Harp 150_ Lab 12 Data Analysis.ipynb

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Specify dtype option on import or set low_memory=False.\n","  ct_df = pd.read_csv (\"Real_Estate_Sales_2001-2020_GL.csv\")\n"]}]},{"cell_type":"code","source":["ct_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":652},"id":"f_f4Aavfl9Wi","executionInfo":{"status":"ok","timestamp":1712863051801,"user_tz":240,"elapsed":35,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"b6a41115-16c8-43b1-e2d2-3c2a4f73e40e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Serial Number  List Year Date Recorded           Town  \\\n","0             2020348       2020    09/13/2021        Ansonia   \n","1               20002       2020    10/02/2020        Ashford   \n","2              200212       2020    03/09/2021           Avon   \n","3              200243       2020    04/13/2021           Avon   \n","4              200377       2020    07/02/2021           Avon   \n","...               ...        ...           ...            ...   \n","997208         190272       2019    06/24/2020     New London   \n","997209         190284       2019    11/27/2019      Waterbury   \n","997210         190129       2019    04/27/2020  Windsor Locks   \n","997211         190504       2019    06/03/2020     Middletown   \n","997212         190344       2019    12/20/2019        Milford   \n","\n","                      Address  Assessed Value  Sale Amount  Sales Ratio  \\\n","0             230 WAKELEE AVE        150500.0     325000.0     0.463000   \n","1             390 TURNPIKE RD        253000.0     430000.0     0.588300   \n","2            5 CHESTNUT DRIVE        130400.0     179900.0     0.724800   \n","3       111 NORTHINGTON DRIVE        619290.0     890000.0     0.695800   \n","4          70 FAR HILLS DRIVE        862330.0    1447500.0     0.595700   \n","...                       ...             ...          ...          ...   \n","997208            4 BISHOP CT         60410.0      53100.0     1.137665   \n","997209        126 PERKINS AVE         68280.0      76000.0     0.898400   \n","997210         19 HATHAWAY ST        121450.0     210000.0     0.578300   \n","997211           8 BYSTREK DR        203360.0     280000.0     0.726300   \n","997212        250 RESEARCH DR       4035970.0    7450000.0     0.541700   \n","\n","        Property Type Residential Type      Non Use Code Assessor Remarks  \\\n","0          Commercial              NaN               NaN              NaN   \n","1         Residential    Single Family               NaN              NaN   \n","2         Residential            Condo               NaN              NaN   \n","3         Residential    Single Family               NaN              NaN   \n","4         Residential    Single Family               NaN              NaN   \n","...               ...              ...               ...              ...   \n","997208  Single Family    Single Family  14 - Foreclosure              NaN   \n","997209  Single Family    Single Family        25 - Other     PRIVATE SALE   \n","997210  Single Family    Single Family               NaN              NaN   \n","997211  Single Family    Single Family               NaN              NaN   \n","997212            NaN              NaN               NaN              NaN   \n","\n","       OPM remarks Location  \n","0              NaN      NaN  \n","1              NaN      NaN  \n","2              NaN      NaN  \n","3              NaN      NaN  \n","4              NaN      NaN  \n","...            ...      ...  \n","997208         NaN      NaN  \n","997209         NaN      NaN  \n","997210         NaN      NaN  \n","997211         NaN      NaN  \n","997212         NaN      NaN  \n","\n","[997213 rows x 14 columns]"],"text/html":["\n","  <div id=\"df-fc20cfc6-6b9c-4f22-99a9-4218c7f8101e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Serial Number</th>\n","      <th>List Year</th>\n","      <th>Date Recorded</th>\n","      <th>Town</th>\n","      <th>Address</th>\n","      <th>Assessed Value</th>\n","      <th>Sale Amount</th>\n","      <th>Sales Ratio</th>\n","      <th>Property Type</th>\n","      <th>Residential Type</th>\n","      <th>Non Use Code</th>\n","      <th>Assessor Remarks</th>\n","      <th>OPM remarks</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2020348</td>\n","      <td>2020</td>\n","      <td>09/13/2021</td>\n","      <td>Ansonia</td>\n","      <td>230 WAKELEE AVE</td>\n","      <td>150500.0</td>\n","      <td>325000.0</td>\n","      <td>0.463000</td>\n","      <td>Commercial</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>20002</td>\n","      <td>2020</td>\n","      <td>10/02/2020</td>\n","      <td>Ashford</td>\n","      <td>390 TURNPIKE RD</td>\n","      <td>253000.0</td>\n","      <td>430000.0</td>\n","      <td>0.588300</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>200212</td>\n","      <td>2020</td>\n","      <td>03/09/2021</td>\n","      <td>Avon</td>\n","      <td>5 CHESTNUT DRIVE</td>\n","      <td>130400.0</td>\n","      <td>179900.0</td>\n","      <td>0.724800</td>\n","      <td>Residential</td>\n","      <td>Condo</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>200243</td>\n","      <td>2020</td>\n","      <td>04/13/2021</td>\n","      <td>Avon</td>\n","      <td>111 NORTHINGTON DRIVE</td>\n","      <td>619290.0</td>\n","      <td>890000.0</td>\n","      <td>0.695800</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>200377</td>\n","      <td>2020</td>\n","      <td>07/02/2021</td>\n","      <td>Avon</td>\n","      <td>70 FAR HILLS DRIVE</td>\n","      <td>862330.0</td>\n","      <td>1447500.0</td>\n","      <td>0.595700</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>997208</th>\n","      <td>190272</td>\n","      <td>2019</td>\n","      <td>06/24/2020</td>\n","      <td>New London</td>\n","      <td>4 BISHOP CT</td>\n","      <td>60410.0</td>\n","      <td>53100.0</td>\n","      <td>1.137665</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>14 - Foreclosure</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>997209</th>\n","      <td>190284</td>\n","      <td>2019</td>\n","      <td>11/27/2019</td>\n","      <td>Waterbury</td>\n","      <td>126 PERKINS AVE</td>\n","      <td>68280.0</td>\n","      <td>76000.0</td>\n","      <td>0.898400</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>25 - Other</td>\n","      <td>PRIVATE SALE</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>997210</th>\n","      <td>190129</td>\n","      <td>2019</td>\n","      <td>04/27/2020</td>\n","      <td>Windsor Locks</td>\n","      <td>19 HATHAWAY ST</td>\n","      <td>121450.0</td>\n","      <td>210000.0</td>\n","      <td>0.578300</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>997211</th>\n","      <td>190504</td>\n","      <td>2019</td>\n","      <td>06/03/2020</td>\n","      <td>Middletown</td>\n","      <td>8 BYSTREK DR</td>\n","      <td>203360.0</td>\n","      <td>280000.0</td>\n","      <td>0.726300</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>997212</th>\n","      <td>190344</td>\n","      <td>2019</td>\n","      <td>12/20/2019</td>\n","      <td>Milford</td>\n","      <td>250 RESEARCH DR</td>\n","      <td>4035970.0</td>\n","      <td>7450000.0</td>\n","      <td>0.541700</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>997213 rows × 14 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc20cfc6-6b9c-4f22-99a9-4218c7f8101e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-fc20cfc6-6b9c-4f22-99a9-4218c7f8101e button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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If you want to modify your df: df.sort_values(”column name”, inplace=True)\n","\n","ct_df.sort_values(\"List Year\")\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":687},"id":"vJvbih12mBJE","executionInfo":{"status":"ok","timestamp":1712863091641,"user_tz":240,"elapsed":385,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"763d0356-58fa-4afe-8e91-f22f4571b102"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Serial Number  List Year Date Recorded         Town  \\\n","91405          11611       2001    07/19/2002      Danbury   \n","81567          12433       2001    08/01/2002   Bridgeport   \n","81566          10339       2001    06/24/2002       Bethel   \n","81565          10002       2001    10/04/2001       Goshen   \n","81564          10089       2001    09/19/2002  Barkhamsted   \n","...              ...        ...           ...          ...   \n","44594       20160710       2020    06/28/2021      Redding   \n","44593         202869       2020    08/17/2021      Norwalk   \n","44592          20468       2020    04/30/2021   Ridgefield   \n","44590         200003       2020    10/01/2020      Shelton   \n","0            2020348       2020    09/13/2021      Ansonia   \n","\n","                    Address  Assessed Value  Sale Amount  Sales Ratio  \\\n","91405           16 ALAN AVE        126700.0     285000.0     0.444561   \n","81567      106-108 SAVOY ST        111300.0     262000.0     0.424809   \n","81566       17 CASTLE HL DR        107750.0     236000.0     0.456568   \n","81565        16 DEER RUN LN         36830.0      95000.0     0.387684   \n","81564           9 SCHOOL ST         95810.0     157000.0     0.610255   \n","...                     ...             ...          ...          ...   \n","44594  14 GALLOWS HILL ROAD        404300.0     770000.0     0.525000   \n","44593  5 VAN ZANT ST UNIT 5        177660.0     295000.0     0.602200   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Residential    Single Family          NaN              NaN   \n","0        Commercial              NaN          NaN              NaN   \n","\n","      OPM remarks                    Location  \n","91405         NaN  POINT (-73.46164 41.41207)  \n","81567         NaN                         NaN  \n","81566         NaN                         NaN  \n","81565         NaN                         NaN  \n","81564         NaN                         NaN  \n","...           ...                         ...  \n","44594         NaN  POINT (-73.38738 41.31428)  \n","44593         NaN                         NaN  \n","44592         NaN                         NaN  \n","44590         NaN  POINT (-73.12783 41.31357)  \n","0             NaN                         NaN  \n","\n","[997213 rows x 14 columns]"],"text/html":["\n","  <div id=\"df-1a71fc01-2a4e-4f5b-b8ff-322ffd0110fa\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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<td>295000.0</td>\n","      <td>0.602200</td>\n","      <td>Residential</td>\n","      <td>Condo</td>\n","      <td>NaN</td>\n","      <td>3-35-3-5</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>44592</th>\n","      <td>20468</td>\n","      <td>2020</td>\n","      <td>04/30/2021</td>\n","      <td>Ridgefield</td>\n","      <td>16 MCKEON PLACE</td>\n","      <td>403560.0</td>\n","      <td>620000.0</td>\n","      <td>0.650900</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>44590</th>\n","      <td>200003</td>\n","      <td>2020</td>\n","      <td>10/01/2020</td>\n","      <td>Shelton</td>\n","      <td>45 INDEPENDENCE DR</td>\n","      <td>394400.0</td>\n","      <td>655000.0</td>\n","      <td>0.602100</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (-73.12783 41.31357)</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>2020348</td>\n","      <td>2020</td>\n","      <td>09/13/2021</td>\n","      <td>Ansonia</td>\n","      <td>230 WAKELEE AVE</td>\n","      <td>150500.0</td>\n","      <td>325000.0</td>\n","      <td>0.463000</td>\n","      <td>Commercial</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>997213 rows × 14 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a71fc01-2a4e-4f5b-b8ff-322ffd0110fa')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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Danbury   \n","\n","                     Address  Assessed Value  Sale Amount  Sales Ratio  \\\n","0            230 WAKELEE AVE        150500.0     325000.0     0.463000   \n","68      14 ESSEX GLEN DR #12        335800.0     520000.0     0.645700   \n","31             1467 SOUTH ST        359800.0     480000.0     0.749500   \n","30            74 LAKEVIEW DR        142800.0     245000.0     0.582800   \n","14               609 CAMP ST        144340.0     249900.0     0.577500   \n","...                      ...             ...          ...          ...   \n","113648        297 CHARLES ST         56500.0     127000.0     0.444882   \n","113649     20 SCARBOROUGH RD         72170.0      75000.0     0.962267   \n","113650    243 B HERITAGE VLG         77440.0     169000.0     0.458225   \n","113651         56 JESSICA DR        128030.0     220000.0     0.581955   \n","91405            16 ALAN AVE        126700.0     285000.0     0.444561   \n","\n","       Property Type Residential Type Non Use Code Assessor Remarks  \\\n","0         Commercial              NaN          NaN              NaN   \n","68       Residential            Condo          NaN              NaN   \n","31        Commercial              NaN          NaN              NaN   \n","30       Residential    Single Family          NaN              NaN   \n","14       Residential    Single Family          NaN              NaN   \n","...              ...              ...          ...              ...   \n","113648           NaN              NaN          NaN              NaN   \n","113649           NaN              NaN          NaN              NaN   \n","113650           NaN              NaN          NaN              NaN   \n","113651           NaN              NaN          NaN              NaN   \n","91405            NaN              NaN          NaN              NaN   \n","\n","       OPM remarks                    Location  \n","0              NaN                         NaN  \n","68             NaN         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 <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Serial Number</th>\n","      <th>List Year</th>\n","      <th>Date Recorded</th>\n","      <th>Town</th>\n","      <th>Address</th>\n","      <th>Assessed Value</th>\n","      <th>Sale Amount</th>\n","      <th>Sales Ratio</th>\n","      <th>Property Type</th>\n","      <th>Residential Type</th>\n","      <th>Non Use Code</th>\n","      <th>Assessor Remarks</th>\n","      <th>OPM remarks</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2020348</td>\n","      <td>2020</td>\n","      <td>09/13/2021</td>\n","      <td>Ansonia</td>\n","      <td>230 WAKELEE AVE</td>\n","      <td>150500.0</td>\n","      <td>325000.0</td>\n","      <td>0.463000</td>\n","      <td>Commercial</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>68</th>\n","      <td>200140</td>\n","      <td>2020</td>\n","      <td>04/28/2021</td>\n","      <td>Essex</td>\n","      <td>14 ESSEX GLEN DR #12</td>\n","      <td>335800.0</td>\n","      <td>520000.0</td>\n","      <td>0.645700</td>\n","      <td>Residential</td>\n","      <td>Condo</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>200282</td>\n","      <td>2020</td>\n","      <td>07/15/2021</td>\n","      <td>Coventry</td>\n","      <td>1467 SOUTH ST</td>\n","      <td>359800.0</td>\n","      <td>480000.0</td>\n","      <td>0.749500</td>\n","      <td>Commercial</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>200101</td>\n","      <td>2020</td>\n","      <td>01/07/2021</td>\n","      <td>Coventry</td>\n","      <td>74 LAKEVIEW DR</td>\n","      <td>142800.0</td>\n","      <td>245000.0</td>\n","      <td>0.582800</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>201295</td>\n","      <td>2020</td>\n","      <td>09/09/2021</td>\n","      <td>Bristol</td>\n","      <td>609 CAMP ST</td>\n","      <td>144340.0</td>\n","      <td>249900.0</td>\n","      <td>0.577500</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>113648</th>\n","      <td>10991</td>\n","      <td>2001</td>\n","      <td>09/30/2002</td>\n","      <td>Torrington</td>\n","      <td>297 CHARLES ST</td>\n","      <td>56500.0</td>\n","      <td>127000.0</td>\n","      <td>0.444882</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>113649</th>\n","      <td>10569</td>\n","      <td>2001</td>\n","      <td>08/19/2002</td>\n","      <td>Windsor</td>\n","      <td>20 SCARBOROUGH RD</td>\n","      <td>72170.0</td>\n","      <td>75000.0</td>\n","      <td>0.962267</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>113650</th>\n","      <td>10552</td>\n","      <td>2001</td>\n","      <td>08/23/2002</td>\n","      <td>Southbury</td>\n","      <td>243 B HERITAGE VLG</td>\n","      <td>77440.0</td>\n","      <td>169000.0</td>\n","      <td>0.458225</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (-73.22516 41.48984)</td>\n","    </tr>\n","    <tr>\n","      <th>113651</th>\n","      <td>10016</td>\n","      <td>2001</td>\n","      <td>10/12/2001</td>\n","      <td>South Windsor</td>\n","      <td>56 JESSICA DR</td>\n","      <td>128030.0</td>\n","      <td>220000.0</td>\n","      <td>0.581955</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>91405</th>\n","      <td>11611</td>\n","      <td>2001</td>\n","      <td>07/19/2002</td>\n","      <td>Danbury</td>\n","      <td>16 ALAN AVE</td>\n","      <td>126700.0</td>\n","      <td>285000.0</td>\n","      <td>0.444561</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (-73.46164 41.41207)</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>997213 rows × 14 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dee87cff-42c6-4748-a374-5c0cbac8347b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","  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NaN   \n","...                                                   ...         ...   \n","856309                                                NaN         NaN   \n","853637                                                NaN         NaN   \n","123445                                                NaN         NaN   \n","198947                                                NaN         NaN   \n","667843                    CAMPSITE LOT AT COOP CAMPGROUND         NaN   \n","\n","                          Location  \n","444657                         NaN  \n","338981                         NaN  \n","837786                         NaN  \n","19637                          NaN  \n","826756                         NaN  \n","...                            ...  \n","856309                         NaN  \n","853637                         NaN  \n","123445  POINT (-72.04527 41.95857)  \n","198947  POINT (-72.06184 41.95154)  \n","667843                         NaN  \n","\n","[997213 rows x 14 columns]"],"text/html":["\n","  <div id=\"df-086c579f-607c-44da-9f54-0fb98623daa2\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Serial Number</th>\n","      <th>List Year</th>\n","      <th>Date Recorded</th>\n","      <th>Town</th>\n","      <th>Address</th>\n","      <th>Assessed Value</th>\n","      <th>Sale Amount</th>\n","      <th>Sales Ratio</th>\n","      <th>Property Type</th>\n","      <th>Residential Type</th>\n","      <th>Non Use Code</th>\n","      <th>Assessor Remarks</th>\n","      <th>OPM remarks</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>444657</th>\n","      <td>70086</td>\n","      <td>2007</td>\n","      <td>12/18/2007</td>\n","      <td>***Unknown***</td>\n","      <td>18 MATHIEU LANE</td>\n","      <td>66540.0</td>\n","      <td>282450.0</td>\n","      <td>0.235582</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>07 - Change in Property</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>338981</th>\n","      <td>50060</td>\n","      <td>2005</td>\n","      <td>09/21/2006</td>\n","      <td>Andover</td>\n","      <td>141 BOSTON HL RD</td>\n","      <td>133500.0</td>\n","      <td>247000.0</td>\n","      <td>0.540486</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>7</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>837786</th>\n","      <td>17031</td>\n","      <td>2017</td>\n","      <td>03/26/2018</td>\n","      <td>Andover</td>\n","      <td>48 PINE RIDGE DR</td>\n","      <td>122500.0</td>\n","      <td>170000.0</td>\n","      <td>0.720500</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>19637</th>\n","      <td>20046</td>\n","      <td>2020</td>\n","      <td>06/16/2021</td>\n","      <td>Andover</td>\n","      <td>31 BURNAP BROOK</td>\n","      <td>169000.0</td>\n","      <td>250000.0</td>\n","      <td>0.676000</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>826756</th>\n","      <td>17054</td>\n","      <td>2017</td>\n","      <td>07/11/2018</td>\n","      <td>Andover</td>\n","      <td>TOWNSEND 27/20/10</td>\n","      <td>2690.0</td>\n","      <td>13878.0</td>\n","      <td>0.193832</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>25 - Other</td>\n","      <td>3 PARCELS MARKETED TOGETHER SOLD AS 3 SEPARATE...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>856309</th>\n","      <td>170164</td>\n","      <td>2017</td>\n","      <td>07/31/2018</td>\n","      <td>Woodstock</td>\n","      <td>261 CHILD RD</td>\n","      <td>142970.0</td>\n","      <td>241000.0</td>\n","      <td>0.593200</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>853637</th>\n","      <td>170124</td>\n","      <td>2017</td>\n","      <td>06/11/2018</td>\n","      <td>Woodstock</td>\n","      <td>NIGHTINGALE RD</td>\n","      <td>870.0</td>\n","      <td>65000.0</td>\n","      <td>0.013300</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>28 - Use Assessment</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>123445</th>\n","      <td>10059</td>\n","      <td>2001</td>\n","      <td>12/13/2001</td>\n","      <td>Woodstock</td>\n","      <td>66 BRADFORD CORNER RD</td>\n","      <td>111550.0</td>\n","      <td>210000.0</td>\n","      <td>0.531190</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (-72.04527 41.95857)</td>\n","    </tr>\n","    <tr>\n","      <th>198947</th>\n","      <td>20041</td>\n","      <td>2002</td>\n","      <td>11/19/2002</td>\n","      <td>Woodstock</td>\n","      <td>30 RED CEDAR DR</td>\n","      <td>60390.0</td>\n","      <td>135000.0</td>\n","      <td>0.447333</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (-72.06184 41.95154)</td>\n","    </tr>\n","    <tr>\n","      <th>667843</th>\n","      <td>120140</td>\n","      <td>2012</td>\n","      <td>08/16/2013</td>\n","      <td>Woodstock</td>\n","      <td>47 MEADOWSIDE COOP</td>\n","      <td>0.0</td>\n","      <td>4000.0</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>25 - Other</td>\n","      <td>CAMPSITE LOT AT COOP CAMPGROUND</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>997213 rows × 14 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-086c579f-607c-44da-9f54-0fb98623daa2')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-086c579f-607c-44da-9f54-0fb98623daa2 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Foreclosure   \n","318167            NaN              NaN                      NaN   \n","793407            NaN              NaN            24 - Plottage   \n","985826  Single Family    Single Family                      NaN   \n","299873            NaN              NaN                      NaN   \n","...               ...              ...                      ...   \n","487686  Single Family    Single Family                      NaN   \n","752495  Single Family    Single Family                      NaN   \n","693537  Single Family    Single Family                      NaN   \n","333523            NaN              NaN                      NaN   \n","444657  Single Family    Single Family  07 - Change in Property   \n","\n","       Assessor Remarks OPM remarks                    Location  \n","500485   FORECLSED SALE         NaN  POINT (-72.07386 42.02309)  \n","318167              NaN         NaN                         NaN  \n","793407              NaN         NaN                         NaN  \n","985826              NaN         NaN                         NaN  \n","299873              NaN         NaN                         NaN  \n","...                 ...         ...                         ...  \n","487686              NaN         NaN  POINT (-72.38405 41.70116)  \n","752495              NaN         NaN   POINT (-72.36092 41.7078)  \n","693537              NaN         NaN  POINT (-72.39085 41.74249)  \n","333523              NaN         NaN  POINT (-72.39845 41.75072)  \n","444657              NaN         NaN                         NaN  \n","\n","[997213 rows x 14 columns]"],"text/html":["\n","  <div id=\"df-999a327f-826d-4cfa-8131-5dc06fb60671\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Serial Number</th>\n","      <th>List Year</th>\n","      <th>Date Recorded</th>\n","      <th>Town</th>\n","      <th>Address</th>\n","      <th>Assessed Value</th>\n","      <th>Sale Amount</th>\n","      <th>Sales Ratio</th>\n","      <th>Property Type</th>\n","      <th>Residential Type</th>\n","      <th>Non Use Code</th>\n","      <th>Assessor Remarks</th>\n","      <th>OPM remarks</th>\n","      <th>Location</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>500485</th>\n","      <td>80086</td>\n","      <td>2008</td>\n","      <td>07/01/2009</td>\n","      <td>Woodstock</td>\n","      <td>1979 RT 198</td>\n","      <td>229700.0</td>\n","      <td>196000.0</td>\n","      <td>1.171939</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>14 - 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Change in Property</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>997213 rows × 14 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-999a327f-826d-4cfa-8131-5dc06fb60671')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-999a327f-826d-4cfa-8131-5dc06fb60671 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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In comparison, a dataframe is two-dimensional array, that is a collections of columns.\n"],"metadata":{"id":"rOrKspjHn_oH"}},{"cell_type":"markdown","source":["* Next, we want to turn our returned pandas series into a df and give it associated column names “Year” and “Count” !\n","(Note: you may wonder since the definition of a series is that it is only one column or a row. Then, how come we have two columns of data, i e. years and counts, in our series? The thing is years is actually used as the index of the series, which does not count as a column.)\n","\n","\n","* This also explains why .reset_index() actually makes a series into a df. By adding index, the original index years become a real column. Thus, it turned a series from one dimensional to two, which is a dataframe!)\n","pd.DataFrame() is also a common way to form a df!\n","\n"],"metadata":{"id":"0y0dQ5NwoJBJ"}},{"cell_type":"code","source":["# you're adding a new dataframe\n","\n","yearly_counts = ct_df[\"List Year\"].value_counts()\n","\n","yearly_counts\n","\n","# below are series, a one dimensional array/ long list of data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u_vCdca8oFVt","executionInfo":{"status":"ok","timestamp":1712863704871,"user_tz":240,"elapsed":207,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"a5ea4eb2-8cae-4f20-f5ba-c63da2dc4273"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["List Year\n","2004    84056\n","2020    66592\n","2003    64239\n","2005    61602\n","2002    59682\n","2001    59584\n","2019    58954\n","2018    50709\n","2016    49773\n","2014    49563\n","2006    48785\n","2015    46651\n","2017    45691\n","2009    42508\n","2013    39943\n","2012    35973\n","2007    35617\n","2010    33491\n","2008    32735\n","2011    31065\n","Name: count, dtype: int64"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["# two ways to ocnvert a series into a data frame (DF)\n","# using reset_index(). this makes a series into a data frame\n","\n","yearly_counts_df1 = yearly_counts.reset_index()\n","yearly_counts_df1.columns = [\"Year\",\"Count\"]\n","\n","# using pd.dataframe\n","yearly_counts_df2 = pd.DataFrame({\"Year\": yearly_counts.index, \"Count\":yearly_counts.values})\n","\n","yearly_counts_df2\n","\n","# this kinda pairs the data together like a dictionary, so the year correspons to the cout"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":677},"id":"cR6Vz9oDogdw","executionInfo":{"status":"ok","timestamp":1712863835238,"user_tz":240,"elapsed":216,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"34fe49bc-757e-48f8-d3c8-9a1eda88e503"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Year  Count\n","0   2004  84056\n","1   2020  66592\n","2   2003  64239\n","3   2005  61602\n","4   2002  59682\n","5   2001  59584\n","6   2019  58954\n","7   2018  50709\n","8   2016  49773\n","9   2014  49563\n","10  2006  48785\n","11  2015  46651\n","12  2017  45691\n","13  2009  42508\n","14  2013  39943\n","15  2012  35973\n","16  2007  35617\n","17  2010  33491\n","18  2008  32735\n","19  2011  31065"],"text/html":["\n","  <div id=\"df-8ae65f3c-d7a2-4dbd-b7bc-6aa7d19d8376\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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\"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","source":["#Exercise #2: #\n","\n","\n","*   Can you find out  how many homes are listed per town\n","* Can you make your series into a df and give column names “Town” and “Count” (using both reset_index() and pd.DataFrame())?\n","\n","*   List item\n","\n"],"metadata":{"id":"MVf-NUH-pdTX"}},{"cell_type":"code","source":["town_count = ct_df[\"Town\"].value_counts()\n","\n","town_count"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EhxLsK3Ipbqa","executionInfo":{"status":"ok","timestamp":1712864455367,"user_tz":240,"elapsed":192,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"d8109c59-afa5-44d1-eef1-4776b4929c91"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Town\n","Bridgeport       34201\n","Stamford         32529\n","Waterbury        28506\n","Norwalk          23960\n","New Haven        21346\n","                 ...  \n","Hartland           448\n","Scotland           430\n","Canaan             429\n","Union              261\n","***Unknown***        1\n","Name: count, Length: 170, dtype: int64"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","source":["town_counts_df1 = town_count.reset_index()\n","town_counts_df1.columns = [\"Town\", \"Count\"]\n","\n","town_counts_df1\n","\n","town_counts_df2 = pd.DataFrame({\"Town\": town_count.index, \"Count\": town_count.values})\n","\n","town_counts_df2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"-ZRmKuL2qMb-","executionInfo":{"status":"ok","timestamp":1712864563296,"user_tz":240,"elapsed":23,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"cff75cd5-628e-4e77-c05f-7c8ef9b5dd07"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              Town  Count\n","0       Bridgeport  34201\n","1         Stamford  32529\n","2        Waterbury  28506\n","3          Norwalk  23960\n","4        New Haven  21346\n","..             ...    ...\n","165       Hartland    448\n","166       Scotland    430\n","167         Canaan    429\n","168          Union    261\n","169  ***Unknown***      1\n","\n","[170 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-0555a62b-8111-44b7-8dfe-4a4e0c1c3585\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Town</th>\n","      <th>Count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Bridgeport</td>\n","      <td>34201</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Stamford</td>\n","      <td>32529</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Waterbury</td>\n","      <td>28506</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Norwalk</td>\n","      <td>23960</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>New Haven</td>\n","      <td>21346</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>165</th>\n","      <td>Hartland</td>\n","      <td>448</td>\n","    </tr>\n","    <tr>\n","      <th>166</th>\n","      <td>Scotland</td>\n","      <td>430</td>\n","    </tr>\n","    <tr>\n","      <th>167</th>\n","      <td>Canaan</td>\n","      <td>429</td>\n","    </tr>\n","    <tr>\n","      <th>168</th>\n","      <td>Union</td>\n","      <td>261</td>\n","    </tr>\n","    <tr>\n","      <th>169</th>\n","      <td>***Unknown***</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>170 rows × 2 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0555a62b-8111-44b7-8dfe-4a4e0c1c3585')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: 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First, let’s see how many of them have null values using the seaborn heatmap.\n","\n","We can also use *.isnull()* to check whether a series or a dataframe has missing values. *df.info()* works too! On the right, we use *seaborn.heatmap()* to show the missing value distribution.\n","\n","Looks like we can get rid of a few columns that are mostly empty!\n"],"metadata":{"id":"qbeyCP9Hrze9"}},{"cell_type":"code","source":["!pip install seaborn"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qDgZvRX7r6wB","executionInfo":{"status":"ok","timestamp":1712864642183,"user_tz":240,"elapsed":8848,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"45e2a534-b48d-4f3c-a511-f1647ee4fe62"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.1)\n","Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.25.2)\n","Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn) (2.0.3)\n","Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.10/dist-packages (from seaborn) (3.7.1)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.0)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (9.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2023.4)\n","Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2024.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"]}]},{"cell_type":"code","source":["import seaborn as sns\n","\n","columns = ct_df.columns\n","colors = [\"#000099\",\"#ffff00\"]\n","\n","sns.heatmap(ct_df[columns].isnull(), cmap = sns.color_palette(colors))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"id":"KAHLCF8NsDRR","executionInfo":{"status":"ok","timestamp":1712864761029,"user_tz":240,"elapsed":25211,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"496118f5-26ca-4f7e-d839-9673ee08b84f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":43},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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learned to use .drop() to delete unwanted columns*\n","\n","I created a copy of the dataframe first because I wanted to keep an original version. Therefore, if we do anything wrong with the copy, we can easily roll back! (In the same vein, before you make any permanent changes on the df, you can test it out on a new copy first!)\n","Refresher: axis = 1 lets the computer know you’re deleting a column\n","inplace = True makes the change permanent\n","Once you do this, you should have 11 columns instead of 14\n"],"metadata":{"id":"3QILL0JHsrnr"}},{"cell_type":"code","source":["ct_df1 = ct_df.copy()\n","\n","ct_df1.drop(labels=[\"OPM remarks\",\"Location\",\"Non Use Code\"], axis = 1, inplace = True)\n","\n","\n","ct_df1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":617},"id":"diV5GFYKscyE","executionInfo":{"status":"ok","timestamp":1712865116177,"user_tz":240,"elapsed":681,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"1f6cf707-85db-453d-f98d-cd59e45dcd28"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Serial Number  List Year Date Recorded         Town  \\\n","91405          11611       2001    07/19/2002      Danbury   \n","81567          12433       2001    08/01/2002   Bridgeport   \n","81566          10339       2001    06/24/2002       Bethel   \n","81565          10002       2001    10/04/2001       Goshen   \n","81564          10089       2001    09/19/2002  Barkhamsted   \n","...              ...        ...           ...          ...   \n","44594       20160710       2020    06/28/2021      Redding   \n","44593         202869       2020    08/17/2021      Norwalk   \n","44592          20468       2020    04/30/2021   Ridgefield   \n","44590         200003       2020    10/01/2020      Shelton   \n","0            2020348       2020    09/13/2021      Ansonia   \n","\n","                    Address  Assessed Value  Sale Amount  Sales Ratio  \\\n","91405           16 ALAN AVE        126700.0     285000.0     0.444561   \n","81567      106-108 SAVOY ST        111300.0     262000.0     0.424809   \n","81566       17 CASTLE HL DR        107750.0     236000.0     0.456568   \n","81565        16 DEER RUN LN         36830.0      95000.0     0.387684   \n","81564           9 SCHOOL ST         95810.0     157000.0     0.610255   \n","...                     ...             ...          ...          ...   \n","44594  14 GALLOWS HILL ROAD        404300.0     770000.0     0.525000   \n","44593  5 VAN ZANT ST UNIT 5        177660.0     295000.0     0.602200   \n","44592       16 MCKEON PLACE        403560.0     620000.0     0.650900   \n","44590    45 INDEPENDENCE DR        394400.0     655000.0     0.602100   \n","0           230 WAKELEE AVE        150500.0     325000.0     0.463000   \n","\n","      Property Type Residential Type Assessor Remarks  \n","91405           NaN              NaN              NaN  \n","81567           NaN              NaN              NaN  \n","81566           NaN              NaN              NaN  \n","81565           NaN              NaN              NaN  \n","81564           NaN              NaN              NaN  \n","...             ...              ...              ...  \n","44594   Residential    Single Family              NaN  \n","44593   Residential            Condo         3-35-3-5  \n","44592   Residential    Single Family              NaN  \n","44590   Residential    Single Family              NaN  \n","0        Commercial              NaN              NaN  \n","\n","[997213 rows x 11 columns]"],"text/html":["\n","  <div id=\"df-1db3c72a-b37a-4c2f-ab83-0457f5df4749\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Serial Number</th>\n","      <th>List Year</th>\n","      <th>Date Recorded</th>\n","      <th>Town</th>\n","      <th>Address</th>\n","      <th>Assessed Value</th>\n","      <th>Sale Amount</th>\n","      <th>Sales Ratio</th>\n","      <th>Property Type</th>\n","      <th>Residential Type</th>\n","      <th>Assessor Remarks</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>91405</th>\n","      <td>11611</td>\n","      <td>2001</td>\n","      <td>07/19/2002</td>\n","      <td>Danbury</td>\n","      <td>16 ALAN AVE</td>\n","      <td>126700.0</td>\n","      <td>285000.0</td>\n","      <td>0.444561</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>81567</th>\n","      <td>12433</td>\n","      <td>2001</td>\n","      <td>08/01/2002</td>\n","      <td>Bridgeport</td>\n","      <td>106-108 SAVOY ST</td>\n","      <td>111300.0</td>\n","      <td>262000.0</td>\n","      <td>0.424809</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>81566</th>\n","      <td>10339</td>\n","      <td>2001</td>\n","      <td>06/24/2002</td>\n","      <td>Bethel</td>\n","      <td>17 CASTLE HL DR</td>\n","      <td>107750.0</td>\n","      <td>236000.0</td>\n","      <td>0.456568</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>81565</th>\n","      <td>10002</td>\n","      <td>2001</td>\n","      <td>10/04/2001</td>\n","      <td>Goshen</td>\n","      <td>16 DEER RUN LN</td>\n","      <td>36830.0</td>\n","      <td>95000.0</td>\n","      <td>0.387684</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>81564</th>\n","      <td>10089</td>\n","      <td>2001</td>\n","      <td>09/19/2002</td>\n","      <td>Barkhamsted</td>\n","      <td>9 SCHOOL ST</td>\n","      <td>95810.0</td>\n","      <td>157000.0</td>\n","      <td>0.610255</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>44594</th>\n","      <td>20160710</td>\n","      <td>2020</td>\n","      <td>06/28/2021</td>\n","      <td>Redding</td>\n","      <td>14 GALLOWS HILL ROAD</td>\n","      <td>404300.0</td>\n","      <td>770000.0</td>\n","      <td>0.525000</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>44593</th>\n","      <td>202869</td>\n","      <td>2020</td>\n","      <td>08/17/2021</td>\n","      <td>Norwalk</td>\n","      <td>5 VAN ZANT ST UNIT 5</td>\n","      <td>177660.0</td>\n","      <td>295000.0</td>\n","      <td>0.602200</td>\n","      <td>Residential</td>\n","      <td>Condo</td>\n","      <td>3-35-3-5</td>\n","    </tr>\n","    <tr>\n","      <th>44592</th>\n","      <td>20468</td>\n","      <td>2020</td>\n","      <td>04/30/2021</td>\n","      <td>Ridgefield</td>\n","      <td>16 MCKEON PLACE</td>\n","      <td>403560.0</td>\n","      <td>620000.0</td>\n","      <td>0.650900</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>44590</th>\n","      <td>200003</td>\n","      <td>2020</td>\n","      <td>10/01/2020</td>\n","      <td>Shelton</td>\n","      <td>45 INDEPENDENCE DR</td>\n","      <td>394400.0</td>\n","      <td>655000.0</td>\n","      <td>0.602100</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>2020348</td>\n","      <td>2020</td>\n","      <td>09/13/2021</td>\n","      <td>Ansonia</td>\n","      <td>230 WAKELEE AVE</td>\n","      <td>150500.0</td>\n","      <td>325000.0</td>\n","      <td>0.463000</td>\n","      <td>Commercial</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>997213 rows × 11 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1db3c72a-b37a-4c2f-ab83-0457f5df4749')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 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axis\"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-57-bb05b1445900>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# ct_df1.drop)(colmns - [\"OPM remarks\", \"Location\", \"Non Use Code\"], inplace = true\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mct_df1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"OPM remarks\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Location\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Non Use Code\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m 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\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6697\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6698\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6699\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{list(labels[mask])} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6700\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6701\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: \"['OPM remarks', 'Location', 'Non Use Code'] not found in axis\""]}]},{"cell_type":"code","source":["ct_df1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":617},"id":"wZ5AlBCnu2rj","executionInfo":{"status":"ok","timestamp":1712865391908,"user_tz":240,"elapsed":26,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"57718a10-c78f-4a06-8dae-250a6d2a4ac7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Serial Number  List Year Date Recorded         Town  \\\n","91405          11611       2001    07/19/2002      Danbury   \n","81567          12433       2001    08/01/2002   Bridgeport   \n","81566          10339       2001    06/24/2002       Bethel   \n","81565          10002       2001    10/04/2001       Goshen   \n","81564          10089       2001    09/19/2002  Barkhamsted   \n","...              ...        ...           ...          ...   \n","44594       20160710       2020    06/28/2021      Redding   \n","44593         202869       2020    08/17/2021      Norwalk   \n","44592          20468       2020    04/30/2021   Ridgefield   \n","44590         200003       2020    10/01/2020      Shelton   \n","0            2020348       2020    09/13/2021      Ansonia   \n","\n","                    Address  Assessed Value  Sale Amount  Sales Ratio  \\\n","91405           16 ALAN AVE        126700.0     285000.0     0.444561   \n","81567      106-108 SAVOY ST        111300.0     262000.0     0.424809   \n","81566       17 CASTLE HL DR        107750.0     236000.0     0.456568   \n","81565        16 DEER RUN LN         36830.0      95000.0     0.387684   \n","81564           9 SCHOOL ST         95810.0     157000.0     0.610255   \n","...                     ...             ...          ...          ...   \n","44594  14 GALLOWS HILL ROAD        404300.0     770000.0     0.525000   \n","44593  5 VAN ZANT ST UNIT 5        177660.0     295000.0     0.602200   \n","44592       16 MCKEON PLACE        403560.0     620000.0     0.650900   \n","44590    45 INDEPENDENCE DR        394400.0     655000.0     0.602100   \n","0           230 WAKELEE AVE        150500.0     325000.0     0.463000   \n","\n","      Property Type Residential Type Assessor Remarks  \n","91405           NaN              NaN              NaN  \n","81567           NaN              NaN              NaN  \n","81566           NaN              NaN              NaN  \n","81565           NaN              NaN              NaN  \n","81564           NaN              NaN              NaN  \n","...             ...              ...              ...  \n","44594   Residential    Single Family              NaN  \n","44593   Residential            Condo         3-35-3-5  \n","44592   Residential    Single Family              NaN  \n","44590   Residential    Single Family              NaN  \n","0        Commercial              NaN              NaN  \n","\n","[997213 rows x 11 columns]"],"text/html":["\n","  <div id=\"df-e58a92f6-3ffd-4a79-ba40-51934c53a239\" 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ALAN AVE</td>\n","      <td>126700.0</td>\n","      <td>285000.0</td>\n","      <td>0.444561</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>81567</th>\n","      <td>12433</td>\n","      <td>2001</td>\n","      <td>08/01/2002</td>\n","      <td>Bridgeport</td>\n","      <td>106-108 SAVOY ST</td>\n","      <td>111300.0</td>\n","      <td>262000.0</td>\n","      <td>0.424809</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>81566</th>\n","      <td>10339</td>\n","      <td>2001</td>\n","      <td>06/24/2002</td>\n","      <td>Bethel</td>\n","      <td>17 CASTLE HL DR</td>\n","      <td>107750.0</td>\n","      <td>236000.0</td>\n","      <td>0.456568</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>81565</th>\n","      <td>10002</td>\n","      <td>2001</td>\n","      <td>10/04/2001</td>\n","      <td>Goshen</td>\n","      <td>16 DEER RUN LN</td>\n","      <td>36830.0</td>\n","      <td>95000.0</td>\n","      <td>0.387684</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>81564</th>\n","      <td>10089</td>\n","      <td>2001</td>\n","      <td>09/19/2002</td>\n","      <td>Barkhamsted</td>\n","      <td>9 SCHOOL ST</td>\n","      <td>95810.0</td>\n","      <td>157000.0</td>\n","      <td>0.610255</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>44594</th>\n","      <td>20160710</td>\n","      <td>2020</td>\n","      <td>06/28/2021</td>\n","      <td>Redding</td>\n","      <td>14 GALLOWS HILL ROAD</td>\n","      <td>404300.0</td>\n","      <td>770000.0</td>\n","      <td>0.525000</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>44593</th>\n","      <td>202869</td>\n","      <td>2020</td>\n","      <td>08/17/2021</td>\n","      <td>Norwalk</td>\n","      <td>5 VAN ZANT ST UNIT 5</td>\n","      <td>177660.0</td>\n","      <td>295000.0</td>\n","      <td>0.602200</td>\n","      <td>Residential</td>\n","      <td>Condo</td>\n","      <td>3-35-3-5</td>\n","    </tr>\n","    <tr>\n","      <th>44592</th>\n","      <td>20468</td>\n","      <td>2020</td>\n","      <td>04/30/2021</td>\n","      <td>Ridgefield</td>\n","      <td>16 MCKEON PLACE</td>\n","      <td>403560.0</td>\n","      <td>620000.0</td>\n","      <td>0.650900</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>44590</th>\n","      <td>200003</td>\n","      <td>2020</td>\n","      <td>10/01/2020</td>\n","      <td>Shelton</td>\n","      <td>45 INDEPENDENCE DR</td>\n","      <td>394400.0</td>\n","      <td>655000.0</td>\n","      <td>0.602100</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>2020348</td>\n","      <td>2020</td>\n","      <td>09/13/2021</td>\n","      <td>Ansonia</td>\n","      <td>230 WAKELEE AVE</td>\n","      <td>150500.0</td>\n","      <td>325000.0</td>\n","      <td>0.463000</td>\n","      <td>Commercial</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>997213 rows × 11 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e58a92f6-3ffd-4a79-ba40-51934c53a239')\"\n","            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sns.color_palette(colors))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"id":"G3ErVf4PwWcN","executionInfo":{"status":"ok","timestamp":1712865793942,"user_tz":240,"elapsed":20274,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"72d61a00-d119-4c77-f61e-acc909c2d98e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":62},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAioAAAIUCAYAAAA0QAduAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADPhklEQVR4nOzdd1RUV/fw8S9FEGkqgoIiYgM1AnZBY0XATkyiolHsmoAFE6PEArZg1/zUaIy9xd41KEqwgV0SsaBiIRbsDVRAmPcPXm6cMChjUEef/Vlr1nLOPffcc3nWs2bntK2nUqlUCCGEEELoIP333QEhhBBCiNxIoCKEEEIInSWBihBCCCF0lgQqQgghhNBZEqgIIYQQQmdJoCKEEEIInSWBihBCCCF0lgQqQgghhNBZEqgIIYQQQmdJoCKEEEIInfVWApUnT54waNAgHBwcMDExwcPDg6NHjyrXN2zYgJeXF1ZWVujp6REbG5trWyqViubNm6Onp8emTZvUriUmJtKyZUsKFSqEjY0NQ4YM4cWLF8r1qKgo9PT0cnySkpLy+5WFEEII8Ra8lUClV69eREREsGzZMk6dOoWXlxeenp5cv34dgJSUFOrXr8/EiRNf29aMGTPQ09PLUZ6RkUHLli1JS0sjOjqaJUuWsHjxYkaNGpWjbnx8PDdv3lQ+NjY2//0lhRBCiP8h+/bto3Xr1tjZ2WkcPNAkKiqK6tWrY2xsTPny5Vm8eLH2D1bls6dPn6oMDAxU27ZtUyuvXr26avjw4Wplly9fVgGqkydPamzr5MmTqpIlS6pu3rypAlQbN25Uru3YsUOlr6+vSkpKUsrmzJmjsrCwUKWmpqpUKpXqjz/+UAGqBw8e5Mu7CSGEEP+rduzYoRo+fLhqw4YNOX6TNbl06ZKqUKFCqsGDB6vOnDmjmjlzpsrAwEAVHh6u1XPzfUTlxYsXZGRkULBgQbVyExMTDhw4kOd2nj59SqdOnZg9ezYlSpTIcT0mJoaqVatSvHhxpczb25vHjx9z+vRptbpubm7Y2trSrFkzDh48qOUbCSGEEKJ58+aMGzeOzz77LE/1586di6OjI1OnTqVSpUoEBgbyxRdfMH36dK2em++Birm5Oe7u7owdO5YbN26QkZHB8uXLiYmJ4ebNm3luJygoCA8PD9q2bavxelJSklqQAijfs9eg2NraMnfuXNavX8/69euxt7enUaNGnDhx4g3fTgghhBB5ERMTg6enp1qZt7c3MTExWrVjmJ+dyrZs2TJ69OhByZIlMTAwoHr16vj5+XH8+PE83b9lyxYiIyM5efLkf+qHk5MTTk5OyncPDw8SEhKYPn06y5Yt03hPamoqqampamXGxsYYGxv/p74IIYQQuuZt/ublNqDw+PFjnj17homJSZ7aeSuBSrly5di7dy8pKSk8fvwYW1tbOnToQNmyZfN0f2RkJAkJCRQuXFit/PPPP+fTTz8lKiqKEiVKcOTIEbXrt27dAtA4VZStdu3ar5yCCgsLY/To0f8qbQW0zlPfhRAiv6lUfd93F4RWVO/gGTk3mbyJsLCQHL95ISEhhIaG5kv7+eGtnqNiamqKra0tDx48YOfOnblO4/zbsGHD+Ouvv4iNjVU+ANOnT2fRokUAuLu7c+rUKW7fvq3cFxERgYWFBZUrV8617djYWGxtbXO9HhwczKNHj9Q+4JOnfgshhBAfEk2/ecHBwfnSdokSJZQBhGy3bt3CwsIiz6Mp8JZGVHbu3IlKpcLJyYmLFy8yZMgQnJ2d6d69OwD3798nMTGRGzduAFnbhyHrpV7+/Fvp0qVxdHQEwMvLi8qVK9OlSxcmTZpEUlISI0aMICAgQBmymjFjBo6OjlSpUoXnz58zf/58IiMj2bVrV6591zzkVeC//kmEEEIInfM2lza4u7uzY8cOtbKIiAjc3d21auetjKg8evSIgIAAnJ2d6dq1K/Xr12fnzp0UKJD1g79lyxaqVatGy5YtAejYsSPVqlVj7ty5eX6GgYEB27Ztw8DAAHd3d7766iu6du3KmDFjlDppaWl8++23VK1alYYNG/Lnn3+ye/dumjZtmr8vLIQQQnzkkpOT1WY5Ll++TGxsLImJiUDW6EzXrl2V+v369ePSpUt8//33nDt3jp9//pk1a9YQFBSk1XP1VCrVu5hM+6Dp6c17310QQvwPkzUqH5oPZ42KNn2NioqicePGOcr9/f1ZvHgx3bp148qVK0RFRandExQUxJkzZyhVqhQjR46kW7duWvVQApU8kEBFCPE+SaDyofk4A5X3RZISCiGEEEJnSaAihBBCCJ2ldaDyuqREecmMnJCQwGeffYa1tTUWFha0b98+xxambKmpqbi5uWls66+//uLTTz+lYMGC2NvbM2nSpFz7vWrVKvT09PD19dXyjYUQQgjxvmgdqKSkpODq6srs2bNzvf6qzMgpKSl4eXmhp6dHZGQkBw8eJC0tjdatW5OZmZmj/vfff4+dnV2O8sePH+Pl5YWDgwPHjx9n8uTJhIaGMm9ezvUkV65c4bvvvuPTTz/V8m2FEEII8T5pfY5K8+bNad68ea7Xu3TpAmQFB5ocPHiQK1eucPLkSSwsLABYsmQJRYoUITIyUi0vwO+//86uXbtYv349v//+u1o7K1asIC0tjYULF2JkZESVKlWIjY1l2rRp9OnTR6mXkZFB586dGT16NPv37+fhw4favrIQQggh3pN3vkYlNTUVPT09tQNmChYsiL6+vtrR9rdu3aJ3794sW7aMQoUK5WgnJiaGBg0aYGRkpJR5e3sTHx/PgwcPlLIxY8ZgY2NDz54939IbCSGEEOJteeeBSt26dTE1NWXo0KE8ffqUlJQUvvvuOzIyMpTsyiqVim7dutGvXz9q1qypsZ28ZE8+cOAACxYs4Ndff32LbySEEEKIt+WdByrW1tasXbuWrVu3YmZmhqWlJQ8fPqR69ero62d1Z+bMmTx58uQ/5Rt48uQJXbp04ddff6VYsWJ5vi81NZXHjx+rfSD9jfshhBBCiDf3VnL9vI6XlxcJCQncvXsXQ0NDChcuTIkSJZTsypGRkcTExOTIP1CzZk06d+7MkiVLck12BFk5gxISErhy5QqtW/+T9Th7sa6hoSHx8fGUK1cuR98ke7IQQgihO95LoJIte6QjMjKS27dv06ZNGwD+7//+j3Hjxin1bty4gbe3N6tXr6ZOnTpAVrKj4cOHk56eruQQioiIwMnJiSJFimBiYsKpU6fUnjdixAiePHnCTz/9hL29vcY+BQcHM3jwYLUyS8tl+fPCQgghhNCK1oFKcnIyFy9eVL5nJyUqWrQopUuXfm1mZIBFixZRqVIlrK2tiYmJYeDAgQQFBeHk5ARkZUl+mZmZGQDlypWjVKlSAHTq1InRo0fTs2dPhg4dSlxcHD/99BPTp08HshbofvLJJ2rtFC5cGCBH+cske7IQQgihO7QOVI4dO6aWlCh79CE7KdGWLVvo3r27cr1jx44AhISEEBoaCmQFL8HBwdy/f58yZcowfPhwrbMpWlpasmvXLgICAqhRowbFihVj1KhRaluThRBCCPFhk6SEeSBJCYUQ75MkJfzQSFLC/CS5foQQQgihsyRQEUIIIYTOkkBFCCGEEDor37Mn6+npafxMnjxZrd727dupU6cOJiYmFClSJEdW4wEDBlCjRg2MjY1xc3PT2Jc1a9bg5uZGoUKFcHBwyPGMlx08eBBDQ8Nc2xJCCCGE7sn37Mk3b95U+yxcuBA9PT0+//xzpc769evp0qUL3bt3588//+TgwYN06tQpR1s9evSgQ4cOGp/z+++/07lzZ/r160dcXBw///wz06dPZ9asWTnqPnz4kK5du9K0aVNtX1cIIYQQ79F/2vWjp6fHxo0bc4yGvMzX15cnT56wZ88eAF68eEGZMmWUM1BeJzQ0lE2bNhEbG6tW3qlTJ9LT01m7dq1SNnPmTCZNmkRiYiJ6ev+siO7YsSMVKlTAwMBAY1uvf0/Z9SOEeH9k18+HRnb95Ke3ukbl1q1bbN++XS0gOXHiBNevX0dfX59q1apha2tL8+bNiYuL06rt1NRUChYsqFZmYmLCtWvXuHr1qlK2aNEiLl26REhIyH97GSGEEEK8c281UFmyZAnm5ua0a9dOKbt06RKQNVIyYsQItm3bRpEiRWjUqBH379/Pc9ve3t5s2LCBPXv2kJmZyfnz55k6dSqAkoX5woULDBs2jOXLl2No+F6zBQghhBDiDbzVQGXhwoV07txZbeQjOzHg8OHD+fzzz6lRowaLFi1CT09PbRrndXr37k1gYCCtWrXCyMiIunXrKqfg6uvrk5GRoRyzX7FixTy3K9mThRBCCN3x1gKV/fv3Ex8fT69evdTKbW1tAahcubJSZmxsTNmyZUlMTMxz+3p6ekycOJHk5GSuXr1KUlIStWvXBqBs2bI8efKEY8eOERgYiKGhIYaGhowZM4Y///wTQ0NDIiMjNbYbFhaGpaWl2gfCtXx7IYQQQuSHtxaoLFiwgBo1auDq6qpWnr3lODtZIUB6ejpXrlzBwcFB6+cYGBhQsmRJjIyM+O2333B3d8fa2hoLCwtOnTpFbGys8unXrx9OTk7ExsYqWZj/LTg4mEePHql9wEfrfgkhhBDiv8v37MkAjx8/Zu3atcqakZdZWFjQr18/QkJCsLe3Vzv/5Msvv1TqXbx4keTkZJKSknj27JmyU6dy5coYGRlx9+5d1q1bR6NGjXj+/DmLFi1i7dq17N27F8ia/vl3lmQbGxuNWZVfJtmThRBCCN2R79mTAVatWoVKpcLPz09jG5MnT8bQ0JAuXbrw7Nkz6tSpQ2RkJEWKFFHq9OrVSwk6AKpVqwZkBUZlypQBshbrfvfdd6hUKtzd3YmKilKmf4QQQgjx4ZPsyXkg56gIId4nOUflQyPnqOQnyfUjhBBCCJ0lgYoQQgghdJYEKkIIIYTQWRKoCCGEEEJnaR2o7Nu3j9atW2NnZ4eenh6bNm1Sux4aGoqzszOmpqYUKVIET09PDh8+rFbn/PnztG3blmLFimFhYUH9+vX5448/1OoMGDBAOXPFzc1NY19UKhVTpkyhYsWKGBsbU7JkScaPH6+x7sGDBzE0NMy1LSGEEELoHq0DlZSUFFxdXZk9e7bG6xUrVmTWrFmcOnWKAwcOUKZMGby8vLhz545Sp1WrVrx48YLIyEiOHz+Oq6srrVq1IikpSa2tHj160KFDh1z7MnDgQObPn8+UKVM4d+4cW7Zs0bg9+eHDh3Tt2pWmTZtq+7pCCCGEeI/+0/ZkPT09Nm7ciK+vb651Hj9+jKWlJbt376Zp06bcvXsXa2tr9u3bx6effgrAkydPsLCwICIiAk9PT7X7Q0ND2bRpk3LgW7azZ8/i4uJCXFwcTk5Or+xnx44dqVChAgYGBhrbev17yvZkIcT7I9uTPzSyPTk/vdU1KmlpacybNw9LS0vlKH0rKyucnJxYunQpKSkpvHjxgl9++QUbGxtq1KiR57a3bt1K2bJl2bZtG46OjpQpU4ZevXrlyMC8aNEiLl26REhISL6+mxBCCCHePq1Pps2Lbdu20bFjR54+fYqtrS0REREUK1YMyBqF2b17N76+vpibm6Ovr4+NjQ3h4eFqJ9O+zqVLl7h69Spr165l6dKlZGRkEBQUxBdffKEkHLxw4QLDhg1j//79GBq+lVcVQgghxFv0Vn69GzduTGxsLHfv3uXXX3+lffv2HD58GBsbG1QqFQEBAdjY2LB//35MTEyYP38+rVu35ujRo0p25dfJzMwkNTWVpUuXUrFiReCfRIjx8fGUL1+eTp06MXr0aOV6XqSmppKamvqv0nQk348QQgjx7r2VqR9TU1PKly9P3bp1WbBgAYaGhixYsACAyMhItm3bxqpVq6hXrx7Vq1fn559/xsTEhCVLluT5Gba2thgaGqoFIZUqVQIgMTGRJ0+ecOzYMQIDAzE0NMTQ0JAxY8bw559/YmhoqIy6/FtYWBiWlpZqHwh/8z+GEEIIId7YOzlHJXv0A+Dp06dZD9ZXf7S+vj6ZmZl5brNevXq8ePGChIQEpez8+fMAODg4YGFhwalTp4iNjVU+/fr1w8nJidjYWOrUqaOx3eDgYB49eqT2AR9tXlcIIYQQ+UTrqZ/k5GQuXryofL98+TKxsbEULVoUKysrxo8fT5s2bbC1teXu3bvMnj2b69ev8+WXXwLg7u5OkSJF8Pf3Z9SoUZiYmPDrr79y+fJlWrZsqbR78eJFkpOTSUpK4tmzZ8pOncqVK2NkZISnpyfVq1enR48ezJgxg8zMTAICAmjWrJkyyvLJJ5+o9d3GxoaCBQvmKH+ZsbExxsbG/yqVaR8hhBDifdA6UDl27BiNGzdWvg8ePBgAf39/5s6dy7lz51iyZAl3797FysqKWrVqsX//fqpUqQJAsWLFCA8PZ/jw4TRp0oT09HSqVKnC5s2blZ1BAL169WLv3r3K92rVqgFZgVGZMmXQ19dn69at9O/fnwYNGmBqakrz5s2ZOnXqm/0lhBBCCKFz/tM5Kv8r5BwVIcT7JOeofGjkHJX8JLl+hBBCCKGzJFARQgghhM6SQEUIIYQQOivfsyffunWLbt26YWdnR6FChfDx8eHChQtqdfr27Uu5cuUwMTHB2tqatm3bcu7cOeX64sWL0dPT0/i5ffs2AFFRURqvv5zY8MmTJwwaNAgHBwdMTEzw8PDg6NGj2r6yEEIIId6TfM2erFKp8PX15dKlS2zevJmTJ0/i4OCAp6cnKSkpSr0aNWqwaNEizp49y86dO1GpVHh5eZGRkQFAhw4duHnzptrH29ubhg0bYmNjo/bM+Ph4tXovX+/VqxcREREsW7aMU6dO4eXlhaenJ9evX9f2tYUQQgjxHuRr9uTz58/j5OREXFycsh05MzOTEiVK8OOPP9KrVy+N7fz111+4urpy8eJFypUrl+P6nTt3KFmyJAsWLKBLly5A1ohK48aNefDgAYULF85xz7NnzzA3N2fz5s1q57PUqFGD5s2bM27cOC3eU3b9CCHeH9n186GRXT/5KV/XqGSfPluwYMF/HqCvj7GxMQcOHNB4T0pKCosWLcLR0RF7e3uNdZYuXUqhQoX44osvclxzc3PD1taWZs2acfDgQaX8xYsXZGRkqPUFwMTEJNe+CCGEEEK35Gug4uzsTOnSpQkODubBgwekpaUxceJErl27xs2bN9Xq/vzzz5iZmWFmZsbvv/9OREQERkZGGttdsGABnTp1wsTERCmztbVl7ty5rF+/nvXr12Nvb0+jRo04ceIEAObm5ri7uzN27Fhu3LhBRkYGy5cvJyYmJkdfhBBCCKGb8nXqB+D48eP07NmTP//8EwMDAzw9PdHX10elUvH7778r9R49esTt27e5efMmU6ZM4fr16xw8eDDHCEhMTAweHh4cO3aMGjVqvLI/DRs2pHTp0ixbtgyAhIQEevTowb59+zAwMKB69epUrFiR48ePc/bsWY1taMqebGm5DDlGXwjxvsjUz4dGpn7yU75vT65RowaxsbE8fPiQmzdvEh4ezr179yhbtqxaPUtLSypUqECDBg1Yt24d586dY+PGjTnamz9/Pm5ubq8NUgBq166tloeoXLly7N27l+TkZP7++2+OHDlCenp6jr68TLInCyGEELrjrZ2jYmlpibW1NRcuXODYsWO0bds217oqlQqVSpVjJCM5OZk1a9bQs2fPPD0zNjYWW1vbHOWmpqbY2try4MEDdu7c+cq+SPZkIYQQQnfka/bk0qVLs3btWqytrSldujSnTp1i4MCB+Pr64uXlBcClS5dYvXo1Xl5eWFtbc+3aNSZMmICJiQktWrRQe9bq1at58eIFX331VY5+zJgxA0dHR6pUqcLz58+ZP38+kZGR7Nq1S6mTvfXZycmJixcvMmTIEJydnenevXuu7yfZk4UQQgjdka/ZkxcvXszNmzcZPHgwt27dwtbWlq5duzJy5EilfsGCBdm/fz8zZszgwYMHFC9enAYNGhAdHZ3jjJQFCxbQrl07jduP09LS+Pbbb7l+/TqFChXCxcWF3bt3q/Xt0aNHBAcHc+3aNYoWLcrnn3/O+PHjKVBAAg8hhBDiQyDZk/NAzlERQrxPspj2QyOLafOT5PoRQgghhM6SQEUIIYQQOksCFSGEEELoLK0ClbCwMGrVqoW5uTk2Njb4+voSHx+vVicpKYkuXbpQokQJTE1NqV69OuvXr1euX7lyhZ49e+Lo6IiJiQnlypUjJCSEtLQ0tXZ27txJ3bp1MTc3x9rams8//5wrV66o1Zk9ezaVKlXCxMQEJycnli5dqnZdUxbmfx8oJ4QQQgjdpVWgsnfvXgICAjh06BARERGkp6fj5eWllhm5a9euxMfHs2XLFk6dOkW7du1o3749J0+eBODcuXNkZmbyyy+/cPr0aaZPn87cuXP54YcflDYuX75M27ZtadKkCbGxsezcuZO7d+/Srl07pc6cOXMIDg4mNDSU06dPM3r0aAICAti6datany0sLNSyK1+9evWN/lBCCCGEePf+066fO3fuYGNjw969e2nQoAEAZmZmzJkzR8lyDGBlZcXEiRNzzZ48efJk5syZw6VLlwBYt24dfn5+pKamoq+fFUtt3bqVtm3bkpqaSoECBfDw8KBevXpMnjxZaefbb7/l8OHDStLBxYsXM2jQIB4+fPimrwjIrh8hxPslu34+NLLrJz/9pzUqWae2QtGiRZUyDw8PVq9ezf3798nMzGTVqlU8f/6cRo0avbKdl9uoUaMG+vr6LFq0iIyMDB49esSyZcvw9PRUzkBJTU3VmBk5+5j8bMnJyTg4OGBvb0/btm05ffr0f3llIYQQQrxDbxyoZGZmMmjQIOrVq8cnn3yilK9Zs4b09HSsrKwwNjamb9++bNy4kfLly2ts5+LFi8ycOZO+ff/5LwZHR0d27drFDz/8gLGxMYULF+batWusWbNGqePt7c38+fM5fvw4KpWKY8eOMX/+fNLT07l79y4ATk5OLFy4kM2bN7N8+XIyMzPx8PDg2rVrb/raQgghxP+02bNnU6ZMGQoWLEidOnU4cuTIK+vPmDEDJycnTExMsLe3JygoiOfPn+f5eW8cqAQEBBAXF8eqVavUykeOHMnDhw/ZvXs3x44dY/DgwbRv355Tp07laOP69ev4+Pjw5Zdf0rt3b6U8KSmJ3r174+/vz9GjR9m7dy9GRkZ88cUXZM9UjRw5kubNm1O3bl0KFChA27Zt8ff3z3qp/z9d5O7uTteuXXFzc6Nhw4Zs2LABa2trfvnll1zfKzU1lcePH6t9ID3X+kIIIcT/itWrVzN48GBCQkI4ceIErq6ueHt7c/v2bY31V65cybBhwwgJCeHs2bMsWLCA1atXq61LfZ03WqMSGBjI5s2b2bdvH46Ojkp5QkIC5cuXJy4ujipVqijlnp6elC9fnrlz5yplN27coFGjRtStW5fFixcrwQVkBSHh4eEcPXpUKbt27Rr29vbExMRQt25dpTw9PV05rn/evHkMHTqUhw8fqrX3si+//BJDQ0N+++03jddDQ0MZPXr0v0pbAa3z8qcRQoh8J2tUPjQf7xqVOnXqUKtWLWbNmgVkza7Y29vTv39/hg0blqN+YGAgZ8+eZc+ePUrZv9eTvo5WIyoqlYrAwEA2btxIZGSkWpAC8PTp06xG/xUkGBgYkJmZqXy/fv06jRo1okaNGixatChH/adPn2psA1BrB6BAgQKUKlUKAwMDVq1aRatWrXINUjIyMjh16pTGDMvZJHuyEEIIkVNaWhrHjx/H09NTKdPX18fT05OYmBiN93h4eHD8+HFleujSpUvs2LEjRxLiV9EqKWFAQAArV65k8+bNmJubk5SUBIClpSUmJiY4OztTvnx5+vbty5QpU7CysmLTpk1ERESwbds24J8gxcHBgSlTpnDnzh2l/RIlSgDQsmVLpk+fzpgxY/Dz8+PJkyf88MMPODg4UK1aNQDOnz/PkSNHqFOnDg8ePGDatGnExcWxZMkSpb0xY8ZQt25dypcvz8OHD5k8eTJXr17NdfcRSPZkIYQQ/ztSU1NJTU1VK9P8Owh3794lIyOD4sWLq5UXL16cc+fOaWy/U6dO3L17l/r166NSqXjx4gX9+vXTaupHqxGVOXPm8OjRIxo1aoStra3yWb16NZA1urFjxw6sra1p3bo1Li4uLF26lCVLlijRU0REBBcvXmTPnj2UKlVKrZ1sTZo0YeXKlWzatIlq1arh4+ODsbEx4eHhmJiYAFmjI1OnTsXV1ZVmzZrx/PlzoqOjKVOmjNLOgwcP6N27N5UqVaJFixY8fvyY6OhoKleurM1rCyGEEB+lsLAwLC0t1T5hYWH51n5UVBQ//vgjP//8MydOnGDDhg1s376dsWPH5rkNyZ6cB3KOihDifZI1Kh+aD2eNSmrq8zyPqKSlpVGoUCHWrVuHr6+vUu7v78/Dhw/ZvHlzjns+/fRT6tatq3bm2fLly+nTpw/Jycm5LtV4meT6EUIIIf5HGRsbY2FhofbRFKQAGBkZUaNGDbWFsZmZmezZswd3d3eN97xqzWlex0m0WqMihBBCiP9dgwcPxt/fn5o1a1K7dm1mzJhBSkoK3bt3B7LS6JQsWVKZPmrdujXTpk2jWrVq1KlTh4sXLzJy5Ehat26tBCyvI4GKEEIIIfKkQ4cO3Llzh1GjRpGUlISbmxvh4eHKAtvExES1EZQRI0agp6fHiBEjuH79urKGdfz48Xl+ptZTP3PmzMHFxUUZInJ3d+f333/PUU+lUtG8eXP09PTYtGmTxrbu3btHqVKl0NPTU8vHs2HDBpo1a4a1tbXyjJ07d6rdu2/fPlq3bo2dnV2uz0hOTiYwMJBSpUphYmJC5cqV1c5yEUIIIYR2AgMDuXr1KqmpqRw+fJg6deoo16Kioli8eLHy3dDQkJCQEC5evMizZ89ITExk9uzZFC5cOM/P0zpQKVWqFBMmTOD48eMcO3aMJk2aaMyhM2PGDPT0Xr3Yp2fPnri4uOQo37dvH82aNWPHjh0cP36cxo0b07p1ayUDM0BKSgqurq7Mnj071/YHDx5MeHg4y5cv5+zZswwaNIjAwEC2bNmi5VsLIYQQ4n3Il10/RYsWZfLkyfTs2ROA2NhYWrVqxbFjx7C1tWXjxo1qK4Qha2Rm9erVjBo1iqZNm/LgwYNXRlhVqlShQ4cOjBo1KudL6OlpfMYnn3xChw4dGDlypFJWo0YNmjdvzrhx4/L8frLrRwjxPsmunw/Nh7Pr56PPnpyRkcGqVatISUlRVvw+ffqUTp06MXv2bOUAt387c+YMY8aMYenSpXnampSZmcmTJ0/UMiznhYeHB1u2bOH69euoVCr++OMPzp8/j5eXl1btCCGEEOL9eKPFtKdOncLd3Z3nz59jZmbGxo0blUPUgoKC8PDwoG3bthrvTU1Nxc/Pj8mTJ1O6dGkuXbr02udNmTKF5ORk2rdvr1U/Z86cSZ8+fShVqhSGhobo6+vz66+/0qBBA63aEUIIIcT78UaBipOTE7GxsTx69Ih169bh7+/P3r17uXjxIpGRkWprSf4tODiYSpUq8dVXX+XpWStXrmT06NFs3rwZGxsbrfo5c+ZMDh06xJYtW3BwcGDfvn0EBARgZ2enlqvgZZqOE87KnizH6AshhBDvWr6sUfH09KRcuXKYmJjwf//3f2rTORkZGejr6/Ppp58SFRWFm5sbp06dUhbaqlQqMjMzMTAwYPjw4WqZi1etWkWPHj1Yu3YtLVu2zP0lNKxRefbsGZaWlmzcuFHt3l69enHt2jXCw8M1tiXZk4UQukbWqHxoZI1KfsqXc1QyMzNJTU1l9OjRORL+Va1alenTp9O6ddYP/fr163n27Jly/ejRo/To0YP9+/dTrlw5pfy3336jR48erFq16pVBSm7S09NJT09/bSbnfwsODmbw4MFqZZaWy7R+vhBCCCH+O60DleDgYJo3b07p0qV58uQJK1euJCoqip07d1KiRAmNC2hLly6No6MjgFowAlnZGAEqVaqk7PpZuXIl/v7+/PTTT9SpU0fJ0mxiYoKlpSWQdUbKxYsXlXYuX75MbGwsRYsWpXTp0lhYWNCwYUOGDBmCiYkJDg4O7N27l6VLlzJt2rRc30+yJwshhBC6Q+tA5fbt23Tt2pWbN29iaWmJi4sLO3fupFmzZvnWqXnz5vHixQsCAgIICAhQyv39/ZWDZI4dO0bjxo2Va9mjIC/XWbVqFcHBwXTu3Jn79+/j4ODA+PHj6devX771VQghhBBvj2RPzgM5R0UI8T7JGpUPjaxRyU+SPVkIIYQQOksCFSGEEELoLAlUhBBCCKGzJFARQgghhM7SOlCZM2cOLi4uWFhYYGFhgbu7O7///jsAV65cQU9PT+Nn7dq1Odq6d+8epUqVQk9Pj4cPH6pdW7FiBa6urhQqVAhbW1t69OjBvXv3lOvp6emMGTOGcuXKUbBgQVxdXXMc4hYWFkatWrUwNzfHxsYGX19f4uPjtX1lIYQQQrwnWgcqpUqVYsKECRw/fpxjx47RpEkT2rZty+nTp7G3t+fmzZtqn9GjR2NmZkbz5s1ztNWzZ09cXFxylB88eJCuXbvSs2dPTp8+zdq1azly5Ai9e/dW6owYMYJffvmFmTNncubMGfr168dnn32mdnz/3r17CQgI4NChQ0RERJCeno6XlxcpKSnavrYQQggh3oN82Z5ctGhRJk+eTM+ePXNcq1atGtWrV2fBggVq5XPmzGH16tWMGjWKpk2b8uDBA+XAtylTpjBnzhwSEhKU+jNnzmTixIlcu3YNADs7O4YPH652zsrnn3+OiYkJy5cv19jPO3fuYGNjw969e7VKTCjbk4UQ75NsT/7QyPbk/PSf1qhkZGSwatUqUlJScHd3z3H9+PHjxMbG5ghgzpw5w5gxY1i6dGmOI+4B3N3d+fvvv9mxYwcqlYpbt26xbt06WrRoodRJTU2lYMGCaveZmJhw4MCBXPv76NEjICuwEkIIIYTue6NA5dSpU5iZmWFsbEy/fv3YuHEjlStXzlFvwYIFVKpUCQ8PD6UsNTUVPz8/Jk+eTOnSpTW2X69ePVasWEGHDh0wMjKiRIkSWFpaMnv2bKWOt7c306ZN48KFC2RmZhIREcGGDRu4efOmxjYzMzMZNGgQ9erV45NPPsn13VJTU3n8+LHaJyt7shBCCCHetTcKVJycnIiNjeXw4cN8/fXX+Pv7c+bMGbU6z549Y+XKlTlGU4KDg6lUqRJfffVVru2fOXOGgQMHMmrUKI4fP054eDhXrlxRO/r+p59+okKFCjg7O2NkZERgYCDdu3fXOEIDEBAQQFxcHKtWrXrlu4WFhWFpaan2Ac2ZloUQQgjxduXLGhVPT0/KlSvHL7/8opQtW7aMnj17cv36daytrZVyNzc3Tp06hZ5e1vyaSqUiMzMTAwMDhg8fzujRo+nSpQvPnz9X2yl04MABPv30U27cuIGtra1S/vz5c+7du4ednR3Dhg1j27ZtnD59Wq1/gYGBbN68mX379inJEXOTmppKamqqWllW9mRJTCiEeD9kjcqHRtao5CetkxJqkpmZmePHfcGCBbRp00YtSAFYv349z549U74fPXqUHj16sH//fiWz8tOnTzE0VO+agYEBkBXYvKxgwYKULFmS9PR01q9fT/v27ZVrKpWK/v37s3HjRqKiol4bpIBkTxZCCCF0idaBSnBwMM2bN6d06dI8efKElStXEhUVxc6dO5U6Fy9eZN++fezYsSPH/dnBSLa7d+8CUKlSJWXXT+vWrenduzdz5szB29ubmzdvMmjQIGrXro2dnR0Ahw8f5vr167i5uXH9+nVCQ0PJzMzk+++/V9oOCAhg5cqVbN68GXNzc5KSkgCwtLTExMRE21cXQgghxDumdaBy+/Ztunbtys2bN7G0tMTFxYWdO3fSrFkzpc7ChQspVaoUXl5eb9Spbt268eTJE2bNmsW3335L4cKFadKkCRMnTlTqPH/+nBEjRnDp0iXMzMxo0aIFy5YtU4IdyNoCDdCoUSO19hctWkS3bt3eqG9CCCGEeHfyZY3Kx07OURFCvE+yRuVDI2tU8pPk+hFCCCGEzpJARQghhBA6SwIVIYQQQuisfM2eDFkLV/+dOfnlg9r+/PNP/Pz8sLe3x8TEhEqVKvHTTz+pPePAgQPUq1cPKysrTExMcHZ2Zvr06Tn6cv36db766iulXtWqVTl27BiQlV156NChVK1aFVNTU+zs7OjatSs3btzQ9pWFEEII8Z5ovesnO3tyhQoVUKlULFmyhLZt23Ly5EmqVKkCQO/evRkzZoxyT6FChZR/Hz9+HBsbG5YvX469vT3R0dH06dMHAwMDAgMDATA1NSUwMBAXFxdMTU05cOAAffv2xdTUlD59+gDw4MED6tWrR+PGjfn999+xtrbmwoULFClSBMg6i+XEiROMHDkSV1dXHjx4wMCBA2nTpo0SzAghhBBCt+V79uRGjRrh5ubGjBkz8nx/QEAAZ8+eJTIyMtc67dq1w9TUlGXLlgEwbNgwDh48yP79+/P8nKNHj1K7dm2uXr2aa54hTWTXjxDifZJdPx8a2fWTn95K9uQVK1ZQrFgxPvnkE4KDg3n69Okr23n06NErMxqfPHmS6OhoGjZsqJRt2bKFmjVr8uWXX2JjY0O1atX49ddfX/scPT09tbNWhBBCCKG73ugI/VOnTuHu7s7z588xMzNTy57cqVMnHBwcsLOz46+//mLo0KHEx8ezYcMGjW1FR0ezevVqtm/fnuNaqVKluHPnDi9evCA0NJRevXop1y5dusScOXMYPHgwP/zwA0ePHmXAgAEYGRnh7++fo63nz58zdOhQ/Pz8sLCweJPXFkIIIcQ79kZTP2lpaSQmJvLo0SPWrVvH/Pnz2bt3rxKsvCwyMpKmTZty8eLFHMfnx8XF0bhxYwYOHMiIESNy3Hv58mWSk5M5dOgQw4YNY9asWfj5+QFgZGREzZo1iY6OVuoPGDCAo0ePEhMTo9ZOeno6n3/+OdeuXSMqKuqVgYokJRRC6BqZ+vnQyNRPfnqjqR8jIyPKly9PjRo1CAsLw9XVNcfOnWx16tQBsvL/vOzMmTM0bdqUPn36aAxSABwdHalatSq9e/cmKCiI0NBQ5ZqtrW2OwKhSpUokJiaqlaWnp9O+fXuuXr1KRETEa0dTwsLCsLS0VPtA+CvvEUIIIcTbkS/nqGjKnpwtNjYWyAossp0+fZrGjRvj7+/P+PHj3+gZ9erVIz4+Xq3O+fPncXBwUL5nBykXLlxg9+7dWFlZvfY5wcHBPHr0SO0DPnnqoxBCCCHyV75mT05ISGDlypW0aNECKysr/vrrL4KCgmjQoAEuLi5A1nRPkyZN8Pb2ZvDgwUpGYwMDA6ytrQGYPXs2pUuXxtnZGYB9+/YxZcoUBgwYoPQjKCgIDw8PfvzxR9q3b8+RI0eYN28e8+Zl7dBJT0/niy++4MSJE2zbto2MjAzlWUWLFsXIyEjj+xkbG2NsbPyvUpn2EUIIId4Hrdeo9OzZkz179qhlTx46dCjNmjXj77//5quvviIuLo6UlBTs7e357LPPGDFihDLlEhoayujRo3O06+DgwJUrVwCYOXMmv/zyC5cvX8bQ0JBy5crRu3dv+vbti77+P4NA27ZtIzg4mAsXLuDo6MjgwYPp3bs3AFeuXMHR0VHjO/zxxx85Miq/imxPFkK8T7JG5UMja1Tyk2RPzgMJVIQQ75MEKh8aCVTyk+T6EUIIIYTOkkBFCCGEEDpLAhUhhBBC6Kw3Opk224QJEwgODmbgwIFKbp++ffuye/dubty4gZmZGR4eHkycOFHZwQOQmJjI119/zR9//IGZmRn+/v6EhYVhaPhPd1JTUxkzZgzLly8nKSkJW1tbRo0aRY8ePZQ6a9euZeTIkVy5coUKFSowceJEWrRoodbHs2fPMnToUPbu3cuLFy+oXLky69ev1yrXjxBCvE96er+87y7oNFnD83F74xGVo0eP8ssvvyjbjrPVqFGDRYsWcfbsWXbu3IlKpcLLy4uMjAwgKz9Qy5YtSUtLIzo6miVLlrB48WJGjRql1k779u3Zs2cPCxYsID4+nt9++w0nJyflenR0NH5+fvTs2ZOTJ0/i6+uLr68vcXFxSp2EhATq16+Ps7MzUVFR/PXXX4wcOZKCBQu+6WsLIYQQ4h16o10/ycnJVK9enZ9//plx48a9MlvyX3/9haurq3KE/u+//06rVq24ceMGxYsXB2Du3LkMHTqUO3fuYGRkRHh4OB07duTSpUu5Jivs0KEDKSkpbNu2TSmrW7cubm5uzJ07F4COHTtSoEABJePym5JdP0IIobt0b0RFdv3kpzcaUQkICKBly5Z4enq+sl5KSgqLFi3C0dERe3t7AGJiYqhataoSpAB4e3vz+PFjTp8+DfyTGXnSpEmULFmSihUr8t133/Hs2TPlnpiYmBzP9/b2VvL8ZGZmsn37dipWrIi3tzc2NjbUqVOHTZs2vckrCyGEEOI90DpQWbVqFSdOnCAsLCzXOj///DNmZmaYmZnx+++/ExERoZwEm5SUpBakAMr37JNjL126xIEDB4iLi2Pjxo3MmDGDdevW8c033yj35NZOdhu3b98mOTmZCRMm4OPjw65du/jss89o164de/fu1fa1hRBCCPEeaLWY9u+//2bgwIFERES8cp1H586dadasGTdv3mTKlCm0b9+egwcP5nltSGZmJnp6eqxYseL/JwWEadOm8cUXX/Dzzz9jYmKSpzYA2rZtS1BQEABubm5ER0czd+5cGjZsqPE+TdmTIR05Rl8IIYR497QaUTl+/Di3b9+mevXqGBoaYmhoyN69e/m///s/DA0NlQWzlpaWVKhQgQYNGrBu3TrOnTvHxo0bAShRogS3bt1Sazf7e4kSJYCsBIYlS5ZUghTIyoysUqm4du3aK9vJbqNYsWIYGhrmKcPyyyR7shBCCKE7tApUmjZtyqlTp4iNjVU+NWvWpHPnzsTGxmJgYJDjHpVKhUqlUkYp3N3dOXXqFLdv31bqREREYGFhoQQV9erV48aNGyQnJyt1zp8/j76+PqVKlVLa2bNnj9qzIiIicHd3B8DIyIhatWq9NsPyv0n2ZCGEEEJ3aDX1Y25uzieffKJWZmpqipWVFZ988gmXLl1i9erVeHl5YW1tzbVr15gwYQImJibK+SZeXl5UrlyZLl26MGnSJJKSkhgxYgQBAQFK1uJOnToxduxYunfvzujRo7l79y5DhgyhR48eyrTPwIEDadiwIVOnTqVly5asWrWKY8eOKdmTAYYMGUKHDh1o0KABjRs3Jjw8nK1btxIVFZXrO0r2ZCGEEEJ3/KcD3/6tYMGC7N+/nxkzZvDgwQOKFy9OgwYNiI6OxsbGBgADAwO2bdvG119/jbu7O6ampvj7+zNmzBilHTMzMyIiIujfvz81a9bEysqK9u3bM27cOKWOh4cHK1euZMSIEfzwww9UqFCBTZs2qQVSn332GXPnziUsLIwBAwbg5OTE+vXrqV+/fn6+thBCvFW6t/1WiHdHsifngZyjIoQQukv3Ajk5RyU/5euIihBCiPynez/EQrw7EqgIIYSOk1w/ryaB3MdNsicLIYQQQmf9p0BlwoQJ6OnpMWjQIACuXLmCnp6exs/atWtz3H/v3j1KlSqFnp4eDx8+VLsWFRVF9erVMTY2pnz58ixevFjtelhYGLVq1cLc3BwbGxt8fX1zbEWGrKP2mzRpgqmpKRYWFjRo0EDtKH4hhBBC6K58zZ5sb2/PzZs31T6jR4/GzMyM5s2b52ijZ8+eObIvA1y+fJmWLVvSuHFjYmNjGTRoEL169WLnzp1Knb179xIQEMChQ4eIiIggPT0dLy8vUlJSlDoxMTH4+Pjg5eXFkSNHOHr0KIGBgejry0CSEEII8SF469mTq1WrRvXq1VmwYIFa+Zw5c1i9ejWjRo2iadOmPHjwgMKFCwMwdOhQtm/fTlxcnFK/Y8eOPHz4kPBwzafE3rlzBxsbG/bu3UuDBg2ArGzKzZo1Y+zYsdq+ohrZ9SOEELpL99aoyK6f/PRWsycfP36c2NhYevbsqVZ+5swZxowZw9KlSzWObrwuM7ImWSfIQtGiRYGspISHDx/GxsYGDw8PihcvTsOGDTlw4ECe3lEIIYQQ799byZ6cbcGCBVSqVAkPDw+lLDU1FT8/PyZPnkzp0qU13pdbZuTHjx9rXF+SmZnJoEGDqFevnnLg26VLlwAIDQ2ld+/ehIeHU716dZo2bcqFCxfy/L5CCCGEeH/eSvZkgGfPnrFy5UpGjhypVh4cHEylSpX46quvtO9tLgICAoiLi1MbLcnOnty3b1+6d+8OZE1D7dmzh4ULF+YaaEn2ZCGEEEJ3aBWovJw9OVtGRgb79u1j1qxZpKamKokJ161bx9OnT+natataG5GRkZw6dYp169YBWUkLISvb8fDhwxk9enSumZEtLCyUXD/ZAgMD2bZtG/v27VMSFkJWBmbgjbInjx49+l+lrYDWud4jhBDi/dG1c2bkvPf8pVWgkp09+WXdu3fH2dmZoUOHqmVPXrBgAW3atMHa2lqt/vr169Wmb44ePUqPHj3Yv38/5cqVA7IyI+/YsUPtvpczI0NWgNO/f382btxIVFQUjo6OavXLlCmDnZ2dxuzJmnYgZQsODmbw4MFqZZaWy3KtL4QQ4v3SvcW0fd53B96q2bNnM3nyZJKSknB1dWXmzJnUrl071/oPHz5k+PDhbNiwgfv37+Pg4MCMGTOUZMWvk6/Zk7NdvHiRffv25Qg2ACUYyXb37l0ga6Qje9dPv379mDVrFt9//z09evQgMjKSNWvWsH37duW+gIAAVq5cyebNmzE3NycpKQkAS0tLTExM0NPTY8iQIYSEhODq6oqbmxtLlizh3LlzymiOJpI9WQghhNBs9erVDB48mLlz51KnTh1mzJiBt7c38fHxSvLhl6WlpdGsWTNsbGxYt24dJUuW5OrVq8rvfV68lSP0Fy5cSKlSpfDy8nqj+x0dHdm+fTtBQUH89NNPlCpVivnz5+Pt7a3UmTNnDgCNGjVSu3fRokV069YNgEGDBvH8+XOCgoK4f/8+rq6uRERE5AiWhBBCl+neiIFukamfd2fatGn07t1bWfs5d+5ctm/fzsKFCxk2bFiO+gsXLuT+/ftER0dToEDWf/SXKVNGq2dK9uQ8kHNUhBDvkwQqr6Z7gcq7mPp59+eopKWlUahQIdatW4evr69S7u/vz8OHD9m8eXOOe1q0aEHRokUpVKgQmzdvxtramk6dOuVYLvIqkpRQCCF0nK79EOsa3QvkPpw1Kpp2umpeApG1VCMjI0Pj8SHnzp3T2P6lS5eIjIykc+fO7Nixg4sXL/LNN9+Qnp5OSEhInvoogYoQQug43fshFh8LTTtdQ0JCCA0NzZf2MzMzsbGxYd68eRgYGFCjRg2uX7/O5MmTJVARQoiPhYyovJoEcm9O005XTaMpkHWMiIGBgcbjQ0qUKKHxHltbWwoUKKA2zVOpUiWSkpJIS0vDyMjotX3UOlAJDQ3NEX05OTkpwz7z5s1j5cqVnDhxgidPnqjl8MnWpk0bYmNjuX37NkWKFMHT05OJEydiZ2cHwPPnz+nXrx/Hjx/n7NmztGrVik2bNqm1ceDAAYYOHcq5c+d4+vQpDg4O9O3bl6CgII39njBhAsHBwQwcODDXvERCCKGL5IdYvC25TfNoYmRkRI0aNdizZ4+yRiUzM5M9e/YQGBio8Z569eqxcuVKMjMzlZQ558+fx9bWNk9BCrzhiEqVKlXYvXv3P40Y/tPM06dP8fHxwcfHh+DgYI33N27cmB9++AFbW1uuX7/Od999xxdffEF0dDSQdYiciYkJAwYMYP369RrbMDU1JTAwEBcXF0xNTTlw4AB9+/bF1NSUPn3U5wc1ZXoWQogPhYyofFg+5i0qgwcPxt/fn5o1a1K7dm1mzJhBSkqKsguoa9eulCxZUjn9/euvv2bWrFkMHDiQ/v37c+HCBX788UcGDBiQ52e+UaBiaGiY6zDPoEGDAIiKisr1/pdHPRwcHBg2bBi+vr6kp6dToEABTE1Nle3HBw8e5OHDhznaqFatGtWqVVO+lylThg0bNrB//361QCU5OZnOnTvz66+/Mm7cOC3eUgghxIdA90acPpzFtNrq0KEDd+7cYdSoUSQlJeHm5kZ4eLiywDYxMVEt2bC9vT07d+4kKCgIFxcXSpYsycCBAxk6dGien/lGgcqFCxews7OjYMGCuLu7ExYWlmuCwde5f/8+K1aswMPDQ9lj/SZOnjxJdHR0jmDk5UzPEqgIIcTHR9dGnD7mERXISl2T21SPpkEKd3d3Dh069MbP0zp7cp06dVi8eDHh4eHMmTOHy5cv8+mnn/LkyROt2hk6dKhyqm1iYqLG/dd5UapUKYyNjalZsyYBAQH06tVLuaZNpmchhBBC6B6tR1RezpPj4uJCnTp1cHBwYM2aNfTs2TPP7QwZMoSePXty9epVRo8eTdeuXdm2bRt6etodYrN//36Sk5M5dOgQw4YNo3z58vj5+WmV6fllkj1ZCCE+LDL183H7z9uTCxcuTMWKFbl48aJW9xUrVoxixYpRsWJFKlWqhL29PYcOHVJLPJgX2ckIq1atyq1btwgNDcXPz0+rTM8vk+zJQghdo3s/xEK8O/85UElOTiYhIYEuXbq8cRuZmZkAGkYytG8nuw1tMj2/TLInCyF0ja6twRCv9rGvUXnXtA5UvvvuO1q3bo2DgwM3btwgJCQEAwMD/Pz8AEhKSiIpKUkZYTl16hTm5uaULl2aokWLcvjwYY4ePUr9+vUpUqQICQkJjBw5knLlyqmNppw5c4a0tDTu37/PkydPiI2NBcDNzQ3ISjNdunRpnJ2dAdi3bx9TpkxRtjzlNdPzv0n2ZCGErpERlVeTQO7jpnWgcu3aNfz8/Lh37x7W1tbUr1+fQ4cOYW1tDWRlUnx56qRBgwbAP1mNCxUqxIYNGwgJCSElJQVbW1t8fHwYMWKEWoDQokULrl69qnzP3oqcnUMxMzOT4OBgLl++jKGhIeXKlWPixIn07Sv/hxZCfFzkh/jVdC+QkzUq+UmyJ+eBZE8WQgjdpXuByrv4WX332ZPfF623JwshhBBCvCsSqAghhBBCZ0mgIoQQQgidJYGKEEIIIXSWVoFKaGgoenp6ap/s7cHZYmJiaNKkCaamplhYWNCgQQOePXumXC9TpkyONiZMmKBcf/78Od26daNq1aoYGhoqqaRzc/DgQQwNDZVty9n27dtH69atsbOzQ09Pj02bNmnzqkIIIYTQAVpvT65SpQq7d+/+pwHDf5qIiYnBx8eH4OBgZs6ciaGhIX/++adaJkWAMWPG0Lt3b+W7ubm58u+MjAxMTEwYMGAA69evf2VfHj58SNeuXWnatCm3bt1Su5aSkoKrqys9evSgXbt22r6mEELoDN3b1SLEu6N1oGJoaEiJEiU0XgsKCmLAgAEMGzZMKXNycspRz9zcPNc2TE1NmTNnDpA1WvLw4cNc+9KvXz86deqEgYFBjhGT5s2bq+UlEkKID5Wco/JqEsh93LReo3LhwgXs7OwoW7YsnTt3JjExEYDbt29z+PBhbGxs8PDwoHjx4jRs2JADBw7kaGPChAlYWVlRrVo1Jk+ezIsXL7Tu+KJFi7h06RIhISFa3yuEEEKID4NWIyp16tRh8eLFODk5cfPmTUaPHs2nn35KXFwcly5dArLWsUyZMgU3NzeWLl1K06ZNiYuLo0KFCgAMGDCA6tWrU7RoUaKjowkODubmzZtMmzYtz/24cOECw4YNY//+/WpTT0IIIYT4uGj1K//yVIqLiwt16tTBwcGBNWvWUKlSJQD69u1L9+7dgaxj7/fs2cPChQsJCwsDUEv45+LigpGREX379iUsLExDjp2cMjIy6NSpE6NHj6ZixYradD9PUlNTNSRHTEfy/QghhBDv3n/anly4cGEqVqzIxYsXsbW1BaBy5cpqdSpVqqRMD2lSp04dXrx4wZUrV/L0zCdPnnDs2DECAwMxNDTE0NCQMWPG8Oeff2JoaEhkZOQbvw9AWFgYlpaWah8I/09tCiGEEOLN/Kd5k+TkZBISEujSpQtlypTBzs6O+Ph4tTrnz59/5aLW2NhY9PX1sbGxydMzLSwsOHXqlFrZzz//TGRkJOvWrcPR0VH7F3lJcHCw2qgPgKXlsv/UphBCiLdH1xYbSwa9/KVVoPLdd9/RunVrHBwcuHHjBiEhIRgYGODn54eenh5DhgwhJCQEV1dX3NzcWLJkCefOnWPdunVA1vblw4cP07hxY8zNzYmJiSEoKIivvvqKIkWKKM85c+YMaWlp3L9/nydPnhAbGwuAm5sb+vr6fPLJJ2r9srGxoWDBgmrlycnJXLx4Ufl++fJlYmNjKVq0KKVLl871HY2NjTVMQcm0jxBC6Crd2/Uj2ZPzk1aByrVr1/Dz8+PevXtYW1tTv359Dh06hLW1NQCDBg3i+fPnBAUFcf/+fVxdXYmIiKBcuXJAVhCwatUqQkNDSU1NxdHRkaCgoBwjGC1atODq1avK92rVqgGgTaLnY8eO0bhxY+V79jP8/f1ZvHixNq8thBBCh8mIysdNT6XNr///KD29ee+7C0IIIXKheyMq7+JnVS+f2tH9EED29gohhPigyYjKx00CFSGEEB803RtRkTUq+UmyJwshhBBCZ2kdqLwug3KjRo1yXO/Xr59yffHixTmuZ39u376t1IuKiqJ69eoYGxtTvnz5HAtgw8LCqFWrFubm5tjY2ODr65tja/Tr+iKEEEII3fZGUz+vyqAM0Lt3b8aMGaN8L1SokPLvDh064OPjo1a/W7duPH/+XDlL5fLly7Rs2ZJ+/fqxYsUK9uzZQ69evbC1tcXb2xuAvXv3EhAQQK1atXjx4gU//PADXl5enDlzBlNT0zz1RQghhBC67Y0ClVdlUIasYCC36yYmJpiYmCjf79y5Q2RkJAsWLFDK5s6di6OjI1OnTgWyTrc9cOAA06dPVwKV8HD102IXL16MjY0Nx48fp0GDBnnqixBCCCF02xsFKtkZlAsWLIi7uzthYWFqh6itWLGC5cuXU6JECVq3bs3IkSNzHclYunQphQoV4osvvlDKYmJi8PT0VKvn7e3NoEGDcu3To0ePAChatKhauTZ9EUII8eGRXT8fN60DlVdlUDY3N6dTp044ODhgZ2fHX3/9xdChQ4mPj2fDhg0a21uwYAGdOnVSG2VJSkqiePHiavWKFy/O48ePefbsmVpdgMzMTAYNGkS9evXUTqfVti9CCCE+PLLr5+OmdaDyqgzKPXv2pE+ff/4Hqlq1Kra2tjRt2pSEhATlhNpsMTExnD17lmXL/lsunYCAAOLi4jhw4IBauTZ9ySbZk4UQ4sMiIyoft/98jsrLGZQ1qVOnDgAXL17MERzMnz8fNzc3atSooVZeokQJbt26pVZ269YtLCwscoymBAYGsm3bNvbt20epUqVe2ddX9SVbWFgYo0eP/ldpK6D1K9sWQoi3RfdGDHSLrgUq70J+vfOHEFT950Dl5QzKmmQnFLS1tc1x35o1awgLC8txj7u7Ozt27FAri4iIwN3dXfmuUqno378/GzduJCoqKk9Zk3Pry8ske7IQQtf8L/4Qa0P3AjmZ+slPWgcqr8qgnJCQwMqVK2nRogVWVlb89ddfBAUF0aBBA1xcXNTaWb16NS9evOCrr77K8Yx+/foxa9Ysvv/+e3r06EFkZCRr1qxh+/btSp2AgABWrlzJ5s2bMTc3JykpCQBLS0tMTEy06svLJHuyEEIIoTu0TkrYsWNH9u3bp5ZBefz48ZQrV46///6br776iri4OFJSUrC3t+ezzz5jxIgRWFhYqLXj4eGBo6MjK1as0PicqKgogoKCOHPmDKVKlWLkyJF069btn47raU7ItGjRIrp166ZVX15HkhIKIYTu0r0Rlbc/n5Jfv0sqle6P/kj25DyQQEUIIXSXBCpv7kMIVCQpoRBCiA+arq3hkf/8z18SqAghhPig6d6Iiu6PUnxIJFARQggdp3s/xEK8O1oFKqGhoTnOGHFycuLcuXMAJCQk8N1333HgwAFSU1Px8fFh5syZaqfMnj9/niFDhnDw4EHS0tJwcXFh7NixNG7cWKmTmJjI119/zR9//IGZmRn+/v6EhYWpJT+cPXs2s2bN4sqVK5QuXZrhw4fTtWtXtb6tXbuWkSNHcuXKFSpUqMDEiRNp0aKFNq8shBDvna5NbegaCeQ+bvra3lClShVu3rypfLJPg01JScHLyws9PT0iIyOVQKR169ZkZmYq97dq1YoXL14QGRnJ8ePHcXV1pVWrVsr24oyMDFq2bElaWhrR0dEsWbKExYsXM2rUKKWNOXPmEBwcTGhoKKdPn2b06NEEBASwdetWpU50dDR+fn707NmTkydP4uvri6+vL3FxcW/8xxJCCCHEu6XVrp/Q0FA2bdqkHJz2sl27dtG8eXMePHigbP999OgRRYoUYdeuXXh6enL37l2sra3Zt28fn376KQBPnjzBwsKCiIgIPD09+f3332nVqhU3btxQRmLmzp3L0KFDuXPnDkZGRnh4eFCvXj0mT56sPP/bb7/l8OHDSuDUoUMHUlJS2LZtm1Knbt26uLm5MXfuXO3+SLLrRwghRB69i500/0u7frQeUcnOnFy2bFk6d+5MYmIikJUjR09PT+2wtIIFC6Kvr68ED1ZWVjg5ObF06VJSUlJ48eIFv/zyCzY2Nsox+jExMVStWlVtusjb25vHjx9z+vRp5VkFCxZU65eJiQlHjhwhPT1daUdTBuaYmBhtX1kIIYQQ74lWa1RelTm5bt26mJqaMnToUH788UdUKhXDhg0jIyODmzdvAlmHtO3evRtfX1/Mzc3R19fHxsaG8PBwihQpAuSeOTn7GmQFHPPnz8fX15fq1atz/Phx5s+fT3p6Onfv3sXW1jbXdrLbEEII8XHQvTUquj9K8SHRakSlefPmfPnll7i4uODt7c2OHTt4+PAha9aswdramrVr17J161bMzMywtLTk4cOHVK9eHX39rMeoVCoCAgKwsbFh//79HDlyBF9fX1q3bq0EM3kxcuRImjdvTt26dSlQoABt27bF398/64X0tR4kUpOamsrjx4/VPlnZk4UQQgjxrv2nX/V/Z0728vIiISGB27dvc/fuXZYtW8b169cpW7YsAJGRkWzbto1Vq1ZRr149qlevzs8//4yJiQlLliwBcs+cnH0NsqZ5Fi5cyNOnT7ly5QqJiYmUKVMGc3NzrK2tX9lOdhu5CQsLw9LSUu0D4f/lzySEEEKIN/SfzlHJLXNysWLFgKzA5Pbt27Rp0waAp0+fAjlHPfT19ZWdQe7u7owfP57bt29jY2MDZGVOtrCwoHLlymr3FShQgFKlSgGwatUqWrVqpbTt7u7Onj17GDRokFL/3xmYNZHsyUII8WHRte3bcjJt/tIqUHlV5mTISghYqVIlrK2tiYmJYeDAgQQFBeHk5ARkBQ9FihTB39+fUaNGYWJiwq+//srly5dp2bIlkDUqU7lyZbp06cKkSZNISkpixIgRBAQEKAt1z58/z5EjR6hTpw4PHjxg2rRpxMXFKaMyAAMHDqRhw4ZMnTqVli1bsmrVKo4dO8a8ea9eKS3Zk4UQQgjdoVWgcu3aNfz8/NQyJx86dEiZbomPjyc4OJj79+9TpkwZhg8fTlBQkHJ/sWLFCA8PZ/jw4TRp0oT09HSqVKnC5s2bcXV1BcDAwIBt27bx9ddf4+7ujqmpKf7+/owZM0ZpJyMjg6lTpxIfH0+BAgVo3Lgx0dHRlClTRqnj4eHBypUrGTFiBD/88AMVKlRg06ZNfPLJJ//l7yWEEELHyGLaj5tkT84DOUdFCCF0l+4FKpI9OT/9ty0yQgghhBBvkQQqQgghhNBZkj1ZCCHEB012/XzctA5Url+/ztChQ/n99995+vQp5cuXZ9GiRdSsWVOpc/bsWYYOHcrevXt58eIFlStXZv369ZQuXRqAefPmsXLlSk6cOMGTJ0948OABhQsXVu6PiopSy6b8siNHjlCrVi2uXLmCo6NjjusxMTHUrVsXgA0bNvDjjz9y8eJF0tPTqVChAt9++22O7dRCCKHLdG8Nhng13V/38SHRKlB58OAB9erVo3Hjxvz+++9YW1tz4cIF5fh7gISEBOrXr0/Pnj0ZPXo0FhYWnD59Wi03z9OnT/Hx8cHHx4fg4OAcz/Hw8MhxUu3IkSPZs2ePWkAEsHv3bqpUqaJ8t7KyUv5dtGhRhg8fjrOzM0ZGRmzbto3u3btjY2ODt7e3Nq8uhBDvja6NGOgaCeQ+blrt+hk2bBgHDx5k//79udbp2LEjBQoUYNmy1x+Slj1y8u8RlX9LT0+nZMmS9O/fn5EjRwIoIyonT57Ezc0tr69A9erVadmyJWPHjs3zPbLrRwghdJfuBSqy6yc/aTWismXLFry9vfnyyy/Zu3cvJUuW5JtvvqF3794AZGZmsn37dr7//nu8vb05efIkjo6OBAcH4+vr+8ad3LJlC/fu3aN79+45rrVp04bnz59TsWJFvv/+e+UU3H9TqVRERkYSHx/PxIkT37gvQgghdIuujTjJGpX8pdWun0uXLjFnzhwqVKjAzp07+frrrxkwYIByIuzt27dJTk5mwoQJ+Pj4sGvXLj777DPatWvH3r1737iTCxYswNvbWzkuH8DMzIypU6eydu1atm/fTv369fH19WXLli1q9z569AgzMzOMjIxo2bIlM2fOpFmzZm/cFyGEEEK8O1pN/RgZGVGzZk2io6OVsgEDBnD06FFiYmK4ceMGJUuWxM/Pj5UrVyp12rRpg6mpKb/99ptae3mZ+rl27RoODg6sWbOGzz///JX969q1K5cvX1abmsrMzOTSpUskJyezZ88exo4dy6ZNm2jUqJHGNlJTU0lNTVUry8r1I8foCyGEeL13MZ0iUz+5sLW1zZEYsFKlSqxfvx7IOiLf0NBQY50DBw68UQcXLVqElZVVrlM6L6tTpw4RERFqZfr6+pQvXx4ANzc3zp49S1hYWK6BSlhYGKNHj/5XaSug9Rv0XgghxNume2tUdP/H/0Oi1dRPvXr1iI+PVys7f/48Dg4OQNaIS61atV5ZRxsqlYpFixbRtWtXChR4/YhGbGwstra2r6yTmZmZY8TkZcHBwTx69EjtAz7adl0IIYQQ+UCrEZWgoCA8PDz48ccfad++PUeOHGHevHlqGYmHDBlChw4daNCgAY0bNyY8PJytW7cSFRWl1ElKSiIpKYmLFy8CcOrUKczNzSldujRFixZV6kVGRnL58mV69eqVoy9LlizByMiIatWqAVlnpixcuJD58+crdcLCwqhZsyblypUjNTWVHTt2sGzZMubMmZPrO0r2ZCGEEEJ3aBWo1KpVi40bNxIcHMyYMWNwdHRkxowZdO7cWanz2WefMXfuXMLCwhgwYABOTk6sX7+e+vXrK3Xmzp2rNr3SoEEDIGuap1u3bkr5ggUL8PDwwNnZWWN/xo4dy9WrVzE0NMTZ2ZnVq1fzxRdfKNdTUlL45ptvuHbtGiYmJjg7O7N8+XI6dOigzWsLIYQQ4j2R7Ml5IOeoCCGEyKuPfTHt7NmzmTx5MklJSbi6ujJz5kxq16792vtWrVqFn58fbdu2ZdOmTXl+nuT6EUII8UGTxbTvzurVqxk8eDBz586lTp06zJgxA29vb+Lj47Gxscn1vitXrvDdd9/x6aefav1MyZ4shBBCiDyZNm0avXv3pnv37lSuXJm5c+dSqFAhFi5cmOs9GRkZdO7cmdGjR1O2bFmtnymBihBCCCFeKy0tjePHj+Pp6amU6evr4+npSUxMTK73jRkzBhsbG3r27PlGz9U6ULl+/TpfffUVVlZWmJiYULVqVY4dO6ZW5+zZs7Rp0wZLS0tMTU2pVasWiYmJyvV58+bRqFEjLCws0NPT4+HDhzmeM378eDw8PChUqFCuh8ENGDCAGjVqYGxsnGu+H5VKxZQpU6hYsSLGxsaULFmS8ePHa/vaQgghxEcnNTWVx48fq31yO8Lj7t27ZGRkULx4cbXy4sWLk5SUpPGeAwcOsGDBAn799dc37qNWgUp29uQCBQrw+++/c+bMGaZOnaoxe7KzszNRUVH89ddfjBw5UmP25B9++CHXZ6WlpfHll1/y9ddfv7JPPXr0eOUunoEDBzJ//nymTJnCuXPn2LJlS54W/QghhBAfu7CwMCwtLdU+YWFh+dL2kydP6NKlC7/++ivFihV743Z0Pnvy4sWLGTRokMZRl2yhoaFs2rSJ2NhYtfKzZ8/i4uJCXFwcTk5Or+1PbmTXjxBC6C7dW0z74WRPfv7cP8cIiubzxLIGEAoVKsS6devUEg37+/vz8OFDNm/erFY/NjaWatWqYWBgoJRlZmYCWVNG8fHxlCtX7rV91GpEZcuWLdSsWZMvv/wSGxsbqlWrpjack509uWLFinh7e2NjY0OdOnW02oaUn7Zu3UrZsmXZtm0bjo6OlClThl69enH//v330h8hhBBClxgbG2NhYaH20RSkQNbp8zVq1GDPnj1KWWZmJnv27MHd3T1HfWdnZ06dOkVsbKzyadOmDY0bNyY2NhZ7e/s89VGr7cnZ2ZMHDx7MDz/8wNGjRxkwYABGRkb4+/urZU8eN24cEydOJDw8nHbt2vHHH3/QsGFDbR73n126dImrV6+ydu1ali5dSkZGBkFBQXzxxRdERka+074IIYR4O/T0fnnfXVDzMZ9ONnjwYPz9/alZsya1a9dmxowZpKSk0L17dyArOXDJkiUJCwujYMGCfPLJJ2r3Z8+e/Lv8VbQKVDIzM6lZsyY//vgjANWqVSMuLo65c+fi7++vDOm0bduWoKAgICsRYHR0NHPnzn3ngUp2Xp+lS5dSsWJFIOu02xo1ahAfH69xOkhT9mRIR47RF0II3aR7Uz8f7zkqHTp04M6dO4waNYqkpCTc3NwIDw9XFtgmJiair5+/G4p1Pnvyf2Fra4uhoaESpGT3BbL+mJoCFcmeLITQNbr3Qyz+lwUGBhIYGKjx2st5/TRZvHix1s/TKlB519mT/6t69erx4sULEhISlAU758+fB8i1P8HBwQwePFitzNLy9QuDhRBCvB8y9fNx09nsyYmJidy/f5/ExEQyMjKUHT3ly5fHzMwMgIsXL5KcnExSUhLPnj1T6lSuXBkjIyM8PT2pXr06PXr0YMaMGWRmZhIQEECzZs3URlleJtmThRC6Rtd+iIV4l7ROSrht2zaCg4O5cOECjo6ODB48mN69e6vVWbhwIWFhYVy7dg0nJydGjx5N27ZtleuhoaEaplfUsyd369aNJUuW5Kjzxx9/0KhRIwAaNWrE3r17c9S5fPkyZcqUAeDGjRv079+fXbt2YWpqSvPmzZk6daoSEOWFbE8WQgjdpXtTYx/O9uR3kUDxv5LsyXkggYoQQuguCVTe3IcQqEiuHyGEEELoLAlUhBBCCKGzJFARQgghhM6SQEUIIYQQOkur7ckAZcqU4erVqznKv/nmG2bPnk3fvn3ZvXs3N27cwMzMDA8PDyZOnIizs7NSV09PL8f9v/32Gx07dlS+p6amMmbMGJYvX05SUhK2traMGjWKHj16ALnv+GnRogXbt28HYMOGDcydO5fjx49z//59Tp48iZubm7avLIQQQofp2vZt2aKSv7QOVI4ePUpGRobyPS4ujmbNmvHll18CUKNGDTp37kzp0qW5f/8+oaGheHl5cfnyZbUMiosWLcLHx0f5/u/sye3bt+fWrVssWLCA8uXLc/PmTeWIfsgKQtLS0pTv9+7dw9XVVekHQEpKCvXr16d9+/Y5tlALIYQQQvdpHahYW1urfZ8wYQLlypVT8vj06fPPVqcyZcowbtw4XF1duXLlilo658KFC1OiRAmNzwgPD2fv3r1cunRJOe8k+1yUbP8+B2XVqlUUKlRILVDp0qULAFeuXNHuJYUQQnwwdG97su5v+f2Q/Kc1KmlpaSxfvpwePXponM5JSUlh0aJFODo65kjnHBAQQLFixahduzYLFy7k5eNctmzZQs2aNZk0aRIlS5akYsWKfPfddzx79izXvixYsICOHTtiamr6X15JCCGEEDpE6xGVl23atImHDx8qp8lm+/nnn/n+++9JSUnBycmJiIgIjIyMlOtjxoyhSZMmFCpUiF27dvHNN9+QnJzMgAEDALh06RIHDhygYMGCbNy4kbt37/LNN99w7949Fi1alKMfR44cIS4ujgULFvyX1xFCCCGEjvlPJ9N6e3tjZGTE1q1b1cofPXrE7du3uXnzJlOmTOH69escPHiQggULamxn1KhRLFq0iL///hsALy8v9u/fT1JSEpaWlkDWmpQvvviClJQUTExM1O7v27cvMTEx/PXXXxrbv3LlCo6OjnlaTJuamkpqaqpaWVZSQsn3I4QQukj3pn7kZNr89MYjKlevXmX37t1s2LAhxzVLS0ssLS2pUKECdevWpUiRImzcuBE/Pz+NbdWpU4exY8eSmpqKsbExtra2lCxZUglSACpVqoRKpeLatWtUqFBBKU9JSWHVqlWMGTPmTV9FTVhYmIY8RK2A1vnSvhBCiPwlu34+bm8cqCxatAgbGxtatmz5ynoqlQqVSpVjlOJlsbGxFClSRMlaXK9ePdauXUtycrKSKfn8+fPo6+tTqlQptXvXrl1LamoqX3311Zu+iprg4GAGDx6sVpY1oiKEEEIX6d6Iiu6PUnxI3ihQyczMZNGiRfj7+2No+E8Tly5dYvXq1Xh5eWFtbc21a9eYMGECJiYmtGjRAoCtW7dy69Yt6tatS8GCBYmIiODHH3/ku+++U9rp1KkTY8eOpXv37owePZq7d+8yZMgQevTokWPaZ8GCBfj6+mJlZZWjn/fv3ycxMZEbN24AEB8fD0CJEiVy3XFkbGysBEz/kGkfIYTQVTKi8nF7o0Bl9+7dJCYmKoevZStYsCD79+9nxowZPHjwgOLFi9OgQQOio6OxsbEBoECBAsyePZugoCBUKhXly5dn2rRpauecmJmZERERQf/+/alZsyZWVla0b9+ecePGqT0vPj6eAwcOsGvXLo393LJlC927d1e+Zx8oFxISQmho6Ju8uhBCCB0jIyoft/+0mPZ/RX4tWhJCCJH/dC9QkcW0+Uly/QghhBBCZ0mgIoQQQgidJYGKEEIIIXSW1oFKmTJl0NPTy/EJCAjgypUrGq/p6emxdu1aICt5oI+PD3Z2dhgbG2Nvb09gYCCPHz9WnnHgwAHq1auHlZUVJiYmODs7M336dLV+hIWFUatWLczNzbGxscHX11fZ1fOymJgYmjRpgqmpKRYWFjRo0OCVR/ELIYQQQnfka/Zke3t7bt68qVZ/3rx5TJ48mebNmwOgr69P27ZtGTduHNbW1ly8eJGAgADu37/PypUrATA1NSUwMBAXFxdMTU05cOAAffv2xdTUVEl6uHfvXgICAqhVqxYvXrzghx9+wMvLizNnzij5fmJiYvDx8SE4OJiZM2diaGjIn3/+ib6+DCQJIT4curdYVLfI9uSP23/e9TNo0CC2bdvGhQsXNCYmrFatGtWrV39lHp7/+7//Y/LkycoR+pq0a9cOU1NTli3TfPjanTt3sLGxYe/evTRo0ACAunXr0qxZM8aOHavlW6mTXT9CCKG7dC+Qk10/+ek/JSXMzp48ePBgjUHK8ePHiY2NZfbs2bm2cePGDTZs2EDDhg1zrXPy5Emio6NznKPyskePHgFQtGhRAG7fvs3hw4fp3LkzHh4eJCQk4OzszPjx46lfv35eX1EIId473fshFuLd+U9zILllT862YMECKlWqhIeHR45rfn5+FCpUiJIlS2JhYcH8+fNz1ClVqhTGxsbUrFmTgIAAevXqpfE5mZmZDBo0iHr16vHJJ58AWafkAoSGhtK7d2/Cw8OpXr06TZs25cKFC2/4xkIIIYR4l95K9mSAZ8+eYWtry8iRI/n2229zXE9KSuLhw4ecP3+e4OBgGjZsyM8//6xW5/LlyyQnJ3Po0CGGDRvGrFmzNCY2/Prrr/n99985cOCAkgsoOjqaevXqERwczI8//qjUdXFxoWXLloSFhWl8J8meLIQQHxbdG3GSqZ/89FayJwOsW7eOp0+f0rVrV43Xs/PtODs7U7RoUT799FNGjhyJra2tUsfR0RGAqlWrcuvWLUJDQ3MEKoGBgWzbto19+/apJSzMbqdy5cpq9StVqkRiYmKu7yXZk4UQQgjd8cZTP6/LnrxgwQLatGmDtbX1a9vKzMwEeGWG5czMTLXrKpWKwMBANm7cSGRkpBLUZCtTpgx2dnY5tiyfP38eBweHXJ8THBzMo0eP1D7g89p3EEIIIUT+y9fsydkuXrzIvn372LFjR45rO3bs4NatW9SqVQszMzNOnz7NkCFDqFevHmXKlAFg9uzZlC5dGmdnZwD27dvHlClTGDBggNJOQEAAK1euZPPmzZibm5OUlASApaUlJiYm6OnpMWTIEEJCQnB1dcXNzY0lS5Zw7tw51q1bl+u7SfZkIYQQQnfka/bkbAsXLqRUqVJ4eXnluGZiYsKvv/5KUFAQqamp2Nvb065dO4YNG6bUyczMJDg4mMuXL2NoaEi5cuWYOHEiffv+Mw85Z84cABo1aqTW/qJFi5TFvYMGDeL58+cEBQVx//59XF1diYiIoFy5cm/y2kIIIYR4xyR7ch7IOSpCCCHy6l0sUJXFtEIIIcQHQvd2/ej+j/+HRAIVIYQQHzQ5Qv/jJoGKEEKID5qMqHzctN6enJGRwciRI3F0dMTExIRy5coxduxYclvq0q9fP/T09JgxY4Za+fnz52nbti3FihXDwsKC+vXr88cffyjX//zzT/z8/LC3t8fExIRKlSrx008/qbURFRWlMVNz9g4gyHuWZSGEEELoHq1HVCZOnMicOXNYsmQJVapU4dixY3Tv3h1LS0u17cMAGzdu5NChQ9jZ2eVop1WrVlSoUIHIyEhMTEyYMWMGrVq1IiEhgRIlSnD8+HFsbGxYvnw59vb2REdH06dPHwwMDAgMDFRrKz4+HgsLC+W7jY2N8u+8ZFkWQgghhG7SetdPq1atKF68uFo25M8//xwTExOWL1+ulF2/fp06deqwc+dOWrZsyaBBgxg0aBAAd+/exdramn379vHpp58C8OTJEywsLIiIiMDT01PjswMCAjh79iyRkZFA1ohK48aNefDgAYULF85T/zVlWX4d2fUjhBC6S/emfuQI/fyk9dSPh4cHe/bs4fz580DWFM2BAwdo3ry5UiczM5MuXbowZMgQqlSpkqMNKysrnJycWLp0KSkpKbx48YJffvkFGxsbatSokeuzHz16pGRHfpmbmxu2trY0a9aMgwcPvrL//86yLIQQQgjdpfXUz7Bhw3j8+DHOzs4YGBiQkZHB+PHj6dy5s1Jn4sSJGBoa5pgKyqanp8fu3bvx9fXF3NwcfX19bGxsCA8Pp0iRIhrviY6OZvXq1Wzfvl0ps7W1Ze7cudSsWZPU1FTmz59Po0aNOHz4MNWrV8/RhqYsy0IIIT5ssuvn46Z1oLJmzRpWrFjBypUrqVKlCrGxsQwaNAg7Ozv8/f05fvw4P/30EydOnEBPT09jGyqVioCAAGxsbNi/fz8mJibMnz+f1q1bc/ToUbXEhABxcXG0bduWkJAQtdNunZyccHJyUr57eHiQkJDA9OnTWbZsWY7nBgQEEBcXx4EDB3J9P03ZkyEdOUZfCCF0k+5N/ej+dMqHROs1Kvb29gwbNoyAgAClbNy4cSxfvpxz584xY8YMBg8ejL7+P7NKGRkZ6OvrY29vz5UrV9izZw9eXl48ePBAbRFshQoV6Nmzp9px+mfOnKFx48b06tWL8ePHv7Z/Q4YM4cCBA8TExKiVBwYGsnnzZvbt25cjgeHLQkNDJXuyEEKINyYn0+YvrUdUnj59qhaEABgYGCgZkLt06ZJjMay3tzddunShe/fuShtAjnb09fWVdgBOnz5NkyZN8Pf3z1OQAhAbG6s2IqNSqejfvz8bN24kKirqlUEKZGVPHjx4sFqZpWXO0RkhhBC6QUZUPm5aByqtW7dm/PjxlC5dmipVqnDy5EmmTZumJCi0srLCyspK7Z4CBQpQokQJZZrG3d2dIkWK4O/vz6hRo5REhZcvX6Zly5ZA1nRPkyZN8Pb2ZvDgwcrZKAYGBlhbWwMwY8YMHB0dqVKlCs+fP2f+/PlERkaya9cu5dmvy7L8b5I9WQghhNAdWgcqM2fOZOTIkXzzzTfcvn0bOzs7+vbty6hRo/LcRrFixQgPD2f48OE0adKE9PR0qlSpwubNm3F1dQVg3bp13Llzh+XLl6tte3ZwcODKlSsApKWl8e2333L9+nUKFSqEi4sLu3fvpnHjxkr9vGRZFkII8eGSxbQfN8menAdyjooQQugu3Zv6kXNU8pPW56gIIYQQQrwrEqgIIYQQQmdJoCKEEEIInaX1YtqMjAxCQ0NZvnw5SUlJ2NnZ0a1bN0aMGKEc8KZSqQgJCeHXX3/l4cOH1KtXjzlz5lChQgWlnTJlynD16lW1tsPCwtTOUFGpVEydOpV58+Zx9epVihUrxjfffMPw4cMBOHDgAEOHDuXcuXM8ffoUBwcH+vbtS1BQkFq7s2fPZvLkySQlJeHq6srMmTOpXbu2tq8uhBDvhe6twRDi3Xkr2ZMnTZrE//3f/7FkyRIcHR0ZOXIk3t7enDlzhoIFCyptjRkzht69eyvfzc3N1Z41cOBAdu3axZQpU6hatSr379/n/v37ynVTU1MCAwNxcXHB1NSUAwcO0LdvX0xNTenTJ2uB0OrVqxk8eDBz586lTp06zJgxA29vb+Lj49WyLAshhK7StV0tukYCuY9bvmdPVqlU2NnZ8e233/Ldd98BWYkAixcvzuLFi+nYsSOQNaLyckblfzt79iwuLi7ExcWpHZP/Ou3atcPU1FQ5Qr9OnTrUqlWLWbNmAVn5fuzt7enfv7/a6M2ryK4fIYTQXboXqMiun/yk9YiKh4cH8+bN4/z581SsWFHJnjxt2jQALl++TFJSktrptJaWltSpU4eYmBglUAGYMGECY8eOpXTp0nTq1ImgoCAMDbO6tHXrVsqWLcu2bdvw8fFBpVLh6enJpEmTcs18fPLkSaKjoxk3bhyQdc7K8ePHCQ4OVuro6+vj6emZ44h9IYTQVbr3Q6xbdG3ESQ79yF/5nj05++TX4sWLq91XvHhx5RrAgAEDqF69OkWLFiU6Oprg4GBu3rypBDyXLl3i6tWrrF27lqVLl5KRkUFQUBBffPEFkZGRam2XKlWKO3fu8OLFC0JDQ+nVqxcAd+/eJSMjQ2Nfzp07p+2rCyGE0EG6F8jp/ijFhyTfsyfn1cv5dFxcXDAyMqJv376EhYVhbGxMZmYmqampLF26lIoVKwKwYMECatSoQXx8vNp00P79+0lOTubQoUMMGzaM8uXL4+fnp+2rAZI9WQihe3RtxEDX6F6gIvKT1tuThwwZwrBhw+jYsSNVq1alS5cuBAUFERYWBkCJEiUAuHXrltp9t27dUq5pUqdOHV68eKEcj29ra4uhoaESpABUqlQJgMTERLV7HR0dqVq1Kr179yYoKIjQ0FAg66h+AwMDrfoSFhaGpaWl2gfCX/1HEUIIIcRboXWg8rrsyY6OjpQoUYI9e/Yo1x8/fszhw4dxd3fPtd3Y2Fj09fWVnTj16tXjxYsXJCQkKHXOnz8PZOX7yU32SAyAkZERNWrUUOtLZmYme/bsybUvwcHBPHr0SO0DPrk+TwghhBBvT75nT9bT02PQoEGMGzeOChUqKNuT7ezs8PX1BSAmJobDhw/TuHFjzM3NiYmJISgoiK+++ooiRYoA4OnpSfXq1enRowczZswgMzOTgIAAmjVrpoyyzJ49m9KlS+Ps7AzAvn37mDJlirJNGrKmmPz9/alZsya1a9dmxowZpKSk0L17d43vJ9mThRBCCN3xVrInf//996SkpNCnTx8ePnxI/fr1CQ8PV85QMTY2ZtWqVYSGhpKamoqjoyNBQUFq61b09fXZunUr/fv3p0GDBpiamtK8eXOmTp2q1MnMzCQ4OJjLly9jaGhIuXLlmDhxIn37/jNf2aFDB+7cucOoUaNISkrCzc2N8PDwHAtshRBCV8kaDPG/TLIn54GcoyKEELpL9wI5OUclP0muHyGEEELoLK2nfoQQQrxbujdioFt0bfu2zFPkLwlUhBBCx+naD7Gu0b1ATvenU/4LbRL9/vrrryxdupS4uDgAatSowY8//qhVYmAJVIQQQnzQdC2Q+5hHVLRN9BsVFYWfnx8eHh4ULFiQiRMn4uXlxenTpylZsmSenqnVGpWMjAxGjhyJo6MjJiYmlCtXjrFjx/Lyetzk5GQCAwMpVaoUJiYmVK5cmblz5+ZoKyYmhiZNmmBqaoqFhQUNGjTg2bNnAFy5coWePXuqPSckJIS0tDTl/vj4eBo3bkzx4sUpWLAgZcuWZcSIEaSnpyt1Tp8+zeeff06ZMmXQ09NjxowZ2ryuEEIIIV4ybdo0evfuTffu3ZXf90KFCrFw4UKN9VesWME333yDm5sbzs7OzJ8/XznPLK+0GlGZOHEic+bMYcmSJVSpUoVjx47RvXt3LC0tlbNLBg8eTGRkJMuXL6dMmTLs2rWLb775Bjs7O9q0aQNkBSk+Pj4EBwczc+ZMDA0N+fPPP5WD5M6dO0dmZia//PIL5cuXJy4ujt69e5OSksKUKVMAKFCgAF27dqV69eoULlyYP//8k969e5OZmcmPP/4IZB1OV7ZsWb788kuCgoK0eVUhhBAfCJn6eTfyI9Hv06dPSU9PzzW5sCZaBSrR0dG0bduWli1bAlCmTBl+++03jhw5olbH39+fRo0aAdCnTx9++eUXjhw5ogQqQUFBDBgwgGHDhin3vZy7x8fHBx+ff06DLVu2LPHx8cyZM0cJVMqWLUvZsmWVOg4ODkRFRbF//36lrFatWtSqVQtA7VlCCCE+HjL18+Y05bfTfPBp/iT6HTp0KHZ2dnh6eua5j1oFKh4eHsybN4/z589TsWJF/vzzTw4cOKBkPM6us2XLFnr06IGdnR1RUVGcP3+e6dOnA3D79m0OHz5M586d8fDwICEhAWdnZ8aPH0/9+vVzffajR49eGYFdvHiR8PBw2rVrp80rCSGE+MDJiMqbCwsLY/To0WplISEhSs68/DRhwgRWrVpFVFSUcgBsXmgVqAwbNozHjx/j7OyMgYEBGRkZjB8/ns6dOyt1Zs6cSZ8+fShVqhSGhobo6+vz66+/0qBBAwAuXboEQGhoKFOmTMHNzY2lS5fStGlT4uLiqFChQo7nXrx4kZkzZyqjKS/z8PDgxIkTpKam0qdPH8aMGaPNK+Ug2ZOFEEL8rwgODlY7FR7QOJoCb5boN9uUKVOYMGECu3fvxsXFRas+arWYds2aNaxYsYKVK1dy4sQJlixZwpQpU1iyZIlSZ+bMmRw6dIgtW7Zw/Phxpk6dSkBAALt37wZQkhf27duX7t27U61aNaZPn46Tk5PGxTjXr1/Hx8eHL7/8kt69e+e4vnr1ak6cOMHKlSvZvn27xmBGG5I9WQghxP8KY2NjLCws1D65BSpvkugXYNKkSYwdO5bw8HBq1qypdR+1GlEZMmQIw4YNo2PHjgBUrVqVq1evEhYWhr+/P8+ePeOHH35g48aNyjoWFxcXYmNjmTJlCp6entja2gJQuXJltbYrVapEYmKiWtmNGzdo3LixMuWkib29vdJeRkYGffr04dtvv8XAwECbV1Noii4tLZe9UVtCCCHEx+R1iX67du1KyZIlCQsLA7I24YwaNYqVK1dSpkwZkpKSADAzM8PMzCxPz9QqUHn69KmyMyebgYGBMkqSnp5Oenr6K+uUKVMGOzs74uPj1eqcP3+e5s2bK9+vX79O48aNqVGjBosWLcrRpiaZmZmkp6eTmZn5xoGKZE8WQgghNHtdot/ExES13+s5c+aQlpbGF198odaONutgtApUWrduzfjx4yldujRVqlTh5MmTTJs2jR49egBgYWFBw4YNGTJkCCYmJjg4OLB3716WLl2qLLjV09NjyJAhhISE4OrqipubG0uWLOHcuXOsW7cOyApSGjVqhIODA1OmTOHOnTtKH7LnwVasWEGBAgWoWrUqxsbGHDt2jODgYDp06ECBAlmBRVpaGmfOnFH+ff36dWJjYzEzM6N8+fLavLoQQrw3urdYVLfIrp93KzAwkMDAQI3XoqKi1L5fuXLlPz9Pq+zJT548YeTIkWzcuJHbt29jZ2eHn58fo0aNwsjICICkpCSCg4PZtWsX9+/fx8HBgT59+hAUFISenp7S1oQJE5g9ezb379/H1dWVSZMmKbt+Fi9erAwj/Vt2d1evXs2kSZM4f/48KpUKBwcHvvrqK4KCgpTVxFeuXMHR0TFHGw0bNszxx3wVyZ4shBC6S/cCOcmenJ+0ClT+V0mgIoQQuksClTf3IQQqWu36EUIIIYR4lyRQEUIIIYTOkuzJQgih43RvakO3yGLaj5vWgcq/F9RWq1aNn376Scmps2HDBubOncvx48e5f/8+J0+exM3NTa2NhIQEvvvuOw4cOEBqaio+Pj7MnDlTLX9AmzZtiI2N5fbt2xQpUgRPT08mTpyInZ0dkPtC2ZiYGOrWrQtoXpRrbGzM8+fPtX1tIYR4b3Tth1jX6F4gp/vrPj4kWk/99OrVi4iICJYtW8apU6fw8vLC09OT69evA5CSkkL9+vWZOHGixvtTUlLw8vJCT0+PyMhIDh48SFpaGq1bt1bOWgFo3Lgxa9asIT4+nvXr15OQkJBjHzbA7t27uXnzpvKpUaOG2nULCwu161evXtX2lYUQQgjxnmg1ovLs2TPWr1/P5s2bldw9oaGhbN26lTlz5jBu3Di6dOkC5L53+uDBg1y5coWTJ09iYWEBwJIlSyhSpAiRkZFKRsWgoCDlHgcHB4YNG4avry/p6enKOSkAVlZWr8wxoKen99ocBEIIIYTQTVqNqLx48YKMjIwcWQ9NTEw4cOBAntpITU1FT09P7fTXggULoq+vn2sb9+/fZ8WKFXh4eKgFKZA1RWRjY0P9+vXZsmVLjnuTk5NxcHDA3t6etm3bcvr06Tz1UwghhBDvn1aBirm5Oe7u7owdO5YbN26QkZHB8uXLiYmJ4ebNm3lqo27dupiamjJ06FCePn1KSkoK3333HRkZGTnaGDp0KKamplhZWZGYmMjmzZuVa2ZmZkydOpW1a9eyfft26tevj6+vr1qwkp3ocPPmzSxfvpzMzEw8PDy4du2aNq8thBBCiPdE6wPfEhIS6NGjB/v27cPAwIDq1atTsWJFjh8/ztmzZ5V62YtdNS2m3bVrF19//TWXL19GX18fPz8/zpw5Q+3atZkzZ45S7+7du9y/f5+rV68yevRoLC0t2bZtm9oJty/r2rUrly9fZv/+/Rqvp6enU6lSJfz8/Bg7dqzGOqmpqaSmpqqVZSUllHw/Qoj3Q/cWi4pXkwPf8pPWu37KlSvH3r17SUlJ4fHjx9ja2tKhQwfKli2b5za8vLxISEjg7t27GBoaUrhwYUqUKJGjjWLFilGsWDEqVqxIpUqVsLe359ChQ7mmk65Tpw4RERG5PrdAgQJUq1aNixcv5lonLCyM0aNH/6u0FdA6r68nhBD5Snb9vJoEch+3Nz7wzdTUFFtbWx48eMDOnTtp27at1m0UK1aMwoULExkZye3bt2nTpk2udbN3BP17tONlsbGx2Nra5no9IyODU6dOvbJOcHAwjx49UvuAz+tfRgghhBD5TusRlZ07d6JSqXBycuLixYsMGTIEZ2dn5byS+/fvk5iYyI0bNwCIj48HsrIeZ+++WbRoEZUqVcLa2pqYmBgGDhxIUFAQTk5OABw+fJijR49Sv359ihQpQkJCAiNHjqRcuXLKaMqSJUswMjKiWrVqQNb5LQsXLmT+/PlKX8eMGUPdunUpX748Dx8+ZPLkyVy9epVevXrl+n7GxsZqC32zyLSPEOL9kRED8b9M60Dl0aNHBAcHc+3aNYoWLcrnn3/O+PHjld04W7ZsUTtkrWPHjgCEhIQQGhoKZAUvwcHB3L9/nzJlyjB8+HC17ciFChViw4YNhISEkJKSgq2tLT4+PowYMUItiBg7dixXr17F0NAQZ2dnVq9erXbWyoMHD+jduzdJSUkUKVKEGjVqEB0dTeXKlbV9bSGEeG9k6ufVJJD7uEn25DyQ7MlCCKG7dC9QkcW0+UmSEgohhBBCZ0mgIoQQQgidJYGKEEIIIXSW1oHKkydPGDRoEA4ODpiYmODh4cHRo0fV6pw9e5Y2bdpgaWmJqakptWrVIjExUa1OTEwMTZo0wdTUFAsLCxo0aMCzZ88AiIqKQk9PT+Pn5Wft3LmTunXrYm5ujrW1NZ9//nmOHEMrVqzA1dWVQoUKYWtrS48ePbh37562ry2EEEKI9yDfsycnJCRQv359nJ2diYqK4q+//mLkyJFq+YFiYmLw8fHBy8uLI0eOcPToUQIDA9HXz+qOh4eHWsbjmzdv0qtXLxwdHalZsyYAly9fpm3btjRp0oTY2Fh27tzJ3bt3adeunfKcgwcP0rVrV3r27Mnp06dZu3YtR44coXfv3v/pjyaEEEKId0OrXT/Pnj3D3NyczZs307JlS6W8Ro0aNG/enHHjxtGxY0cKFCjAsmXLcm2nbt26NGvWLNdj7P8tPT2dkiVL0r9/f0aOHAnAunXr8PPzIzU1VQlwtm7dStu2bUlNTaVAgQJMmTKFOXPmkJCQoLQ1c+ZMJk6cqFW+H9n1I4QQukt2/by5j27Xz+uyJ2dmZrJ9+3YqVqyIt7c3NjY21KlTh02bNil1b9++zeHDh7GxscHDw4PixYvTsGHDV2Zf3rJlC/fu3VM7n6VGjRro6+uzaNEiMjIyePToEcuWLcPT01M508Xd3Z2///6bHTt2oFKpuHXrFuvWraNFixbavLYQQggh3pN8zZ58+/ZtkpOTmTBhAj4+PuzatYvPPvuMdu3asXfvXgAuXboEQGhoKL179yY8PJzq1avTtGlTLly4oPG5CxYswNvbm1KlSilljo6O7Nq1ix9++AFjY2MKFy7MtWvXWLNmjVKnXr16rFixgg4dOmBkZESJEiWwtLRk9uzZWv+hhBBCCPHu5Wv25D179lCyZEn8/PxYuXKlck+bNm0wNTXlt99+Izo6mnr16hEcHMyPP/6o1HFxcaFly5aEhYWpPe/atWs4ODiwZs0aPv/8c6U8KSmJBg0a4Ovri5+fH0+ePGHUqFEYGhoSERGBnp4eZ86cwdPTk6CgILy9vbl58yZDhgyhVq1aLFiwQOP7SfZkIYQQ/8W7mE75X5r6ydfsycWKFcPQ0DDHEfWVKlVSpnayEwJqqvPvnUGQlRfIysoqR8LC2bNnY2lpyaRJk5Sy5cuXY29vz+HDh6lbty5hYWHUq1ePIUOGAFnBkKmpKZ9++injxo3TmJxQsicLIcSHRffWqOj+j/+HJF+zJxsZGVGrVi0lEWG28+fP4+DgAECZMmWws7N7ZZ1sKpWKRYsW0bVrV2XdSbanT58qi2izGRgYAP9kWn5VndwGkiR7shBCCKE78j178pAhQ+jQoQMNGjSgcePGhIeHs3XrVqKiogDQ09NjyJAhhISE4OrqipubG0uWLOHcuXOsW7dO7VmRkZFcvnxZY7bjli1bMn36dMaMGaNM/fzwww84ODgoGZVbt25N7969mTNnjjL1M2jQIGrXro2dnZ3G95PsyUII8WHRtaSNkkEvf+V79uTPPvuMuXPnEhYWxoABA3BycmL9+vXUr19faWPQoEE8f/6coKAg7t+/j6urKxEREZQrV07tWQsWLMDDwwNnZ+cc/WjSpAkrV65k0qRJTJo0iUKFCuHu7k54eDgmJiYAdOvWjSdPnjBr1iy+/fZbChcuTJMmTZg4caK2ry2EEEKI90CyJ+eBnKMihBC6S/fWqMg5KvlJcv0IIYQQQmdJoCKEEEIInSWBihBCCCF01nvJnpyUlESXLl0oUaIEpqamVK9enfXr16u1ceLECZo1a0bhwoWxsrKiT58+JCcnK9fv3buHj48PdnZ2GBsbY29vT2BgII8fP1bq5JaFOSkpSdvXFkIIIcR78F6yJ3ft2pX4+Hi2bNnCqVOnaNeuHe3bt+fkyZMA3LhxA09PT8qXL8/hw4cJDw/n9OnTdOvW7Z+O6+vTtm1btmzZwvnz51m8eDG7d++mX79+OfocHx+vlonZxsZG29cWQgghxHvwXrInm5mZMWfOHLp06aKUWVlZMXHiRHr16sW8efMYOXIkN2/eVA5sO3XqFC4uLly4cIHy5ctrbPf//u//mDx5Mn///TeQNaLSuHFjHjx4QOHChfP6mjnIrh8hhBB5JUfo5y+tzlHJa/bk77//Hm9vb06ePImjoyPBwcH4+voq9T08PFi9ejUtW7akcOHCrFmzhufPn9OoUSMgK9+OkZGR2qmy2WejHDhwQGOgcuPGDTZs2EDDhg1zXHNzcyM1NZVPPvmE0NBQ6tWrp81rCyGE0GG6tz1Z93/8PyTvPHsywJo1a0hPT8fKygpjY2P69u3Lxo0blQCkSZMmJCUlMXnyZNLS0njw4AHDhg0D4ObNm2p98vPzo1ChQpQsWRILCwvmz5+vXLO1tWXu3LmsX7+e9evXY29vT6NGjThx4sQb/8GEEEII8e5ovUZl2bJlqFQqSpYsibGxMf/3f/+Hn58f+vr6So6dtm3bEhQUhJubG8OGDaNVq1bMnTtXaWPkyJE8fPiQ3bt3c+zYMQYPHkz79u05deoUAFWqVGHJkiVMnTqVQoUKUaJECRwdHSlevHiO3D3Tp0/nxIkTbN68mYSEBAYPHqxcc3Jyom/fvtSoUQMPDw8WLlyIh4cH06dPz/X9UlNTefz4sdoH0rX9MwkhhBAiH2gdqGRnT05OTubvv//myJEjpKenvzZ7cvaun4SEBGbNmsXChQtp2rQprq6uhISEULNmTWbPnq3c06lTJ5KSkrh+/Tr37t0jNDSUO3fuULZsWbW2S5QogbOzM23atOGXX35hzpw5OUZdXla7dm0uXryY6/WwsDAsLS3VPhCu7Z9JCCGEEPngnWdPfvr0adaDNWQ1zh6ReVnx4sUxMzNj9erVFCxYkGbNmuXap+z7U1NTc60TGxuLra1trtcle7IQQgihO9559mRnZ2fKly9P3759mTJlClZWVmzatImIiAi2bdumPGfWrFl4eHhgZmZGREQEQ4YMYcKECcrunR07dnDr1i1q1aqFmZkZp0+fZsiQIdSrV48yZcoAMGPGDBwdHalSpQrPnz9n/vz5REZGsmvXrlzfT7InCyGEELrjnWdPLlCgADt27GDYsGG0bt2a5ORkypcvz5IlS2jRooXynCNHjhASEkJycjLOzs788ssvatuZTUxM+PXXXwkKCiI1NRV7e3vatWunLLoFSEtL49tvv+X69esUKlQIFxcXdu/eTePGjd/4DyaEEEKId0eyJ+eBnKMihBC6S/e2J0v25Pyk9YiKEEIIoUv09H55311QI//5n78kUBFCCPFB070RFd0fpfiQSPZkIYQQQuistxKo7Nu3j9atW2NnZ4eenh6bNm1Su96tW7ccGY19fNS3AI8fPx4PDw8KFSqUa56ePXv24OHhgbm5OSVKlGDo0KG8ePFCrc5ff/3Fp59+SsGCBbG3t2fSpEn5+apCCCGEeIveSqCSkpKCq6ur2gFu/+bj46OW0fi3335Tu56WlsaXX37J119/rfH+P//8kxYtWuDj48PJkydZvXo1W7ZsUdv18/jxY7y8vHBwcOD48eNMnjyZ0NBQ5s2TxbFCCCHEh+CtrFFp3rw5zZs3f2UdY2NjSpQokev10aNHA7B48WKN11evXo2LiwujRo0CoHz58kyaNIn27dsTEhKCubk5K1asIC0tjYULF2JkZESVKlWIjY1l2rRp9Okjc4hCCPEmdG9NiPiYvbfFtFFRUdjY2FCkSBGaNGnCuHHjsLKyyvP9qampGrM4P3/+nOPHj9OoUSNiYmJo0KABRkZGSh1vb28mTpzIgwcPKFKkSL69jxBC/K/QvV02Ejh9zN5LoOLj40O7du1wdHQkISGBH374gebNmxMTE4OBgUGe2vD29mbGjBn89ttvtG/fnqSkJMaMGQP8k2E5KSkJR0dHtfuKFy+uXJNARQghPny6Fzi97x58XN5LoNKxY0fl31WrVsXFxYVy5coRFRVF06ZN89SGl5cXkydPpl+/fnTp0gVjY2NGjhzJ/v37c+QR0kZqaqqGXEHpyDH6Qgihm3RvREWWFuQnndienJ15+VVZjTUZPHgwDx8+JDExkbt379K2bVulPcjKrHzr1i21e7K/57Y+RrInCyGEELpDJwKVa9euce/evVdmNc6Nnp4ednZ2mJiY8Ntvv2Fvb0/16tUBcHd3Z9++faSnpyv1IyIicHJyynXaR7InCyGEELrjrUz9JCcnq42OXL58mdjYWIoWLUrRokUZPXo0n3/+OSVKlCAhIYHvv/+e8uXL4+3trdyTmJjI/fv3SUxMJCMjg9jYWCBrd4+ZmRkAkydPxsfHB319fTZs2MCECRNYs2aNss6lU6dOjB49mp49ezJ06FDi4uL46aefmD59eq59l+zJQgghhO54K0kJo6KiNGYo9vf3Z86cOfj6+nLy5EkePnyInZ0dXl5ejB07VlnoClmHwi1ZsiRHG3/88QeNGjUCoEmTJpw4cYLU1FRcXV0JCQnJsS36r7/+IiAggKNHj1KsWDH69+/P0KFDtXofSUoohBC6S/fWqEhSwvwk2ZPzQAIVIYT4h+4FBrpGApX8JEkJhRBCfNBke/LHTQIVIYQQWtG9wEDXRnh0f5TiQ6ITu36EEEIIITSRQEUIIYQQOkumfoQQQnzQdG8q6n334OMigYoQQogPmqxRebdmz57N5MmTSUpKwtXVlZkzZ1K7du1c669du5aRI0dy5coVKlSowMSJE2nRokWenydTP0IIIYTIk9WrVzN48GBCQkI4ceIErq6ueHt7c/v2bY31o6Oj8fPzo2fPnpw8eRJfX198fX2Ji4vL8zPlHJU8kHNUhBBCd+neiMrHe45KnTp1qFWrFrNmzQIgMzMTe3t7+vfvz7Bhw3LU79ChAykpKWzbtk0pq1u3Lm5ubsydOzdPz5QRFSGEEEK8VlpaGsePH8fT01Mp09fXx9PTk5iYGI33xMTEqNUH8Pb2zrW+JrJGRQghhPgflZqaSmpqqlqZ5px3cPfuXTIyMtTS3QAUL16cc+fOaWw/KSlJY/2kpKS8d1Il3onnz5+rQkJCVM+fP3/fXVGpVNKf15H+vJr05/V0rU/Sn1fTtf68KyEhISqy5qqUT0hIiMa6169fVwGq6OhotfIhQ4aoateurfGeAgUKqFauXKlWNnv2bJWNjU2e+yhrVN6Rx48fY2lpyaNHj7CwsHjf3ZH+SH+kP2+ZrvVJ+vNh9edd0WZEJS0tjUKFCrFu3Tp8fX2Vcn9/fx4+fMj/a+/Ow2pO+z+Av89p30vJEnVKDaIoxhaKMoSo5pG9pBhMiWyNJfuWLTMaW2kxY4vGLrIVyRotqGhRqBBFiup0//7o6TwdleX3OPf3eNyv6zrX1Pecub7vq1L3uZfP58iRI/X+H319ffj6+mLGjBmia4sXL8bhw4eRlJT0WRnZHhWGYRiG+U4pKChAXV1d7NHQIAUA5OXl0aVLF5w7d050rbq6GufOnUPPnj0b/H969uwp9noAiImJafT1DWF7VBiGYRiG+Sy+vr5wc3ND165d0a1bNwQGBuLt27dwd3cHALi6ukJPTw+rV68GAPj4+MDa2hobNmzAkCFDsG/fPty8eRM7dnz+qSU2UGEYhmEY5rOMHDkSz58/h7+/PwoKCtC5c2dER0eLNszm5uaCz//PYk2vXr2wZ88eLFy4EPPnz4eJiQkOHz6Mjh07fvY92UCFEgUFBSxevLjRKTXaWJ6PY3k+juX5NGnLxPJ8nLTlkWZeXl7w8vJq8LmLFy/WuzZixAiMGDHi/30/tpmWYRiGYRipxTbTMgzDMAwjtdhAhWEYhmEYqcUGKgzDMAzDSC02UJGAqqoqLFu2DI8fP+Y6CgCgsrISsrKyX9StkmGkWVxcHKqqqupdr6qqQlxcHAeJ/uP58+e4fPkyLl++jOfPn3OaRRpVVFQgPT29we8f14qLi7mOwDSAbaaVEDU1NaSkpEAgEHAdBQBgZGSEf/75B506deIsg4WFBXg83me9NjExUcJp6jt37hzOnTuHZ8+eobq6Wuy5Xbt2Uc8jbTIzMxEaGorMzExs3rwZurq6OHXqFPT19dGhQweqWWRkZJCfnw9dXV2x60VFRdDV1YVQKKSaBwDevn0Lb29v7N69W3R/GRkZuLq64o8//oCysjL1TNL0PSsrK4O3tzfCw8MBABkZGTAyMoK3tzf09PQa7LwrSWvXroVAIMDIkSMBAC4uLjh06BCaN2+OkydPcvq7khHHZlQkpH///oiNjeU6hsiCBQswf/58vHz5krMMjo6OGD58OIYPH46BAwciMzMTCgoKsLGxgY2NDRQVFZGZmYmBAwdSz7Z06VL89NNPOHfuHF68eIFXr16JPWjLy8sTm5G7fv06ZsyY8UVFkr6m2NhYmJmZ4dq1a4iKikJpaSkAICkpCYsXL6aehxDS4KC3qKgIKioq1PMANYWwYmNjcfToURQXF4tKisfGxmLWrFnU80jb9+y3335DUlISLl68CEVFRdF1Ozs77N+/n3qebdu2oXXr1gBqKqXGxMTg1KlTsLe3x5w5c6jnYT7is7sCMV9k69atpHnz5mTWrFlkz5495MiRI2IP2jp37kxUVVWJgoIC+eGHH4iFhYXYgzYPDw+ycOHCetf9/f2Ju7s79TzNmzcnERER1O/bmN69e4vy5OfnE3V1ddKzZ0+io6NDli5dSj1Pjx49yIYNGwghhKiqqpLMzExCCCHXrl0jenp61HI4OTkRJycnwufzyeDBg0WfOzk5kWHDhhGBQEAGDhxILU9d2tra5MKFC/Wunz9/nujo6FDPIy3fs1r6+vokISGhXp4HDx4QNTU16nkUFRVJbm4uIYSQ6dOnk8mTJxNCCElPTyeamprU8zCNYwXfJGTatGkAgI0bN9Z7jsfjUZ+arttAShpERkbi5s2b9a6PGzcOXbt2pb7UUlFRgV69elG958ekpqaiW7duAIADBw6gY8eOiI+Px5kzZzBlyhT4+/tTzZOSkoI9e/bUu66rq4sXL15Qy6GhoQGgZkZFTU0NSkpKoufk5eXRo0cPTJo0iVqeusrKyuq1swdqvkZlZWXU80jL96zW8+fP6y3VATVLZp+7JPw1aWlpIS8vD61bt0Z0dDRWrFgBoOZni4ulQ6ZxbKAiIR/uceAaF1O9H6OkpIT4+HiYmJiIXY+PjxebFqbF09MTe/bswaJFi6jfuyGVlZWiCplnz57FsGHDAADt2rVDfn4+9TyamprIz8+HoaGh2PXbt29DT0+PWo7Q0FAAgEAgwOzZszlb5mlIz549sXjxYkRERIh+hsvLy7F06dIvasD2tUjL96xW165dceLECXh7ewOAaHASHBzMydfH2dkZY8aMgYmJCYqKimBvbw+g5utjbGxMPQ/TODZQoeDdu3ec/PGVZjNmzMDUqVORmJgomjm4du0adu3axclg4d27d9ixYwfOnj0Lc3NzyMnJiT3f0MyYJHXo0AHbtm3DkCFDEBMTg+XLlwMAnj59Cm1tbapZAGDUqFGYN28eIiMjwePxUF1djfj4eMyePRuurq7U80jbwBsANm/ejIEDB6JVq1aijZhJSUlQVFTE6dOnqeeRtu/ZqlWrYG9vj3v37qGqqgqbN2/GvXv3cOXKFU72823atAkCgQB5eXkICAiAqqoqACA/P180I85IB3bqR0KEQiFWrVqFbdu2obCwULTDfdGiRRAIBPDw8KCeZ9OmTThw4AByc3NRUVEh9jwXm2wPHDiAzZs34/79+wCA9u3bw8fHBy4uLtSz9OvXr9HneDwezp8/TzFNTb8MJycnvH79Gm5ubqKlsPnz5yMtLQ1RUVFU81RUVODXX39FWFgYhEIhZGVlIRQKMWbMGISFhUFGRoZqnsLCQsyePVt0SuvDX2NcTd2XlZXh77//RlpaGoCan+mxY8eKLVHRIm3fM6DmFNKaNWuQlJSE0tJSWFpaYt68eTAzM6Oe5fXr11BXV2/wuYcPH7JZFSnCBioSsmzZMoSHh2PZsmWYNGkSUlNTYWRkhP379yMwMBAJCQlU8/j7+yM4OBizZs3CwoULsWDBAuTk5ODw4cPw9/fH9OnTqeZhPk0oFOL169fQ0tISXcvJyYGysnKDa/005ObmIjU1FaWlpbCwsKi3dEeLvb09cnNz4eXlhRYtWtTb4zB8+HBOckkjafmeSZs+ffrg7Nmz9ZoQpqenw9bWVmrqYDFsoCIxxsbG2L59O2xtbaGmpoakpCQYGRkhLS0NPXv2pH7ktU2bNvj9998xZMgQqKmp4c6dO6JrV69ebXDTnaQVFxfj4MGDyMrKwuzZs9GkSRMkJiaiWbNm1NfQz58/DysrK6npnFpeXg5CiKj2xqNHj/DPP/+gffv2nBzfljZqamq4dOkSOnfuzGmOo0ePwt7eHnJycjh69OhHX1u7z4gLtb/mudi0WpdQKMQ///wjmkU1NTXF8OHDIStLfxeCvb09eDwejh49Krr//fv30b9/f7i4uGDz5s3UMzENYwMVCVFSUkJaWhoMDAzEBir37t1Dt27dRDUNaFFRUcH9+/ehr6+PFi1a4MSJE7C0tERWVhYsLCxQUlJCNU9ycjLs7OygoaGBnJwcpKenw8jICAsXLkRubi4iIiKo5lFVVUVVVRV+/PFH2NjYwNraGlZWVpxM2QPATz/9BGdnZ0yZMgXFxcVo164d5OTk8OLFC2zcuBFTp06lmmfixIkffZ72KS1TU1P8/fffsLCwoHrfD/H5fBQUFEBXVxd8fuNlqbg46QcAISEh2LRpEx48eAAAMDExwYwZM+Dp6Uk9y927dzFs2DAUFBSgbdu2AGqKvjVt2hTHjh1Dx44dqeYpLy+HnZ0dWrVqhX379uHu3buwtbXF2LFjqe9JYz6OFXyTEFNTU1y6dKne9YMHD3Lyy7VVq1ai0yJt2rTBmTNnAAA3btzgZBbB19cXEyZMwIMHD8Q2Gg8ePJiTEuivXr3CuXPnYG9vj+vXr8PJyQmampqwsrLCwoULqedJTExEnz59ANT8zDRr1gyPHj1CREQEfv/9d+p5PiyA9+zZM5w/fx5RUVGclB0PDAyEn58fcnJyqN+7rurqatEyXHV1daMPLgYp/v7+8PHxgYODAyIjIxEZGQkHBwfMnDmT+vF2oOZkXYcOHfD48WMkJiYiMTEReXl5MDc3x+TJk6nnUVJSwokTJ5Ceng4XFxfY2trC1dWVDVKkESfVW74Dhw8fJhoaGmTNmjVEWVmZrFu3jnh6ehJ5eXly5swZ6nnmzZtHVq5cSQghZN++fURWVpYYGxsTeXl5Mm/ePOp51NXVycOHDwkh4sWfcnJyiIKCAvU8H0pNTSVubm5EVlaW8Pl86vdXUlIijx49IoQQMmLECLJkyRJCCCG5ublESUmJep6GCIVCMnnyZLJ27Vrq99bU1CTy8vKEz+cTVVVVoqWlJfbgQnh4OHn37l296+/fvyfh4eHU8+jo6JA9e/bUu75nzx6ira1NPY+ioiJJTU2tdz0lJYUoKipSyVBSUlLvkZaWRlq3bk2mTp0qdp2RHmzpR4IuXbqEZcuWie1w9/f3x08//cR1NCQkJCAhIQEmJiZwcHCgfn9dXV2cPn0aFhYWYktjMTExmDhxIvLy8qjmycjIwMWLF3Hx4kXExsbi/fv36NOnj6i8P+2+H+bm5vD09ISTkxM6duyI6Oho9OzZE7du3cKQIUNQUFBANU9j0tPTYWNjQ722S22/mMa4ublRSvIf0tZ/SFNTEzdu3Ki3eTYjIwPdunWjPhPWqVMnbNq0Cf379xe7fv78efj4+CAlJUXiGfh8foP7dEidPTzk3+0ZWNE36cEGKgwnPD09UVRUhAMHDqBJkyZITk6GjIwMHB0d0bdvXwQGBlLNw+fz0bRpU/j4+GDo0KEwMzPjdOPhwYMHMWbMGAiFQvTv3x8xMTEAgNWrVyMuLg6nTp3iLFtdJ0+ehJubG+sSjJqfocLCQjRt2lTselJSEvr160e9BIC3tzfk5OTqLWXMnj0b5eXlCAoKoprn5MmTmDt3LpYsWYIePXoAAK5evYply5ZhzZo16N27t+i1jR0b/m99Sb0Wa2triWRgvhwbqEjYzZs3xXa4d+nShbMsu3fvxrZt25CdnY2EhAQYGBggMDAQhoaG1I9zlpSU4F//+hdu3ryJN2/eoGXLligoKEDPnj1x8uRJ6hVHZ8yYgbi4ONy7dw+WlpaimZTevXtz0vUWAAoKCpCfn49OnTqJNmpev34d6urqaNeuHdUsvr6+Yp8TQpCfn48TJ07Azc0NW7ZsoZonNzf3o8/r6+tTSvKfruBJSUno0KGD2AkWoVCI7OxsDBo0CAcOHKCWCagZqERERKB169aigcG1a9eQm5sLV1dXsaKGNPZl1N1sXPsmgHxwGonWbEZVVRVWrVqFiRMnolWrVhK9F/PfYwMVCXn8+DFGjx6N+Ph4aGpqAqg5jturVy/s27eP+j+OrVu3wt/fHzNmzMDKlStFdV3CwsIQHh6OCxcuUM1T6/Lly0hOThYtjdnZ2XGSo1ZxcTEuXbqE2NhYxMbG4u7du7CwsEB8fDwneR4+fIjMzEz07dsXSkpKjXYNlrQPC+LVzkD1798fEydOpH68tLEp/Fo0p+2XLl0q+u+sWbNEFU6Bmv5DAoEAP//8M+Tl5allAj5exLAuWgUNL168+Nk/uzRmM9TU1JCSkgKBQCDxezH/HTZQkZBBgwahuLgY4eHhoqN46enpcHd3h7q6OqKjo6nmMTU1xapVq+Do6Ci2JyQ1NRU2NjacNCmTRkVFRYiNjcWFCxdw8eJF3Lt3D1paWtS/PkVFRXBxccGFCxfA4/Hw4MEDGBkZYeLEidDS0sKGDRuo5pE2SUlJYp9XVlbi9u3b2LhxI1auXAlnZ2fqmcLDwzFy5EjWLuMbMXz4cDg7O3Oyn4n5MmygIiFKSkq4cuVKvaPIt27dQp8+fah3U22srsuDBw9gbm6O8vJyiWf4kmO1tCvlTp8+XWxg0rdvX1hbW8PGxoaT/Squrq549uwZgoOD0b59e9H36/Tp0/D19cXdu3ep5vlWnDhxAuvWrcPFixe5jsK50NBQjBo1irNaQB8yNDSEu7s7JkyYQHVprjHbtm3D0qVLMXbsWHTp0qXecjOXBfoYcawpoYS0bt0alZWV9a4LhUK0bNmSeh5DQ0PcuXMHBgYGYtejo6PRvn17Khk2bdok9vnz589RVlYmtjRWWx6e9kAlPz8fkydPho2NDfXCUw05c+YMTp8+XW+J0MTEBI8ePaKSoXbvxedITEyUcJrP07ZtW9y4cYOTe0tbPy0/Pz/4+PhgxIgR8PDwQK9evaje/0M+Pj4ICwvDsmXL0K9fP3h4eMDJyYmzatC1jQcb2p/DTv1IFzZQkZB169bB29sbQUFB6Nq1K4CajbU+Pj5Yv3499Ty+vr749ddf8e7dOxBCcP36dezduxerV69GcHAwlQzZ2dmij/fs2YM///wTISEhYktjkyZNwi+//EIlT12RkZHU7/kxb9++bXAT78uXL6n9Ynd0dKRyn/+P169fi31eu7l3yZIlnPWyWbp06Uf7adH25MkTHDt2DGFhYbCxsYGRkRHc3d3h5uaG5s2bU88zY8YMzJgxA4mJiQgLC4O3tzemTZuGMWPGYOLEibC0tKSap7q6mur9mP8C3bIt/9s0NTXFik7VFqSSl5cX+5hmQaoRI0aQly9fEkII+euvv4ixsTHh8XiEx+MRPT09EhwcTC1LXUZGRiQxMbHe9Zs3bxKBQMBBIkIePnxIvLy8iK2tLbG1tSXe3t6ionS02dvbk4ULFxJCagriZWVlEaFQSEaMGEF+/vlnTjJJEx6PR/h8vtiDx+MRfX19cuXKFU4yGRkZkePHjxNCar5ntT87mzdvJqNHj+YkU62CggKyfv16YmZmRuTk5IiDgwM5fPgwEQqFnGWqqKgggYGBREFBgfD5fNKpUycSEhJCqqurOcvESCc2o/IV0a798TkeP36MDh06YOfOnRg7dizGjh2LsrIylJaWctaBF6hZaqmqqqp3XSgUorCwkHqe06dPY9iwYejcuTOsrKwAAPHx8ejQoQOOHTuGAQMGUM0TEBAAW1tb3Lx5ExUVFZg7dy7u3r2Lly9fcnYCSZp8eEqt9hSSsbExJw3ugJrj5GZmZgBqekfV9s8aOnQoFi1axEmmWs2aNUPv3r2RkZGBjIwMpKSkwM3NDVpaWggNDYWNjQ21LJWVlfjnn38QGhqKmJgY9OjRAx4eHnj8+DHmz5+Ps2fPUmuS+vbtW8TGxja4VMc6yksRrkdKjGRVV1eTgIAAoqSkRDw8PEhpaSnXkQghhAwdOpRYWFiQW7duia7dvHmTWFpaEgcHB+p5Onfu3GArgXnz5hELCwvqeQghpLi4mKxYsYKMGDGC2NvbkwULFpCnT59ykqWqqoqsW7eO/Pjjj6RZs2ZSUbJe2vzwww/k6tWrhBBCrKysyOrVqwkhNS0rmjZtykmmgoICsm7dOmJqakoUFRXJqFGjSExMDCGEkNLSUjJ37lyir68v0Qy1rQVu3bpFvLy8iLa2NmnatCmZNWsWuX//vthraZbTT0xMJM2bNyfq6upERkaGNG3alPB4PKKiokIMDQ2pZGA+DxuoSFhhYSFJSUkhSUlJYg/a7t+/T3r06EEEAgHZsGED2bx5s9iDtmfPnhF7e3vC4/HElsbs7e1JYWEh9TwKCgokIyOj3vX09HTqvYcqKipI//79G8zDlUWLFpEWLVqQ9evXE0VFRbJ8+XLi4eFBtLW1Ofn5IUS6luoIkZ5+WoaGhuTFixdk6NChRE5OjnTo0IFs2rSJFBUV1XttYWEh4fF4Es3D5/NJYWEh4fP5ZODAgeTAgQOkoqKiwdeWlpaSCRMmSDRPLWtrazJp0iQiFApF/cZyc3NJ3759yaFDh6hkYD4PG6hIyM2bN0mHDh1Ea+d1H1w0uSOEkJ07dxIZGRnSqlUrIhAIRA/a7x6qq6vJo0ePSFlZGcnIyCBHjhwhR44cIenp6VRz1NWqVSty4MCBetf3799PWrduTT2Pjo6OVA1UpG3/RXR0NJGXlyfdunUjM2fOJDNnziTdunUjCgoKnDT9bEhCQgLZsGEDOXr0KNX71g4MJk6c+Mn9OtXV1SQnJ0eieXg8HiksLJT4fb6UhoYGSUtLE3187949QgghV69eJW3btuUyGvMBNlCREHNzc+Lk5ESuXr1KsrOzSU5OjtiDpoKCAjJ06FCiqalJwsLCqN67IUKhkMjJyUnFH+KlS5eSt2/fkqVLlxJNTU2yZs0aEhcXR+Li4sjq1auJpqYmWbZsGfVcM2bM4KSrdWOUlZVF3ZybN28uWrLLzMwk6urq1PNI41Ldx9y4cYPavWoHBtKCx+ORZ8+ecR2jnrpvBkxMTEh0dDQhpGb2WVlZmctozAfYZloJycrKwqFDh2BsbMxpjn379sHLywudO3dGUlKSVBRa4vP5MDExQVFREWdHSWstXboUU6ZMwaJFi6CmpoYNGzbgt99+AwC0bNkSS5Ys4WRTXVVVFXbt2oWzZ882WIyKRm+Wulq1aoX8/Hzo6+ujTZs2OHPmDCwtLXHjxg1O6mDcv3+/wd45EydO5GxTe2lpKWRkZMQKrN25cweLFi3CyZMnqdblOH36NDQ0ND76GpoFzWxtbT+5yZl2LR4LCwtRd2lra2v4+/vjxYsX2L17t1TUUmL+gw1UJMTW1hZJSUmcD1Q8PDywZs0aeHt7c5rjQ2vWrMGcOXOwdetWTn8pkDpN0WbOnImZM2fizZs3AGp6gXAlNTVVVFciIyND7Dkuev04OTnh3Llz6N69O7y9vTFu3DiEhIQgNzcXM2fOpJ6nadOmuHPnTr2B7p07d6ifZsvLy4OLiwuuX78OGRkZeHl5YcWKFZgyZQr2798PJycnXLlyhWqmT5WFp13QbODAgWI9kKTBqlWrRP/WV65cCVdXV0ydOhUmJibYtWsXx+mYulgJfQl58eIF3Nzc0K1bN3Ts2FGsUylA793MgwcPOJ+1aIiWlhbKyspQVVUFeXn5emW+aVXx5PP5KCwsRNOmTanc71uzZcsWjBs3TlQ9uFZCQgISEhJgYmICBwcH6rmWLVuGTZs2wc/PT1RxNT4+HmvXroWvry/V48CjRo1Ceno6PDw8EBUVhdjYWFhaWqJ79+7w8/Oj3oCUz+ejoKCA0/IDdUlbHubbwwYqEnLs2DGMHz++XgVNgJVnBmoauH0MrUZhfD4fGhoan5yloF3+XFpoaGigsrISTk5O8PDwQP/+/bmOBKBmJiwwMBAbNmzA06dPAdQs1c2ZMwfTp0+nOuvUsmVLREVFoUePHnj27BmaN2+OjRs3YsaMGdQy1CUjI4P8/HypGRhIWx7m28MGKhIiEAhEhZ6aNWvGdRymEXw+H4GBgZ9cz6cxcPqSjr9RUVESTPIf5eXliIyMRGhoKOLi4qCvr4+JEydiwoQJaN26NZUMn8L1Up2MjAyePn0q+neuqqqKW7duiVpD0CZtMxjSlqdWUVER/P39ceHCBTx79qxeSf3v9c2JNGJ7VCSkqKgIM2fOZIOUjxAKhTh8+DDu378PAOjQoQOGDRsGGRkZqjlGjRolFb9E6w6WCCH4559/oKGhIeoVdevWLRQXF3/RgOa/paSkBFdXV7i6uiIrKwthYWEICQnB0qVLYWdnBw8PDzg6OtZb2qSJy71Etfh8vtjH8vLynGVxc3OTmo7JQE2PL2lcWh0/fjwePnwIDw8PNGvWjJO9X8znYTMqEuLm5oY+ffrA09OT6yhS6eHDhxg8eDCePHki1pSwdevWOHHiBNq0aUMlh7ROS8+bNw8vX77Etm3bRAM3oVCIadOmQV1dHevWreMsGyEEZ8+eRVhYGA4fPgwVFRU8e/aMagZpejf84fJhcXEx1NXVxQYvtDMxn6ampobLly+jU6dOXEdhPoHNqEjIDz/8gN9++w2XL1+GmZlZvXectI+8GhkZ4caNG9DW1ha7XlxcDEtLS2RlZVHNM336dLRp0wZXr15FkyZNANT88Rk3bhymT5+OEydOUMkhreP0Xbt24fLly2KzSzIyMvD19UWvXr04HajweDzIysqCx+OBEILKykrqGaTp3XBoaChn92b+/9q1a4fy8nKuYzCfgc2oSIihoWGjz/F4POoDg8bWiQsLC6Gvr4/3799TzaOiooKrV6+KmrjVSkpKgpWVFUpLS6nmkTZaWloICwvD8OHDxa4fOXIEEyZMwKtXr6hnysvLQ2hoKMLCwpCbm4u+ffvCw8MDP//8MxQVFalmYe+Gmf/WjRs34OfnB39//wZPZqqrq3OUjPkQm1GRkOzsbK4jAACOHj0q+vjDIlBCoRDnzp2DQCCgnktBQUG0CbKu0tJSTtf3pYW7uzs8PDyQmZmJbt26AQCuXbuGNWvWwN3dnVqOiooKREVFYdeuXTh//jxatGgBNzc3TJw4EUZGRtRyfIi9G2b+W5qamnj9+nW9k2yEEHYyU8qwGZX/cbXr5LXT9HXJyclBIBBgw4YNGDp0KNVcrq6uSExMREhIiNgf4kmTJqFLly4ICwujmkfaVFdXY/369di8eTPy8/MBAC1atICPjw9mzZpFbcNxkyZNUFZWhqFDh8LDwwMDBw6st/eCC+zd8KeFhoZi5MiRUFZW5ixDcnLyZ7/W3Nxcgknq69atG2RlZeHj49Pg8qG1tTXVPEzj2EBFQiZOnPjR52lXPjQ0NMSNGzego6ND9b6NKS4uhpubG44dOyb6I1NVVYVhw4YhLCzsk8eFvye1tXi4+OO7ceNGjB8/XupObTx48ABjxoypV3advRv+j2bNmqG8vBwjRoyAh4eHqDAeTXw+v8E3SbVqn+Pie6asrIzbt29zdoyc+Xxs6UdCPtxDUFlZidTUVBQXF3NSNKuhpaji4uJ6FUdp0dTUxJEjR/Dw4UPR8eT27dtz3nJAGnE5O+Dr68vZvT9m7NixkJOTw549ezjfTCutnjx5gmPHjiEsLAw2NjYwMjKCu7s73Nzc0Lx5cyoZpGUJvCFdu3ZFXl4eG6h8A9iMCkXV1dWYOnUq2rRpg7lz51K999q1ayEQCDBy5EgAwIgRI3Do0CG0aNECJ0+eZJsSpYCFhcVn/8Gl3cBN2nwL74aFQiFSUlJgYGAALS0tTrMUFhbir7/+Qnh4ONLS0jBo0CB4eHjAwcFBKpbyuBAZGYklS5Zgzpw5DZ7MpL0UxTSODVQoS09Ph42NjWjfAS2Ghob4+++/0atXL8TExMDFxQX79+/HgQMHkJubizNnzlDN8/PPP6Nbt26YN2+e2PWAgADcuHEDkZGRVPNIg6VLl4o+fvfuHf7880+YmpqiZ8+eAICrV6/i7t27mDZtGlavXs1VTKnQt29f+Pv7w87OjusoIjNmzICZmRk8PDwgFAphbW2NK1euQFlZGcePH4eNjQ2n+a5du4Zdu3YhPDwcLVq0wKtXr6ClpYXQ0FCq2e7du4fc3FxUVFSIXafZzRlAgwM0LpeimMaxpR/KMjMzUVVVRf2+BQUFopLnx48fh4uLC3766ScIBAJ0796dep64uDgsWbKk3nV7e3ts2LCBeh5psHjxYtHHnp6emD59OpYvX17vNXl5ebSjSR1vb2/4+PhI1bvhgwcPYty4cQBqen1lZ2cjLS0Nu3fvxoIFCxAfH089U2FhIXbv3o3Q0FBkZWXB0dERx48fh52dHd6+fYtly5bBzc0Njx49kniWrKwsODk5ISUlRWzfSu0sIu2BgTQvSzHi2IyKhHy4tk8IQX5+Pk6cOAE3Nzds2bKFap6WLVvi4MGD6NWrF9q2bYsVK1ZgxIgRSE9Px48//thg80RJUlJSwp07d+pN3aelpcHCwuK7P3qqoaGBmzdv1ut8/eDBA3Tt2hUlJSUcJZMO0vhuWFFREQ8fPkSrVq0wefJkKCsrIzAwENnZ2ejUqRP1f2MODg44ffo0fvjhB3h6esLV1VVUXLFWbRPFDyv7SiqPjIwMgoODYWhoiOvXr6OoqAizZs3C+vXr0adPH4lnYL5NbEZFQm7fvi32OZ/PR9OmTbFhw4ZPngiSBGdnZ4wZMwYmJiYoKiqCvb29KCcXG1jNzMywf/9++Pv7i13ft28fTE1NqeeRNkpKSoiPj683UImPj6dWXO1LNtJu3LhRgknqk8Z3w82aNcO9e/fQokULREdHY+vWrQCAsrIy6v2rAEBXVxexsbGipcOGNG3alNrXMiEhAefPn4eOjg74fD74fD569+6N1atXY/r06fV+Z9Kwe/dubNu2DdnZ2UhISICBgQECAwNhaGhYr9giwx02UJGQCxcucB1BzKZNmyAQCJCXl4eAgACoqqoCAPLz8zFt2jTqeRYtWgRnZ2dkZmaKTkGdO3cOe/fu/S73p3xoxowZmDp1KhITE8XqzISEhNQb3EnKh384EhMTUVVVJZoFy8jIgIyMDLp06UIlT10GBgYNXq+ursbJkycbfV6S3N3d4eLighYtWoDH44n2z1y7dg3t2rWjnsfa2hqWlpb1rldUVGDfvn1wdXUFj8ej9rUSCoWiBpI6Ojp4+vQp2rZtCwMDA6Snp1PJUNfWrVvh7++PGTNmYOXKlaJZOE1NTQQGBrKBijQhDMOR48ePk169ehFlZWWira1N+vXrRy5evMh1LKmxf/9+0qtXL6KlpUW0tLSIlZUVOXDgAElJSaGeZcOGDcTBwYG8fPlSdO3ly5dk+PDhZP369dTzfOjBgwfkt99+Iy1atCCysrKc5YiMjCQbN24keXl5omthYWHk8OHD1LPw+XxSWFhY7/qLFy8In8+nnqd3797kn3/+IYQQMnr0aDJo0CBy+fJl4urqSjp06EA9T/v27UV5VFVVSWZmJiGEkJSUFKKtrU09D9M4tkflK+vXr98nj5jyeDycO3dO4lmOHj0Ke3t7yMnJiZXSbwjtHffMl3n9+jX27t2LkJAQ3Lp1i/oeDD09PZw5cwYdOnQQu56amoqffvoJT58+pZoHAMrLyxEZGYng4GDEx8ejT58+GDVqFJycnNCsWTPqeep69+4d9f5HH+Lz+SgsLKxXrC8pKQn9+vWj3s359OnTePv2LZydnfHw4UMMHToUGRkZ0NbWxv79+6nXl1JSUkJaWhoMDAygpqaGpKQkGBkZ4cGDBzA3N//u98lJE7b085V17ty50efevHmDPXv2UGsA6OjoKGpE6Ojo2OjruNp8WFxcjIMHDyIrKwuzZ89GkyZNkJiYiGbNmkFPT496HmkUFxeHkJAQHDp0CC1btoSzszOCgoKo53j9+jWeP39e7/rz588b7NkkSTdu3EBwcDD27duHNm3aYOzYsbhy5YroODdXhEIhVq1ahW3btqGwsBAZGRkwMjLCokWLIBAI4OHhQSVHbT0eHo8HW1tbyMr+59e8UChEdnY2Bg0aRCVLXQMHDhR9bGxsjLS0NLx8+RJaWlqcFOwzNDTEnTt36i19RUdHo3379tTzMI1jA5WvbNOmTfWuVVVVISgoCCtXroSenl69I6eSUncnP41d/V8iOTkZdnZ20NDQQE5ODjw9PdGkSRNERUUhNzcXERERXEfkTEFBAcLCwhASEoLXr1/DxcUF79+/x+HDhzn7Q+zk5AR3d3ds2LBBbM/MnDlz4OzsTC2Hubk5Xr9+jTFjxuDKlSuiGR4/Pz9qGRqzcuVKhIeHIyAgAJMmTRJd79ixIwIDA6kNVGrflNy5cwcDBw4U7UcDAHl5eQgEAvz8889UsnzKh6eQaPL19cWvv/6Kd+/egRCC69evY+/evVi9ejWCg4M5y8U0gOOlp/95f/31FzEyMiItWrQgQUFBpLKykutIYvLy8sikSZOo39fW1pbMmTOHECK+PhwfH08MDAyo55EWQ4cOJerq6mT06NHk+PHjpKqqihBCiKysLLl79y5nud6+fUumTp1KFBQUCJ/PJ3w+n8jLy5OpU6eS0tJSajnk5eXJ+PHjyZkzZ0h1dbXoOtdfH0IIadOmDTl79iwhRPxn+v79+0RTU5NqlqqqKhIWFkaePn1K9b4fcnJyIiUlJaKPP/bgwl9//UWMjY0Jj8cjPB6P6OnpkeDgYE6yMI1jMyoSEh0dDT8/P2RnZ2P27Nnw9fWFiooK17HqKSoqQkhICHbs2EH1vjdu3MD27dvrXdfT00NBQQHVLNLk1KlTmD59OqZOnVrvaDKXlJWV8eeff2LdunXIzMwEALRp04b6z3RWVhbCwsIwdepUlJeXY/To0Rg7dqxU9Pp58uRJg0f9q6urUVlZSTWLjIwMfvnlF1EfLa5oaGiIvjfq6upS8X2qa+zYsRg7dizKyspQWloKXV1dADXfS7b8LD3YQOUru379OubNm4erV69iypQpOHv2rNR0LJYmCgoKDRbAysjIkLpOvTRdvnwZISEh6NKlC9q3b4/x48dj1KhRXMcSyc/PR35+Pvr27QslJSVRgTVa9PT0sGDBAixYsADnz5/Hrl27YGVlhaqqKoSFhcHT0xM//PADtTx1mZqa4tKlS/X2PBw8eBAWFhbU83Ts2BFZWVkwNDSkfu9aoaGhoo/DwsI4y/EpysrKUFZWRkFBAVauXImQkBCUlZVxHYupxfWUzv8aHo9HlJWVyYwZM8jmzZsbfUiLO3fucHJU0cPDgzg6OpKKigqiqqpKsrKyyKNHj4iFhQXx8fGhnkfalJaWkpCQEGJlZUXk5OQIn88ngYGB5PXr15zkefHiBenfvz/h8XiEz+eLljXc3d2Jr68vJ5lqFRcXk6CgINKlSxfC4/GImZkZJzkOHz5MNDQ0yJo1a4iysjJZt24d8fT0JPLy8uTMmTPU85w6dYp07tyZHDt2jDx9+pSUlJSIPWjr168fefXqVb3rJSUlpF+/ftRyvHz5kowaNYpoa2uTFi1akM2bNxOhUEgWLVpElJSUSPfu3cm+ffuo5WE+jQ1UvjIDAwMiEAg++jA0NOQ6pghXA5Xi4mJiZ2dHNDU1iYyMDGndujWRk5Mjffr0obrn4VuQlpZG5syZQ5o3b04UFRWJg4MD9Qzjx48nAwcOJHl5eWL7L6Kjo4mpqSn1PI25ffs28fb25uz+cXFxxM7OjjRt2pQoKSkRKysrcvr0aU6y1O67qB1c1j5qP+ciT0N1XQoLC6nWvpk8eTLR19cns2bNIh07diR8Pp/Y29uTIUOGkISEBGo5mM/H6qj8j/vUiYzi4mLExsZy1in08uXLSE5ORmlpKSwtLaWqG660EQqFOHbsGHbt2vXJujhfW/PmzXH69Gl06tRJrOZEVlYWzM3NUVpaSjUP82mxsbEffd7a2ppKjuTkZAA1pRvOnz8vdtJHKBQiOjoa27dvR05ODpU8+vr6CAsLQ//+/ZGTkwMjIyP4+flh1apVVO7PfDk2UPkf5+7u/lmvq7uWzKXExET4+/vj+PHjXEdh6lBTU0NiYiJMTEzEBio3b97EwIEDUVRUxHVERkrx+XzRPqaG/twoKSnhjz/+oNYDTVZWFnl5eWjRogWAmv0pN2/eZD3GpBjbTPs/TloGIHWdPn0aMTExkJeXh6enJ4yMjJCWlgY/Pz8cO3ZMrDAUIx369OmDiIgIUQ0gHo+H6upqBAQEoF+/fhyn486XFCujXQkWAC5duoTt27cjKysLkZGR0NPTw+7du2FoaIjevXtTyZCdnQ1CCIyMjHD9+nWxzfLy8vLQ1dWl2rSRECJWBE9GRgZKSkrU7s98OTZQYagKCQnBpEmT0KRJE7x69QrBwcHYuHEjvL29MXLkSKSmprKqkFIoICAAtra2uHnzJioqKjB37lzcvXsXL1++RHx8PNfxOBMYGMh1hEYdOnQI48ePx9ixY5GYmCiqiF1SUoJVq1bh5MmTVHLUnoKSlqKThBCxir3l5eVwcHCAvLy82OsSExO5iMc0gC39MFSZm5tj/PjxmDNnDg4dOoQRI0agR48eOHDgAFq1asV1POYjSkpKsGXLFiQlJYn2FP3666+iKXSuSENfHWlkYWGBmTNnwtXVVWy57vbt27C3t+ekXtGDBw9w4cIFPHv2rN7AhVZX8KVLl37W6xYvXizhJMznYgMVhioVFRXcvXsXAoEAhBAoKCjgwoULsLKy4joa8w2prq7GypUrOe+r05h3796hoqJC7Jq6ujrVDMrKyrh37x4EAkG9DdCmpqZ49+4d1Tw7d+7E1KlToaOjg+bNm4stmfF4PDaDwTSKz3WA/yWvX7/+7Mf3qry8HMrKygBqfjkpKChw/o6c+bTo6GhcvnxZ9HlQUBA6d+6MMWPG4NWrV9TzrFixAmFhYQgICBCbsu/YsSNnfVrevn0LLy8v6OrqQkVFBVpaWmIP2po3b46HDx/Wu3758mUYGRlRz7NixQqsXLkSBQUFuHPnDm7fvi16sEEK8zFsj8pXpKmp+cmNdeTflTy5Og4sDYKDg0WN0morin5YvXf69OlcRGMaMWfOHKxduxYAkJKSAl9fX8yaNQsXLlyAr68v9U3bERER2LFjB2xtbTFlyhTR9U6dOiEtLY1qllpz587FhQsXsHXrVowfPx5BQUF48uQJtm/fjjVr1lDPM2nSJPj4+GDXrl3g8Xh4+vQpEhISMHv2bCxatIh6nlevXmHEiBHU78t8+9jSz1f0qboFddGqYSBtBALBJwdzPB4PWVlZlBIxn0NVVRWpqakQCARYsmQJUlNTcfDgQSQmJmLw4MHU9zsoKSkhLS0NBgYGYssa9+7dQ7du3Tip66Kvr4+IiAjY2NhAXV0diYmJMDY2xu7du7F3715qm1drEUKwatUqrF69WlQOXkFBAbNnz6bWwb0uDw8P/Pjjj2IDS4b5HGxG5Sv6XgcfX4JWUSfm65KXlxf9sTt79ixcXV0BAE2aNOFkKVPa+uoANcePa5dU1NXVRceRe/fujalTp1LPw+PxsGDBAsyZMwcPHz5EaWkpTE1NRbOZtBkbG2PRokW4evUqzMzMICcnJ/Y8m0VlGsMGKhJWVlaG3NzcehvrzM3NOUrEMF+ud+/e8PX1hZWVFa5fv479+/cDqGkiycVpLX9/f7i5ueHJkyeorq5GVFQU0tPTERERwVmxQCMjI2RnZ0NfXx/t2rXDgQMH0K1bNxw7dgyampqcZAJqBplqampQU1PjbJACADt27ICqqipiY2PrzT7zeDyqA5XKykoMGjQI27Ztk6ou5UwjqBft/048e/aMDBkyRKzHRt0Hw3xLHj16RIYMGULMzc1JcHCw6PqMGTM4660jTX11CCFk48aNooajMTExRFFRkSgoKIgaStJWWVlJFi5cSNTV1UW/d9TV1cmCBQtIRUUF9TzSRkdHh2RkZHAdg/kMbI+KhIwdOxaPHj1CYGAgbGxs8M8//6CwsBArVqzAhg0bMGTIEK4jMgwjQTk5OaJ9KlzMoE6dOhVRUVFYtmwZevbsCQBISEjAkiVL4OjoiK1bt1LPBAAVFRXIzs5GmzZtxCrE0jZz5kwoKChwstGZ+TJsoCIhLVq0wJEjR9CtWzeoq6vj5s2b+OGHH3D06FEEBASIHfVkGGmXmJgIOTk5mJmZAQCOHDmC0NBQmJqaYsmSJfWqejLc09DQwL59+2Bvby92/eTJkxg9ejRKSkqo5ikrK4O3tzfCw8MBQFT7xtvbG3p6evDz86Oax9vbGxERETAxMUGXLl2goqIi9vzGjRup5mEax+qoSMjbt2+hq6sLoKYfyPPnzwEAZmZmrGYA88355ZdfkJGRAQDIysrCqFGjoKysjMjISMydO5dKBi0tLTRp0uSzHjQlJCTU2xcTEREBQ0ND6OrqYvLkyaLy9TQpKChAIBDUu25oaMjJwPK3335DUlISLl68KFZJ2M7OTrTniabU1FRYWlpCTU0NGRkZYnVd7ty5Qz0P0zi2mVZC2rZti/T0dAgEAnTq1Anbt2+HQCDAtm3bWIGzf8vMzERoaCgyMzOxefNm6Orq4tSpU9DX10eHDh24jsfUkZGRgc6dOwMAIiMj0bdvX+zZswfx8fEYNWoUlZ430tpXZ9myZbCxscHQoUMB1NSZ8fDwwIQJE9C+fXusW7cOLVu2xJIlS6jm8vLywvLlyxEaGgoFBQUAwPv377Fy5Up4eXlRzQIAhw8fxv79+9GjRw+xEgUdOnRAZmYm9TwXLlygfk/m/4cNVCTEx8cH+fn5AGp6RgwaNAh///035OXlERYWxm04KRAbGwt7e3tYWVkhLi4OK1euhK6uLpKSkhASEoKDBw9yHZGpgxAi6s1y9uxZ0R/l1q1b48WLF1QyuLm5UbnPl7pz545YXZJ9+/ahe/fu2LlzJ4Car9HixYupD1Ru376Nc+fOoVWrVujUqRMAICkpCRUVFbC1tYWzs7PotVFRURLP8/z5c9Esc11v37797A7UkvL48WMAYP3GpBQbqEjIuHHjRB936dIFjx49QlpaGvT19etVYf0e+fn5YcWKFfD19YWamproev/+/bFlyxYOkzEN6dq1K1asWAE7OzvExsaKNmJmZ2ejWbNmnGbjuq/Oq1evxL4GtYPwWj/++CPy8vKo5amlqamJn3/+Wexa69atqeeo1bVrV5w4cQLe3t4AIBqcBAcHizb70lRdXS063FBbIFBNTQ2zZs3CggULwOeznRFSg9MzR8x3S0VFhWRlZRFCCFFVVSWZmZmEEEKys7OJgoICl9GYBiQlJZGOHTsSdXV1smTJEtF1Ly8vMnr0aOp5SktLya+//kqaNm3K+fF/fX19EhsbSwgh5P3790RJSYmcPXtW9HxycjLR0tKimkkaXbp0iaiqqpIpU6YQRUVF4uPjQwYMGEBUVFTIzZs3qefx8/MjTZs2JX/++SdJSkoiSUlJJCgoiDRt2pTMnz+feh6mcWxG5Svy9fXF8uXLoaKiAl9f34++9nvfUa6pqYn8/HwYGhqKXb99+zb09PQ4SsU0xtzcHCkpKfWur1u3DjIyMtTzSFNfncGDB8PPzw9r167F4cOHoaysjD59+oieT05ORps2bahmquv58+dIT08HULN3rmnTppzk6N27N+7cuYM1a9bAzMwMZ86cgaWlJRISEkSnyWgKDw9HcHAwhg0bJrpmbm4OPT09TJs2DStXrqSeiWkYG6h8Rbdv30ZlZSWAmuOcja27cr0eKw1GjRqFefPmITIyEjweD9XV1YiPj8fs2bNF5dkZ6VJcXIyDBw8iMzMTc+bMQZMmTXDv3j00a9aM+uDy2LFjor467u7u6NOnD4yNjWFgYIC///4bY8eOpZZl+fLlcHZ2hrW1NVRVVREeHi52qmbXrl346aefqOWp9fbtW9ER3Nr9RTIyMnB1dcUff/wh6mJOU5s2bUR7d7j28uVLtGvXrt71du3aidofMFKC6ykd5vv0/v174unpSWRlZQmPxyNycnKEz+eTcePGkaqqKq7jMR9ISkoiOjo6xNjYmMjKyoqW6hYsWEDGjx9PPY+Kigp59OgRIYQQPT09cu3aNUIIIVlZWURFRYV6HkIIKS4ubvBnt6ioiLx//556nsmTJxMjIyNy8uRJUlJSQkpKSsiJEydImzZtyJQpU6hkqL3v5zxo69atW4NVlb28vEj37t2p52Eaxwq+SUBlZSWUlJRw584ddOzYkes4Ui0vLw8pKSkoLS2FhYUF67shpezs7GBpaYmAgACxbsVXrlzBmDFjqDebNDc3xx9//AFra2vY2dmhc+fOWL9+PX7//XcEBASITnF8z3R0dHDw4EHY2NiIXb9w4QJcXFxEtZ0kic/nf/YMslAolHAacbGxsRgyZAj09fXFKvfm5eXh5MmTYst3DLfY0o8EyMnJQV9fn/o/vG/JsmXLMHv2bLRu3VrsJEJ5eTnWrVsHf39/DtMxH7px4wa2b99e77qenh4KCgqo53F3d0dSUhKsra3h5+cHBwcHbNmyBZWVld/9/q9aZWVlDZ7I0tXVFXXClrS6tUpycnLg5+eHCRMmiA0MwsPDsXr1aip56rK2tkZGRgaCgoKQlpYGAHB2dsa0adPQsmVL6nmYxrEZFQkJCQlBVFQUdu/eTb1S5rdARkYG+fn59eoqFBUVQVdXlw3ypIyuri5Onz4NCwsLsRmVmJgYTJw4kZPjt3Vx3VdHGtna2kJbWxsRERGiSrDl5eVwc3PDy5cvcfbsWep5PD09MXr0aLHre/bswY4dO3Dx4kWqeZhvBxuoSIiFhQUePnyIyspKGBgY1Osj8b2X0efz+SgsLKx3AuH8+fMYOXIklWlp5vN5enqiqKgIBw4cQJMmTZCcnAwZGRk4Ojqib9++Uls19nuWkpKCQYMG4f3792IF3xQVFXH69Gnq1Z+VlZWRlJRUb3m3tuoxrVmeWtHR0VBVVUXv3r0BAEFBQdi5cydMTU0RFBQELS0tqnmYxrGBioQsXbr0o88vXryYUhLpoqWlBR6Ph5KSEqirq4utXwuFQpSWlmLKlCkICgriMCXzoZKSEvzrX//CzZs38ebNG7Rs2RIFBQXo2bMnTp48WW8gLikJCQkoKioSVcYFavrqLF68GG/fvoWjoyP++OMPUcn4711ZWRn+/vtv0dJG+/btMXbsWCgpKVHP0rZtWwwfPhwBAQFi1+fOnYsjR46IjlDTYmZmhrVr12Lw4MFISUlB165dMWvWLFy4cAHt2rVDaGgo1TxM49hAhaEqPDwchBBMnDgRgYGB0NDQED0nLy8PgUDASZVK5vPEx8cjKSkJpaWlsLS0hJ2dHdX729vbw8bGBvPmzQNQM2tgaWkp1lfnl19+oV6uXtpUVlaiXbt2OH78ONq3b891HAA1XZt//vlnGBsbo3v37gCA69ev48GDBzh06BAGDx5MNY+qqipSU1MhEAiwZMkSpKam4uDBg0hMTMTgwYM52XvFNIxtppWghupOJCYmclJ3QlrU9msxNDREr169ICcnx3Ei5ktYWVnBysoKQM3PN23S2ldH2sjJyeHdu3dcxxAzePBgZGRkYOvWraIZHgcHB0yZMoWT0v7y8vKi5aazZ8+K6jc1adIEr1+/pp6HaRybUZGQ5ORk2NnZQUNDAzk5OUhPT4eRkREWLlyI3NxcREREcB1RanDdq4X5tLVr10IgEGDkyJEAABcXFxw6dAjNmzfHyZMnRXsgJE1RUREPHjwQ/WHr3bs37O3tsWDBAgA1m2rNzMzw5s0bKnmk2apVq5CRkYHg4GDIyrL3pB8aNmwYKioqYGVlheXLlyM7Oxt6eno4c+YMvLy8kJGRwXVE5t9Y1yUJ8fX1xYQJE/DgwQPRjnug5l1FXFwch8mkQ1lZGby8vKCrqwsVFRVoaWmJPRjpsm3bNtHgICYmBjExMTh16hTs7e0xZ84cajmaNWuG7OxsAEBFRQUSExPRo0cP0fNv3rxhs3T/duPGDURFRUFfXx8DBw6Es7Oz2IOG5ORkUVXc5OTkjz5o27JlC2RlZXHw4EFs3bpVNMt96tQpDBo0iHoepnFsmC0h0lZ3QtrMmTNHanq1MJ9WUFAgGqgcP34cLi4u+OmnnyAQCET7DWiQ9r460qSh7sm0de7cGQUFBdDV1UXnzp3B4/HQ0CQ+j8ejXpJAX18fx48fr3d906ZNVHMwn8YGKhKioKDQ4DpnRkYGZ03BpIk09WphPk1LSwt5eXlo3bo1oqOjsWLFCgAAIYTqHxhp7asjjaTh1Ep2drbo913tTJi0SExMhJycnKgh4pEjRxAaGgpTU1MsWbJE7OeK4RgHZfu/Cx4eHsTR0ZFUVFQQVVVVkpWVRR49ekQsLCyIj48P1/E4J429WpjG/frrr8TAwIDY2dkRbW1t8ubNG0IIIXv37iUWFhbU80hbXx1pIhQKyZo1a0ivXr1I165dybx580hZWRnXsaRO165dycGDBwkhhGRmZhJFRUUyevRoYmxszH5HSxm2R0VCNmzYgNLSUujq6qK8vBzW1tYwNjaGmpoaax8OwMjISPQOq127djhw4ACAmpkWTU1NDpMxDdm0aRO8vLxgamqKmJgYqKqqAgDy8/Mxbdo06nk0NDQgIyNT73qTJk2++3fCK1euxPz586Gqqgo9PT1s3rwZv/76K9exEB4ejhMnTog+nzt3LjQ1NdGrVy88evSIep7aQnMAEBkZib59+2LPnj0ICwvDoUOHqOdhGsdO/UgY13UnpNWmTZsgIyOD6dOn4+zZs3BwcAAhRNSrxcfHh+uIDPNNMjExwezZs/HLL78AqDl6O2TIEJSXl4PP5+69adu2bbF161b0798fCQkJsLW1RWBgII4fPw5ZWVlERUVRzaOuro5bt27BxMQEAwYMwNChQ+Hj44Pc3Fy0bdsW5eXlVPMwjWMDFUYqPHr0CLdu3WK9WqRUeHg4dHR0MGTIEAA174Z37NgBU1NT7N27FwYGBhwnZGopKCjg4cOHYrVJFBUV8fDhQ7Rq1YqzXMrKykhLS4O+vj7mzZuH/Px8RERE4O7du7CxsaHeNqN///5o3bo17Ozs4OHhgXv37sHY2BixsbFwc3Oj3hGcaRxb+vnKEhIS6u0kj4iIgKGhIXR1dTF58mS8f/+eo3TSy8DAAM7OzmyQIqVWrVolKruekJCAoKAgBAQEQEdHBzNnzuQ4HVNXVVWVWEkEoKYAXGVlJUeJaqiqqqKoqAgAcObMGQwYMABAzSCKi9mLwMBAJCYmwsvLCwsWLICxsTEA4ODBg+jVqxf1PEzj2IzKV8ZKfH9adXU1wsLCEBUVhZycHPB4PBgaGuJf//oXxo8fL9b/h5EO0vZumGkcn8+Hvb29WL+jY8eOoX///mI9mWgvtYwdOxZpaWmwsLDA3r17kZubC21tbRw9ehTz589Hamoq1TyNeffuHWRkZFg9HinCZlS+sjt37sDW1lb0ed0S376+vvj9999FG0e/R4QQDBs2DJ6ennjy5AnMzMzQoUMHPHr0CBMmTICTkxPXEZkGSNu7YaZxbm5u0NXVhYaGhugxbtw4tGzZUuwabUFBQejZsyeeP3+OQ4cOQVtbGwBw69YtjB49mnoeoKYNRHBwMH777Te8fPkSAHDv3j08e/aMkzxMI7g7cPS/SUFBgeTm5oo+t7KyIitWrBB9np2dTVRVVbmIJhV27dpF1NTUyPnz5+s9d+7cOaKmpkbCw8M5SMZ8zJgxY4ilpSXx8PAgysrK5MWLF4QQQo4cOUJMTU05TscwXy4pKYno6OgQY2NjIisrSzIzMwkhhCxYsICMHz+e43RMXWxG5StjJb4/bu/evZg/fz769etX77n+/fvDz88Pf//9NwfJmI/52LvhMWPGcJyO+VZcunQJ48aNQ69evfDkyRMAwO7du3H58mXqWXx9feHu7s7anHwD2EDlK6st8X3p0iX89ttvrMT3B5KTkz/aR8Pe3h5JSUkUEzGfQ1NTE1u2bMGRI0fEvn9Lly7F8OHDOUzGfCsOHTqEgQMHQklJCYmJiaJDBSUlJVi1ahX1PDdu3BAd4a6LtTmRPmyg8pUtX74csrKysLa2xs6dO7Fz505W4ruOly9folmzZo0+36xZM7x69YpiIub/482bN9ixYwe6d+9OrXMy821bsWIFtm3bhp07d4rNKltZWSExMZF6Htbm5NvBev18ZTo6OoiLi0NJSQlUVVXrVc+MjIwUVfX8HgmFwo+2nJeRkUFVVRXFRMyXiIuLQ0hICA4dOoSWLVvC2dkZW7Zs4ToW8w1IT09H3759613X0NBAcXEx9TzDhg3DsmXLRIcbeDwecnNzMW/ePM6bOTLi2EBFQhrbVd+kSRPKSaQLIQQTJkwQOzpZF6sxI30KCgoQFhaGkJAQvH79Gi4uLnj//j0OHz4MU1NTruMx34jmzZvj4cOHEAgEYtcvX74MIyMj6nk2bNiAf/3rX2JtTgoKCtCzZ0/W5kTKsIEKQ5Wbm9snX+Pq6kohCfM5HBwcEBcXhyFDhiAwMBCDBg2CjIwMtm3bxnU05hszadIk+Pj4YNeuXeDxeHj69CkSEhIwe/ZsLFq0iHoeDQ0NxMTEsDYn3wBW8I1hmEbJyspi+vTpmDp1KkxMTETX5eTkkJSUxGZUmM9GCMGqVauwevVqlJWVAajZJzJ79mwsX76c43Q1iouLWVNUKcQ20zIM06jLly/jzZs36NKlC7p3744tW7bgxYsXXMdivkE8Hg8LFizAy5cvkZqaiqtXr+L58+dYvnw5J0UD165di/3794s+d3Fxgba2NvT09NjJQynDBioMwzSqR48e2LlzJ/Lz8/HLL79g3759aNmyJaqrqxETE4M3b95wHZH5xsjLy8PU1BTdunWDnJwcNm7cCENDQ+o5tm3bJmrcGBMTg5iYGJw6dQr29vaYM2cO9TxM49jSD8MwXyQ9PR0hISHYvXs3iouLMWDAABw9epTrWIyUev/+PZYsWYKYmBjIy8tj7ty5cHR0RGhoKBYsWAAZGRl4eXmJ+qPRoqSkhIyMDLRu3Ro+Pj549+4dtm/fjoyMDHTv3p2VSZAibEaFYZgv0rZtWwQEBODx48fYu3cv13EYKefv74+tW7dCIBAgJycHI0aMwOTJk7Fp0yZs3LgROTk51AcpAKClpYW8vDwAQHR0tGgTLSEEQqGQeh6mcezUD8Mw/y8yMjJwdHSEo6Mj11EYKRYZGYmIiAgMGzYMqampMDc3R1VVFZKSkjjtlO7s7IwxY8bAxMQERUVFsLe3BwDcvn0bxsbGnOVi6mMDFYZhGEZiHj9+jC5dugAAOnbsCAUFBcycOZPTQQoAbNq0CQKBAHl5eQgICBAV4szPz8e0adM4zcaIY3tUGIZhGImRkZFBQUGBqCy9mpoakpOTOdlAy3yb2IwKwzAMIzEfVqN+9+4dpkyZAhUVFbHXRUVFUc0VHh4OHR0dDBkyBAAwd+5c7NixA6ampti7dy8MDAyo5mEax2ZUGIZhGIlxd3f/rNeFhoZKOIm4tm3bYuvWrejfvz8SEhJgZ2eHTZs24fjx45CVlaU+cGIaxwYqDMMwzHdHWVkZaWlp0NfXx7x585Cfn4+IiAjcvXsXNjY2eP78OdcRmX9jx5MZhmGY746qqiqKiooAAGfOnMGAAQMAAIqKipxUymUax/aoMAzDMN+dAQMGwNPTExYWFsjIyMDgwYMBAHfv3mX7U6QMm1FhGIZhvjtBQUHo2bMnnj9/jkOHDkFbWxsAcOvWLYwZM4bjdExdbI8KwzAMw9SRmpqKjh07ch2D+Tc2o8IwDMN89968eYMdO3age/fu6NSpE9dxmDrYQIVhGIb5bsXFxcHNzQ0tWrTA+vXr0a9fP1y9epXrWEwdbDMtwzAM810pKChAWFgYQkJC8Pr1a7i4uOD9+/c4fPgwTE1NuY7HfIDNqDAMwzDfDQcHB7Rt2xbJyckIDAzE06dP8ccff3Adi/kINqPCMAzDfDdOnTqF6dOnY+rUqTAxMeE6DvMZ2IwKwzAM8924fPky3rx5gy5duqB79+7YsmULXrx4wXUs5iPY8WSGYRjmu/P27Vvs378fu3btwvXr1yEUCrFx40ZMnDgRampqXMdj6mADFYZhGOa7lp6ejpCQEOzevRvFxcUYMGAAjh49ynUs5t/YQIVhGIZhAAiFQhw7dgy7du1iAxUpwgYqDMMwDMNILbaZlmEYhmEYqcUGKgzDMAzDSC02UGEYhmEYRmqxgQrDMAzDMFKLDVQYhmEYhpFabKDCMAzDMIzUYgMVhmEYhmGkFhuoMAzDMAwjtf4PdPDZH4a3QI8AAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["new_df = pd.DataFrame(filtered_df1)\n","# new_df = filtered_df1.copy()\n","new_df[\"Property Type\"].fillna(\"Not Specified\",inplace = True)\n"],"metadata":{"id":"L4agZiuGuoeH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import seaborn as sns\n","\n","columns = ct_df2.columns\n","colors = [\"#000099\",\"#ffff00\"]\n","\n","sns.heatmap(ct_df2[columns].isnull(), cmap = sns.color_palette(colors))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":566},"id":"GWWilJT4wcGi","executionInfo":{"status":"ok","timestamp":1712865812393,"user_tz":240,"elapsed":17715,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"55db259d-da9c-484b-974e-d342145fdce0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":63},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# find mean\n","\n","* We’re ready to find the mean (average) house price per year, we can accomplish this with .groupby().\n","* This takes a column (in this case “List Year”) and groups it with “Sale * * * Amount”, which we then combining them to get the average using mean()\n","* We can also use common aggregation functions like sum(), max(), min() for other scenarios\n","\n","* Notice that I am storing this new data frame in the variable avg_sale and using the .round() to make the numbers more readable\n","* You can understand groupby() as “group column A by column B”; [[]] ensures the result being a df, [] will result in a series.\n","\n"],"metadata":{"id":"cDoZURWowp7j"}},{"cell_type":"code","source":["avg_sale = new_df.groupby(\"List Year\")[[\"Sale Amount\"]].mean().round()\n","\n","avg_sale"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":426},"id":"HwhcAk8FwnaB","executionInfo":{"status":"ok","timestamp":1712865925928,"user_tz":240,"elapsed":26,"user":{"displayName":"Joshua Cheuk","userId":"09800220979386665615"}},"outputId":"34b17c05-74ea-49d4-e370-042cb16495e3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           Sale Amount\n","List Year             \n","2010          331657.0\n","2011          391684.0\n","2012          395478.0\n","2013          413516.0\n","2014          401422.0\n","2015          345884.0\n","2016          507761.0\n","2017          393251.0\n","2018          383728.0\n","2019          420297.0\n","2020          604964.0"],"text/html":["\n","  <div id=\"df-c6e92af4-27f5-4e04-9528-909238d19eed\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: 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