>\n",
 419 |        "      <td>NaN</td>\n",
 420 |        "      <td>NaN</td>\n",
 421 |        "      <td>NaN</td>\n",
 422 |        "      <td>NaN</td>\n",
 423 |        "    </tr>\n",
 424 |        "    <tr>\n",
 425 |        "      <td>31</td>\n",
 426 |        "      <td>NaN</td>\n",
 427 |        "      <td>NaN</td>\n",
 428 |        "      <td>NaN</td>\n",
 429 |        "      <td>NaN</td>\n",
 430 |        "      <td>NaN</td>\n",
 431 |        "      <td>NaN</td>\n",
 432 |        "      <td>NaN</td>\n",
 433 |        "    </tr>\n",
 434 |        "    <tr>\n",
 435 |        "      <td>32</td>\n",
 436 |        "      <td>NaN</td>\n",
 437 |        "      <td>NaN</td>\n",
 438 |        "      <td>NaN</td>\n",
 439 |        "      <td>NaN</td>\n",
 440 |        "      <td>NaN</td>\n",
 441 |        "      <td>NaN</td>\n",
 442 |        "      <td>NaN</td>\n",
 443 |        "    </tr>\n",
 444 |        "    <tr>\n",
 445 |        "      <td>33</td>\n",
 446 |        "      <td>NaN</td>\n",
 447 |        "      <td>NaN</td>\n",
 448 |        "      <td>NaN</td>\n",
 449 |        "      <td>NaN</td>\n",
 450 |        "      <td>NaN</td>\n",
 451 |        "      <td>NaN</td>\n",
 452 |        "      <td>NaN</td>\n",
 453 |        "    </tr>\n",
 454 |        "    <tr>\n",
 455 |        "      <td>34</td>\n",
 456 |        "      <td>NaN</td>\n",
 457 |        "      <td>NaN</td>\n",
 458 |        "      <td>NaN</td>\n",
 459 |        "      <td>NaN</td>\n",
 460 |        "      <td>NaN</td>\n",
 461 |        "      <td>NaN</td>\n",
 462 |        "      <td>NaN</td>\n",
 463 |        "    </tr>\n",
 464 |        "  </tbody>\n",
 465 |        "</table>\n",
 466 |        "</div>"
 467 |       ],
 468 |       "text/plain": [
 469 |        "   House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2018–19  \\\n",
 470 |        "0                                                 NaN                             \n",
 471 |        "1                                  Financial year (c)                             \n",
 472 |        "2                                                 NaN                             \n",
 473 |        "3                                                 NaN                             \n",
 474 |        "4                                             2002–03                             \n",
 475 |        "5                                             2003–04                             \n",
 476 |        "6                                             2004–05                             \n",
 477 |        "7                                             2005–06                             \n",
 478 |        "8                                             2006–07                             \n",
 479 |        "9                                             2007–08                             \n",
 480 |        "10                                            2008–09                             \n",
 481 |        "11                                            2009–10                             \n",
 482 |        "12                                            2010–11                             \n",
 483 |        "13                                            2011–12                             \n",
 484 |        "14                                            2012–13                             \n",
 485 |        "15                                            2013–14                             \n",
 486 |        "16                                            2014–15                             \n",
 487 |        "17                                            2015–16                             \n",
 488 |        "18                                            2016–17                             \n",
 489 |        "19                                            2017–18                             \n",
 490 |        "20                                            2018–19                             \n",
 491 |        "21                                                NaN                             \n",
 492 |        "22                              n.a. = not available.                             \n",
 493 |        "23                            (a) Established houses.                             \n",
 494 |        "24             (b) Base of each index: 2011–12 = 100.                             \n",
 495 |        "25                         (c) Average four quarters.                             \n",
 496 |        "26                                                                                \n",
 497 |        "27  Source: ABS 6416.0, Residential Property Price...                             \n",
 498 |        "28                                                NaN                             \n",
 499 |        "29                                                NaN                             \n",
 500 |        "30                                                NaN                             \n",
 501 |        "31                                                NaN                             \n",
 502 |        "32                                                NaN                             \n",
 503 |        "33                                                NaN                             \n",
 504 |        "34                                                NaN                             \n",
 505 |        "\n",
 506 |        "      Unnamed: 1       Unnamed: 2 Unnamed: 3       Unnamed: 4 Unnamed: 5  \\\n",
 507 |        "0            NaN              NaN        NaN              NaN        NaN   \n",
 508 |        "1   Capital city              NaN        NaN              NaN        NaN   \n",
 509 |        "2       Brisbane              NaN     Sydney              NaN  Melbourne   \n",
 510 |        "3          Index  Annual % change      Index  Annual % change      Index   \n",
 511 |        "4           52.6             n.a.       78.2             n.a.       54.1   \n",
 512 |        "5           69.7             32.5       87.5             11.9       60.1   \n",
 513 |        "6           72.6              4.2       84.1             –3.9       61.2   \n",
 514 |        "7           75.4              3.9       81.6             –3.0       63.9   \n",
 515 |        "8           83.1             10.2       83.6              2.5       70.4   \n",
 516 |        "9           98.8             18.9       89.1              6.6       84.1   \n",
 517 |        "10          97.4             –1.4       85.8             –3.7       83.5   \n",
 518 |        "11         105.7              8.5       97.8             14.0      100.2   \n",
 519 |        "12         104.6             –1.0      102.2              4.5      104.8   \n",
 520 |        "13         100.0             –4.4      100.0             –2.2      100.0   \n",
 521 |        "14         101.8              1.8      104.4              4.4      100.5   \n",
 522 |        "15         108.0              6.1      120.4             15.3      110.3   \n",
 523 |        "16         113.2              4.8      140.0             16.3      118.1   \n",
 524 |        "17         118.4              4.6      157.3             12.4      131.2   \n",
 525 |        "18         123.2              4.1      175.4             11.5      148.9   \n",
 526 |        "19         126.6              2.8      178.8              1.9      162.1   \n",
 527 |        "20         126.1             –0.4      163.5             –8.6      149.7   \n",
 528 |        "21           NaN              NaN        NaN              NaN        NaN   \n",
 529 |        "22           NaN              NaN        NaN              NaN        NaN   \n",
 530 |        "23           NaN              NaN        NaN              NaN        NaN   \n",
 531 |        "24           NaN              NaN        NaN              NaN        NaN   \n",
 532 |        "25           NaN              NaN        NaN              NaN        NaN   \n",
 533 |        "26           NaN              NaN        NaN              NaN        NaN   \n",
 534 |        "27           NaN              NaN        NaN              NaN        NaN   \n",
 535 |        "28           NaN              NaN        NaN              NaN        NaN   \n",
 536 |        "29           NaN              NaN        NaN              NaN        NaN   \n",
 537 |        "30           NaN              NaN        NaN              NaN        NaN   \n",
 538 |        "31           NaN              NaN        NaN              NaN        NaN   \n",
 539 |        "32           NaN              NaN        NaN              NaN        NaN   \n",
 540 |        "33           NaN              NaN        NaN              NaN        NaN   \n",
 541 |        "34           NaN              NaN        NaN              NaN        NaN   \n",
 542 |        "\n",
 543 |        "         Unnamed: 6  \n",
 544 |        "0               NaN  \n",
 545 |        "1               NaN  \n",
 546 |        "2               NaN  \n",
 547 |        "3   Annual % change  \n",
 548 |        "4              n.a.  \n",
 549 |        "5              11.1  \n",
 550 |        "6               1.8  \n",
 551 |        "7               4.4  \n",
 552 |        "8              10.2  \n",
 553 |        "9              19.5  \n",
 554 |        "10             –0.7  \n",
 555 |        "11             20.0  \n",
 556 |        "12              4.6  \n",
 557 |        "13             –4.6  \n",
 558 |        "14              0.5  \n",
 559 |        "15              9.8  \n",
 560 |        "16              7.1  \n",
 561 |        "17             11.1  \n",
 562 |        "18             13.5  \n",
 563 |        "19              8.9  \n",
 564 |        "20             –7.6  \n",
 565 |        "21              NaN  \n",
 566 |        "22              NaN  \n",
 567 |        "23              NaN  \n",
 568 |        "24              NaN  \n",
 569 |        "25              NaN  \n",
 570 |        "26              NaN  \n",
 571 |        "27              NaN  \n",
 572 |        "28              NaN  \n",
 573 |        "29              NaN  \n",
 574 |        "30              NaN  \n",
 575 |        "31              NaN  \n",
 576 |        "32              NaN  \n",
 577 |        "33              NaN  \n",
 578 |        "34              NaN  "
 579 |       ]
 580 |      },
 581 |      "execution_count": 2,
 582 |      "metadata": {},
 583 |      "output_type": "execute_result"
 584 |     }
 585 |    ],
 586 |    "source": [
 587 |     "#Viewing HPI data\n",
 588 |     "data1"
 589 |    ]
 590 |   },
 591 |   {
 592 |    "cell_type": "code",
 593 |    "execution_count": 3,
 594 |    "metadata": {
 595 |     "collapsed": true
 596 |    },
 597 |    "outputs": [
 598 |     {
 599 |      "name": "stdout",
 600 |      "output_type": "stream",
 601 |      "text": [
 602 |       "<class 'pandas.core.frame.DataFrame'>\n",
 603 |       "RangeIndex: 35 entries, 0 to 34\n",
 604 |       "Data columns (total 7 columns):\n",
 605 |       "House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2018–19    24 non-null object\n",
 606 |       "Unnamed: 1                                                                      20 non-null object\n",
 607 |       "Unnamed: 2                                                                      18 non-null object\n",
 608 |       "Unnamed: 3                                                                      19 non-null object\n",
 609 |       "Unnamed: 4                                                                      18 non-null object\n",
 610 |       "Unnamed: 5                                                                      19 non-null object\n",
 611 |       "Unnamed: 6                                                                      18 non-null object\n",
 612 |       "dtypes: object(7)\n",
 613 |       "memory usage: 2.0+ KB\n"
 614 |      ]
 615 |     }
 616 |    ],
 617 |    "source": [
 618 |     "#knowing the datasets\n",
 619 |     "data1.info()"
 620 |    ]
 621 |   },
 622 |   {
 623 |    "cell_type": "code",
 624 |    "execution_count": 4,
 625 |    "metadata": {
 626 |     "collapsed": true
 627 |    },
 628 |    "outputs": [
 629 |     {
 630 |      "data": {
 631 |       "text/html": [
 632 |        "<div>\n",
 633 |        "<style scoped>\n",
 634 |        "    .dataframe tbody tr th:only-of-type {\n",
 635 |        "        vertical-align: middle;\n",
 636 |        "    }\n",
 637 |        "\n",
 638 |        "    .dataframe tbody tr th {\n",
 639 |        "        vertical-align: top;\n",
 640 |        "    }\n",
 641 |        "\n",
 642 |        "    .dataframe thead th {\n",
 643 |        "        text-align: right;\n",
 644 |        "    }\n",
 645 |        "</style>\n",
 646 |        "<table border=\"1\" class=\"dataframe\">\n",
 647 |        "  <thead>\n",
 648 |        "    <tr style=\"text-align: right;\">\n",
 649 |        "      <th></th>\n",
 650 |        "      <th>House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2018–19</th>\n",
 651 |        "      <th>Unnamed: 1</th>\n",
 652 |        "      <th>Unnamed: 2</th>\n",
 653 |        "      <th>Unnamed: 3</th>\n",
 654 |        "      <th>Unnamed: 4</th>\n",
 655 |        "      <th>Unnamed: 5</th>\n",
 656 |        "      <th>Unnamed: 6</th>\n",
 657 |        "    </tr>\n",
 658 |        "  </thead>\n",
 659 |        "  <tbody>\n",
 660 |        "    <tr>\n",
 661 |        "      <td>4</td>\n",
 662 |        "      <td>2002–03</td>\n",
 663 |        "      <td>52.6</td>\n",
 664 |        "      <td>n.a.</td>\n",
 665 |        "      <td>78.2</td>\n",
 666 |        "      <td>n.a.</td>\n",
 667 |        "      <td>54.1</td>\n",
 668 |        "      <td>n.a.</td>\n",
 669 |        "    </tr>\n",
 670 |        "    <tr>\n",
 671 |        "      <td>5</td>\n",
 672 |        "      <td>2003–04</td>\n",
 673 |        "      <td>69.7</td>\n",
 674 |        "      <td>32.5</td>\n",
 675 |        "      <td>87.5</td>\n",
 676 |        "      <td>11.9</td>\n",
 677 |        "      <td>60.1</td>\n",
 678 |        "      <td>11.1</td>\n",
 679 |        "    </tr>\n",
 680 |        "    <tr>\n",
 681 |        "      <td>6</td>\n",
 682 |        "      <td>2004–05</td>\n",
 683 |        "      <td>72.6</td>\n",
 684 |        "      <td>4.2</td>\n",
 685 |        "      <td>84.1</td>\n",
 686 |        "      <td>–3.9</td>\n",
 687 |        "      <td>61.2</td>\n",
 688 |        "      <td>1.8</td>\n",
 689 |        "    </tr>\n",
 690 |        "    <tr>\n",
 691 |        "      <td>7</td>\n",
 692 |        "      <td>2005–06</td>\n",
 693 |        "      <td>75.4</td>\n",
 694 |        "      <td>3.9</td>\n",
 695 |        "      <td>81.6</td>\n",
 696 |        "      <td>–3.0</td>\n",
 697 |        "      <td>63.9</td>\n",
 698 |        "      <td>4.4</td>\n",
 699 |        "    </tr>\n",
 700 |        "    <tr>\n",
 701 |        "      <td>8</td>\n",
 702 |        "      <td>2006–07</td>\n",
 703 |        "      <td>83.1</td>\n",
 704 |        "      <td>10.2</td>\n",
 705 |        "      <td>83.6</td>\n",
 706 |        "      <td>2.5</td>\n",
 707 |        "      <td>70.4</td>\n",
 708 |        "      <td>10.2</td>\n",
 709 |        "    </tr>\n",
 710 |        "    <tr>\n",
 711 |        "      <td>9</td>\n",
 712 |        "      <td>2007–08</td>\n",
 713 |        "      <td>98.8</td>\n",
 714 |        "      <td>18.9</td>\n",
 715 |        "      <td>89.1</td>\n",
 716 |        "      <td>6.6</td>\n",
 717 |        "      <td>84.1</td>\n",
 718 |        "      <td>19.5</td>\n",
 719 |        "    </tr>\n",
 720 |        "    <tr>\n",
 721 |        "      <td>10</td>\n",
 722 |        "      <td>2008–09</td>\n",
 723 |        "      <td>97.4</td>\n",
 724 |        "      <td>–1.4</td>\n",
 725 |        "      <td>85.8</td>\n",
 726 |        "      <td>–3.7</td>\n",
 727 |        "      <td>83.5</td>\n",
 728 |        "      <td>–0.7</td>\n",
 729 |        "    </tr>\n",
 730 |        "    <tr>\n",
 731 |        "      <td>11</td>\n",
 732 |        "      <td>2009–10</td>\n",
 733 |        "      <td>105.7</td>\n",
 734 |        "      <td>8.5</td>\n",
 735 |        "      <td>97.8</td>\n",
 736 |        "      <td>14.0</td>\n",
 737 |        "      <td>100.2</td>\n",
 738 |        "      <td>20.0</td>\n",
 739 |        "    </tr>\n",
 740 |        "    <tr>\n",
 741 |        "      <td>12</td>\n",
 742 |        "      <td>2010–11</td>\n",
 743 |        "      <td>104.6</td>\n",
 744 |        "      <td>–1.0</td>\n",
 745 |        "      <td>102.2</td>\n",
 746 |        "      <td>4.5</td>\n",
 747 |        "      <td>104.8</td>\n",
 748 |        "      <td>4.6</td>\n",
 749 |        "    </tr>\n",
 750 |        "    <tr>\n",
 751 |        "      <td>13</td>\n",
 752 |        "      <td>2011–12</td>\n",
 753 |        "      <td>100.0</td>\n",
 754 |        "      <td>–4.4</td>\n",
 755 |        "      <td>100.0</td>\n",
 756 |        "      <td>–2.2</td>\n",
 757 |        "      <td>100.0</td>\n",
 758 |        "      <td>–4.6</td>\n",
 759 |        "    </tr>\n",
 760 |        "    <tr>\n",
 761 |        "      <td>14</td>\n",
 762 |        "      <td>2012–13</td>\n",
 763 |        "      <td>101.8</td>\n",
 764 |        "      <td>1.8</td>\n",
 765 |        "      <td>104.4</td>\n",
 766 |        "      <td>4.4</td>\n",
 767 |        "      <td>100.5</td>\n",
 768 |        "      <td>0.5</td>\n",
 769 |        "    </tr>\n",
 770 |        "    <tr>\n",
 771 |        "      <td>15</td>\n",
 772 |        "      <td>2013–14</td>\n",
 773 |        "      <td>108.0</td>\n",
 774 |        "      <td>6.1</td>\n",
 775 |        "      <td>120.4</td>\n",
 776 |        "      <td>15.3</td>\n",
 777 |        "      <td>110.3</td>\n",
 778 |        "      <td>9.8</td>\n",
 779 |        "    </tr>\n",
 780 |        "    <tr>\n",
 781 |        "      <td>16</td>\n",
 782 |        "      <td>2014–15</td>\n",
 783 |        "      <td>113.2</td>\n",
 784 |        "      <td>4.8</td>\n",
 785 |        "      <td>140.0</td>\n",
 786 |        "      <td>16.3</td>\n",
 787 |        "      <td>118.1</td>\n",
 788 |        "      <td>7.1</td>\n",
 789 |        "    </tr>\n",
 790 |        "    <tr>\n",
 791 |        "      <td>17</td>\n",
 792 |        "      <td>2015–16</td>\n",
 793 |        "      <td>118.4</td>\n",
 794 |        "      <td>4.6</td>\n",
 795 |        "      <td>157.3</td>\n",
 796 |        "      <td>12.4</td>\n",
 797 |        "      <td>131.2</td>\n",
 798 |        "      <td>11.1</td>\n",
 799 |        "    </tr>\n",
 800 |        "    <tr>\n",
 801 |        "      <td>18</td>\n",
 802 |        "      <td>2016–17</td>\n",
 803 |        "      <td>123.2</td>\n",
 804 |        "      <td>4.1</td>\n",
 805 |        "      <td>175.4</td>\n",
 806 |        "      <td>11.5</td>\n",
 807 |        "      <td>148.9</td>\n",
 808 |        "      <td>13.5</td>\n",
 809 |        "    </tr>\n",
 810 |        "    <tr>\n",
 811 |        "      <td>19</td>\n",
 812 |        "      <td>2017–18</td>\n",
 813 |        "      <td>126.6</td>\n",
 814 |        "      <td>2.8</td>\n",
 815 |        "      <td>178.8</td>\n",
 816 |        "      <td>1.9</td>\n",
 817 |        "      <td>162.1</td>\n",
 818 |        "      <td>8.9</td>\n",
 819 |        "    </tr>\n",
 820 |        "    <tr>\n",
 821 |        "      <td>20</td>\n",
 822 |        "      <td>2018–19</td>\n",
 823 |        "      <td>126.1</td>\n",
 824 |        "      <td>–0.4</td>\n",
 825 |        "      <td>163.5</td>\n",
 826 |        "      <td>–8.6</td>\n",
 827 |        "      <td>149.7</td>\n",
 828 |        "      <td>–7.6</td>\n",
 829 |        "    </tr>\n",
 830 |        "  </tbody>\n",
 831 |        "</table>\n",
 832 |        "</div>"
 833 |       ],
 834 |       "text/plain": [
 835 |        "   House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2018–19  \\\n",
 836 |        "4                                             2002–03                             \n",
 837 |        "5                                             2003–04                             \n",
 838 |        "6                                             2004–05                             \n",
 839 |        "7                                             2005–06                             \n",
 840 |        "8                                             2006–07                             \n",
 841 |        "9                                             2007–08                             \n",
 842 |        "10                                            2008–09                             \n",
 843 |        "11                                            2009–10                             \n",
 844 |        "12                                            2010–11                             \n",
 845 |        "13                                            2011–12                             \n",
 846 |        "14                                            2012–13                             \n",
 847 |        "15                                            2013–14                             \n",
 848 |        "16                                            2014–15                             \n",
 849 |        "17                                            2015–16                             \n",
 850 |        "18                                            2016–17                             \n",
 851 |        "19                                            2017–18                             \n",
 852 |        "20                                            2018–19                             \n",
 853 |        "\n",
 854 |        "   Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6  \n",
 855 |        "4        52.6       n.a.       78.2       n.a.       54.1       n.a.  \n",
 856 |        "5        69.7       32.5       87.5       11.9       60.1       11.1  \n",
 857 |        "6        72.6        4.2       84.1       –3.9       61.2        1.8  \n",
 858 |        "7        75.4        3.9       81.6       –3.0       63.9        4.4  \n",
 859 |        "8        83.1       10.2       83.6        2.5       70.4       10.2  \n",
 860 |        "9        98.8       18.9       89.1        6.6       84.1       19.5  \n",
 861 |        "10       97.4       –1.4       85.8       –3.7       83.5       –0.7  \n",
 862 |        "11      105.7        8.5       97.8       14.0      100.2       20.0  \n",
 863 |        "12      104.6       –1.0      102.2        4.5      104.8        4.6  \n",
 864 |        "13      100.0       –4.4      100.0       –2.2      100.0       –4.6  \n",
 865 |        "14      101.8        1.8      104.4        4.4      100.5        0.5  \n",
 866 |        "15      108.0        6.1      120.4       15.3      110.3        9.8  \n",
 867 |        "16      113.2        4.8      140.0       16.3      118.1        7.1  \n",
 868 |        "17      118.4        4.6      157.3       12.4      131.2       11.1  \n",
 869 |        "18      123.2        4.1      175.4       11.5      148.9       13.5  \n",
 870 |        "19      126.6        2.8      178.8        1.9      162.1        8.9  \n",
 871 |        "20      126.1       –0.4      163.5       –8.6      149.7       –7.6  "
 872 |       ]
 873 |      },
 874 |      "execution_count": 4,
 875 |      "metadata": {},
 876 |      "output_type": "execute_result"
 877 |     }
 878 |    ],
 879 |    "source": [
 880 |     "#pre-processing data- removing unnecessary columns, rows and replacing missing values\n",
 881 |     "#changing column names\n",
 882 |     "data1 = data1[4:21]                                     #removing descriptions and empty cells\n",
 883 |     "data1"
 884 |    ]
 885 |   },
 886 |   {
 887 |    "cell_type": "code",
 888 |    "execution_count": 5,
 889 |    "metadata": {
 890 |     "collapsed": true
 891 |    },
 892 |    "outputs": [
 893 |     {
 894 |      "data": {
 895 |       "text/html": [
 896 |        "<div>\n",
 897 |        "<style scoped>\n",
 898 |        "    .dataframe tbody tr th:only-of-type {\n",
 899 |        "        vertical-align: middle;\n",
 900 |        "    }\n",
 901 |        "\n",
 902 |        "    .dataframe tbody tr th {\n",
 903 |        "        vertical-align: top;\n",
 904 |        "    }\n",
 905 |        "\n",
 906 |        "    .dataframe thead th {\n",
 907 |        "        text-align: right;\n",
 908 |        "    }\n",
 909 |        "</style>\n",
 910 |        "<table border=\"1\" class=\"dataframe\">\n",
 911 |        "  <thead>\n",
 912 |        "    <tr style=\"text-align: right;\">\n",
 913 |        "      <th></th>\n",
 914 |        "      <th>House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2018–19</th>\n",
 915 |        "      <th>Unnamed: 1</th>\n",
 916 |        "      <th>Unnamed: 3</th>\n",
 917 |        "      <th>Unnamed: 5</th>\n",
 918 |        "    </tr>\n",
 919 |        "  </thead>\n",
 920 |        "  <tbody>\n",
 921 |        "    <tr>\n",
 922 |        "      <td>4</td>\n",
 923 |        "      <td>2002–03</td>\n",
 924 |        "      <td>52.6</td>\n",
 925 |        "      <td>78.2</td>\n",
 926 |        "      <td>54.1</td>\n",
 927 |        "    </tr>\n",
 928 |        "    <tr>\n",
 929 |        "      <td>5</td>\n",
 930 |        "      <td>2003–04</td>\n",
 931 |        "      <td>69.7</td>\n",
 932 |        "      <td>87.5</td>\n",
 933 |        "      <td>60.1</td>\n",
 934 |        "    </tr>\n",
 935 |        "    <tr>\n",
 936 |        "      <td>6</td>\n",
 937 |        "      <td>2004–05</td>\n",
 938 |        "      <td>72.6</td>\n",
 939 |        "      <td>84.1</td>\n",
 940 |        "      <td>61.2</td>\n",
 941 |        "    </tr>\n",
 942 |        "    <tr>\n",
 943 |        "      <td>7</td>\n",
 944 |        "      <td>2005–06</td>\n",
 945 |        "      <td>75.4</td>\n",
 946 |        "      <td>81.6</td>\n",
 947 |        "      <td>63.9</td>\n",
 948 |        "    </tr>\n",
 949 |        "    <tr>\n",
 950 |        "      <td>8</td>\n",
 951 |        "      <td>2006–07</td>\n",
 952 |        "      <td>83.1</td>\n",
 953 |        "      <td>83.6</td>\n",
 954 |        "      <td>70.4</td>\n",
 955 |        "    </tr>\n",
 956 |        "    <tr>\n",
 957 |        "      <td>9</td>\n",
 958 |        "      <td>2007–08</td>\n",
 959 |        "      <td>98.8</td>\n",
 960 |        "      <td>89.1</td>\n",
 961 |        "      <td>84.1</td>\n",
 962 |        "    </tr>\n",
 963 |        "    <tr>\n",
 964 |        "      <td>10</td>\n",
 965 |        "      <td>2008–09</td>\n",
 966 |        "      <td>97.4</td>\n",
 967 |        "      <td>85.8</td>\n",
 968 |        "      <td>83.5</td>\n",
 969 |        "    </tr>\n",
 970 |        "    <tr>\n",
 971 |        "      <td>11</td>\n",
 972 |        "      <td>2009–10</td>\n",
 973 |        "      <td>105.7</td>\n",
 974 |        "      <td>97.8</td>\n",
 975 |        "      <td>100.2</td>\n",
 976 |        "    </tr>\n",
 977 |        "    <tr>\n",
 978 |        "      <td>12</td>\n",
 979 |        "      <td>2010–11</td>\n",
 980 |        "      <td>104.6</td>\n",
 981 |        "      <td>102.2</td>\n",
 982 |        "      <td>104.8</td>\n",
 983 |        "    </tr>\n",
 984 |        "    <tr>\n",
 985 |        "      <td>13</td>\n",
 986 |        "      <td>2011–12</td>\n",
 987 |        "      <td>100.0</td>\n",
 988 |        "      <td>100.0</td>\n",
 989 |        "      <td>100.0</td>\n",
 990 |        "    </tr>\n",
 991 |        "    <tr>\n",
 992 |        "      <td>14</td>\n",
 993 |        "      <td>2012–13</td>\n",
 994 |        "      <td>101.8</td>\n",
 995 |        "      <td>104.4</td>\n",
 996 |        "      <td>100.5</td>\n",
 997 |        "    </tr>\n",
 998 |        "    <tr>\n",
 999 |        "      <td>15</td>\n",
1000 |        "      <td>2013–14</td>\n",
1001 |        "      <td>108.0</td>\n",
1002 |        "      <td>120.4</td>\n",
1003 |        "      <td>110.3</td>\n",
1004 |        "    </tr>\n",
1005 |        "    <tr>\n",
1006 |        "      <td>16</td>\n",
1007 |        "      <td>2014–15</td>\n",
1008 |        "      <td>113.2</td>\n",
1009 |        "      <td>140.0</td>\n",
1010 |        "      <td>118.1</td>\n",
1011 |        "    </tr>\n",
1012 |        "    <tr>\n",
1013 |        "      <td>17</td>\n",
1014 |        "      <td>2015–16</td>\n",
1015 |        "      <td>118.4</td>\n",
1016 |        "      <td>157.3</td>\n",
1017 |        "      <td>131.2</td>\n",
1018 |        "    </tr>\n",
1019 |        "    <tr>\n",
1020 |        "      <td>18</td>\n",
1021 |        "      <td>2016–17</td>\n",
1022 |        "      <td>123.2</td>\n",
1023 |        "      <td>175.4</td>\n",
1024 |        "      <td>148.9</td>\n",
1025 |        "    </tr>\n",
1026 |        "    <tr>\n",
1027 |        "      <td>19</td>\n",
1028 |        "      <td>2017–18</td>\n",
1029 |        "      <td>126.6</td>\n",
1030 |        "      <td>178.8</td>\n",
1031 |        "      <td>162.1</td>\n",
1032 |        "    </tr>\n",
1033 |        "    <tr>\n",
1034 |        "      <td>20</td>\n",
1035 |        "      <td>2018–19</td>\n",
1036 |        "      <td>126.1</td>\n",
1037 |        "      <td>163.5</td>\n",
1038 |        "      <td>149.7</td>\n",
1039 |        "    </tr>\n",
1040 |        "  </tbody>\n",
1041 |        "</table>\n",
1042 |        "</div>"
1043 |       ],
1044 |       "text/plain": [
1045 |        "   House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2018–19  \\\n",
1046 |        "4                                             2002–03                             \n",
1047 |        "5                                             2003–04                             \n",
1048 |        "6                                             2004–05                             \n",
1049 |        "7                                             2005–06                             \n",
1050 |        "8                                             2006–07                             \n",
1051 |        "9                                             2007–08                             \n",
1052 |        "10                                            2008–09                             \n",
1053 |        "11                                            2009–10                             \n",
1054 |        "12                                            2010–11                             \n",
1055 |        "13                                            2011–12                             \n",
1056 |        "14                                            2012–13                             \n",
1057 |        "15                                            2013–14                             \n",
1058 |        "16                                            2014–15                             \n",
1059 |        "17                                            2015–16                             \n",
1060 |        "18                                            2016–17                             \n",
1061 |        "19                                            2017–18                             \n",
1062 |        "20                                            2018–19                             \n",
1063 |        "\n",
1064 |        "   Unnamed: 1 Unnamed: 3 Unnamed: 5  \n",
1065 |        "4        52.6       78.2       54.1  \n",
1066 |        "5        69.7       87.5       60.1  \n",
1067 |        "6        72.6       84.1       61.2  \n",
1068 |        "7        75.4       81.6       63.9  \n",
1069 |        "8        83.1       83.6       70.4  \n",
1070 |        "9        98.8       89.1       84.1  \n",
1071 |        "10       97.4       85.8       83.5  \n",
1072 |        "11      105.7       97.8      100.2  \n",
1073 |        "12      104.6      102.2      104.8  \n",
1074 |        "13      100.0      100.0      100.0  \n",
1075 |        "14      101.8      104.4      100.5  \n",
1076 |        "15      108.0      120.4      110.3  \n",
1077 |        "16      113.2      140.0      118.1  \n",
1078 |        "17      118.4      157.3      131.2  \n",
1079 |        "18      123.2      175.4      148.9  \n",
1080 |        "19      126.6      178.8      162.1  \n",
1081 |        "20      126.1      163.5      149.7  "
1082 |       ]
1083 |      },
1084 |      "execution_count": 5,
1085 |      "metadata": {},
1086 |      "output_type": "execute_result"
1087 |     }
1088 |    ],
1089 |    "source": [
1090 |     "#This analysis focuses on House price index alone, hence dropping Annual % change column \n",
1091 |     "#(Unnamed:2,Unnamed:4, Unnamed:6)\n",
1092 |     "data1 = data1.drop(columns = [\"Unnamed: 2\", 'Unnamed: 4','Unnamed: 6' ])\n",
1093 |     "data1"
1094 |    ]
1095 |   },
1096 |   {
1097 |    "cell_type": "code",
1098 |    "execution_count": 6,
1099 |    "metadata": {
1100 |     "collapsed": true
1101 |    },
1102 |    "outputs": [
1103 |     {
1104 |      "data": {
1105 |       "text/html": [
1106 |        "<div>\n",
1107 |        "<style scoped>\n",
1108 |        "    .dataframe tbody tr th:only-of-type {\n",
1109 |        "        vertical-align: middle;\n",
1110 |        "    }\n",
1111 |        "\n",
1112 |        "    .dataframe tbody tr th {\n",
1113 |        "        vertical-align: top;\n",
1114 |        "    }\n",
1115 |        "\n",
1116 |        "    .dataframe thead th {\n",
1117 |        "        text-align: right;\n",
1118 |        "    }\n",
1119 |        "</style>\n",
1120 |        "<table border=\"1\" class=\"dataframe\">\n",
1121 |        "  <thead>\n",
1122 |        "    <tr style=\"text-align: right;\">\n",
1123 |        "      <th></th>\n",
1124 |        "      <th>Financial_Year</th>\n",
1125 |        "      <th>Brisbane_HPI</th>\n",
1126 |        "      <th>Sydney_HPI</th>\n",
1127 |        "      <th>Melbourne_HPI</th>\n",
1128 |        "    </tr>\n",
1129 |        "  </thead>\n",
1130 |        "  <tbody>\n",
1131 |        "    <tr>\n",
1132 |        "      <td>4</td>\n",
1133 |        "      <td>2002–03</td>\n",
1134 |        "      <td>52.6</td>\n",
1135 |        "      <td>78.2</td>\n",
1136 |        "      <td>54.1</td>\n",
1137 |        "    </tr>\n",
1138 |        "    <tr>\n",
1139 |        "      <td>5</td>\n",
1140 |        "      <td>2003–04</td>\n",
1141 |        "      <td>69.7</td>\n",
1142 |        "      <td>87.5</td>\n",
1143 |        "      <td>60.1</td>\n",
1144 |        "    </tr>\n",
1145 |        "    <tr>\n",
1146 |        "      <td>6</td>\n",
1147 |        "      <td>2004–05</td>\n",
1148 |        "      <td>72.6</td>\n",
1149 |        "      <td>84.1</td>\n",
1150 |        "      <td>61.2</td>\n",
1151 |        "    </tr>\n",
1152 |        "    <tr>\n",
1153 |        "      <td>7</td>\n",
1154 |        "      <td>2005–06</td>\n",
1155 |        "      <td>75.4</td>\n",
1156 |        "      <td>81.6</td>\n",
1157 |        "      <td>63.9</td>\n",
1158 |        "    </tr>\n",
1159 |        "    <tr>\n",
1160 |        "      <td>8</td>\n",
1161 |        "      <td>2006–07</td>\n",
1162 |        "      <td>83.1</td>\n",
1163 |        "      <td>83.6</td>\n",
1164 |        "      <td>70.4</td>\n",
1165 |        "    </tr>\n",
1166 |        "    <tr>\n",
1167 |        "      <td>9</td>\n",
1168 |        "      <td>2007–08</td>\n",
1169 |        "      <td>98.8</td>\n",
1170 |        "      <td>89.1</td>\n",
1171 |        "      <td>84.1</td>\n",
1172 |        "    </tr>\n",
1173 |        "    <tr>\n",
1174 |        "      <td>10</td>\n",
1175 |        "      <td>2008–09</td>\n",
1176 |        "      <td>97.4</td>\n",
1177 |        "      <td>85.8</td>\n",
1178 |        "      <td>83.5</td>\n",
1179 |        "    </tr>\n",
1180 |        "    <tr>\n",
1181 |        "      <td>11</td>\n",
1182 |        "      <td>2009–10</td>\n",
1183 |        "      <td>105.7</td>\n",
1184 |        "      <td>97.8</td>\n",
1185 |        "      <td>100.2</td>\n",
1186 |        "    </tr>\n",
1187 |        "    <tr>\n",
1188 |        "      <td>12</td>\n",
1189 |        "      <td>2010–11</td>\n",
1190 |        "      <td>104.6</td>\n",
1191 |        "      <td>102.2</td>\n",
1192 |        "      <td>104.8</td>\n",
1193 |        "    </tr>\n",
1194 |        "    <tr>\n",
1195 |        "      <td>13</td>\n",
1196 |        "      <td>2011–12</td>\n",
1197 |        "      <td>100.0</td>\n",
1198 |        "      <td>100.0</td>\n",
1199 |        "      <td>100.0</td>\n",
1200 |        "    </tr>\n",
1201 |        "    <tr>\n",
1202 |        "      <td>14</td>\n",
1203 |        "      <td>2012–13</td>\n",
1204 |        "      <td>101.8</td>\n",
1205 |        "      <td>104.4</td>\n",
1206 |        "      <td>100.5</td>\n",
1207 |        "    </tr>\n",
1208 |        "    <tr>\n",
1209 |        "      <td>15</td>\n",
1210 |        "      <td>2013–14</td>\n",
1211 |        "      <td>108.0</td>\n",
1212 |        "      <td>120.4</td>\n",
1213 |        "      <td>110.3</td>\n",
1214 |        "    </tr>\n",
1215 |        "    <tr>\n",
1216 |        "      <td>16</td>\n",
1217 |        "      <td>2014–15</td>\n",
1218 |        "      <td>113.2</td>\n",
1219 |        "      <td>140.0</td>\n",
1220 |        "      <td>118.1</td>\n",
1221 |        "    </tr>\n",
1222 |        "    <tr>\n",
1223 |        "      <td>17</td>\n",
1224 |        "      <td>2015–16</td>\n",
1225 |        "      <td>118.4</td>\n",
1226 |        "      <td>157.3</td>\n",
1227 |        "      <td>131.2</td>\n",
1228 |        "    </tr>\n",
1229 |        "    <tr>\n",
1230 |        "      <td>18</td>\n",
1231 |        "      <td>2016–17</td>\n",
1232 |        "      <td>123.2</td>\n",
1233 |        "      <td>175.4</td>\n",
1234 |        "      <td>148.9</td>\n",
1235 |        "    </tr>\n",
1236 |        "    <tr>\n",
1237 |        "      <td>19</td>\n",
1238 |        "      <td>2017–18</td>\n",
1239 |        "      <td>126.6</td>\n",
1240 |        "      <td>178.8</td>\n",
1241 |        "      <td>162.1</td>\n",
1242 |        "    </tr>\n",
1243 |        "    <tr>\n",
1244 |        "      <td>20</td>\n",
1245 |        "      <td>2018–19</td>\n",
1246 |        "      <td>126.1</td>\n",
1247 |        "      <td>163.5</td>\n",
1248 |        "      <td>149.7</td>\n",
1249 |        "    </tr>\n",
1250 |        "  </tbody>\n",
1251 |        "</table>\n",
1252 |        "</div>"
1253 |       ],
1254 |       "text/plain": [
1255 |        "   Financial_Year Brisbane_HPI Sydney_HPI Melbourne_HPI\n",
1256 |        "4         2002–03         52.6       78.2          54.1\n",
1257 |        "5         2003–04         69.7       87.5          60.1\n",
1258 |        "6         2004–05         72.6       84.1          61.2\n",
1259 |        "7         2005–06         75.4       81.6          63.9\n",
1260 |        "8         2006–07         83.1       83.6          70.4\n",
1261 |        "9         2007–08         98.8       89.1          84.1\n",
1262 |        "10        2008–09         97.4       85.8          83.5\n",
1263 |        "11        2009–10        105.7       97.8         100.2\n",
1264 |        "12        2010–11        104.6      102.2         104.8\n",
1265 |        "13        2011–12        100.0      100.0         100.0\n",
1266 |        "14        2012–13        101.8      104.4         100.5\n",
1267 |        "15        2013–14        108.0      120.4         110.3\n",
1268 |        "16        2014–15        113.2      140.0         118.1\n",
1269 |        "17        2015–16        118.4      157.3         131.2\n",
1270 |        "18        2016–17        123.2      175.4         148.9\n",
1271 |        "19        2017–18        126.6      178.8         162.1\n",
1272 |        "20        2018–19        126.1      163.5         149.7"
1273 |       ]
1274 |      },
1275 |      "execution_count": 6,
1276 |      "metadata": {},
1277 |      "output_type": "execute_result"
1278 |     }
1279 |    ],
1280 |    "source": [
1281 |     "#Renaming the columns\n",
1282 |     "data1 = data1.rename(columns={'House Price Index (a)(b): Brisbane, Sydney and Melbourne, 2002–03 to 2018–19' : 'Financial_Year', \"Unnamed: 1\": \"Brisbane_HPI\", \"Unnamed: 3\" :\"Sydney_HPI\", \"Unnamed: 5\":\"Melbourne_HPI\"})\n",
1283 |     "data1"
1284 |    ]
1285 |   },
1286 |   {
1287 |    "cell_type": "code",
1288 |    "execution_count": 7,
1289 |    "metadata": {
1290 |     "collapsed": true
1291 |    },
1292 |    "outputs": [
1293 |     {
1294 |      "name": "stdout",
1295 |      "output_type": "stream",
1296 |      "text": [
1297 |       "<class 'pandas.core.frame.DataFrame'>\n",
1298 |       "RangeIndex: 17 entries, 4 to 20\n",
1299 |       "Data columns (total 4 columns):\n",
1300 |       "Financial_Year    17 non-null object\n",
1301 |       "Brisbane_HPI      17 non-null object\n",
1302 |       "Sydney_HPI        17 non-null object\n",
1303 |       "Melbourne_HPI     17 non-null object\n",
1304 |       "dtypes: object(4)\n",
1305 |       "memory usage: 676.0+ bytes\n"
1306 |      ]
1307 |     }
1308 |    ],
1309 |    "source": [
1310 |     "#viewing pre-processed dataset\n",
1311 |     "data1.info()"
1312 |    ]
1313 |   },
1314 |   {
1315 |    "cell_type": "code",
1316 |    "execution_count": 8,
1317 |    "metadata": {
1318 |     "collapsed": true
1319 |    },
1320 |    "outputs": [
1321 |     {
1322 |      "name": "stdout",
1323 |      "output_type": "stream",
1324 |      "text": [
1325 |       "<class 'pandas.core.frame.DataFrame'>\n",
1326 |       "RangeIndex: 17 entries, 4 to 20\n",
1327 |       "Data columns (total 4 columns):\n",
1328 |       "Financial_Year    17 non-null object\n",
1329 |       "Brisbane_HPI      17 non-null float64\n",
1330 |       "Sydney_HPI        17 non-null float64\n",
1331 |       "Melbourne_HPI     17 non-null float64\n",
1332 |       "dtypes: float64(3), object(1)\n",
1333 |       "memory usage: 676.0+ bytes\n"
1334 |      ]
1335 |     }
1336 |    ],
1337 |    "source": [
1338 |     "#converting datatypes of Brisbane_HPI, Sydney_HPI, Melbourne_HPI to float (for easy plotting)\n",
1339 |     "\n",
1340 |     "data1['Brisbane_HPI'] = data1.Brisbane_HPI.astype(float)\n",
1341 |     "data1['Sydney_HPI'] = data1.Sydney_HPI.astype(float)\n",
1342 |     "data1['Melbourne_HPI']= data1.Melbourne_HPI.astype(float)\n",
1343 |     "data1.info()"
1344 |    ]
1345 |   },
1346 |   {
1347 |    "cell_type": "markdown",
1348 |    "metadata": {},
1349 |    "source": [
1350 |     "VISUALISATION"
1351 |    ]
1352 |   },
1353 |   {
1354 |    "cell_type": "code",
1355 |    "execution_count": 9,
1356 |    "metadata": {
1357 |     "collapsed": true
1358 |    },
1359 |    "outputs": [
1360 |     {
1361 |      "data": {
1362 |       "image/png": 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9TsBosXj//fcPmc1FX7p//vnnoJkzZ9Ywm806ICDA9vXXXx8E8Pf3t+/YscO/ZcuWNYKCgmyLFi06APDCCy8ktG/fvnnt2rVzmzdvnnXmzBmvosofMGBA+o4dO/zatWvXDIwWnK+//vpg7dq1S7KgbJE8uraOUioC+FFr3crx+mdgjtZ6nlLqHqCv1vrfSqkPgdVa628dx+0BummtjxdVflRUlJbROkKIcm1qJGS6WNsssBqMKZsVCZRSMVrrqDKpvAKJjY091Lp1a4/durjSxcbGhrVu3TrC1b7S7nPyDPCzUmoaxi2lTo7ttTl31dl4x7bzkhOl1FAcK8XWq1fPo8EKIcQlc5WYFLVdCFHqM8QOA0ZqresCI4H/Ora76s3ssklHa/2R1jpKax0VHi5zCAkhhBCXm9JuOXkQeNrx/Dsgf9n5eKCu03F1ALdO6CKEEKUm/TjsXwlxK8s6EiEqpNJOThKArsBqoDuQf8P1B+BJpdRcjI6wacX1NxFCiHLDaoEjf0DcCti/Ck44BilWql62cQlRQXksOVFKfYsxEidMKRUPvAw8CryjlDIDOTj6jgBLgFuAOCALGOKpuIQQwi1OHzBaRuJWwMG1kJcJJm+o1xF6ToDGPaB6K5hQpawjFaLC8VhyorW+p5BdbV0cq4EnPBWLEEKUSGEjbPyC4apBxi2b0weMbVXqw7/ugUY9oMH14Bt07jmB1VyX5VvZ/XELcZmQGWKFEKKgwkbS5KTB1q8h4nroMMxoHQlpCKqIGcoLDhe2WuCzW+HETuNRvYX74haXneeff77GwoULQ00mkzaZTMyePftw9+7dM4s7b+bMmaGbNm0K/OKLL46URpzuJsmJEEKUxPOHwOx78eebfWHQl/BhF5h3Lzz6K/jLrZ8K740Grck+ff411T/EyvMHYy+myBUrVgT+/PPPVbZv377T399fHz9+3GyxWC5prZ6KorSHEgshRMV2KYlJvso1YdAXkHoEFg0Fu8slUkRF4ioxKWr7BTh27Jh3SEiI1d/fXwPUrFnTGhsb69erV69G+cdER0dXvvHGGxsBvPPOO6ERERGt2rVr13TDhg2V8o8ZOHBgxODBg+u2adOmWZ06da7Kn8IeYNy4cdVbtWrVvEmTJi1GjhxZC+Dpp5+uNWnSpGr5xzz11FO1X3311bOvS4O0nAghRFmofy3cPAWWjIbfpsANY8s6IlGUxU/UJWlnwEWd+9ENTV1ur9Yii37vFbqgYL9+/dJff/31WhEREa06d+6cfs8995zu27dvxjPPPFMvISHBXKtWLeucOXNCBw8enHz48GHvKVOm1IqJidkVEhJi69SpU9NWrVpl5Zd14sQJ702bNu3eunWrX//+/RsPGTIkZdGiRZXj4uL8tm3btktrTc+ePRsvXbq00vDhw5P79+/faNy4cUk2m43FixdX/euvv3Zd1Hu/SNJyIoQQzhK3l15d7R6B1v+G396A3UtKr15RIQQHB9v//vvvnbNmzTocHh5uffDBBxvNmjUrdNCgQac+/vjjkOTkZK/NmzdXuvPOO9PWrFkT2LFjx4xatWpZ/fz89IABA85Z0+e2225L9fLyom3btjmnTp3yBli2bFnlNWvWVG7RokWLli1btti/f7/f7t27/Zo2bZpbpUoV6/r16/2jo6Mrt2zZMqtGjRq20nzv0nIihBD5rLmweBjGpNUuJqkOdHPLtlLQ5y1I2gHRj8GjqyAs0r11CPcoooUDgFeCzxuJetbQX/dcbLVms5k+ffpk9OnTJ+Pqq6/O/vLLL0PnzJlz+NZbb23s5+en+/btm+Lt7Q2AKqJjtp+f39kvdP6aelprnnnmmeNjxow5b/2gIUOGJH/yySdhSUlJ3kOGDDl1sfFfLGk5EUKIfGunGy0nd38Nr6Sd//DEQn3e/nDXV+DlDXPvBUuG++sQFVJsbKzv9u3bz3Zy2rJli3+dOnVyIyIi8qpXr543ffr0mo8++mgyQJcuXTL/+OOPoMTERC+LxaKio6OrFl6yoXfv3ulffvllWFpamgng4MGD3seOHTMD3H///am//vprcGxsbODAgQPTPPUeCyMtJ0IIAXA8FtZOg6vvgma3lm7dVerBHZ/Cl/2MlptBXxY9PFmUP/4h1kJH61yk9PR0rxEjRtRLT0/38vLy0hEREZbPP//8MMDdd9996r333jO3bds2B6B+/fp5zz//fELHjh2bh4eH51199dVZNputyC/RgAED0nfs2OHXrl27ZgABAQH2r7/++mDt2rWtfn5+ulOnTulVqlSxmc2lnyqo/OadiigqKkpv2rSprMMQQlR01lz4qBtknYLhv0NASNnEseFd+OU/0GM8XD/KY9UopWK01lEeq+AyERsbe6h169bn3fIoDx544IF6bdq0yRo5cqRH4rPZbLRs2bLFd999t/+qq66yeKKO2NjYsNatW0e42ie3dYQQYs2bRr+Pvu+UXWICcO2T0GogrJxkTIsvhAstW7ZsvnPnTv/HH3/cI31BYmJi/OrXr3/V9ddfn+6pxKQ4cltHCHFlO7YZ1r5ljJppenPZxqIU3PYuJO2GBQ/D0NUQ0qBsYxLlzo4dOzw6rLdt27Y58fHxpThs7XzSciKEuHJZLbB4OFSqBje/XtbRGHwC4e6vAA3z7ofcrGJPEeJyI8mJEOLKtXoKnNxltFaUpynkQxrCwDlw4m/43wiowH0DhbgYkpwIIa5M8TGwfga0uR8ie5V1NOeL7AndX4Lt38Efs8s6GiFKlSQnQogrT14OLH4cgmrCTZPLOprCdR4FzfrAL+Pg4JqyjkaIUiPJiRDiyvPrZEjea9zO8Qsu62gKZzJBv/chtBF8NwTS4ss6IlGMHck7/G5ZdEuLHck7/NxRnlKqbb9+/c72is7Ly6Nq1aqtb7jhhsZFnffjjz8G5R/z7LPP1ho/fnx1d8RTWiQ5EUJcWY5uhN9nQdvB0LhHWUdTPL/KcPc3RufdefcZrT6iXDqTe8b05KonI+Mz4v2fWvVU5JncM5d8jfX397fv2bPH/8yZMwqMVYirV6+ed+nRXjqr9aLnlyuWJCdCiCtHXrYxA2vlOnDjq2UdzYULi4QBH0LCFphcHV4JPvcxVdbjKQ/GrBkTkWZJ89ZoUi2p3s+tea6+O8rt0aNH2nfffVcF4Ntvvw0ZOHDg2UX90tPTTXfeeWdEq1atmjdv3rzFV1995bJn97Zt2wI6duzYpH79+q2mT58eBmC323nsscfqREZGtmzSpEmLjz/+uCqc2+oCxoRvM2fODAWoXbv2VaNHj67Ztm3bpnPmzKnavn37psOGDat91VVXNY+IiGi1bNmySmAkLo899lidVq1aNW/SpEmLqVOnhpXkPcs8J0KIK8eqV+FUHDzwPfgGlWrVcSlxjF4zmmldptG4apEt8q4VNaV+ZtLFByYuyLj14+rGpcQFFLb/dM5p8/HM437asWBknj1PrTu2LuSmBTcFhPi5nsK+cdXGWZOum1T0goLA/ffff/rll1+uedddd6Xu2rUr4OGHHz61YcOGSgBjx46tecMNN6R/9913h5KTk72ioqKa33bbbekFy9i1a5d/TEzMroyMDK82bdq0GDhwYNrq1asDt2/f7r9r164dx48fN7dv3775jTfeeKa4ePz8/OwxMTF7AD755JNqVqtVbd++fde8efOCJ06cWOvmm2/eO2PGjLDg4GDb33//vSs7O1u1a9euWd++fdObNWuWW1z5IC0nQogrxeHf4ff3IOphaNitVKvOysti+MrhHEg9wBMrnyArT+YuudycyDrhqwusZK3RnMg64VvIKResQ4cO2fHx8b4ff/xxSM+ePc9ZhG/16tWV33777ZrNmjVr0blz56YWi0XFxcX5FCyjd+/eqZUqVdI1a9a0Xnvttelr164NXLt2bdCgQYNOm81m6tata+3QocOZdevWFZqA5XvggQdSnF/feeedKQCdOnXKjI+P9wFYsWJF5fnz54c2a9asRZs2bZqnpKSYd+7cecH9cKTlRAhx+cvNgu+HQ5W60GtiqVc/fsN4TuecRqM5lX2Klze8zNSuU0s9DnHximvh+HLnl6HvbH6nnsVmOftHv6+Xr/2Zts8cua/5fZc8zfzNN9+c+vLLL9f95Zdf9iQlJZ29dmutWbBgQVzr1q3PmWY+ISHB2/m1KrCQpFKKwtbW8/b21na7/exri8VyzslBQUF259d+fn4awGw2k7/YoNZaTZ8+/cjAgQPPa8W5ENJyIoS4/K2cCKcPwO2zwbdSqVYdvS+aNfFrsNiMa4fFbmH10dVE74su1TiEZ93f4v5T7Wq0S/M2eWsAb5O3bl+jfao7EhOAYcOGJY8aNSqhffv22c7bb7jhhvTp06dXz08m1q9f7+/q/KVLl1bJyspSiYmJXn/88UdQ586dM7t27ZqxYMGCEKvVSkJCgnnjxo2Vrr/++sxGjRpZ4uLi/LOzs9WpU6e81q1bV7mk8fbq1Svt/fffD89PbLZt2+abnp5+wTmHtJwIIS5vh9bBn+9D+6HQ4PpSr37G5hlkW8+5npBjy2HG5hn0j+xf6vEIz5naZeqhvov7tkzOTvap4lsl780ubx52V9mNGjXKGzdu3Hmdi6ZMmZIwdOjQes2aNWuhtVZ16tSx/Prrr3EFj2vTpk1mjx49IhMSEnxGjx59PCIiIq9evXqpGzZsqNS8efOWSik9YcKE+Hr16lkB+vbtm9K8efOWDRo0yGnZsmWJ70OOHDky+dChQ75XXXVVc621CgkJyVuyZMn+Cz1fFdasc6mUUnOAPkCS1rqV0/angCcBK/CT1vo5x/YXgYcBGzBCa/1zcXVERUXpTZs2eSJ8IcTlwHIGPrjOeD5sg7FuTSmb/Mdk5u6Ze842Py8/Xur4Ev0a9ytZYVMjXXd+DawGY/ZdcDFKqRitdVTJKr/yxMbGHmrdunVySc7ZkbzDb8yaMQ2ndpl6oGVYSxn3XYTY2Niw1q1bR7ja58mWk8+AWcAX+RuUUjcAtwNXa60tSqlqju0tgLuBlkAtYIVSqonW2ubB+IQQl7sVr0DKYRiypEwSk43HN7Jw30KCfYPJtmaTazMGKphNZrrW6VryAkuQgIiy0TKsZc6SAUt2lnUcFZ3H+pxordcApwtsHgZM0VpbHMfk/wlwOzBXa23RWh8E4oD2nopNCHEFOPAb/PUxdBwG9TuVevU7Tu1gxK8jqF+5Pgv6LiDULxSFItQvlFxbLkOXDyXNklZ8QUJcgUq7Q2wT4Hql1J9Kqd+UUu0c22sDzj2h4x3bzqOUGqqU2qSU2nTy5EkPhyuEqJAsGfDDkxDSCLqPK/XqD6UdYviK4QT7BPNBzw+oEViD2T1m07BKQz658RNmdp/J/tT9DF0+lPTcixrMIEqH3W63q+IPEyXl+Fzthe0v7eTEDFQFOgJjgPnKGN/k6ofvsjOM1vojrXWU1joqPDzcc5EKISqWqZH/zJj6eh1IPQKn98M7rUs1jMTMRIYuHwrAh70+pHqgsaRJ46qNWXz7YhpXbcx1ta/j7W5vszdlL8OWD+NMbrHzXomy8ffJkyeDJUFxL7vdrk6ePBkM/F3YMaU9WiceWKSNXrgblVJ2IMyxva7TcXWAhFKOTQhRkRU2S2opzp6ampPK48sfJz03nTk3zSEiOKLQY7vW7cq0rtMYvXo0w1cO54OeHxDgXez8V6IUWa3WRxITEz9JTExshUy94U524G+r1fpIYQeUdnKyGOgOrFZKNQF8gGTgB+AbpdRbGB1iI4GNpRybEEJctKy8LJ5Y+QRHM47yQa8PaBHaothzetTrwRtd3uC5Nc8xfOVwZveYLQlKOdK2bdsk4LayjuNK5LFMUCn1LfA70FQpFa+UehiYAzRUSv0NzAUe1IYdwHxgJ7AMeEJG6gghKoo8Wx4jV4/k71N/82bXN2lXo13xJzncGHEjr3V+jS1JWxixagQ5Vhl9KoTHWk601vcUsuu+Qo6fDEz2VDxCCOEJNruNsevGsiFhAxM7TaRHvR4lLuOWhrdg0zZeWvcST//6NDO7z8TX65KXZBGiwpJ7aEKIii/tWJlUq7Xm9Y2vs+zQMka2HXlJM772bdSXCZ0msCFhAyN/HXl2ThQhrkSSnAghKpbmTrQAACAASURBVDa7HRY/Xvj+wGoeq3p27Gzm7ZnHkJZDeKjVQ5dcXv/I/oy/djxrj61l1G+jyLPluSFKISoeWVtHCFGx/fk+HFwDfWdC2wdLrdqvd33NB7Ef0L9xf0a2Hem2cu9scidWu5XX/nyN59Y8x5td38Tb5F38iUJcRqTlRAhRcZ3YYUxR3/RWuOaBUqv2pwM/MWXjFLrX7c74a8eftxz9pbqn2T081+45VhxZwdi1Y7HarW4tX4jyTlpOhBAVU14OLHwU/KrAbTPBzQlCYdbGr+U/6/5DVPUo3uz6JmaTZ36N3t/ifqx2K2/FvIWXyYvJ103Gy+TlkbqEKG8kORFCVEyrJkHSDvj3dxAYVipVbk3ayrOrnyWyaiTvdn/X4yNqhrQagtVuZeaWmZiVmYnXTcSkpMFbXP7kWy6EqHgOrIbfZ0G7R6DJjaVS5d6UvQxfOZzqgdV5v+f7VPKpVCr1Pnr1owxrPYzv93/PxN8nYtd24lLi6Pd9P+JS4kolBiFKm7ScCCEqluwUiB4GoZHQa5JHq4pLiWP0mtGMiRrDuPXj8Pfy58NeHxLqH+rRegsa1noYVruVj7d/DMCGhA0kZibyxMoniL49WmaVFZcdSU6EEBWH1vDjs8Z6OfesAB/PXZSz8rIYvnL42SQgwBzA570/p3Yllwume5RSiqfaPIXVbuXTHZ/ipbzQaE5ln+LlDS8ztevUUo9JCE+S2zpCiIpj+3ewYxF0exFqtfFoVeM3jOdUzik0Gpu20SK0BZFVIz1aZ1GUUkQER2BWZmyO1T0sdgurj64mel90mcUlhCdIciKEqBhSj8BPo6BuR+jsvnlFXIneF82a+DXnzNIaezK2zJOAdza/g1WfO6w4x5bDjM0zyigiITxDkhMhRPlntxn9TLSGAR+Ch4fUztg8g2xr9jnbykMS8Mw1z+Bv9j9nm5+Xn1sngROiPJDkRAhR/m14Fw6vg1vehKoRHq/uziZ3nretPCQB/SP706VOl7NDmH1NvnSr241+jfuVaVxCuJskJ0KI8u14LKx6FVrcDq0LW+zcfSw2CysOr8DX5IuPlw9QvpKAiZ0mEuIXgkIR6h/KhE4TyjokIdxOkhMhRPmVl23MAhsYBn1mlMossO9ufpf9aft5s+ubhPqFlrskIMA7gNk9ZtOwSkPe6/GeDCMWlyUZSiyEKL9WvALJe+D+aAgI8Xh1fyX+xRc7v2BQk0F0r9edekH1GL1mNNO6TCtXSUDjqo1ZfPvisg5DCI+R5EQIUT7FrYA/P4AOw6BRd49Xl5mXybj146gTVIdRUaMASQKEKCuSnAghyp+s07D4CQhvDj1fLpUqp/41lYQzCXze+/Ny1UoixJVIkhMhRPmiNfxvBGSfhvsWgLd/8edcojXxa1i4byEPtXqINtU8O7mbEKJ40iFWCFG+bP0Gdv0Puv8Halzl8epSclIYv348kVUjeeJfT3i8PiFE8aTlRAhRfpw+CEufg4jr4donPV6d1ppJf0wiLTeND3t9eHbosBCibEnLiRCifLBZIfpxUF7Q732PzwILsOTgEpYfXs4T/3qCpiFNPV6fEOLCeKzlRCk1B+gDJGmtWxXYNxqYCoRrrZOVUgp4B7gFyAIGa603eyo2IUQ5MTXSWGG4oI+7w5h9Hq36ROYJJv85mdbhrRnccrBH6xJClIwnW04+A24uuFEpVRfoBRxx2twbiHQ8hgLvezAuIUR54SoxKWq7m2itGb9hPFa7lcmdJ2M2yR1uIcoTjyUnWus1wGkXu94GngO007bbgS+04Q+gilKqpqdiE0Jc2ebvmc+GhA082/ZZ6leuX9bhCCEKKNU+J0qp24BjWuvYArtqA0edXsc7trkqY6hSapNSatPJkyc9FKkQ4nJ1JP0I02Omc23Na7mr6V1lHY4QwoVSS06UUgHAS8B4V7tdbNMutqG1/khrHaW1jgoPD3dniEKIy5zNbuOldS9hVmYmXjcRVQpr9QghSq40W04aAQ2AWKXUIaAOsFkpVQOjpaSu07F1gIRSjE0IUdq0y78/POqzHZ+x9eRWxnYcS43AGqVevxDiwpRacqK13q61rqa1jtBaR2AkJNdorROBH4AHlKEjkKa1Pl5asQkhykDst4XvC6zm9ur2nN7DrK2z6FW/F7c2uNXt5Qsh3MeTQ4m/BboBYUqpeOBlrfV/Czl8CcYw4jiMocRDPBWXEKIcyDoNv/wH6naAIcvA5Nm/k3JtuYxdN5Zgn2DGdRwnt3OEKOc8lpxore8pZn+E03MNyLzRQlwplo+HnDTo87bHExOA92PfZ2/KXt7t/i5V/ap6vD4hxKWRGWKFEKXr8O+w5Uu49gmo3tLj1W1N2sqcv+cwIHIA3ep283h9QohLJ8mJEKL0WHPhx5EQXA+6Pu/x6rLyshi7biw1AmowJmqMx+sTQriHTIsohCg9v8+Ck7vgnnngE+jx6t6KeYv4jHj+e9N/qeRTyeP1CSHcQ1pOhBClI+UQ/PYmNO8LTc9b2cLt1h9bz7w987i/xf20q9HO4/UJIdxHWk6EEJ6nNSwZY6w0fPMbHq0qLiWOZ1c/S5oljYbBDRlxzQiP1ieEcD9JToQQnrfze9j3C9z0OgS7XJnCLbLyshi+cjjHM41pkt664S18vXw9Vp8QwjPkto4QwrNy0mHZC1Djamg/1KNVjd8wnpNZxppbXsqLubvnerQ+IYRnSHIihPCsXydDRiL0mQFenmusjd4Xzeqjq7FqKwA2bWP10dVE74v2WJ1CCM+Q5EQI4TkJW2DjR9DuEajT1qNVvRXzFhab5ZxtObYcZmye4dF6hRDuJ8mJEMIz7Db43zMQGA49xnm0qpScFLyU13nb/bz8GNl2pEfrFkK4nyQnQgjP+OsTOL4Vbn4d/II9Vk1mXibDVgzjTN4Z2lVvd7YDrK/Jl251u9GvcT+P1S2E8AxJToQQ7pd+HFZOgkY9oOUAj1VjsVkYsWoEu0/vZnrX6czqMYsQvxAUilD/UCZ0muCxuoUQniPJiRDC/Za9APY8uHUaeGgFYKvdypjfxrAxcSOvdn6VrnW7EuAdwOwes2lYpSHv9XiPAO8Aj9RdUou3HOO6Kato8MJPXDdlFYu3HCvrkIQo12SeEyGEe+1bDjsXQ/f/QEhDj1Rh13Ze2fAKvx79lRfav0Cfhn3O7mtctTGLb1/skXovxuItx3hx0Xay82wAHEvN5sVF2wHo16bkc74s3nKMqT/vISE1m1pV/BlzU9OLKkeI8kySEyGE++RmwU+jIKwpdHraI1VorZm+aTrf7/+e4a2Hc2/zez1SjztorXltya6ziUm+7DwbLy7aTszhFPx9vPD39sLfx4sAHy/8vI1/87f5e3sR4GMmwMeLX/ck8dqSXeTk2YFLT3SEKK8kORFCuM+aqZB6GAb/BGYfj1TxyfZP+GLnF/y72b95vPXjHqnjUqRl5bEuLpnf9iaxZm8ySRkWl8dl59n4cVsC2Xm2s8nGxcjOszH15z2SnIjLiiQnQgj3SNoFG2bCv+6FiM4eqWLe7nnM3DKTPg378Hz751Ee6s9SEja7Zlt8Kmv2GgnJ1qOp2DVU9jNzfWQ46+OSSc3OO++82lX8Wf9CdwDsdk2O1UZWro3sXBvZec7PrWTn2snKtTJmwTaXMSSkZnv0PQpR2iQ5EUJcOrsdfhwJvkHQa5JHqlh6cCmT/5xM1zpdmXjdREzKs/35i+rbkZSew297T7JmXzJr950kNSsPpeDqOlV4snskXZuE0bpOFcxepvP6nAD4e3sx5qamZ1+bTMpx66boX8kzVuzjmItEpFYVfze9ayHKB0lOhBCXbuvXcOR3uG0WBIa6vfh1x9Yxdu1Yrql+DdO6TsPb5O32Opy56sT63MJt/BB7jITUHHYnZgAQHuRLj2bV6do0nM6NwwgJPP9WVn5C445OrGNualpsoiPE5UBprcs6hosWFRWlN23aVNZhCHFlyzwFs9pCeHOjr4nJvS0aW5K2MPSXoTQIbsB/b/ovQT5Bbi3fleumrHLZQgFwbcNQujQJp2uTcJrXDCr1W0vuGK2jlIrRWkd5KEQhLpm0nAghLs3ycWDJgD5vuT0x2XN6D0+sfILqgdV5v+f7Hk9MtNbEHE4pNDFRwLdDO3o0huL0a1NbOr+Ky54kJ0KIi3donXFLp/OzUK25W4s+mn6Ux5Y/hr/Zn496fUSov/tvF+VLycxl0ZZjzN14hH1JZ1CAqzZl6dshROnwWHKilJoD9AGStNatHNumAn2BXGA/MERrnerY9yLwMGADRmitf/ZUbEKISzA1EjKTzt227i3Y8hWM2eeWKpKyknh0+aPYtI05veZQq1Itt5TrTGvN7wdOMXfjUZb9nUiuzc6/6lbhzYFXo7Xmlf/tlL4dQpQRT7acfAbMAr5w2rYceFFrbVVKvQG8CDyvlGoB3A20BGoBK5RSTbTWNoQQ5UvBxKS47SWUZknjseWPkZKTwn9v+i8Nq7h3ltmTGRYWxMQz768jHDqVRWU/M//uUI+729elWY3KZ4/z9faSmViFKCMeS0601muUUhEFtv3i9PIP4A7H89uBuVprC3BQKRUHtAd+91R8QojyIy4ljtFrRvNqp1d5/a/XOZx+mPd7vk+rsFZuKd9u16yNS2buxiMs33kCq13TPiKEp3tG0rtVTfy8vc47R/p2CFF2yrLPyUPAPMfz2hjJSr54x7bzKKWGAkMB6tWr58n4hBClICsvi+Erh5OYmcjgnweTa8vl7RvepkPNDiUuq+BIlke7NCA928q8v45yLDWbkEAfhlwXwV3t6tG4WiUPvBshhDuUSXKilHoJsAJf529ycZjLMc5a64+Aj8AYSuyRAIUQrnlg6oHxG8ZzOuc0Go3FZqFVaCt61OtR4nJczU3yyg87AejcOIwXb2lGrxbV8TWf30oihChfikxOlFLPFrVfa/1WSStUSj2I0VG2h/5nkpV4oK7TYXWAhJKWLYTwsE3/dWtx0fui+S3+Nyy2f9afiUuNI3pfNP0j+5eorCnLdp+3wB5A9cq+fPVIyVthhBBlp7hJCYKKeZSIUupm4HngNq11ltOuH4C7lVK+SqkGQCSwsaTlCyE8KGELLHsRvApZ0C+wWomLnLZpGjnWnHO25dhymLF5xgWXsT0+jVHzY0lMy3G5Pynd9cJ7Qojyq8iWE631BAClVJjWOrkkBSulvgW6AWFKqXjgZYzROb7Acsesin9orR/XWu9QSs0HdmLc7nlCRuoIUY5kp8D8B40E5PG1EBByScVl5WUxa+ss0nPTz9vn5+XHyLYjizw/z2Zn2d+JfLbhEDGHUwjw8SLQx4vM3PN/bcjcJEJUPMXd1ukDfArkKaXswCCt9YYLKVhrfY+LzYW2CWutJwOTL6RsIUQp0hoWPwHpx2DIsktOTDYc28DEPyZy7Mwx7mp6FyezTrI+YT0WmwVfky/d6najX+N+Ls9NPmPh2z+P8NWfhzmRbqF+aADj+rTgzqg6rNqVJOvOCHGZKK5D7GvA9Vrr3UqpDsCbQFfPhyWEKDd+fw/2/AQ3vQ512110Mak5qUzdNJUf9v9AROUIPrv5M9pWb0tWXhb9vu9HYmYiof6hTOg04bxzt8en8emGg/wYe5xcm53rI8N4fcBVdGtSDZPJ6E/vzgX2hBBlq7jkxKq13g2gtf5TKeX5FbeEEOXHkT9hxcvQvC90HHZRRWitWXZoGVM2TiHdks6jVz3KY60fw9fLF4AA7wBm95jN6DWjmdZlGgHeAYBx62bp34l8tv4gm4+kEujjxd3t6/LAtRGFDgOWuUmEuDwUl5xUKzBi55zXFzNaRwhRQWQmw3eDIbgO3P4eXMTqu4mZibz6x6v8Fv8brUJb8VGvj2gacu5tFmNukiMkpD7Kg7uO8FhXM6lZeXztdOtmfJ8W3BFVh8p+3m56c0KI8qy45ORjzh2VU/C1EOJyZLfDoqGQdQoe/gX8gkt2urYzb888ZsTMQKMZEzWGe5vfi5fp3DlGXM1NMv77HQB0aRLOlAERdG0SfvbWjRDiynBBo3WEEFeYtdNh/0ro8zbU+leJTj2QeoCXN7zM1pNbubbmtYy/djx1guq4PPbNQuYmqRbkyxcPtb+o0IUQFV9xo3VmFrVfaz3CveEIIcrcgd9g9Wtw1SBoO+SCT8uz5fHJ35/w8baPCfAOYHLnyfRt2Bfl4nbQyQwL3248QkIhc5OczJC5SYS4khV3WyfG6fkEjLlKhBCXq4xEWPgIhDY2Wk2K6GeSv1jftC7TyLRm8sqGV4hLjaN3RG+ea/8cYf5h550TezSVzzcc4sdtxqgbX7MJi9V+3nEyN4kQV7bibut8nv9cKfWM82shxGXGZoUFD0PuGXjwB/AtfGE858X67lt6H5l5mVQPqM6s7rPoWvfc2QZyrXaW/n2czzYcYotj1M2/O9Tj/mvrsz0+TeYmEUKcpyQL/8kie0Jczla/BofXQf8PoVrzIg8dv2E8ydnJaDSZeZnUDarL/D7zqeTzT0JzMsPCN44J005mWGgQFsgrfVswsG0dghyjbhqFG8fL3CRCCGdlsiqxEKKc2bfc6AR7zQPQ+u4iD124dyErD6/Eqq1nt53MOsnyw8vpH9mf2KOpfLbhED9uSyDPpunaJJzBd0TQNdL1qBuZm0QIUZDSRSyBrpTK4J8WkwAgf7E+BWitdWXPhle0qKgovWnTprIMQYiKLy0ePugMlevAI8vBu/D+Hum56XSb1408e955+wLNVaiZNoUtR1Kp5GvmjrZ1eODa+jQML/z2kCgbSqkYrXVUWcchRGGK63Mic5oIcTmz5hoTrdmsMOjzIhOTA6kHePrXp8mzW9F2L5Tpn34i2u5N8pGe+HvnnXfrRgghSkpu6whxJVvxCsT/BXd+BqGNCj1s9dHVvLD2BXy9fAk49SRp5t8wB+1EmaxouxlrRnOCrZ1Y+XxXmTBNCHHJTGUdgBCijOz6H/zxHrR/DFr2d3mIXdv5MPZDRqwaQb2geky7bg4nkmqTc/wOtLUSWoO2ViLn+B2czsyVxEQI4RaSnAiBMY36dVNW0eCFn7huyioWbzl2SeXFpcTR7/t+xKXEuSlCNzt9ABYPh1rXwI2TXB6SlZfFqNWjmLV1Ft1q30S9nDHcPXuPsVP7kH10CHZLdbKPDgHtI3OTCCHcRm7riCueq/VdXly0HeCiRpHMj9nPq1sfwu6VyoBFD/Gff33KoLaF3zIpdXk5MP9BUCbjdo7Z97xDjqYfZcSvIziQeoCWfvey9NerUCRzf8f6NAwL4PWle8jOrU7WwZGAzE0ihHAvSU7EFe/Nn89f3yU7z8YLC7fx58HThFfyISzIl7BKxiM8yJewSj5U8jWfNzX74i3HmPD7eFRgBkpp7KYMJvz+Mj6mN8p2uOzUSMhMOn/7Jz1hzL5zNm1I2MDo1WPIsdrIOfYQMZmRDIqqy5PdG1Mz2GgdqezvI3OTCCE8RpITcUU7lJxJQqrr9V1yrHaW70zkVGYurkbc+5pNRsIS5Et4JR/Cg3z5cX80KszoKAoY/wbuZPKaz+nXZqwn30rRXCUmBbZrrZm95b98uH0mdkt1co49wMCrWvNk98bUDQk45zSZm0QI4UmSnIgrktaar/44zGtLdhuT9rg4pnYVf9a/0B2rzc7prFySM3JJPmNxeuRyMsN4fijtKJvS1qPDl6PUuaUpUx5Zlb4Hyig5sVmLPeRERjqPLnmBgzlrsaa3ole1p3l2wFVEhAWWQoBCCHEuSU7EFed4WjbPLdjG2n3JdGkSTvdm4byxdE+h67uYvUxUC/KjWpDfOeVk5WWx/PByFsct5sSJTSgUDSxWErwVuSanvuZaYzJn0fObu7iv5SDuadUHX6/z+3m4ldaQuB22zYPt3xV56MSl65l/dBL4JNDQfAfTBz1LZHWZ4kgIUXaKnCG2vJMZYkVJaK2J3nKMl3/Ygc2uGXtLc+7tUA+lFIu3HLugPhRaazYnbWZx3GJ+OfQLWdYs6gXVo1/jfvRt1JcaU5syOjyU1QH+WEwmfO12OmXncLUll7crNcTkcxqTDiAqtCdPt7+fq6s3c++bTDsG2+dD7Dw4uQtM3tDkJtj9o8vDN/n58mB4Q7zNNsa0mci9V/d2bzyiXJIZYkV5J8mJuCKcOmNhbPR2ft5xgqj6VZk+qDX1Q8+9ZRGXEsfoNaOZ1mUajas2PmdfYmYiP+z/ge/jvudIxhECzAHcFHET/Rr3o021Nv90jH0lmCyl6Fe7JolmL2pabUQfO06A1iQ8c5x3f1/GL0e+x+ITizLZqGKKpF+jATwe1Z9An4u8hZKTDrt+MFpJDq4FNNTtAFffZcxfEhACrwQb79Hbm9HVQpmadIpNfr68GVqV8MB6fHDjLBoGN7y4+kWFI8mJKO8kORGXvZ93JDJ20XYycqyMurEJj1zfEK8Ck4Vl5WXR7/t+JGYmUjOwJtG3R+Nl8mLVkVUsjlvM7wm/o9FEVY+iX+N+9KrfiwBvp06iKYdh23z49VXgnyRgWtIpGuc51qFp1geqt0JXb8GGnKq8s3s9OzNXoHySUHY/mgV15Ymoe+ka0eac2FwmTbY82P8rbJsLu5eANRuqNjAW7bt6EDlB9Tl0KpODJzM5kJzJoNXdCTSln02a/LUmy2SiY1Yebz38F0E+chvnSiLJiSjvPJacKKXmAH2AJK11K8e2EGAeEAEcAgZprVOU8WfnO8AtGIsLDtZaby6uDklORFHSsvOY8L8dLNp8jJa1KvPWoH/RtIbri/Do30az+uhqLDYL3iZvagTWINWSSkZuBjUDa3J749u5rdFt1A2q+89J2SmwY7HRYnHk9+IDCo2E0/tB243X3oFYqzVjmTmcL/LS2e19HG2y4a/r06vubbywZSpemSfPb4Ux+4F3IGQlY/OryvG6t7Clyk1szG3IwVNZHEzO5Fhq9nnV+9X65uyU8wC2nHCyD47k4JS+Jf5sRcUmyYko7zyZnHQBzgBfOCUnbwKntdZTlFIvAFW11s8rpW4BnsJITjoA72itOxRXhyQnojDr9iUzZkEsSRkWnujWiCe7R+Jjdj0hcvS+aF7f+DrZ1nMv6FeHXc1T1zxF+xrtMSnHudZc2PeLkZDsXQa2XAhrYtxCuXoQzLiq8KBeSYPcLKMvyIkdkPi38e+J7ZCTRppJ8VNgIHMrV+Ggjwl/u50Qm40kLy/yTCa87Xba5OTycHo6m8zNWW1txE5dE5uyoUy5+PrYqBygCfK34+djw8fbitlsBVMu2xMPo71ScZ6WRdu98U+/g7+eLsMhzqJMSHIiyjuPjdbRWq9RSkUU2Hw70M3x/HNgNfC8Y/sX2siU/lBKVVFK1dRaH/dUfKJsXGjH04uVlWtlytLdfPH7YRqFB7JoWCda161S5DkzNs84LzEBiD8TT8eaHY2RL0c3Quxc2LHIaDEJDIeoh6H1XVDzX5y96gdWcz2nSGA141+fAKjd1njk0xrSjxF8Ygf/PvE3dx/fTkxiLO/5nCHGz/ds2XkmExsD/NgY4AecAk7hDeSv/eulvLCb/bGY/TCZ/fHy8sfbyw9/sz9e3mewFfg7RJny8ApdSpkNcRZCiEKU9lDi6vkJh9b6uFLK8Rub2sBRp+PiHdvOS06UUkOBoQD16tXzbLTCrdw9TXxBMYdTGDV/K4dPZ/Fw5waMuakpft5exZ43IPkEnwSacW5W8LPbGXniOPz6utFKknIQzP7Q7FajlaRRd/By8d+nwGyrF0QpCK5jPJrchAloBxyc0+KcmPJVttn45PaF+Jv98Tf742f2I8AcgNl0/oy1+aL3RTPpj9fIs/8z4Zy38uWFjqNKHq8QQnhYeZnnxNVvVJf3m7TWHwEfgXFbx5NBCfea+vMel9PEj1kQy/KdJ6gZ7EetKv7UqmL8WzPYn7BKPoVecJ1bYQJ9zZyxWKlT1Z9vHunItY1CLyim9Nx0lvgq/LTGrjW5juG/3bKy6Xf6FPz2BjToAl3GQPO+4Ff5kj+HC/XM6VReD61KttOcKX52O2NOp9I8tHmJyuof2Z/1CevP9qvxNflyQ70b6Ne4n7vDFkKIS1baycmJ/Ns1SqmaQH77dzzg1NOQOkBCKccmPCzBRSdNgDybZtfxdFbuPkFOnv2cfT5mk5G0BPufk7gcTD7D5xsOY7Eax5+xWPEyKZ68ofEFJyZaayb9PokksxcfJibxn7BQEpUi1GZnQvJp46CROyC4bKZp738mk/X+fufMmdItK5t+ZzIvqryJnSaeHZEU6h/KhE4T3ByxEEK4R2knJz8ADwJTHP9+77T9SaXUXIwOsWnS3+TykpKZi0kpbC46YNeu4s+q0d3QWpOSlUdCajYJqdkcT8sxnjv+/X1/MonpOdgLaS+z2TXvrorj7vYXdrsvOi6aZYeW8XRKGu1zLMw+cfLs8N+A/DjLKDEBILAaE5Mdo3Wck6b8/islFOAdwOwes88OSz5nKLQQQpQjHktOlFLfYnR+DVNKxQMvYyQl85VSDwNHgDsdhy/BGKkThzGUeIin4hKlz2bXPD1vK6DxNZvOtnbAudPEK6UICfQhJNCHVrWDXZZltdlJyrBw3ZRVLu/7FdY6U9CB1ANM2TiFjtWu4aGDiwFonJfH4mOJJXpvHjVmHwHAbKd5TgIKTA5XUo2rNmbx7YvdE58QQniIJ0fr3FPIrh4ujtXAE56KRZStd1ftY83ek7zW/yoCfLwuabSO2cvkuL3j73Iuj1pV/Istw2Kz8Nya5/Az+fDagR24HmBcfkhCIYS40pSXDrHiMrV6TxLvrNzHwGvqcE/7uiil3DIyZ8xNTc8Z+QPntsIU5e2Yt9mTsof3ssyEpyWBX1XISTn/wIu8fSKEEOLSSHIiPCY+JYtn5m2lafUgXu3XqtBRNxcjP8EpaSvM6qOr+XrX19xnMdEl5STctxDqdXRbXEIIIS6dJCfCIyxWG8O/3ozNCLDoFAAAIABJREFUpvngvrb4+xQ/30hJ9WtTu0StMCcyTzBu3Us0s8LI5FNw/yKo297tcQkhhLg0kpwIj5jwv51si0/jo/vbEhF2kavtupHNbmPs6mexWNJ581Q6Pg8shjoye7cQQpRH5b0voKiAFsTE882fR3i8ayNubFmjrMMBYM5fb7ExeRtj07JocO8iSUyEEKIck5YT4VY7E9J5KXo7HRuGMPrGJmUdDgBb45by3q7P6Z1j5fY7voPa15R1SEIIIYogyYlwm7TsPIZ9HUOwvzfv3nMNZq+yb5hLP7GN59eMoYbWjLvlU5QkJkIIUe5JciLcQmvN6O9iOZaSzdyhHQkP8i3rkNDJcUz6/h7+3959x0dVpv0f/1wppBBa6L0q4tJFRfBBFFbFtYCLq7v7rKIsKhZc29pWV9TH/rj29tj7zwLYFiwoNhakN6lKh0BIAkISQsr1++M+gSEkIWcYMieZ6/16nVdOzsx8556TSebKfe5zn611hFf730O9didEu0nGGGOqwIoTExHPffsLX/y0hdvPPJp+HdLLv9NDR0Du1gO3120W3tV8K7NtJRPfOZsp9RK45ogL6HnUiMjmG2OMOWyi3+9uarz//JzFg1OW8bseLblkYIeK71heYVLZ9nBlruCX18/k/rR4jm/cnUtOuCWy+cYYYw4r6zkJV3X2AgTYll93c/Xbc+nQpC4PjOwZ0YnWwrJ1GQWvnsXfG9UhuU597jvlceLEanBjjKlJ7K92uKqrFyDACotLuPLNueTtKea5/z6GtKRyal1VWPkFvHpW5WFv/B6mPwlbfnKPCceWn+DVM/lXWiLLE+O4Z9D9NE1tGl6WMcaYqLGeExO2+ycvY/baHB67oDdHNK+3/41FBbDoPVdwZC6Feq0qD9u+Dj6/za2ntYDOJ0PnU6DTYEirwjVutiyBV89iWkoSb6bG89/d/ptBbQaF87KMMcZEmRUnh8PM56DLUEjvBNE+zHGYfLpwMy9+v5pRAzpwTu+QKeTzsmHOy24f7NoCzXvAiOfhNyPgnkp6Ma6aBTs2wM9fw89fwYrPYMHb7rbmPaDzYFestDsBElPKPay2JT6e25u24qj0o7j2mGsj/6KNMcZUCytOwlFSUvntk//uvjZs74qULkOh439BUr3KH1dDrNq6i7+/v4A+7Rpy6xnd3MacNTDjGZj7OhTmQuchMOI51/NRWqDVbVbxOB2ABm2g71/cUlICGQv2FSszn4PpT0BCsitQyuQUA7c2bUwByoODHqROfJ3D9OqNMcYcblac+JWXDRMvq/w+4+bBqqluWfAOzH4R4hLd1W+7DHHFSvPuNbJXJbegiLFvzCEpMZ6n/9yXOhnzYPrjsPQjkHjocR6ccCW06H7gg72BwqtyVnHDtzfw8KCH6dKoS/lPFBcHrfq45b+ugz25sHb6vmLFsyoxkRuaNaZ//m5+TEnmrswsOjboeDheujHGmGoiGu7gwwDo16+fzp49u/qecOMceHcU7MqA+CTYs/PA+5Q9W6eoANbPhFVfwqqvYMsitz2tuetd6DIEJt8EedsOnhUNFZyVVJRYn4SW3WHddEhqAP0uhuMvg/qVjy3JK8xj+IfDycjNoGXdlkw8ZyKpian+23VnA/JEGN66JRkJ8Sjw29w8/jczC7lzh/88Y2KIiMxRVbvAlAks6zmpClWY9QJ8dqsrKi6ZAq2PqdpjE5Kg4yC3/PYu+HWz+8//56mwYjIseKvixwbhzJ8K2pBQ+CvsWA+n3ecOw1TxkNUd0+8gKz8LRdmWv41bv7+Vh096mIQ4/2/FO5qkkxUfh4qAKiUCNa8vyhhjTFlWnBxMwS74+BpY/D4ccaobR5FawQyoVVG/JfT5s1tKimHTPHhhSMX3n/OqO7TRrBvEJ4b/vOE4WK/auPkQX/W30MSVE/lq3VcUlhQCsKdkD1PXTaXP631Iik+ibmJdUhNSSU1MdeuJqaQmpO7dHrptWeN0vk5NYU+cdza8CD+kpDAxrS42F6wxxtRsVpxUZusyePdCyFoJp9wOJ17nxkJESlw8tDlIz+rH49zXhGRo0dMVKq37Qqu+0LjLge0JZ3I4Vfh1o3u9maXLcrdUxkdhAvDgrAf3FiahUhJSOL/r+eQV5pFblEtuYS75hfns2L2DzUWbyS3M3XtbiXqDkeunHZCzOy6OR9PTrTgxxpgazoqTiix8zxUGderCXyZBp5Oi046r57relU3zYONcmPcG/Picu61OPWjVO6Rg6VP55HAlJe5QzH4FiPd1z659901tAs26UdR9JAlzXozIy5idMZu8wjziiKOEfWc7Jccnc+vxtzK8y/CDZqgqBcUF5BbmMmnVJJ5Z8AwFxQX7ZV078LaItNcYY0z0WHFSVlGBG1sy6wV3yurIl92hmGhp3NktPUa670uKXTGxaR5smusKlpnPQvGeg2fd1xoK8/Z9n9YCmnaF3n92X5se5b7WbcKMX7K4deIivuLQi5NVOasY9/U42tVvR8cGHZm+aToFxQUkxSUxuO3gKhUmACJCckIyyQnJjO4xmqXZS5m2flpYWcYYY4IrKsWJiFwL/BVQYBFwMdASeAdIB+YCf1HVKnziRlDOWnjvIvfBP2AcDLmjesZ5HGz+j1Bx8dD8aLf0+bPbVrQHti5xhcqn11X8PMeM8oqQbtD0SEhpdMBdduQVct8HC3ln1nrapqewKzGdtMLsA+63O6kxyVV4aVvztjJ26ljqxNXhmaHPkJ6cvvdsncYpjRk/YHwVUsp314C7IpZljDEmOKr9VGIRaQ18Dxytqvki8i7wb+AMYIKqviMizwILVPWZyrIieirxis9gwqVu/MXwp6HbmZHJrW53NqjktopPsVVVPlm4mfEf/0RO3h7+emJHrhl6BKl1Epg0byMPfbacTdvzadUwhRtP68rwPq0rzCq1a88uLppyERt2buDl01/m6MZHA1Wc56SKIpllTKywU4lN0EWrOJkB9AJ+BSYBTwBvAi1UtUhETgDuVNXTKsuKSHFSXATT7oXv/hda9IA/vOamna+pwihONm7P5/ZJi/lq2VZ6tG7Afef2oHvrSnKqoLC4kLFTxzInYw5PDnmSga0HHlKeMSZyrDgxQVfth3VUdaOIPAysA/KBz4E5wHZVLfLutgEo919zEbkUuBSgXbt2/p68ojNZAPpeBMMecNdtqcl8HCIqLlFenb6Ghz9fjir843fdGDWgAwnxh3ZGkqpyx/Q7mLl5JncPvNsKE2OMMb5Ue3EiIo2Ac4COwHbgPWBYOXctt0tHVZ8HngfXc+LrySub1Ozsx31FBVYVZ5RdsmkHt0xYxMINOxjctSl3n9OdtulhzNRajsfmPsYnv3zCVb2vsgGqxhhjfIvGgNihwGpVzQQQkQnAAKChiCR4vSdtgE1RaFutl7+nmEenruCF71bTKDWRx//Yh7N6tkQidJ2fd5a9w4uLX2TkkSO5tOelEck0xhgTW6JRnKwD+otIKu6wzhBgNvA1MBJ3xs5FwIdRaFut9t3KTG6buJh12Xmc368tt5xxFA1TI3f13qnrpnLvzHsZ3GYwtx1/W8QKHmOMMbElGmNOZorI+7jThYuAebjDNJ8C74jIPd62yMz+FYPKnl0zdnBn5q7NYcK8jXRqUpe3x/TnhM6NI/qc87fO56Zvb6J7k+48MOiBsK6VY4wxxkCU5jlR1X8C/yyz+RfguCg0p1aZNG8jt0xYRH5hMeDOxPnHpMUIcPUpXbjy5C4kJ8ZH9DlX71jNVV9dRfPU5jw55MnwrjJsjDHGeCJ4oZgaoLxJzSrbXgM99NmyvYVJqKb1krj+1K4RL0y25W9j7JdjiZd4nh36LOnJh3BRRGOMMYZYm76+imey1CQFRcUs3vgrc9ZmM3tNDhu37y73fpk7C8rdfihyC3O54ssryN6dzUunvUTb+m0j/hzGGGNiT2wVJ7VATu4e5qzNYfbaHOaszWbBhh3sKXIX0mvfOJWUxPhye05aNYzs/C2FJYVcP+16VuSs4PFTHqd7k+4RzTfGGBO7rDgJiPKmiD+ndyvWZOUxa002c9bkMHttNj9n5gKQECd0b92AC/u3p1+HRvRt34hm9ZIPGHMCkJIYz42ndY1YW1WV8dPH88OmH7jzhDsZ1GZQxLKNMcaYap++PpIiem2dKCqvoIgTV1Tk7nHb6icncEz7RvTrkE6/9o3o1bZhheNHwr0WTlU9Oe9Jnlv4HJf3upwre18ZsVxjTPWw6etN0FnPSRTlFhQxd10Ot09afMChmBJ1y70jetCvQyO6NE0jLq5q84YM79M6osUI7LvA3qntT+W5hc8xossIruh1RUSfwxhjjAErTqpV1q4CZq3JYdaabGatyWbJpl8pLqm452p3YTF/Ot7n9YMOg7zCPK6YegUZuRk8s/0Z+rfsz+0n3G6TrBljjDksrDg5BAc7fLIhx40X+XG1K0hWbd0FQJ2EOHq3bcjYkzpzbMd0bv5gIZt3HHiWTaQHsYbrjul3sC1/G4oiCGmJaSTGJUa7WcYYY2opK07CVN5kZzd9sJDvV22jsLiEWauz2eQVHPWSEujXoRHn9m3NcR3S6dGmAUkJ+8aL3HT6UYd9EGu4JqycwNfrvqawpBAARfl+4/dMXDmREUeMiHLrjDHG1EY2IDZMA+//io3b88u9rWm9JI7rkM6xHRpxbMd0jmpRn/iDjBc53INYwzE7YzZjPh9DkRYdcFt6cjrfnP9NFFpljDlUNiDWBJ31nIRpUwWFiQA/3jrE93iMwzGINVzLs5fz2NzH+G7jd9RLqMfukt17e04AkuOTufaYa6PYQmOMMbVZbE1fH0EVjQdp1TClxg4U3bhrI7d8dwvnfXwe8zPn87e+f2Pq+VM5pd0pJMUnAZAUl8TgtoMZ3mV4lFtrjDGmtrLiJEznH9vmgG1BGSfiV/bubB748QHOnHgmX6z9glHdRzH53MmM7jGalIQU7hpwF+nJ6QhC45TGjB8wPtpNNsYYU4vZYZ0w5BYUMXHeJhqmJJBSJ4GMHbsDM07Ej9zCXF5b8hqvLHmF3cW7GdFlBJf3upwWdVvsd7/UxFSeHvI0N3x7Aw8PetiuOmyMMeawsuIkDHd/8hNrsnJ5e0x/+ndqHO3m+FZYXMi7K97l+YXPk707m6HthnJ136vp1KBThY/p0qgLk86ZVI2tNMYYE6usOPFpyuLNvDNrPWMHdw50YVI6o+vDgx6mS6MuAJRoCZNXT+bJeU+yYdcGjm1xLE/0fYKeTXtGubXGGGPMPlac+JCxYzc3T1hEj9YNuHbokdFuToVCZ3S9cuqVTDh7AvMy5/HY3MdYlr2Mro268szQZxjYamCNHbxrjDGm9rLipIpKSpTr35tPQWEJj17QmzoJwR1LfMf0O8jenY2iZOZncsaEM8guyKZ1Wmvu/6/7GdZxGHES3PYbY4yJbVacVNFLP6zmh1VZ3HduDzo3TYt2cyo0YeUEpq2fRkFxAQCFJYVkF2RzRsczuGfgPSTG27Tzxhhjgs2KkypYsmkHD05ZzqlHN+eCY9setucpb5zIweQV5rF422IWZC5gQeYCvt3wLcqBs/7O2DzDChNjjDE1ghUnB5G/p5hr3plPw9RE7v99z8M2RqPsOJGJ50w84JRdVWXdznWuENnqipGV21dSoiUAdGzQkd5Ne7M4a7HN6GqMMabGsuLkIO6bvJRVW3fx+ujjSK9b54Dbw+ntKE/oOJGs/Cz+Of2fjB8wfr9ekYWZC8kpyAGgbmJdejTpwZgeY+jVtBc9m/akQVIDAG745oa9h3ZsRldjjDE1TVQu/CciDYEXgO6AApcAy4H/B3QA1gB/UNWcynIO94X/pi7dwuhXZzP6xI7cfubRB9yeV5jH8A+Hk5GbQcu6Lcvt7TgYVeWtpW/xr7n/2jtOBECQ/Q7PdGzQkZ5NetKrWS96Ne1F5wadiY+LLy8yIu0yxtReduE/E3TR6jl5DJiiqiNFpA6QCtwKTFXV+0XkZuBm4KYotY/MnQX8/f2FHNWiXoVT0pfX2/HQSQ9RoiXsKNhBVn4WWbuzyMrPYlv+tr3re7/mZ5G9O7vcq/4qSnJ8Mo8MfmS/XpGqsBldjTHG1GTV3nMiIvWBBUAnDXlyEVkODFbVzSLSEpimqpVeqOZw9ZyoKpe8MovpP2fx8dUncmTzegfcZ+LKidz3433kF+27OrEgpCWkkV+cX27BkRCXQOPkxjROabz3a5OUJmzcuZGp66ayp2TP3vsmxydzW//b7HCMMSbirOfEBF00ek46AZnAyyLSC5gDXAM0V9XNAF6B0qy8B4vIpcClAO3atTssDXx9xlq+Xp7J+LN/U25hAvDQ7If2K0zA9Xbs0T2M6j6KJilNDihE6tepX+GAWhsnYowxxjjR6DnpB8wABqrqTBF5DPgVuFpVG4bcL0dVG1WWdTh6TlZs2clZT3zPgM6NeWnUsfsVE6rKzIyZPLvgWeZsmXPAYw+lt8PGiRhjqov1nJigi8Y0oRuADao60/v+faAvsMU7nIP3dWt1N6ygqJhxb88jLSmBB0f22luYqCrTN07noikXMebzMazfuZ5bjruFoe2GkhSfBHDIvR2l40Q6NezEU0OessLEGGNMzKr2wzqqmiEi60Wkq6ouB4YAP3nLRcD93tcPq7ttD01ZzrKMnbw0qh9N6yWhqny/8XueXfgsCzMX0jy1ObcdfxsjjhhBUnwSw7sM39vb0TilMeMHjD+k57cr/xpjjDHRO1vnauBN70ydX4CLcb0474rIaGAdcF51Nui7lZm88P1q/tK/PSd3bcY367/h2QXPsjhrMS3rtuT2/rczvMtw6sTvm+vEzooxxhhjIi8q85xESqTGnOTk7uG0R7+lXkoCNw4v4qUlz7M0eymt01ozpscYzu58tk39boypNWzMiQm6mLw07aqcVQz/cDirclahqtz0wXx2xM0lpf1j3Pjdtewq3MXdA+/m4xEf8/sjf2+FiTHGGFONYm76+tBr2Fwx9QqOrf9Hvs9/izqtMpC4Dtx74r0M6ziMhLiY2zXGGGNMIMTcJ3DorK6bczP4KPcRUpNacOeJ9zGs47AKp4Q3xhhjTPWIqeJk4sqJfLXuGwpLSq9ho2hJPFf0Hc2Znc+MatuMMcYY48TUmJMHZj5CYcnu/bZJXDHPL3oqSi0yxhhjTFkxVZwUZw1DS/Yf3KoliRRnDYtSi4wxxhhTVkwVJ9s296JoZze0xB3N0pIEinZ2Y9vmXlFumTHGGGNKxVRx0qphCrs3j0SL0lAFLUpj9+aRtGqYEu2mGWOMMcYTU8XJjad1JSUhhfz1F1NS0Jz89ReTkpDCjad1jXbTjDHGGOOJqbN1hvdpDcBDn9Vh0+pradXQFSal240xxhgTfTFVnIArUKwYMcYYY4Irpg7rGGOMMSb4rDgxxhhjTKBYcWKMMcaYQLHixBhjjDGBYsWJMcYYYwJFVDXabQibiGQCa8N8eBNgW4SaEgtZkc6zLMuyrOhltVfVphFqhzERV6OLk0MhIrNVtZ9lRSfPsizLsoKTZUzQ2GEdY4wxxgSKFSfGGGOMCZRYLk6et6yo5lmWZVlWcLKMCZSYHXNijDHGmGCK5Z4TY4wxxgSQFSfGGGOMCRQrTowxxhgTKFacRICINIt2G2oSEWkS7TYYY4wJrpgoTkTkKBGZLCKfikhnEXlFRLaLyI8i0s1nVnqZpTHwo4g0EpF0n1mnh6w3EJEXRWShiLwlIs39ZJWTXV9EjhGRRoeSc6hEZJiIrBaR70Wkj4gsAWaKyAYRGeIzK01E7hKRJSKyQ0QyRWSGiIw6hPY1F5G+XtsOaZ9XkJ8W5uMSy9nmu6gTkTgRifPW63iv1df7tJLssyOR42WF1SbvNUnI9yeLyPUiMiyMrJ7htOEgme1EpKG33kFERopI90PIa+q9V3uE+94ypkZQ1Vq/AN8CZwF/xE13fwEg3rapPrNKgNVllkLv6y8+s+aGrL8A3AO0B64FJvnMegNo4q2fBqwHvvRe73k+s7K99gzBO6PrEPb9fKAbcAKQBfT3tncLff1VzPoQGAW0Aa4DbgeOAF4F7vWZ1RuYASz19tOXwDJvW98IvvfW+bz/ycAGIBP4HOhQ3vulilnDgS3AZuAcYCbwlZd/ls+sc8ssvwcySr/3mfWPkPWjgRXe788a4HifWQuARt76jcB04B/AF8B9PrOKgVXA3cDREfjZ3+y9rmXAX72vLwJLgOt8Zh3tvUdXAXu8n+Vq4BWgQaTer7bYEpQl6g2olhcJ80LWV5W5ze8f/BuAKUCPkG2rw2xXaHEyv8xt831mLQpZn176oYa7/sYCn1nLgauAH4CNwGOlRcUhvsb1h/gaF5T5fpb3NQ5Y5jNrfnkfhED/MPbXdRUs1wPZPrNmAb/x1kcCK9lX0M3zmTUPaAF0BH4Funrb2wOzfWYVAZ8ALwEve8tO7+tLh/Ce+BQY5q0fB0z3mbU4ZH02kOKtJwALw9hf3YH/8YqABV6B0cFPTkjeEiAFaOztq6be9rqh7a5i1oyQn99xwKve+hjg/XDaZ4stQV5i4rAOEB+y/kiZ2+r4CVLVh3H/Bd0hIo+ISD0g3MlimonIdSJyPVA/tHsa/4fc4kSkvrdeAqzz2rsN94faj1xVfVJVB+J6PDYCT4vILyJyr8+s7SJymYjcCOSIyLUi0lpELgJ2+W2XiJwIICJn4Xp4UNUSXE+YH3VVdWbZjao6A/fh4ce9QCOgXpklDf8/xzqqusRry/u43o9XRWQEYbzPVDVDVVfjenCWe9vWhtGuE3AftLOAS1T1YmCbql6sqpf4bVeIVqo62WvXj95z+PFryGGSbUCyt56A/9eoqrpYVW9T1S64D/5mwHciMt1nFkCxquYD24F8XM8hqpobRlZKyM/vR6CHt/5/uF4VY2oVvx9aNdVTIpKmqrtU9enSjSLSBddV6ouqbgDO8465fwGkhtmu/8N9iIE7NNEEyBSRFrj/7P0YD3wtIk/hejzeE5EPgVNwPT1+7P2gV9V1wIPAgyLSFXdIzI+LcN3sCpyKO7T2Ge5w0xifWZcDL3jtWASMBnccHnjKZ9ZkEfkUeA13CAygLXAh/vfXXNxhuDllbxCRv/rMKhSRFqqaAaCqS7yxOZ8AnX1mISJxXvF2Sci2ePwX5bNE5LfA1cBXInIT4RflnUTkI9z7rI2IpKpqnnfbAWNtDuJy4E0RWQBsBWaLyDdAT1zR6Md+Ba5XBPzo/fMwyGcWwFwReQtX7E7FFZlTcL+TP/nM+llEbvdyzsX7++CNTYqVv+MmhtgMsYdIRFKAzqq6OABtOQLXq3Mk7g/WBtyH5mc+cx5R1esOQxMDxRs0eQ7QGvfBtAH4SFX/7TOnK5Dl9VKVva25qm7xkTUUyFTVBWW2NwCuUtX/8ZF1LO5w3+4y2zsAJ6rqG1XNKvP4VsCjQD9V7RTG408qs2mOqu7yBiSPVFVfhaZXbJ3K/u/7z1R1u8+cP6nqW34ec5C8BOA8XBH3Pu5wzJ9wvZpP+elB8QbV3orrJVkA3K+qO733RTevx8+YWiMmihPvj8RoYATQCvfHYhNugOWLqlroM+80XHd769AsVfX7H3dEs4IoZN+XfY2TcGMVorbvjTHGBFOsFCdv4477vor7rwrcGR8XAemqer6PrEdx/6G9VibrQmClql4TpayKigDfBVgki7mg7vuDPM/zqnppALMmq6rvU2SrIcv2VxTzIrnPjAmKWClOlqtq1wpuW6GqR/rIKvf+3mDWFap6RJSyIlkERDIrqPu+onk1BHe2TpsoZfWtJOsTVW0ZpSzbXz6yDkPbIrbPjKkJYmUgVY6InAd84A0ORNzEVOcBOT6zdovIcd5guVDHArvLe0A1ZfUtpwjYAMwQkRVRzArqvs/EDcoNHQSp3vd+Z/yNZNYs4JsyWaUaRjHL9pd/Qd3/xgRerBQnFwAP4E6HLf1AbISbkMrv2SejgGe8U4hLexXa4uaRGBXFrEgWAZHMCuq+/wUY4p2NtB8RWV/O/asraylwmaquDFiW7S//grr/jQm8mChOVHUNcD6AuOnmpbwzK6qYNRc43jvdd+9ZHqWnfkYri8gWARHLCuq+x51t0ghvPpgyHoxi1p1UPD/H1VHMsv3lXyTzIrnPjAm8mBhzUh4ReVFVR0co625VvT0oWYdaBByurJDMQO57Y4wxwRArM8SWp08Es34XpCxVzVLVbSLyYpCyQgRy30fyNUY4a3JAs2x/RTEvwr+TxgRKLBcnsSCSRUAks4IqqPurRUCzbH9FNy8WfidNjIrl4sTvtVhqYlZQ2f7yJ5LHXmPhOG6Q91cs7H9jDlksFyeXRTBrfECzgloEBHXfB3V/WVbtyIp0XiwU5iZGxUxxIiL1RWTvhdNK58oQkZ5hZLXwzhgpvehcnIj8JkJNPS5CORDZIiCsLBE5W0SSQ7eVM0+Jn7xB3rVsEHeF4i4iEqlxJ1HfXxU4K6BZtr+imxfJfWZMoMTE2Toi8gfcqXhbcVc9HaWqs7zb5qpqRTM5lpd1GXAz7r+WB3DzaywBBgIPqmqVB6mJyONlNwF/wU3PjqqOq2pWOdkdccekf1LVZT4f2w7Yqqq7vdlXRwF9cVdS/T9VLfKRlQ/kApOBt3EXZCv2056QrEdxxVsC7srGQ7zck4B5qnqjz7w04HTcXClFwErg89K5XXxmHcW+iwiWTvf/kaou9ZtVyXNcrKovh9Gu1sBMVd0Vsv10v9cjEpHjAPWuUHw0bt8t83uhxAqyX1PVCyOQcyLuPbJYVT/3+djjgaWq+qu4C3rezL73/b2qusNn3jhgoqoe8jwkIlIHdxr/JlX9UkT+BAzAzaXyvN9rVBkTdLFSnMwHhqnqZu8P7GvArao6QUTmqWqVB5aJyCLgeCAFN2NjF1XNEJFGwNeq2ttH1gZgGvA5+7poHwZuAFDVV31kTVLV4d76ObhibBp88BivAAALMElEQVTuD9h9qvqKj6zFwHGqmiciDwCdcRfqO8Vr1yU+suZ5jxuJ++PaHZgIvK2q31Q1x8ta4j0+BdgItPbamIgrTrr7yPoDcCPuCq8nA9NxPYk9gD+r6iIfWTcBfwTeYf/p/i8A3lHV+6uadZDnWaeq7XzcfxxwJe4DrDdwjap+6N3mtyj/JzAMVxh+gfsdmAYMxRWcfq6W/FHZTbifwVcAqnq2j6wfVfU4b30M7vVOxF2l+GM/+957f/VS1SIReR7Iw11NeIi3/dyqZnl5O3CF+c+4wvw9Vc30kxGS9SZu36fiLi2RBkzw2iaqelE4ucYElqrW+gV32fjQ71sCc4BxwFyfWXND1heUuW2ez6x6uCLiLdwHLcAvYb7GeSHr04GO3nqTsu2sQtZPIetzgLiKXrOf/eV938Lb7/8B1vvMWux9TcbNVJvifR8f2uYqZi0EUkP20Wfeek9gus+sFUBiOdvr4C5I6Ldd5S2LgAKfWYuANG+9AzAbV6CE815d5O3nVNyMvPW97SnAQr/vCeANYDCu12swsNlbP8lnVuj7fhbQ1FuvW/b3vgpZSyt53873k1XaNlzBeyrwIm4K+im4a1TV8/u+8L4mAFuAeO978bv/bbGlJiwxMUMssFNEOqvqzwDqelAG43oD/I4VKRGRRHXdqHvHOnjjKnyN4VHVncDfROQY4A0R+dRvRmhcyHqCqq72nmObiPg9TLFeRE5R1a+ANbjDHmu9Cdn82m/QnrrZXB8HHheR9j6zPhWR73DFyQvAuyIyA/eh9m0Y7cr31nPxrk+iqgtFpL7PrBLc1ZvXltne0rvNj+bAaRx4mQDBFZ1+xKt3KEdV13jv+fe9/e53MGWRusNxeSLys6r+6uXmh/H+6gdcA9wG3Kiq80UkX332pHnivF7LOFwPQqbXrlwRqfLhR8/ikENnC0Skn6rOFpEjgXAOm6i6Q4SfA597PXzDcL1sDwNNfWTFeYd26uIKxAZANpCEO1RtTK0SK8XJWMp86KvqThE5HfiDz6y9XbuquiFke2Pg+nAap6pzROQU4Arg+3AygF4i8ivuQydJRFqoO9xUB/cfrx9/BV4TkTuBHcB87/BMI+A6n1nXVnSDqpb9MK+Uqt4kIie4VZ3hDXAegStU3vfZrn8DU0TkG9wHxntA6dVf/X5w/w2YKiIrgdLxBe2ALsBVPrM+wfV2zC97g4hM85mVISK9S7NUdZeInAm8hDt85cceEUlV1TzgmJA2NcBnAeZ9YP9LRN7zvm4h/L9FDXC9ewJoyPs+Df8/x78Cj4nIP4BtwH/EXbdmvXebX2UL80LgI+Ajb0yLHy8Cy3C/y7cB74nIL0B/3OFEY2qVmBhzUkpEmhMyYFFVt9S2rHKyGwLdVPU/YTy2G3Ak7oNjAzBLwxgs6mUFbn+JyBnA0bhDVV942+Jwh2gKfGbF4QZi7r3mD25/hTX4NxJEpA2ux+OAaw+JyEBV/cFHVlJ5+0REmgAt1ccYnXIyfgcMVNVbw80oJzMVaF7ag+jzsfWATnjv+0N4fx2pqn6v4l1ZXisAVd3k/V4PBdbpIZz9ZkxQxURxIiK9gWdx/2Vt9Da3wQ0sG6uq8wKYdYW6C935ErQiIJKvMej7q4L8NA05S8ayLCvSeZFumzFBECvFyXzcpctnltneH3hOVXvVgqw+wDMErGgK8P6KeKFTwfP4OsPGsmp/VqTzIt02Y4IgVsac1C37gQbgjVuoW0uyXqbiD+5XgCp/cHv3ryjrZZ9ZQd1frxCh1ygiFY3DEdwpn1VmWbUjK9J5kW6bMUEXK8XJZO9MmNfYN2CxLXAh7tS+2pAV1CIgFvbXvcBDuIncyvJ79pVl1Y6sSOdFum3GBFpMHNYBEJFh7JvBs3TA4kcaxuyWQcwSN9tsZ8r/4F6tqlU+aySSWV5ebd9f04GrVXVOObetV9W2lhVbWUFvmzFBFzPFSSwIYhEQZBEsdLoC2VrO7J8i0tzPIFvLqh1ZQW+bMUEXE8WJuLkYbsF9EDXzNm8FPgTuV9XtNT0rqGx/GWOM8StWjlW+i5tx82RVbayqjXHX8tiON/lWTc8SkQYicr+ILBWRLG9Z6m1rGK0sYmt/LbMsy6oJbTMm6GKlOOmgqg9oyGRUqpqh7qJgfk/BC2pWIIsAYmt/DS6TlWNZMZsV9LYZE2wagAv8HO4Fd22Lv+NmjCzd1hy4CfiylmQtD+e2asiy/WVZMZcV9LbZYkvQl1jpOTkfd+2bb0QkR0SycZd7T8f/tXWCmrVWRP4ubsZTwA2UE5Gb2Hc2SjSybH9ZVixmBb1txgRbtKuj6lqAo3DXokgrs/302pCFuyjfA7iLg+Xgrli61NuWHq0s21+WFYtZQW+bLbYEfYl6A6rlRcI4YDkwCVgDnBNy29zakOU9JohFgO0vy4rJrKC3zRZbgrxEvQHV8iJhUekvNNABmA1c430/r5ZkBbIIsP1lWbGYFfS22WJL0JeoN6BaXiT8VOb7NNx0548A82tJVlCLANtflhVzWUFvmy22BH2JlWvrZIhIb1WdD6Cqu0TkTOAloEctyYpX77LpqrpGRAYD74tIe9zsp9HKsv1lWbGYFfS2GRNosXK2zoVARugGVS1S1QuBQbUkK0NEeofk7ALOBJoQZhEQoSzbX5YVi1lBb5sxgRYT09fHAhFpAxRpyARlIbcNVNUfopEVVEHdX5ZVO7KC3jZjgs6KE2OMMcYESqwc1jHGGGNMDWHFiTHGGGMCxYoTY4wxxgSKFSemxhORYhGZH7J0EJF+IvJ4NbZhehXus6s62mKMMTWdDYg1NZ6I7FLVtGi342Cqu50iIrjf8ZLqek5jjIkE6zkxtZKIDBaRT7z1O0XkJRGZJiK/iMi4kPtNEpE5IrJERC4N2b5LRP5HRBaIyIzSq8F6V4Kd6G1fICIDSu/vfU0TkakiMldEFonIOVVs7+uh9xWRN0XkbBGJF5GHRGSWiCwUkcsqex6v12ipiDwNzAXaisgrIrLYu9+1h7pvjTHmcLPixNQGKSGHdCZWcJ+jgNOA44B/ikiit/0SVT0G6AeME5HG3va6wAxV7QV8C4zxtj8OfONt7wssKfM8u4ERqtoXOBn4X68H42BeAC4GEJEGwADg38BoYIeqHgscC4wRkY4HeZ6uwGuq2gc3SVdrVe2uqj2Al6vQFmOMiapYmb7e1G75qtr7IPf5VFULgAIR2Qo0BzbgCpIR3n3aAkcAWcAe4BNv+xzgt976KbiZalHVYmBHmecR4F4RGQSUAK295zpg8qxQqvqNiDwlIs2Ac4EPVLVIRE4FeorISO+uDbw2bqjgeQDWquoMb/0XoJOIPAF8Cnx+kP1kjDFRZ8WJiRUFIevFQIJ3fZKhwAmqmici04Bk7z6Fum9AVjFV/135M9AUOEZVC0VkTUjmwbzuPf4C4BJvmwBXq+pnoXcUkVGVPE9u6f1UNUdEeuF6ja4E/hCSbYwxgWSHdUwsawDkeIXJUUD/KjxmKjAWwBsPUr+czK1ewXAy0N5He14B/gagqqWHiz4DxpYehhKRI0WkblWfR0SaAHGq+gFwO+5QlDHGBJr1nJhYNgW4XEQWAsuBGQe5P8A1wPMiMhrXozIW+E/I7W8CH4vIbGA+sKyqjVHVLSKyFJgUsvkFoAMw1xtTkgkM9/E8rYGXRaT0H5FbqtoeY4yJFjuV2JiAEJFUYBHQV1XLjmUxxpiYYYd1jAkAERmK6/14wgoTY0yss54TY6qRiPTADXwNVaCqx0ejPcYYE0RWnBhjjDEmUOywjjHGGGMCxYoTY4wxxgSKFSfGGGOMCRQrTowxxhgTKP8fUdR1sGwH9i4AAAAASUVORK5CYII=\n",
1363 |       "text/plain": [
1364 |        "<Figure size 432x288 with 1 Axes>"
1365 |       ]
1366 |      },
1367 |      "metadata": {
1368 |       "needs_background": "light"
1369 |      },
1370 |      "output_type": "display_data"
1371 |     }
1372 |    ],
1373 |    "source": [
1374 |     "plt.plot(data1.Financial_Year,data1['Brisbane_HPI'],\"o-\",label = \"Brisbane\")\n",
1375 |     "plt.plot(data1.Financial_Year,data1['Sydney_HPI'], \"s-\",label = \"Sydney\")\n",
1376 |     "plt.plot(data1.Financial_Year,data1['Melbourne_HPI'],\"d-\", label =\"Melbourne\")\n",
1377 |     "plt.ylabel('HPI')\n",
1378 |     "plt.xlabel('Financial_years')\n",
1379 |     "plt.xticks(rotation = 90)\n",
1380 |     "plt.title('HPI of brisbane, sydney and melbourne')\n",
1381 |     "plt.legend(bbox_to_anchor=(1.5, 1.05))\n",
1382 |     "plt.show()"
1383 |    ]
1384 |   },
1385 |   {
1386 |    "cell_type": "markdown",
1387 |    "metadata": {},
1388 |    "source": [
1389 |     "Observation: The above visualisation shows the trends of House Price Index of Brisbane, Sydney and Melbourne from the year 2002 to 2019. From the line chart, it is clear that HPI of Sydney and Melbourne had faced a great fall during the year 2018-19, whereas it also had a gradual rise till 2017-18.\n",
1390 |     "\n",
1391 |     "CPI of either Sydney or melbourne can be analysed to check whether fall in HPI have created a impact on CPI."
1392 |    ]
1393 |   },
1394 |   {
1395 |    "cell_type": "markdown",
1396 |    "metadata": {},
1397 |    "source": [
1398 |     "ANALYSIS for CPI DATA"
1399 |    ]
1400 |   },
1401 |   {
1402 |    "cell_type": "code",
1403 |    "execution_count": 10,
1404 |    "metadata": {
1405 |     "collapsed": true
1406 |    },
1407 |    "outputs": [
1408 |     {
1409 |      "data": {
1410 |       "text/html": [
1411 |        "<div>\n",
1412 |        "<style scoped>\n",
1413 |        "    .dataframe tbody tr th:only-of-type {\n",
1414 |        "        vertical-align: middle;\n",
1415 |        "    }\n",
1416 |        "\n",
1417 |        "    .dataframe tbody tr th {\n",
1418 |        "        vertical-align: top;\n",
1419 |        "    }\n",
1420 |        "\n",
1421 |        "    .dataframe thead th {\n",
1422 |        "        text-align: right;\n",
1423 |        "    }\n",
1424 |        "</style>\n",
1425 |        "<table border=\"1\" class=\"dataframe\">\n",
1426 |        "  <thead>\n",
1427 |        "    <tr style=\"text-align: right;\">\n",
1428 |        "      <th></th>\n",
1429 |        "      <th>Consumer Price Index (CPI)</th>\n",
1430 |        "      <th>Unnamed: 1</th>\n",
1431 |        "      <th>Unnamed: 2</th>\n",
1432 |        "      <th>Unnamed: 3</th>\n",
1433 |        "      <th>Unnamed: 4</th>\n",
1434 |        "      <th>Unnamed: 5</th>\n",
1435 |        "      <th>Unnamed: 6</th>\n",
1436 |        "    </tr>\n",
1437 |        "  </thead>\n",
1438 |        "  <tbody>\n",
1439 |        "    <tr>\n",
1440 |        "      <td>0</td>\n",
1441 |        "      <td>NaN</td>\n",
1442 |        "      <td>Sydney</td>\n",
1443 |        "      <td>NaN</td>\n",
1444 |        "      <td>Australia</td>\n",
1445 |        "      <td>NaN</td>\n",
1446 |        "      <td>NaN</td>\n",
1447 |        "      <td>NaN</td>\n",
1448 |        "    </tr>\n",
1449 |        "    <tr>\n",
1450 |        "      <td>1</td>\n",
1451 |        "      <td>Quarter ending</td>\n",
1452 |        "      <td>Index number</td>\n",
1453 |        "      <td>% change from previous year</td>\n",
1454 |        "      <td>Index number</td>\n",
1455 |        "      <td>% change from previous year</td>\n",
1456 |        "      <td>NaN</td>\n",
1457 |        "      <td>NaN</td>\n",
1458 |        "    </tr>\n",
1459 |        "    <tr>\n",
1460 |        "      <td>2</td>\n",
1461 |        "      <td>2019-06-01 00:00:00</td>\n",
1462 |        "      <td>115.9</td>\n",
1463 |        "      <td>1.7</td>\n",
1464 |        "      <td>114.8</td>\n",
1465 |        "      <td>1.6</td>\n",
1466 |        "      <td>NaN</td>\n",
1467 |        "      <td>NaN</td>\n",
1468 |        "    </tr>\n",
1469 |        "    <tr>\n",
1470 |        "      <td>3</td>\n",
1471 |        "      <td>2019-03-01 00:00:00</td>\n",
1472 |        "      <td>115.1</td>\n",
1473 |        "      <td>1.3</td>\n",
1474 |        "      <td>114.1</td>\n",
1475 |        "      <td>1.3</td>\n",
1476 |        "      <td>NaN</td>\n",
1477 |        "      <td>NaN</td>\n",
1478 |        "    </tr>\n",
1479 |        "    <tr>\n",
1480 |        "      <td>4</td>\n",
1481 |        "      <td>2018-12-01 00:00:00</td>\n",
1482 |        "      <td>115.2</td>\n",
1483 |        "      <td>1.7</td>\n",
1484 |        "      <td>114.1</td>\n",
1485 |        "      <td>1.8</td>\n",
1486 |        "      <td>NaN</td>\n",
1487 |        "      <td>NaN</td>\n",
1488 |        "    </tr>\n",
1489 |        "    <tr>\n",
1490 |        "      <td>...</td>\n",
1491 |        "      <td>...</td>\n",
1492 |        "      <td>...</td>\n",
1493 |        "      <td>...</td>\n",
1494 |        "      <td>...</td>\n",
1495 |        "      <td>...</td>\n",
1496 |        "      <td>...</td>\n",
1497 |        "      <td>...</td>\n",
1498 |        "    </tr>\n",
1499 |        "    <tr>\n",
1500 |        "      <td>72</td>\n",
1501 |        "      <td>2001-12-01 00:00:00</td>\n",
1502 |        "      <td>76.3</td>\n",
1503 |        "      <td>3.4</td>\n",
1504 |        "      <td>75.4</td>\n",
1505 |        "      <td>3.1</td>\n",
1506 |        "      <td>NaN</td>\n",
1507 |        "      <td>NaN</td>\n",
1508 |        "    </tr>\n",
1509 |        "    <tr>\n",
1510 |        "      <td>73</td>\n",
1511 |        "      <td>2001-09-01 00:00:00</td>\n",
1512 |        "      <td>75.6</td>\n",
1513 |        "      <td>2.9</td>\n",
1514 |        "      <td>74.7</td>\n",
1515 |        "      <td>2.5</td>\n",
1516 |        "      <td>NaN</td>\n",
1517 |        "      <td>NaN</td>\n",
1518 |        "    </tr>\n",
1519 |        "    <tr>\n",
1520 |        "      <td>74</td>\n",
1521 |        "      <td>2001-06-01 00:00:00</td>\n",
1522 |        "      <td>75.4</td>\n",
1523 |        "      <td>6.3</td>\n",
1524 |        "      <td>74.5</td>\n",
1525 |        "      <td>6.1</td>\n",
1526 |        "      <td>NaN</td>\n",
1527 |        "      <td>NaN</td>\n",
1528 |        "    </tr>\n",
1529 |        "    <tr>\n",
1530 |        "      <td>75</td>\n",
1531 |        "      <td>2001-03-01 00:00:00</td>\n",
1532 |        "      <td>74.8</td>\n",
1533 |        "      <td>6.4</td>\n",
1534 |        "      <td>73.9</td>\n",
1535 |        "      <td>6</td>\n",
1536 |        "      <td>NaN</td>\n",
1537 |        "      <td>NaN</td>\n",
1538 |        "    </tr>\n",
1539 |        "    <tr>\n",
1540 |        "      <td>76</td>\n",
1541 |        "      <td>Source: Australian Bureau of Statistics.Consum...</td>\n",
1542 |        "      <td>NaN</td>\n",
1543 |        "      <td>NaN</td>\n",
1544 |        "      <td>NaN</td>\n",
1545 |        "      <td>NaN</td>\n",
1546 |        "      <td>NaN</td>\n",
1547 |        "      <td>NaN</td>\n",
1548 |        "    </tr>\n",
1549 |        "  </tbody>\n",
1550 |        "</table>\n",
1551 |        "<p>77 rows × 7 columns</p>\n",
1552 |        "</div>"
1553 |       ],
1554 |       "text/plain": [
1555 |        "                           Consumer Price Index (CPI)    Unnamed: 1  \\\n",
1556 |        "0                                                 NaN        Sydney   \n",
1557 |        "1                                      Quarter ending  Index number   \n",
1558 |        "2                                 2019-06-01 00:00:00         115.9   \n",
1559 |        "3                                 2019-03-01 00:00:00         115.1   \n",
1560 |        "4                                 2018-12-01 00:00:00         115.2   \n",
1561 |        "..                                                ...           ...   \n",
1562 |        "72                                2001-12-01 00:00:00          76.3   \n",
1563 |        "73                                2001-09-01 00:00:00          75.6   \n",
1564 |        "74                                2001-06-01 00:00:00          75.4   \n",
1565 |        "75                                2001-03-01 00:00:00          74.8   \n",
1566 |        "76  Source: Australian Bureau of Statistics.Consum...           NaN   \n",
1567 |        "\n",
1568 |        "                     Unnamed: 2    Unnamed: 3                   Unnamed: 4  \\\n",
1569 |        "0                           NaN     Australia                          NaN   \n",
1570 |        "1   % change from previous year  Index number  % change from previous year   \n",
1571 |        "2                           1.7         114.8                          1.6   \n",
1572 |        "3                           1.3         114.1                          1.3   \n",
1573 |        "4                           1.7         114.1                          1.8   \n",
1574 |        "..                          ...           ...                          ...   \n",
1575 |        "72                          3.4          75.4                          3.1   \n",
1576 |        "73                          2.9          74.7                          2.5   \n",
1577 |        "74                          6.3          74.5                          6.1   \n",
1578 |        "75                          6.4          73.9                            6   \n",
1579 |        "76                          NaN           NaN                          NaN   \n",
1580 |        "\n",
1581 |        "    Unnamed: 5  Unnamed: 6  \n",
1582 |        "0          NaN         NaN  \n",
1583 |        "1          NaN         NaN  \n",
1584 |        "2          NaN         NaN  \n",
1585 |        "3          NaN         NaN  \n",
1586 |        "4          NaN         NaN  \n",
1587 |        "..         ...         ...  \n",
1588 |        "72         NaN         NaN  \n",
1589 |        "73         NaN         NaN  \n",
1590 |        "74         NaN         NaN  \n",
1591 |        "75         NaN         NaN  \n",
1592 |        "76         NaN         NaN  \n",
1593 |        "\n",
1594 |        "[77 rows x 7 columns]"
1595 |       ]
1596 |      },
1597 |      "execution_count": 10,
1598 |      "metadata": {},
1599 |      "output_type": "execute_result"
1600 |     }
1601 |    ],
1602 |    "source": [
1603 |     "#Viewing dataset\n",
1604 |     "data2"
1605 |    ]
1606 |   },
1607 |   {
1608 |    "cell_type": "code",
1609 |    "execution_count": 11,
1610 |    "metadata": {
1611 |     "collapsed": true
1612 |    },
1613 |    "outputs": [
1614 |     {
1615 |      "name": "stdout",
1616 |      "output_type": "stream",
1617 |      "text": [
1618 |       "<class 'pandas.core.frame.DataFrame'>\n",
1619 |       "RangeIndex: 77 entries, 0 to 76\n",
1620 |       "Data columns (total 7 columns):\n",
1621 |       "Consumer Price Index (CPI)    76 non-null object\n",
1622 |       "Unnamed: 1                    76 non-null object\n",
1623 |       "Unnamed: 2                    75 non-null object\n",
1624 |       "Unnamed: 3                    76 non-null object\n",
1625 |       "Unnamed: 4                    75 non-null object\n",
1626 |       "Unnamed: 5                    0 non-null float64\n",
1627 |       "Unnamed: 6                    0 non-null float64\n",
1628 |       "dtypes: float64(2), object(5)\n",
1629 |       "memory usage: 4.3+ KB\n"
1630 |      ]
1631 |     }
1632 |    ],
1633 |    "source": [
1634 |     "#knowing the data\n",
1635 |     "data2.info()"
1636 |    ]
1637 |   },
1638 |   {
1639 |    "cell_type": "code",
1640 |    "execution_count": 12,
1641 |    "metadata": {
1642 |     "collapsed": true
1643 |    },
1644 |    "outputs": [
1645 |     {
1646 |      "data": {
1647 |       "text/html": [
1648 |        "<div>\n",
1649 |        "<style scoped>\n",
1650 |        "    .dataframe tbody tr th:only-of-type {\n",
1651 |        "        vertical-align: middle;\n",
1652 |        "    }\n",
1653 |        "\n",
1654 |        "    .dataframe tbody tr th {\n",
1655 |        "        vertical-align: top;\n",
1656 |        "    }\n",
1657 |        "\n",
1658 |        "    .dataframe thead th {\n",
1659 |        "        text-align: right;\n",
1660 |        "    }\n",
1661 |        "</style>\n",
1662 |        "<table border=\"1\" class=\"dataframe\">\n",
1663 |        "  <thead>\n",
1664 |        "    <tr style=\"text-align: right;\">\n",
1665 |        "      <th></th>\n",
1666 |        "      <th>Consumer Price Index (CPI)</th>\n",
1667 |        "      <th>Unnamed: 1</th>\n",
1668 |        "      <th>Unnamed: 2</th>\n",
1669 |        "      <th>Unnamed: 3</th>\n",
1670 |        "      <th>Unnamed: 4</th>\n",
1671 |        "      <th>Unnamed: 5</th>\n",
1672 |        "      <th>Unnamed: 6</th>\n",
1673 |        "    </tr>\n",
1674 |        "  </thead>\n",
1675 |        "  <tbody>\n",
1676 |        "    <tr>\n",
1677 |        "      <td>2</td>\n",
1678 |        "      <td>2019-06-01 00:00:00</td>\n",
1679 |        "      <td>115.9</td>\n",
1680 |        "      <td>1.7</td>\n",
1681 |        "      <td>114.8</td>\n",
1682 |        "      <td>1.6</td>\n",
1683 |        "      <td>NaN</td>\n",
1684 |        "      <td>NaN</td>\n",
1685 |        "    </tr>\n",
1686 |        "    <tr>\n",
1687 |        "      <td>3</td>\n",
1688 |        "      <td>2019-03-01 00:00:00</td>\n",
1689 |        "      <td>115.1</td>\n",
1690 |        "      <td>1.3</td>\n",
1691 |        "      <td>114.1</td>\n",
1692 |        "      <td>1.3</td>\n",
1693 |        "      <td>NaN</td>\n",
1694 |        "      <td>NaN</td>\n",
1695 |        "    </tr>\n",
1696 |        "    <tr>\n",
1697 |        "      <td>4</td>\n",
1698 |        "      <td>2018-12-01 00:00:00</td>\n",
1699 |        "      <td>115.2</td>\n",
1700 |        "      <td>1.7</td>\n",
1701 |        "      <td>114.1</td>\n",
1702 |        "      <td>1.8</td>\n",
1703 |        "      <td>NaN</td>\n",
1704 |        "      <td>NaN</td>\n",
1705 |        "    </tr>\n",
1706 |        "    <tr>\n",
1707 |        "      <td>5</td>\n",
1708 |        "      <td>2018-09-01 00:00:00</td>\n",
1709 |        "      <td>114.7</td>\n",
1710 |        "      <td>2</td>\n",
1711 |        "      <td>113.5</td>\n",
1712 |        "      <td>1.9</td>\n",
1713 |        "      <td>NaN</td>\n",
1714 |        "      <td>NaN</td>\n",
1715 |        "    </tr>\n",
1716 |        "    <tr>\n",
1717 |        "      <td>6</td>\n",
1718 |        "      <td>2018-06-01 00:00:00</td>\n",
1719 |        "      <td>114</td>\n",
1720 |        "      <td>2.1</td>\n",
1721 |        "      <td>113</td>\n",
1722 |        "      <td>2.1</td>\n",
1723 |        "      <td>NaN</td>\n",
1724 |        "      <td>NaN</td>\n",
1725 |        "    </tr>\n",
1726 |        "    <tr>\n",
1727 |        "      <td>...</td>\n",
1728 |        "      <td>...</td>\n",
1729 |        "      <td>...</td>\n",
1730 |        "      <td>...</td>\n",
1731 |        "      <td>...</td>\n",
1732 |        "      <td>...</td>\n",
1733 |        "      <td>...</td>\n",
1734 |        "      <td>...</td>\n",
1735 |        "    </tr>\n",
1736 |        "    <tr>\n",
1737 |        "      <td>71</td>\n",
1738 |        "      <td>2002-03-01 00:00:00</td>\n",
1739 |        "      <td>77</td>\n",
1740 |        "      <td>2.9</td>\n",
1741 |        "      <td>76.1</td>\n",
1742 |        "      <td>3</td>\n",
1743 |        "      <td>NaN</td>\n",
1744 |        "      <td>NaN</td>\n",
1745 |        "    </tr>\n",
1746 |        "    <tr>\n",
1747 |        "      <td>72</td>\n",
1748 |        "      <td>2001-12-01 00:00:00</td>\n",
1749 |        "      <td>76.3</td>\n",
1750 |        "      <td>3.4</td>\n",
1751 |        "      <td>75.4</td>\n",
1752 |        "      <td>3.1</td>\n",
1753 |        "      <td>NaN</td>\n",
1754 |        "      <td>NaN</td>\n",
1755 |        "    </tr>\n",
1756 |        "    <tr>\n",
1757 |        "      <td>73</td>\n",
1758 |        "      <td>2001-09-01 00:00:00</td>\n",
1759 |        "      <td>75.6</td>\n",
1760 |        "      <td>2.9</td>\n",
1761 |        "      <td>74.7</td>\n",
1762 |        "      <td>2.5</td>\n",
1763 |        "      <td>NaN</td>\n",
1764 |        "      <td>NaN</td>\n",
1765 |        "    </tr>\n",
1766 |        "    <tr>\n",
1767 |        "      <td>74</td>\n",
1768 |        "      <td>2001-06-01 00:00:00</td>\n",
1769 |        "      <td>75.4</td>\n",
1770 |        "      <td>6.3</td>\n",
1771 |        "      <td>74.5</td>\n",
1772 |        "      <td>6.1</td>\n",
1773 |        "      <td>NaN</td>\n",
1774 |        "      <td>NaN</td>\n",
1775 |        "    </tr>\n",
1776 |        "    <tr>\n",
1777 |        "      <td>75</td>\n",
1778 |        "      <td>2001-03-01 00:00:00</td>\n",
1779 |        "      <td>74.8</td>\n",
1780 |        "      <td>6.4</td>\n",
1781 |        "      <td>73.9</td>\n",
1782 |        "      <td>6</td>\n",
1783 |        "      <td>NaN</td>\n",
1784 |        "      <td>NaN</td>\n",
1785 |        "    </tr>\n",
1786 |        "  </tbody>\n",
1787 |        "</table>\n",
1788 |        "<p>74 rows × 7 columns</p>\n",
1789 |        "</div>"
1790 |       ],
1791 |       "text/plain": [
1792 |        "   Consumer Price Index (CPI) Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4  \\\n",
1793 |        "2         2019-06-01 00:00:00      115.9        1.7      114.8        1.6   \n",
1794 |        "3         2019-03-01 00:00:00      115.1        1.3      114.1        1.3   \n",
1795 |        "4         2018-12-01 00:00:00      115.2        1.7      114.1        1.8   \n",
1796 |        "5         2018-09-01 00:00:00      114.7          2      113.5        1.9   \n",
1797 |        "6         2018-06-01 00:00:00        114        2.1        113        2.1   \n",
1798 |        "..                        ...        ...        ...        ...        ...   \n",
1799 |        "71        2002-03-01 00:00:00         77        2.9       76.1          3   \n",
1800 |        "72        2001-12-01 00:00:00       76.3        3.4       75.4        3.1   \n",
1801 |        "73        2001-09-01 00:00:00       75.6        2.9       74.7        2.5   \n",
1802 |        "74        2001-06-01 00:00:00       75.4        6.3       74.5        6.1   \n",
1803 |        "75        2001-03-01 00:00:00       74.8        6.4       73.9          6   \n",
1804 |        "\n",
1805 |        "    Unnamed: 5  Unnamed: 6  \n",
1806 |        "2          NaN         NaN  \n",
1807 |        "3          NaN         NaN  \n",
1808 |        "4          NaN         NaN  \n",
1809 |        "5          NaN         NaN  \n",
1810 |        "6          NaN         NaN  \n",
1811 |        "..         ...         ...  \n",
1812 |        "71         NaN         NaN  \n",
1813 |        "72         NaN         NaN  \n",
1814 |        "73         NaN         NaN  \n",
1815 |        "74         NaN         NaN  \n",
1816 |        "75         NaN         NaN  \n",
1817 |        "\n",
1818 |        "[74 rows x 7 columns]"
1819 |       ]
1820 |      },
1821 |      "execution_count": 12,
1822 |      "metadata": {},
1823 |      "output_type": "execute_result"
1824 |     }
1825 |    ],
1826 |    "source": [
1827 |     "#pre-processing data- removing unnecessary columns, rows and replacing missing values\n",
1828 |     "#changing column names\n",
1829 |     "data2 = data2[2:76]                                     #removing descriptions and empty cells\n",
1830 |     "data2"
1831 |    ]
1832 |   },
1833 |   {
1834 |    "cell_type": "code",
1835 |    "execution_count": 13,
1836 |    "metadata": {
1837 |     "collapsed": true
1838 |    },
1839 |    "outputs": [
1840 |     {
1841 |      "data": {
1842 |       "text/html": [
1843 |        "<div>\n",
1844 |        "<style scoped>\n",
1845 |        "    .dataframe tbody tr th:only-of-type {\n",
1846 |        "        vertical-align: middle;\n",
1847 |        "    }\n",
1848 |        "\n",
1849 |        "    .dataframe tbody tr th {\n",
1850 |        "        vertical-align: top;\n",
1851 |        "    }\n",
1852 |        "\n",
1853 |        "    .dataframe thead th {\n",
1854 |        "        text-align: right;\n",
1855 |        "    }\n",
1856 |        "</style>\n",
1857 |        "<table border=\"1\" class=\"dataframe\">\n",
1858 |        "  <thead>\n",
1859 |        "    <tr style=\"text-align: right;\">\n",
1860 |        "      <th></th>\n",
1861 |        "      <th>Consumer Price Index (CPI)</th>\n",
1862 |        "      <th>Unnamed: 1</th>\n",
1863 |        "      <th>Unnamed: 2</th>\n",
1864 |        "    </tr>\n",
1865 |        "  </thead>\n",
1866 |        "  <tbody>\n",
1867 |        "    <tr>\n",
1868 |        "      <td>2</td>\n",
1869 |        "      <td>2019-06-01 00:00:00</td>\n",
1870 |        "      <td>115.9</td>\n",
1871 |        "      <td>1.7</td>\n",
1872 |        "    </tr>\n",
1873 |        "    <tr>\n",
1874 |        "      <td>3</td>\n",
1875 |        "      <td>2019-03-01 00:00:00</td>\n",
1876 |        "      <td>115.1</td>\n",
1877 |        "      <td>1.3</td>\n",
1878 |        "    </tr>\n",
1879 |        "    <tr>\n",
1880 |        "      <td>4</td>\n",
1881 |        "      <td>2018-12-01 00:00:00</td>\n",
1882 |        "      <td>115.2</td>\n",
1883 |        "      <td>1.7</td>\n",
1884 |        "    </tr>\n",
1885 |        "    <tr>\n",
1886 |        "      <td>5</td>\n",
1887 |        "      <td>2018-09-01 00:00:00</td>\n",
1888 |        "      <td>114.7</td>\n",
1889 |        "      <td>2</td>\n",
1890 |        "    </tr>\n",
1891 |        "    <tr>\n",
1892 |        "      <td>6</td>\n",
1893 |        "      <td>2018-06-01 00:00:00</td>\n",
1894 |        "      <td>114</td>\n",
1895 |        "      <td>2.1</td>\n",
1896 |        "    </tr>\n",
1897 |        "    <tr>\n",
1898 |        "      <td>...</td>\n",
1899 |        "      <td>...</td>\n",
1900 |        "      <td>...</td>\n",
1901 |        "      <td>...</td>\n",
1902 |        "    </tr>\n",
1903 |        "    <tr>\n",
1904 |        "      <td>71</td>\n",
1905 |        "      <td>2002-03-01 00:00:00</td>\n",
1906 |        "      <td>77</td>\n",
1907 |        "      <td>2.9</td>\n",
1908 |        "    </tr>\n",
1909 |        "    <tr>\n",
1910 |        "      <td>72</td>\n",
1911 |        "      <td>2001-12-01 00:00:00</td>\n",
1912 |        "      <td>76.3</td>\n",
1913 |        "      <td>3.4</td>\n",
1914 |        "    </tr>\n",
1915 |        "    <tr>\n",
1916 |        "      <td>73</td>\n",
1917 |        "      <td>2001-09-01 00:00:00</td>\n",
1918 |        "      <td>75.6</td>\n",
1919 |        "      <td>2.9</td>\n",
1920 |        "    </tr>\n",
1921 |        "    <tr>\n",
1922 |        "      <td>74</td>\n",
1923 |        "      <td>2001-06-01 00:00:00</td>\n",
1924 |        "      <td>75.4</td>\n",
1925 |        "      <td>6.3</td>\n",
1926 |        "    </tr>\n",
1927 |        "    <tr>\n",
1928 |        "      <td>75</td>\n",
1929 |        "      <td>2001-03-01 00:00:00</td>\n",
1930 |        "      <td>74.8</td>\n",
1931 |        "      <td>6.4</td>\n",
1932 |        "    </tr>\n",
1933 |        "  </tbody>\n",
1934 |        "</table>\n",
1935 |        "<p>74 rows × 3 columns</p>\n",
1936 |        "</div>"
1937 |       ],
1938 |       "text/plain": [
1939 |        "   Consumer Price Index (CPI) Unnamed: 1 Unnamed: 2\n",
1940 |        "2         2019-06-01 00:00:00      115.9        1.7\n",
1941 |        "3         2019-03-01 00:00:00      115.1        1.3\n",
1942 |        "4         2018-12-01 00:00:00      115.2        1.7\n",
1943 |        "5         2018-09-01 00:00:00      114.7          2\n",
1944 |        "6         2018-06-01 00:00:00        114        2.1\n",
1945 |        "..                        ...        ...        ...\n",
1946 |        "71        2002-03-01 00:00:00         77        2.9\n",
1947 |        "72        2001-12-01 00:00:00       76.3        3.4\n",
1948 |        "73        2001-09-01 00:00:00       75.6        2.9\n",
1949 |        "74        2001-06-01 00:00:00       75.4        6.3\n",
1950 |        "75        2001-03-01 00:00:00       74.8        6.4\n",
1951 |        "\n",
1952 |        "[74 rows x 3 columns]"
1953 |       ]
1954 |      },
1955 |      "execution_count": 13,
1956 |      "metadata": {},
1957 |      "output_type": "execute_result"
1958 |     }
1959 |    ],
1960 |    "source": [
1961 |     "# The current dataset contains CPI of sydney and whole Australia. This analysis focus on Sydney.Hence remove unwanted columns\n",
1962 |     "data2=data2.drop(columns = ['Unnamed: 3', 'Unnamed: 4','Unnamed: 5', 'Unnamed: 6'])\n",
1963 |     "data2"
1964 |    ]
1965 |   },
1966 |   {
1967 |    "cell_type": "code",
1968 |    "execution_count": 14,
1969 |    "metadata": {
1970 |     "collapsed": true
1971 |    },
1972 |    "outputs": [
1973 |     {
1974 |      "data": {
1975 |       "text/html": [
1976 |        "<div>\n",
1977 |        "<style scoped>\n",
1978 |        "    .dataframe tbody tr th:only-of-type {\n",
1979 |        "        vertical-align: middle;\n",
1980 |        "    }\n",
1981 |        "\n",
1982 |        "    .dataframe tbody tr th {\n",
1983 |        "        vertical-align: top;\n",
1984 |        "    }\n",
1985 |        "\n",
1986 |        "    .dataframe thead th {\n",
1987 |        "        text-align: right;\n",
1988 |        "    }\n",
1989 |        "</style>\n",
1990 |        "<table border=\"1\" class=\"dataframe\">\n",
1991 |        "  <thead>\n",
1992 |        "    <tr style=\"text-align: right;\">\n",
1993 |        "      <th></th>\n",
1994 |        "      <th>Quarter_end</th>\n",
1995 |        "      <th>index_number</th>\n",
1996 |        "      <th>percent_change_from_previous_year</th>\n",
1997 |        "    </tr>\n",
1998 |        "  </thead>\n",
1999 |        "  <tbody>\n",
2000 |        "    <tr>\n",
2001 |        "      <td>2</td>\n",
2002 |        "      <td>2019-06-01 00:00:00</td>\n",
2003 |        "      <td>115.9</td>\n",
2004 |        "      <td>1.7</td>\n",
2005 |        "    </tr>\n",
2006 |        "    <tr>\n",
2007 |        "      <td>3</td>\n",
2008 |        "      <td>2019-03-01 00:00:00</td>\n",
2009 |        "      <td>115.1</td>\n",
2010 |        "      <td>1.3</td>\n",
2011 |        "    </tr>\n",
2012 |        "    <tr>\n",
2013 |        "      <td>4</td>\n",
2014 |        "      <td>2018-12-01 00:00:00</td>\n",
2015 |        "      <td>115.2</td>\n",
2016 |        "      <td>1.7</td>\n",
2017 |        "    </tr>\n",
2018 |        "    <tr>\n",
2019 |        "      <td>5</td>\n",
2020 |        "      <td>2018-09-01 00:00:00</td>\n",
2021 |        "      <td>114.7</td>\n",
2022 |        "      <td>2</td>\n",
2023 |        "    </tr>\n",
2024 |        "    <tr>\n",
2025 |        "      <td>6</td>\n",
2026 |        "      <td>2018-06-01 00:00:00</td>\n",
2027 |        "      <td>114</td>\n",
2028 |        "      <td>2.1</td>\n",
2029 |        "    </tr>\n",
2030 |        "    <tr>\n",
2031 |        "      <td>...</td>\n",
2032 |        "      <td>...</td>\n",
2033 |        "      <td>...</td>\n",
2034 |        "      <td>...</td>\n",
2035 |        "    </tr>\n",
2036 |        "    <tr>\n",
2037 |        "      <td>71</td>\n",
2038 |        "      <td>2002-03-01 00:00:00</td>\n",
2039 |        "      <td>77</td>\n",
2040 |        "      <td>2.9</td>\n",
2041 |        "    </tr>\n",
2042 |        "    <tr>\n",
2043 |        "      <td>72</td>\n",
2044 |        "      <td>2001-12-01 00:00:00</td>\n",
2045 |        "      <td>76.3</td>\n",
2046 |        "      <td>3.4</td>\n",
2047 |        "    </tr>\n",
2048 |        "    <tr>\n",
2049 |        "      <td>73</td>\n",
2050 |        "      <td>2001-09-01 00:00:00</td>\n",
2051 |        "      <td>75.6</td>\n",
2052 |        "      <td>2.9</td>\n",
2053 |        "    </tr>\n",
2054 |        "    <tr>\n",
2055 |        "      <td>74</td>\n",
2056 |        "      <td>2001-06-01 00:00:00</td>\n",
2057 |        "      <td>75.4</td>\n",
2058 |        "      <td>6.3</td>\n",
2059 |        "    </tr>\n",
2060 |        "    <tr>\n",
2061 |        "      <td>75</td>\n",
2062 |        "      <td>2001-03-01 00:00:00</td>\n",
2063 |        "      <td>74.8</td>\n",
2064 |        "      <td>6.4</td>\n",
2065 |        "    </tr>\n",
2066 |        "  </tbody>\n",
2067 |        "</table>\n",
2068 |        "<p>74 rows × 3 columns</p>\n",
2069 |        "</div>"
2070 |       ],
2071 |       "text/plain": [
2072 |        "            Quarter_end index_number percent_change_from_previous_year\n",
2073 |        "2   2019-06-01 00:00:00        115.9                               1.7\n",
2074 |        "3   2019-03-01 00:00:00        115.1                               1.3\n",
2075 |        "4   2018-12-01 00:00:00        115.2                               1.7\n",
2076 |        "5   2018-09-01 00:00:00        114.7                                 2\n",
2077 |        "6   2018-06-01 00:00:00          114                               2.1\n",
2078 |        "..                  ...          ...                               ...\n",
2079 |        "71  2002-03-01 00:00:00           77                               2.9\n",
2080 |        "72  2001-12-01 00:00:00         76.3                               3.4\n",
2081 |        "73  2001-09-01 00:00:00         75.6                               2.9\n",
2082 |        "74  2001-06-01 00:00:00         75.4                               6.3\n",
2083 |        "75  2001-03-01 00:00:00         74.8                               6.4\n",
2084 |        "\n",
2085 |        "[74 rows x 3 columns]"
2086 |       ]
2087 |      },
2088 |      "execution_count": 14,
2089 |      "metadata": {},
2090 |      "output_type": "execute_result"
2091 |     }
2092 |    ],
2093 |    "source": [
2094 |     "#renaming column names\n",
2095 |     "data2 = data2.rename(columns={'Unnamed: 1':\"index_number\", 'Unnamed: 2':\"percent_change_from_previous_year\",'Consumer Price Index (CPI)':'Quarter_end'})\n",
2096 |     "data2\n",
2097 |     "\n",
2098 |     "\n",
2099 |     "#column names are chosen as same as provided in the original dataset (index 1)"
2100 |    ]
2101 |   },
2102 |   {
2103 |    "cell_type": "code",
2104 |    "execution_count": 15,
2105 |    "metadata": {
2106 |     "collapsed": true
2107 |    },
2108 |    "outputs": [
2109 |     {
2110 |      "name": "stdout",
2111 |      "output_type": "stream",
2112 |      "text": [
2113 |       "<class 'pandas.core.frame.DataFrame'>\n",
2114 |       "RangeIndex: 74 entries, 2 to 75\n",
2115 |       "Data columns (total 3 columns):\n",
2116 |       "Quarter_end                          74 non-null object\n",
2117 |       "index_number                         74 non-null object\n",
2118 |       "percent_change_from_previous_year    74 non-null object\n",
2119 |       "dtypes: object(3)\n",
2120 |       "memory usage: 1.9+ KB\n"
2121 |      ]
2122 |     }
2123 |    ],
2124 |    "source": [
2125 |     "#knowing processed data\n",
2126 |     "data2.info()"
2127 |    ]
2128 |   },
2129 |   {
2130 |    "cell_type": "code",
2131 |    "execution_count": 16,
2132 |    "metadata": {
2133 |     "collapsed": true
2134 |    },
2135 |    "outputs": [
2136 |     {
2137 |      "name": "stdout",
2138 |      "output_type": "stream",
2139 |      "text": [
2140 |       "<class 'pandas.core.frame.DataFrame'>\n",
2141 |       "RangeIndex: 74 entries, 2 to 75\n",
2142 |       "Data columns (total 4 columns):\n",
2143 |       "Quarter_end                          74 non-null object\n",
2144 |       "index_number                         74 non-null float64\n",
2145 |       "percent_change_from_previous_year    74 non-null object\n",
2146 |       "% change_from_previous_year          74 non-null float64\n",
2147 |       "dtypes: float64(2), object(2)\n",
2148 |       "memory usage: 2.4+ KB\n"
2149 |      ]
2150 |     }
2151 |    ],
2152 |    "source": [
2153 |     "#converting object datatypes to float\n",
2154 |     "data2['index_number']=data2.index_number.astype(float)\n",
2155 |     "data2['% change_from_previous_year'] = data2.percent_change_from_previous_year.astype(float)\n",
2156 |     "data2.info()"
2157 |    ]
2158 |   },
2159 |   {
2160 |    "cell_type": "code",
2161 |    "execution_count": 17,
2162 |    "metadata": {},
2163 |    "outputs": [],
2164 |    "source": [
2165 |     "data2['Year'] = data2.Quarter_end.dt.year #extracting year from Quarter_end"
2166 |    ]
2167 |   },
2168 |   {
2169 |    "cell_type": "code",
2170 |    "execution_count": 18,
2171 |    "metadata": {
2172 |     "collapsed": true
2173 |    },
2174 |    "outputs": [
2175 |     {
2176 |      "data": {
2177 |       "text/html": [
2178 |        "<div>\n",
2179 |        "<style scoped>\n",
2180 |        "    .dataframe tbody tr th:only-of-type {\n",
2181 |        "        vertical-align: middle;\n",
2182 |        "    }\n",
2183 |        "\n",
2184 |        "    .dataframe tbody tr th {\n",
2185 |        "        vertical-align: top;\n",
2186 |        "    }\n",
2187 |        "\n",
2188 |        "    .dataframe thead th {\n",
2189 |        "        text-align: right;\n",
2190 |        "    }\n",
2191 |        "</style>\n",
2192 |        "<table border=\"1\" class=\"dataframe\">\n",
2193 |        "  <thead>\n",
2194 |        "    <tr style=\"text-align: right;\">\n",
2195 |        "      <th></th>\n",
2196 |        "      <th>index_number</th>\n",
2197 |        "      <th>% change_from_previous_year</th>\n",
2198 |        "    </tr>\n",
2199 |        "    <tr>\n",
2200 |        "      <th>Year</th>\n",
2201 |        "      <th></th>\n",
2202 |        "      <th></th>\n",
2203 |        "    </tr>\n",
2204 |        "  </thead>\n",
2205 |        "  <tbody>\n",
2206 |        "    <tr>\n",
2207 |        "      <td>2001</td>\n",
2208 |        "      <td>302.1</td>\n",
2209 |        "      <td>19.0</td>\n",
2210 |        "    </tr>\n",
2211 |        "    <tr>\n",
2212 |        "      <td>2002</td>\n",
2213 |        "      <td>310.8</td>\n",
2214 |        "      <td>11.5</td>\n",
2215 |        "    </tr>\n",
2216 |        "    <tr>\n",
2217 |        "      <td>2003</td>\n",
2218 |        "      <td>318.5</td>\n",
2219 |        "      <td>10.0</td>\n",
2220 |        "    </tr>\n",
2221 |        "    <tr>\n",
2222 |        "      <td>2004</td>\n",
2223 |        "      <td>326.0</td>\n",
2224 |        "      <td>9.4</td>\n",
2225 |        "    </tr>\n",
2226 |        "    <tr>\n",
2227 |        "      <td>2005</td>\n",
2228 |        "      <td>334.2</td>\n",
2229 |        "      <td>10.0</td>\n",
2230 |        "    </tr>\n",
2231 |        "    <tr>\n",
2232 |        "      <td>2006</td>\n",
2233 |        "      <td>345.6</td>\n",
2234 |        "      <td>13.6</td>\n",
2235 |        "    </tr>\n",
2236 |        "    <tr>\n",
2237 |        "      <td>2007</td>\n",
2238 |        "      <td>352.2</td>\n",
2239 |        "      <td>7.6</td>\n",
2240 |        "    </tr>\n",
2241 |        "    <tr>\n",
2242 |        "      <td>2008</td>\n",
2243 |        "      <td>367.1</td>\n",
2244 |        "      <td>16.9</td>\n",
2245 |        "    </tr>\n",
2246 |        "    <tr>\n",
2247 |        "      <td>2009</td>\n",
2248 |        "      <td>373.7</td>\n",
2249 |        "      <td>7.2</td>\n",
2250 |        "    </tr>\n",
2251 |        "    <tr>\n",
2252 |        "      <td>2010</td>\n",
2253 |        "      <td>383.8</td>\n",
2254 |        "      <td>10.8</td>\n",
2255 |        "    </tr>\n",
2256 |        "    <tr>\n",
2257 |        "      <td>2011</td>\n",
2258 |        "      <td>397.1</td>\n",
2259 |        "      <td>13.9</td>\n",
2260 |        "    </tr>\n",
2261 |        "    <tr>\n",
2262 |        "      <td>2012</td>\n",
2263 |        "      <td>404.9</td>\n",
2264 |        "      <td>7.8</td>\n",
2265 |        "    </tr>\n",
2266 |        "    <tr>\n",
2267 |        "      <td>2013</td>\n",
2268 |        "      <td>415.1</td>\n",
2269 |        "      <td>10.1</td>\n",
2270 |        "    </tr>\n",
2271 |        "    <tr>\n",
2272 |        "      <td>2014</td>\n",
2273 |        "      <td>425.0</td>\n",
2274 |        "      <td>9.5</td>\n",
2275 |        "    </tr>\n",
2276 |        "    <tr>\n",
2277 |        "      <td>2015</td>\n",
2278 |        "      <td>433.1</td>\n",
2279 |        "      <td>7.7</td>\n",
2280 |        "    </tr>\n",
2281 |        "    <tr>\n",
2282 |        "      <td>2016</td>\n",
2283 |        "      <td>439.3</td>\n",
2284 |        "      <td>5.7</td>\n",
2285 |        "    </tr>\n",
2286 |        "    <tr>\n",
2287 |        "      <td>2017</td>\n",
2288 |        "      <td>448.8</td>\n",
2289 |        "      <td>8.7</td>\n",
2290 |        "    </tr>\n",
2291 |        "    <tr>\n",
2292 |        "      <td>2018</td>\n",
2293 |        "      <td>457.5</td>\n",
2294 |        "      <td>7.9</td>\n",
2295 |        "    </tr>\n",
2296 |        "    <tr>\n",
2297 |        "      <td>2019</td>\n",
2298 |        "      <td>231.0</td>\n",
2299 |        "      <td>3.0</td>\n",
2300 |        "    </tr>\n",
2301 |        "  </tbody>\n",
2302 |        "</table>\n",
2303 |        "</div>"
2304 |       ],
2305 |       "text/plain": [
2306 |        "      index_number  % change_from_previous_year\n",
2307 |        "Year                                           \n",
2308 |        "2001         302.1                         19.0\n",
2309 |        "2002         310.8                         11.5\n",
2310 |        "2003         318.5                         10.0\n",
2311 |        "2004         326.0                          9.4\n",
2312 |        "2005         334.2                         10.0\n",
2313 |        "2006         345.6                         13.6\n",
2314 |        "2007         352.2                          7.6\n",
2315 |        "2008         367.1                         16.9\n",
2316 |        "2009         373.7                          7.2\n",
2317 |        "2010         383.8                         10.8\n",
2318 |        "2011         397.1                         13.9\n",
2319 |        "2012         404.9                          7.8\n",
2320 |        "2013         415.1                         10.1\n",
2321 |        "2014         425.0                          9.5\n",
2322 |        "2015         433.1                          7.7\n",
2323 |        "2016         439.3                          5.7\n",
2324 |        "2017         448.8                          8.7\n",
2325 |        "2018         457.5                          7.9\n",
2326 |        "2019         231.0                          3.0"
2327 |       ]
2328 |      },
2329 |      "execution_count": 18,
2330 |      "metadata": {},
2331 |      "output_type": "execute_result"
2332 |     }
2333 |    ],
2334 |    "source": [
2335 |     "#Grouping data based on Year\n",
2336 |     "sydney_cpi = data2.groupby( data2.Year ).sum()\n",
2337 |     "sydney_cpi"
2338 |    ]
2339 |   },
2340 |   {
2341 |    "cell_type": "code",
2342 |    "execution_count": 19,
2343 |    "metadata": {
2344 |     "collapsed": true
2345 |    },
2346 |    "outputs": [
2347 |     {
2348 |      "data": {
2349 |       "text/html": [
2350 |        "<div>\n",
2351 |        "<style scoped>\n",
2352 |        "    .dataframe tbody tr th:only-of-type {\n",
2353 |        "        vertical-align: middle;\n",
2354 |        "    }\n",
2355 |        "\n",
2356 |        "    .dataframe tbody tr th {\n",
2357 |        "        vertical-align: top;\n",
2358 |        "    }\n",
2359 |        "\n",
2360 |        "    .dataframe thead th {\n",
2361 |        "        text-align: right;\n",
2362 |        "    }\n",
2363 |        "</style>\n",
2364 |        "<table border=\"1\" class=\"dataframe\">\n",
2365 |        "  <thead>\n",
2366 |        "    <tr style=\"text-align: right;\">\n",
2367 |        "      <th></th>\n",
2368 |        "      <th>index_number</th>\n",
2369 |        "      <th>% change_from_previous_year</th>\n",
2370 |        "    </tr>\n",
2371 |        "    <tr>\n",
2372 |        "      <th>Year</th>\n",
2373 |        "      <th></th>\n",
2374 |        "      <th></th>\n",
2375 |        "    </tr>\n",
2376 |        "  </thead>\n",
2377 |        "  <tbody>\n",
2378 |        "    <tr>\n",
2379 |        "      <td>2010</td>\n",
2380 |        "      <td>383.8</td>\n",
2381 |        "      <td>10.8</td>\n",
2382 |        "    </tr>\n",
2383 |        "    <tr>\n",
2384 |        "      <td>2011</td>\n",
2385 |        "      <td>397.1</td>\n",
2386 |        "      <td>13.9</td>\n",
2387 |        "    </tr>\n",
2388 |        "    <tr>\n",
2389 |        "      <td>2012</td>\n",
2390 |        "      <td>404.9</td>\n",
2391 |        "      <td>7.8</td>\n",
2392 |        "    </tr>\n",
2393 |        "    <tr>\n",
2394 |        "      <td>2013</td>\n",
2395 |        "      <td>415.1</td>\n",
2396 |        "      <td>10.1</td>\n",
2397 |        "    </tr>\n",
2398 |        "    <tr>\n",
2399 |        "      <td>2014</td>\n",
2400 |        "      <td>425.0</td>\n",
2401 |        "      <td>9.5</td>\n",
2402 |        "    </tr>\n",
2403 |        "    <tr>\n",
2404 |        "      <td>2015</td>\n",
2405 |        "      <td>433.1</td>\n",
2406 |        "      <td>7.7</td>\n",
2407 |        "    </tr>\n",
2408 |        "    <tr>\n",
2409 |        "      <td>2016</td>\n",
2410 |        "      <td>439.3</td>\n",
2411 |        "      <td>5.7</td>\n",
2412 |        "    </tr>\n",
2413 |        "    <tr>\n",
2414 |        "      <td>2017</td>\n",
2415 |        "      <td>448.8</td>\n",
2416 |        "      <td>8.7</td>\n",
2417 |        "    </tr>\n",
2418 |        "    <tr>\n",
2419 |        "      <td>2018</td>\n",
2420 |        "      <td>457.5</td>\n",
2421 |        "      <td>7.9</td>\n",
2422 |        "    </tr>\n",
2423 |        "    <tr>\n",
2424 |        "      <td>2019</td>\n",
2425 |        "      <td>231.0</td>\n",
2426 |        "      <td>3.0</td>\n",
2427 |        "    </tr>\n",
2428 |        "  </tbody>\n",
2429 |        "</table>\n",
2430 |        "</div>"
2431 |       ],
2432 |       "text/plain": [
2433 |        "      index_number  % change_from_previous_year\n",
2434 |        "Year                                           \n",
2435 |        "2010         383.8                         10.8\n",
2436 |        "2011         397.1                         13.9\n",
2437 |        "2012         404.9                          7.8\n",
2438 |        "2013         415.1                         10.1\n",
2439 |        "2014         425.0                          9.5\n",
2440 |        "2015         433.1                          7.7\n",
2441 |        "2016         439.3                          5.7\n",
2442 |        "2017         448.8                          8.7\n",
2443 |        "2018         457.5                          7.9\n",
2444 |        "2019         231.0                          3.0"
2445 |       ]
2446 |      },
2447 |      "execution_count": 19,
2448 |      "metadata": {},
2449 |      "output_type": "execute_result"
2450 |     }
2451 |    ],
2452 |    "source": [
2453 |     "#Analysing data from year 2010 to 2018\n",
2454 |     "sydney_cpi = sydney_cpi[9:19]\n",
2455 |     "sydney_cpi"
2456 |    ]
2457 |   },
2458 |   {
2459 |    "cell_type": "markdown",
2460 |    "metadata": {},
2461 |    "source": [
2462 |     "VISUALISATION"
2463 |    ]
2464 |   },
2465 |   {
2466 |    "cell_type": "code",
2467 |    "execution_count": 20,
2468 |    "metadata": {
2469 |     "collapsed": true
2470 |    },
2471 |    "outputs": [
2472 |     {
2473 |      "data": {
2474 |       "image/png": 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\n",
2475 |       "text/plain": [
2476 |        "<Figure size 432x288 with 1 Axes>"
2477 |       ]
2478 |      },
2479 |      "metadata": {
2480 |       "needs_background": "light"
2481 |      },
2482 |      "output_type": "display_data"
2483 |     }
2484 |    ],
2485 |    "source": [
2486 |     "plt.plot(sydney_cpi.index, sydney_cpi.index_number)\n",
2487 |     "plt.xlabel(\"years\")\n",
2488 |     "plt.ylabel(\"Consumer Price Index (CPI)\")\n",
2489 |     "plt.title(\"CPI of sydney by year 2010 to 2019\")\n",
2490 |     "plt.show()"
2491 |    ]
2492 |   },
2493 |   {
2494 |    "cell_type": "markdown",
2495 |    "metadata": {},
2496 |    "source": [
2497 |     "Observation: From the above graph, it is clear that CPI have dropped steadily after 2018.\n",
2498 |     "Conclusion of two visuals: House Price Index (HPI) of sydney has rised from the year 2012 and has reached its peak in 2017-18. CPI of sydney has steadily rised from 2010 till 2017 and faced a fall towards 2019\n",
2499 |     "\n",
2500 |     "INSIGHTS\n",
2501 |     "\n",
2502 |     "Rise in HPI encourages consumer spending which leads to economic growth\n",
2503 |     "Fall in HPI affects consumer confidence and local retail sales, which causes a drop in CPI and hence the economic growth\n",
2504 |     "Drop in HPI may create a high level of negative impact in local economy as HPI are linked with other key indicators of socio-economic conditions like local sales, Construction, household wealth and so on."
2505 |    ]
2506 |   },
2507 |   {
2508 |    "cell_type": "markdown",
2509 |    "metadata": {},
2510 |    "source": [
2511 |     "NOTE: REFER INSIGHT REPORT FOR MORE DETAILED INSIGHTS\n",
2512 |     "    \n",
2513 |     "------------------------------------END OF NOTEBOOK-----------------------------------------------"
2514 |    ]
2515 |   }
2516 |  ],
2517 |  "metadata": {
2518 |   "kernelspec": {
2519 |    "display_name": "Python 3",
2520 |    "language": "python",
2521 |    "name": "python3"
2522 |   },
2523 |   "language_info": {
2524 |    "codemirror_mode": {
2525 |     "name": "ipython",
2526 |     "version": 3
2527 |    },
2528 |    "file_extension": ".py",
2529 |    "mimetype": "text/x-python",
2530 |    "name": "python",
2531 |    "nbconvert_exporter": "python",
2532 |    "pygments_lexer": "ipython3",
2533 |    "version": "3.7.4"
2534 |   }
2535 |  },
2536 |  "nbformat": 4,
2537 |  "nbformat_minor": 2
2538 | }
2539 | 


--------------------------------------------------------------------------------