Harp 150 Lab 12 Demo.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"id":"a9f797a9","metadata":{"id":"a9f797a9"},"outputs":[],"source":["import pandas as pd\n","\n","ct_df = pd.read_csv(\"Real_Estate_Sales_2001-2020_GL.csv\", low_memory = False)"]},{"cell_type":"code","execution_count":null,"id":"416f836e","metadata":{"id":"416f836e","outputId":"d9bc216d-75ba-4362-a0d0-b9e3c69a068f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>"],"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]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["ct_df"]},{"cell_type":"code","execution_count":null,"id":"13ef6a75","metadata":{"id":"13ef6a75"},"outputs":[],"source":["##sort our dates in an ascending order, this way will not notify the df\n","##temporary solution\n","\n","##ct_df.sort_values(\"List Year\")"]},{"cell_type":"code","execution_count":null,"id":"49a3a0d0","metadata":{"id":"49a3a0d0"},"outputs":[],"source":["##To modify your df - ascending order\n","\n","ct_df.sort_values(\"List Year\", inplace=True)\n","\n","##for descending order\n","\n","#ct_df.sort_values(\"List Year\", ascending=False)"]},{"cell_type":"code","execution_count":null,"id":"9a553826","metadata":{"id":"9a553826","outputId":"ffe66fc6-aa9c-4840-e46c-3073b9a8d612"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>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","    <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","      <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","      <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","      <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","      <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>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","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (-73.38738 41.31428)</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>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>"],"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 Non Use Code Assessor Remarks  \\\n","91405           NaN              NaN          NaN              NaN   \n","81567           NaN              NaN          NaN              NaN   \n","81566           NaN              NaN          NaN              NaN   \n","81565           NaN              NaN          NaN              NaN   \n","81564           NaN              NaN          NaN              NaN   \n","...             ...              ...          ...              ...   \n","44594   Residential    Single Family          NaN              NaN   \n","44593   Residential            Condo          NaN         3-35-3-5   \n","44592   Residential    Single Family          NaN              NaN   \n","44590   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]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["ct_df"]},{"cell_type":"code","execution_count":null,"id":"5c852a91","metadata":{"id":"5c852a91"},"outputs":[],"source":["#exercise 1\n","\n","#A-Z\n","\n","ct_df.sort_values(\"Town\", inplace=True)"]},{"cell_type":"code","execution_count":null,"id":"dc8e6d77","metadata":{"id":"dc8e6d77","outputId":"ae958dc2-ad1d-42a4-acd5-f2131e88b051"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>"],"text/plain":["        Serial Number  List Year Date Recorded           Town  \\\n","444657          70086       2007    12/18/2007  ***Unknown***   \n","338981          50060       2005    09/21/2006        Andover   \n","837786          17031       2017    03/26/2018        Andover   \n","19637           20046       2020    06/16/2021        Andover   \n","826756          17054       2017    07/11/2018        Andover   \n","...               ...        ...           ...            ...   \n","856309         170164       2017    07/31/2018      Woodstock   \n","853637         170124       2017    06/11/2018      Woodstock   \n","123445          10059       2001    12/13/2001      Woodstock   \n","198947          20041       2002    11/19/2002      Woodstock   \n","667843         120140       2012    08/16/2013      Woodstock   \n","\n","                      Address  Assessed Value  Sale Amount  Sales Ratio  \\\n","444657        18 MATHIEU LANE         66540.0     282450.0     0.235582   \n","338981       141 BOSTON HL RD        133500.0     247000.0     0.540486   \n","837786       48 PINE RIDGE DR        122500.0     170000.0     0.720500   \n","19637         31 BURNAP BROOK        169000.0     250000.0     0.676000   \n","826756      TOWNSEND 27/20/10          2690.0      13878.0     0.193832   \n","...                       ...             ...          ...          ...   \n","856309           261 CHILD RD        142970.0     241000.0     0.593200   \n","853637         NIGHTINGALE RD           870.0      65000.0     0.013300   \n","123445  66 BRADFORD CORNER RD        111550.0     210000.0     0.531190   \n","198947        30 RED CEDAR DR         60390.0     135000.0     0.447333   \n","667843     47 MEADOWSIDE COOP             0.0       4000.0     0.000000   \n","\n","        Property Type Residential Type             Non Use Code  \\\n","444657  Single Family    Single Family  07 - Change in Property   \n","338981            NaN              NaN                        7   \n","837786  Single Family    Single Family                      NaN   \n","19637     Residential    Single Family                      NaN   \n","826756            NaN              NaN               25 - Other   \n","...               ...              ...                      ...   \n","856309  Single Family    Single Family                      NaN   \n","853637            NaN              NaN      28 - Use Assessment   \n","123445            NaN              NaN                      NaN   \n","198947            NaN              NaN                      NaN   \n","667843            NaN              NaN               25 - Other   \n","\n","                                         Assessor Remarks OPM remarks  \\\n","444657                                                NaN         NaN   \n","338981                                                NaN         NaN   \n","837786                                                NaN         NaN   \n","19637                                                 NaN         NaN   \n","826756  3 PARCELS MARKETED TOGETHER SOLD AS 3 SEPARATE...         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]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["ct_df"]},{"cell_type":"code","execution_count":null,"id":"8ca412a5","metadata":{"id":"8ca412a5","outputId":"3a0fead7-b354-4722-c782-4fa65c5fc228"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>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","    <tr>\n","      <th>339226</th>\n","      <td>40210</td>\n","      <td>2004</td>\n","      <td>06/29/2005</td>\n","      <td>Woodstock</td>\n","      <td>OLDE MDW RD</td>\n","      <td>22400.0</td>\n","      <td>78500.0</td>\n","      <td>0.285350</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>984857</th>\n","      <td>190023</td>\n","      <td>2019</td>\n","      <td>11/12/2019</td>\n","      <td>Woodstock</td>\n","      <td>73 BULL HILL RD</td>\n","      <td>416900.0</td>\n","      <td>620000.0</td>\n","      <td>0.672400</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>53702</th>\n","      <td>200131</td>\n","      <td>2020</td>\n","      <td>05/18/2021</td>\n","      <td>Woodstock</td>\n","      <td>64 LAKE VIEW DR</td>\n","      <td>276200.0</td>\n","      <td>790000.0</td>\n","      <td>0.349600</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 (-72.06451 41.96004)</td>\n","    </tr>\n","    <tr>\n","      <th>580572</th>\n","      <td>100090</td>\n","      <td>2010</td>\n","      <td>08/11/2011</td>\n","      <td>Woodstock</td>\n","      <td>16 MEADOWSIDE COOP</td>\n","      <td>0.0</td>\n","      <td>3500.0</td>\n","      <td>0.000000</td>\n","      <td>Single Family</td>\n","      <td>Single Family</td>\n","      <td>25 - Other</td>\n","      <td>Coop Campground  lot</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>12939</th>\n","      <td>20010</td>\n","      <td>2020</td>\n","      <td>12/02/2020</td>\n","      <td>Andover</td>\n","      <td>3 GILEAD RD</td>\n","      <td>151700.0</td>\n","      <td>271000.0</td>\n","      <td>0.559700</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 (-72.37228 41.72019)</td>\n","    </tr>\n","    <tr>\n","      <th>145047</th>\n","      <td>20035</td>\n","      <td>2002</td>\n","      <td>05/02/2003</td>\n","      <td>Andover</td>\n","      <td>92 WEST ST</td>\n","      <td>99700.0</td>\n","      <td>177500.0</td>\n","      <td>0.561690</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>POINT (-72.3545 41.70755)</td>\n","    </tr>\n","    <tr>\n","      <th>141035</th>\n","      <td>20039</td>\n","      <td>2002</td>\n","      <td>05/19/2003</td>\n","      <td>Andover</td>\n","      <td>176 HEBRON RD</td>\n","      <td>71000.0</td>\n","      <td>218000.0</td>\n","      <td>0.325688</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>12880</th>\n","      <td>20060</td>\n","      <td>2020</td>\n","      <td>08/11/2021</td>\n","      <td>Andover</td>\n","      <td>605 ROUTE 6</td>\n","      <td>62600.0</td>\n","      <td>210000.0</td>\n","      <td>0.298095</td>\n","      <td>Residential</td>\n","      <td>Single Family</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>GOOD SALE PER MLS</td>\n","      <td>NaN</td>\n","    </tr>\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","  </tbody>\n","</table>\n","<p>997213 rows × 14 columns</p>\n","</div>"],"text/plain":["        Serial Number  List Year Date Recorded           Town  \\\n","667843         120140       2012    08/16/2013      Woodstock   \n","339226          40210       2004    06/29/2005      Woodstock   \n","984857         190023       2019    11/12/2019      Woodstock   \n","53702          200131       2020    05/18/2021      Woodstock   \n","580572         100090       2010    08/11/2011      Woodstock   \n","...               ...        ...           ...            ...   \n","12939           20010       2020    12/02/2020        Andover   \n","145047          20035       2002    05/02/2003        Andover   \n","141035          20039       2002    05/19/2003        Andover   \n","12880           20060       2020    08/11/2021        Andover   \n","444657          70086       2007    12/18/2007  ***Unknown***   \n","\n","                   Address  Assessed Value  Sale Amount  Sales Ratio  \\\n","667843  47 MEADOWSIDE COOP             0.0       4000.0     0.000000   \n","339226         OLDE MDW RD         22400.0      78500.0     0.285350   \n","984857     73 BULL HILL RD        416900.0     620000.0     0.672400   \n","53702      64 LAKE VIEW DR        276200.0     790000.0     0.349600   \n","580572  16 MEADOWSIDE COOP             0.0       3500.0     0.000000   \n","...                    ...             ...          ...          ...   \n","12939          3 GILEAD RD        151700.0     271000.0     0.559700   \n","145047          92 WEST ST         99700.0     177500.0     0.561690   \n","141035       176 HEBRON RD         71000.0     218000.0     0.325688   \n","12880          605 ROUTE 6         62600.0     210000.0     0.298095   \n","444657     18 MATHIEU LANE         66540.0     282450.0     0.235582   \n","\n","        Property Type Residential Type             Non Use Code  \\\n","667843            NaN              NaN               25 - Other   \n","339226            NaN              NaN                      NaN   \n","984857  Single Family    Single Family                      NaN   \n","53702     Residential    Single Family                      NaN   \n","580572  Single Family    Single Family               25 - Other   \n","...               ...              ...                      ...   \n","12939     Residential    Single Family                      NaN   \n","145047            NaN              NaN                      NaN   \n","141035            NaN              NaN                        7   \n","12880     Residential    Single Family                      NaN   \n","444657  Single Family    Single Family  07 - Change in Property   \n","\n","                       Assessor Remarks        OPM remarks  \\\n","667843  CAMPSITE LOT AT COOP CAMPGROUND                NaN   \n","339226                              NaN                NaN   \n","984857                              NaN                NaN   \n","53702                               NaN                NaN   \n","580572             Coop Campground  lot                NaN   \n","...                                 ...                ...   \n","12939                               NaN                NaN   \n","145047                              NaN                NaN   \n","141035                              NaN                NaN   \n","12880                               NaN  GOOD SALE PER MLS   \n","444657                              NaN                NaN   \n","\n","                          Location  \n","667843                         NaN  \n","339226                         NaN  \n","984857                         NaN  \n","53702   POINT (-72.06451 41.96004)  \n","580572                         NaN  \n","...                            ...  \n","12939   POINT (-72.37228 41.72019)  \n","145047   POINT (-72.3545 41.70755)  \n","141035                         NaN  \n","12880                          NaN  \n","444657                         NaN  \n","\n","[997213 rows x 14 columns]"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["#Z-A\n","\n","ct_df.sort_values(\"Town\", ascending=False)\n","\n"]},{"cell_type":"code","execution_count":null,"id":"67d1e8db","metadata":{"id":"67d1e8db"},"outputs":[],"source":["##series - 1-dimensional array of information (1 row or 1 column)\n","\n","yearly_counts = ct_df[\"List Year\"].value_counts()\n"]},{"cell_type":"code","execution_count":null,"id":"1506d4ba","metadata":{"id":"1506d4ba","outputId":"4c2a9ddf-e44b-4c39-efae-0c7b8728f170"},"outputs":[{"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"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["yearly_counts"]},{"cell_type":"code","execution_count":null,"id":"f65e5e91","metadata":{"id":"f65e5e91"},"outputs":[],"source":["##now turn the series\n","\n","#2 ways\n","#using reset_index()\n","yearly_counts_df1 = yearly_counts.reset_index()\n","yearly_counts_df1.columns = [\"Year\", \"Count\"]"]},{"cell_type":"code","execution_count":null,"id":"28a48353","metadata":{"id":"28a48353","outputId":"ec14c42f-b6ce-4ce5-a6c9-c4848e183af8"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Year</th>\n","      <th>Count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2004</td>\n","      <td>84056</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2020</td>\n","      <td>66592</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2003</td>\n","      <td>64239</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2005</td>\n","      <td>61602</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2002</td>\n","      <td>59682</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>2001</td>\n","      <td>59584</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2019</td>\n","      <td>58954</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>2018</td>\n","      <td>50709</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>2016</td>\n","      <td>49773</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2014</td>\n","      <td>49563</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2006</td>\n","      <td>48785</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>2015</td>\n","      <td>46651</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>2017</td>\n","      <td>45691</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>2009</td>\n","      <td>42508</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>2013</td>\n","      <td>39943</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>2012</td>\n","      <td>35973</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>2007</td>\n","      <td>35617</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>2010</td>\n","      <td>33491</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>2008</td>\n","      <td>32735</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>2011</td>\n","      <td>31065</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["yearly_counts_df1"]},{"cell_type":"code","execution_count":null,"id":"8c15b4aa","metadata":{"id":"8c15b4aa"},"outputs":[],"source":["#using pd.DataFrame()\n","\n","yearly_counts_df2 = pd.DataFrame({\"Year\": yearly_counts.index, \"Count\": yearly_counts.values})"]},{"cell_type":"code","execution_count":null,"id":"c55c00ae","metadata":{"id":"c55c00ae","outputId":"18cebf10-30bb-4043-c7f7-ed2760598911"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Year</th>\n","      <th>Count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2004</td>\n","      <td>84056</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2020</td>\n","      <td>66592</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2003</td>\n","      <td>64239</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2005</td>\n","      <td>61602</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2002</td>\n","      <td>59682</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>2001</td>\n","      <td>59584</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2019</td>\n","      <td>58954</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>2018</td>\n","      <td>50709</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>2016</td>\n","      <td>49773</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2014</td>\n","      <td>49563</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2006</td>\n","      <td>48785</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>2015</td>\n","      <td>46651</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>2017</td>\n","      <td>45691</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>2009</td>\n","      <td>42508</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>2013</td>\n","      <td>39943</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>2012</td>\n","      <td>35973</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>2007</td>\n","      <td>35617</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>2010</td>\n","      <td>33491</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>2008</td>\n","      <td>32735</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>2011</td>\n","      <td>31065</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["yearly_counts_df2"]},{"cell_type":"code","execution_count":null,"id":"5b382070","metadata":{"id":"5b382070"},"outputs":[],"source":["#Exercise 2\n","\n","count_towns = ct_df[\"Town\"].value_counts()"]},{"cell_type":"code","execution_count":null,"id":"c9d0e3b6","metadata":{"id":"c9d0e3b6","outputId":"057cec15-e6f0-4c4b-be2b-872b77069edf"},"outputs":[{"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"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["count_towns"]},{"cell_type":"code","execution_count":null,"id":"d3a917c5","metadata":{"id":"d3a917c5"},"outputs":[],"source":["#reset_index method\n","\n","count_towns_df = count_towns.reset_index()\n","count_towns_df.columns = [\"Town\", \"Count\"]"]},{"cell_type":"code","execution_count":null,"id":"ccbad1bf","metadata":{"id":"ccbad1bf","outputId":"2b9125b7-1582-4e38-af7d-600ca3f407a5"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>"],"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]"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["count_towns_df"]},{"cell_type":"code","execution_count":null,"id":"913b9989","metadata":{"id":"913b9989"},"outputs":[],"source":["#DataFrame method\n","\n","count_towns_df2 = pd.DataFrame({\"Town\": count_towns.index, \"Count\": count_towns.values})"]},{"cell_type":"code","execution_count":null,"id":"8abef24b","metadata":{"scrolled":true,"id":"8abef24b","outputId":"c569f30c-4c91-4e15-87b4-7a5f803f3282"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>"],"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]"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["count_towns_df2"]},{"cell_type":"code","execution_count":null,"id":"a6ee6e27","metadata":{"id":"a6ee6e27","outputId":"2b07b04d-9d0a-42c0-86f6-7291fb0e26f2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: seaborn in /Users/michelle/anaconda3/lib/python3.11/site-packages (0.12.2)\n","Requirement already satisfied: numpy!=1.24.0,>=1.17 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from seaborn) (1.24.3)\n","Requirement already satisfied: pandas>=0.25 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from seaborn) (2.0.3)\n","Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from seaborn) (3.7.2)\n","Requirement already satisfied: contourpy>=1.0.1 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.5)\n","Requirement already satisfied: cycler>=0.10 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n","Requirement already satisfied: fonttools>=4.22.0 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.25.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n","Requirement already satisfied: packaging>=20.0 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.1)\n","Requirement already satisfied: pillow>=6.2.0 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (10.0.1)\n","Requirement already satisfied: pyparsing<3.1,>=2.3.1 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n","Requirement already satisfied: python-dateutil>=2.7 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from pandas>=0.25->seaborn) (2023.3.post1)\n","Requirement already satisfied: tzdata>=2022.1 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from pandas>=0.25->seaborn) (2023.3)\n","Requirement already satisfied: six>=1.5 in /Users/michelle/anaconda3/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n","Note: you may need to restart the kernel to use updated packages.\n"]}],"source":["pip install seaborn"]},{"cell_type":"code","execution_count":null,"id":"23b9e1ac","metadata":{"id":"23b9e1ac","outputId":"6155272e-c4ec-4c6f-a7ce-5d6498199e54"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":22,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import seaborn as sns\n","columns = ct_df.columns\n","colors =  [\"#000099\", \"#ffff00\"]\n","\n","sns.heatmap(ct_df[columns].isnull(), cmap = sns.color_palette(colors))"]},{"cell_type":"code","execution_count":null,"id":"147901d4","metadata":{"id":"147901d4"},"outputs":[],"source":["ct_df1 = ct_df.copy()"]},{"cell_type":"code","execution_count":null,"id":"fa459d61","metadata":{"id":"fa459d61"},"outputs":[],"source":["ct_df1.drop(labels = [\"OPM remarks\", \"Location\", \"Non Use Code\"], axis = 1, inplace = True)"]},{"cell_type":"code","execution_count":null,"id":"1c68b4e0","metadata":{"id":"1c68b4e0","outputId":"16f7d9bb-cbff-4d22-e770-2d4007a4fe09"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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>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>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>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","    </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","    </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>3 PARCELS MARKETED TOGETHER SOLD AS 3 SEPARATE...</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>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","    </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>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","    </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","    </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>CAMPSITE LOT AT COOP CAMPGROUND</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>997213 rows × 11 columns</p>\n","</div>"],"text/plain":["        Serial Number  List Year Date Recorded           Town  \\\n","444657          70086       2007    12/18/2007  ***Unknown***   \n","338981          50060       2005    09/21/2006        Andover   \n","837786          17031       2017    03/26/2018        Andover   \n","19637           20046       2020    06/16/2021        Andover   \n","826756          17054       2017    07/11/2018        Andover   \n","...               ...        ...           ...            ...   \n","856309         170164       2017    07/31/2018      Woodstock   \n","853637         170124       2017    06/11/2018      Woodstock   \n","123445          10059       2001    12/13/2001      Woodstock   \n","198947          20041       2002    11/19/2002      Woodstock   \n","667843         120140       2012    08/16/2013      Woodstock   \n","\n","                      Address  Assessed Value  Sale Amount  Sales Ratio  \\\n","444657        18 MATHIEU LANE         66540.0     282450.0     0.235582   \n","338981       141 BOSTON HL RD        133500.0     247000.0     0.540486   \n","837786       48 PINE RIDGE DR        122500.0     170000.0     0.720500   \n","19637         31 BURNAP BROOK        169000.0     250000.0     0.676000   \n","826756      TOWNSEND 27/20/10          2690.0      13878.0     0.193832   \n","...                       ...             ...          ...          ...   \n","856309           261 CHILD RD        142970.0     241000.0     0.593200   \n","853637         NIGHTINGALE RD           870.0      65000.0     0.013300   \n","123445  66 BRADFORD CORNER RD        111550.0     210000.0     0.531190   \n","198947        30 RED CEDAR DR         60390.0     135000.0     0.447333   \n","667843     47 MEADOWSIDE COOP             0.0       4000.0     0.000000   \n","\n","        Property Type Residential Type  \\\n","444657  Single Family    Single Family   \n","338981            NaN              NaN   \n","837786  Single Family    Single Family   \n","19637     Residential    Single Family   \n","826756            NaN              NaN   \n","...               ...              ...   \n","856309  Single Family    Single Family   \n","853637            NaN              NaN   \n","123445            NaN              NaN   \n","198947            NaN              NaN   \n","667843            NaN              NaN   \n","\n","                                         Assessor Remarks  \n","444657                                                NaN  \n","338981                                                NaN  \n","837786                                                NaN  \n","19637                                                 NaN  \n","826756  3 PARCELS MARKETED TOGETHER SOLD AS 3 SEPARATE...  \n","...                                                   ...  \n","856309                                                NaN  \n","853637                                                NaN  \n","123445                                                NaN  \n","198947                                                NaN  \n","667843                    CAMPSITE LOT AT COOP CAMPGROUND  \n","\n","[997213 rows x 11 columns]"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["ct_df1"]},{"cell_type":"markdown","id":"c81a1dcc","metadata":{"id":"c81a1dcc"},"source":["## Next Section\n","\n","\n","## How much have house prices increased, on average, between 2010 and 2021 in Connecticut?"]},{"cell_type":"code","execution_count":null,"id":"71e1c660","metadata":{"id":"71e1c660"},"outputs":[],"source":["#making a copy in case we do something wrong, we can always\n","#roll the original dataset back\n","\n","ct_df2 = ct_df1.copy()"]},{"cell_type":"code","execution_count":null,"id":"2d9ef5d2","metadata":{"id":"2d9ef5d2"},"outputs":[],"source":["#filtering the data, we want the years from >= 2010\n","#filtering conditions are always a boolean (true/false)\n","\n","filtered_df1 = ct_df2[ct_df2[\"List Year\"] >= 2010]"]},{"cell_type":"code","execution_count":null,"id":"f9f053f8","metadata":{"id":"f9f053f8"},"outputs":[],"source":["##using the drop() method\n","#we need index numbers to drop rows of data\n","#column labels/indexs to drop columns of data\n","# .index accesses the index labels of the rows in the df\n","\n","filtered_df2 = ct_df2.drop(ct_df2[ct_df2[\"List Year\"] < 2010].index)"]},{"cell_type":"code","execution_count":null,"id":"53b36acf","metadata":{"id":"53b36acf"},"outputs":[],"source":["#iloc - row and column indexes\n","#loc - row and column labels\n","\n","filter_df3 = ct_df2.iloc[(ct_df2[\"List Year\"] >= 2010).values]\n","filtered_df4 = ct_df2.loc[ct_df2[\"List Year\"] >= 2010]"]},{"cell_type":"code","execution_count":null,"id":"7cd3aab1","metadata":{"id":"7cd3aab1"},"outputs":[],"source":["new_df = pd.DataFrame(filtered_df1)\n","\n","new_df[\"Property Type\"].fillna(\"Not Specified\", inplace = True)"]},{"cell_type":"code","execution_count":null,"id":"577c55e4","metadata":{"id":"577c55e4"},"outputs":[],"source":["avg_sale = new_df.groupby(\"List Year\")[[\"Sale Amount\"]].mean().round()"]},{"cell_type":"code","execution_count":null,"id":"8c6ad276","metadata":{"id":"8c6ad276","outputId":"03a2f9a1-dea3-451d-fb3d-18f3c6490a27"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Sale Amount</th>\n","    </tr>\n","    <tr>\n","      <th>List Year</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2010</th>\n","      <td>331657.0</td>\n","    </tr>\n","    <tr>\n","      <th>2011</th>\n","      <td>391684.0</td>\n","    </tr>\n","    <tr>\n","      <th>2012</th>\n","      <td>395478.0</td>\n","    </tr>\n","    <tr>\n","      <th>2013</th>\n","      <td>413516.0</td>\n","    </tr>\n","    <tr>\n","      <th>2014</th>\n","      <td>401422.0</td>\n","    </tr>\n","    <tr>\n","      <th>2015</th>\n","      <td>345884.0</td>\n","    </tr>\n","    <tr>\n","      <th>2016</th>\n","      <td>507761.0</td>\n","    </tr>\n","    <tr>\n","      <th>2017</th>\n","      <td>393251.0</td>\n","    </tr>\n","    <tr>\n","      <th>2018</th>\n","      <td>383728.0</td>\n","    </tr>\n","    <tr>\n","      <th>2019</th>\n","      <td>420297.0</td>\n","    </tr>\n","    <tr>\n","      <th>2020</th>\n","      <td>604964.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["avg_sale"]},{"cell_type":"code","execution_count":null,"id":"756109a0","metadata":{"id":"756109a0"},"outputs":[],"source":["#exercise 3\n","\n","#create a new dataframe\n","new_df2 = pd.DataFrame(filtered_df2)\n","\n","new_df2[\"Town\"].fillna(\"N/A\", inplace = True)"]},{"cell_type":"code","execution_count":null,"id":"2023816c","metadata":{"id":"2023816c"},"outputs":[],"source":["avg_sale2 = new_df2.groupby(\"Town\")[[\"Assessed Value\"]].mean().round()\n","\n","avg_sale2 = avg_sale2.sort_values(\"Assessed Value\", ascending = False)"]},{"cell_type":"code","execution_count":null,"id":"6ca5996f","metadata":{"id":"6ca5996f","outputId":"f681af2d-22a3-4691-d537-c243fa35ebfe"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Assessed Value</th>\n","    </tr>\n","    <tr>\n","      <th>Town</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Greenwich</th>\n","      <td>2701872.0</td>\n","    </tr>\n","    <tr>\n","      <th>New Canaan</th>\n","      <td>1202979.0</td>\n","    </tr>\n","    <tr>\n","      <th>Darien</th>\n","      <td>1088478.0</td>\n","    </tr>\n","    <tr>\n","      <th>Westport</th>\n","      <td>1015508.0</td>\n","    </tr>\n","    <tr>\n","      <th>Stamford</th>\n","      <td>676788.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>Naugatuck</th>\n","      <td>124549.0</td>\n","    </tr>\n","    <tr>\n","      <th>Sterling</th>\n","      <td>121961.0</td>\n","    </tr>\n","    <tr>\n","      <th>Windham</th>\n","      <td>117870.0</td>\n","    </tr>\n","    <tr>\n","      <th>Thompson</th>\n","      <td>116516.0</td>\n","    </tr>\n","    <tr>\n","      <th>Torrington</th>\n","      <td>114682.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>169 rows × 1 columns</p>\n","</div>"],"text/plain":["            Assessed Value\n","Town                      \n","Greenwich        2701872.0\n","New Canaan       1202979.0\n","Darien           1088478.0\n","Westport         1015508.0\n","Stamford          676788.0\n","...                    ...\n","Naugatuck         124549.0\n","Sterling          121961.0\n","Windham           117870.0\n","Thompson          116516.0\n","Torrington        114682.0\n","\n","[169 rows x 1 columns]"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["avg_sale2"]},{"cell_type":"code","execution_count":null,"id":"dcd644d4","metadata":{"id":"dcd644d4","outputId":"df4a0c4f-36fc-4086-a81b-40fba8029e71"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","\n","avg_sale.plot(kind = \"bar\", y = \"Sale Amount\", color = \"Purple\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"0a4cd83e","metadata":{"id":"0a4cd83e","outputId":"2551fbaa-28e6-4e0d-ebee-924d1800dba8"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Sale Amount</th>\n","    </tr>\n","    <tr>\n","      <th>Property Type</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Condo</th>\n","      <td>219673.0</td>\n","    </tr>\n","    <tr>\n","      <th>Four Family</th>\n","      <td>141680.0</td>\n","    </tr>\n","    <tr>\n","      <th>Not Specified</th>\n","      <td>624053.0</td>\n","    </tr>\n","    <tr>\n","      <th>Single Family</th>\n","      <td>342001.0</td>\n","    </tr>\n","    <tr>\n","      <th>Three Family</th>\n","      <td>119343.0</td>\n","    </tr>\n","    <tr>\n","      <th>Two Family</th>\n","      <td>139353.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               Sale Amount\n","Property Type             \n","Condo             219673.0\n","Four Family       141680.0\n","Not Specified     624053.0\n","Single Family     342001.0\n","Three Family      119343.0\n","Two Family        139353.0"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["new_df3 = new_df[new_df[\"List Year\"] == 2010]\n","new_df3 = new_df3.copy()\n","\n","avg_sale_by_type = new_df3.groupby(\"Property Type\")[[\"Sale Amount\"]].mean().round()\n","\n","avg_sale_by_type"]},{"cell_type":"code","execution_count":null,"id":"59f87b63","metadata":{"id":"59f87b63","outputId":"26f7ba67-5be2-4849-8494-fe355581123b"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["avg_sale_by_type.plot(kind = \"bar\", y = \"Sale Amount\", color = \"Blue\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"1261d77f","metadata":{"id":"1261d77f"},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.5"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}