rodriguez_2021_8_14_1140am.ipynb

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    "execution_start": 1628967551831,
    "execution_millis": 886,
    "deepnote_cell_type": "code"
   },
   "source": "import pandas as pd\n\ndf = pd.read_csv('/work/Geographic Factors/crimedata_csv_all_years.csv')\ndf.sample(5)",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "execute_result",
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       ],
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        {
         "TYPE": "Break and Enter Commercial",
         "YEAR": 2010,
         "MONTH": 8,
         "DAY": 9,
         "HOUR": 17,
         "MINUTE": 0,
         "HUNDRED_BLOCK": "23XX BAYSWATER ST",
         "NEIGHBOURHOOD": "Kitsilano",
         "X": 487540.4778,
         "Y": 5456971.6539,
         "_deepnote_index_column": 17641
        },
        {
         "TYPE": "Theft from Vehicle",
         "YEAR": 2014,
         "MONTH": 4,
         "DAY": 16,
         "HOUR": 15,
         "MINUTE": 0,
         "HUNDRED_BLOCK": "26XX LARCH ST",
         "NEIGHBOURHOOD": "Kitsilano",
         "X": 488181.1463,
         "Y": 5456614.0562,
         "_deepnote_index_column": 548849
        },
        {
         "TYPE": "Other Theft",
         "YEAR": 2004,
         "MONTH": 3,
         "DAY": 24,
         "HOUR": 12,
         "MINUTE": 25,
         "HUNDRED_BLOCK": "83XX CHESTER ST",
         "NEIGHBOURHOOD": "Sunset",
         "X": 493494.8305,
         "Y": 5450726.7424,
         "_deepnote_index_column": 433316
        },
        {
         "TYPE": "Theft from Vehicle",
         "YEAR": 2008,
         "MONTH": 1,
         "DAY": 7,
         "HOUR": 9,
         "MINUTE": 0,
         "HUNDRED_BLOCK": "7XX E HASTINGS ST",
         "NEIGHBOURHOOD": "Strathcona",
         "X": 493497.2265,
         "Y": 5458720.2485,
         "_deepnote_index_column": 633633
        },
        {
         "TYPE": "Theft of Bicycle",
         "YEAR": 2013,
         "MONTH": 9,
         "DAY": 27,
         "HOUR": 16,
         "MINUTE": 30,
         "HUNDRED_BLOCK": "CARRALL ST / E PENDER ST",
         "NEIGHBOURHOOD": "Central Business District",
         "X": 492408.4344,
         "Y": 5458642.8036,
         "_deepnote_index_column": 723854
        }
       ],
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      },
      "text/plain": "                              TYPE  YEAR  MONTH  DAY  HOUR  MINUTE  \\\n17641   Break and Enter Commercial  2010      8    9    17       0   \n548849          Theft from Vehicle  2014      4   16    15       0   \n433316                 Other Theft  2004      3   24    12      25   \n633633          Theft from Vehicle  2008      1    7     9       0   \n723854            Theft of Bicycle  2013      9   27    16      30   \n\n                   HUNDRED_BLOCK              NEIGHBOURHOOD            X  \\\n17641          23XX BAYSWATER ST                  Kitsilano  487540.4778   \n548849             26XX LARCH ST                  Kitsilano  488181.1463   \n433316           83XX CHESTER ST                     Sunset  493494.8305   \n633633         7XX E HASTINGS ST                 Strathcona  493497.2265   \n723854  CARRALL ST / E PENDER ST  Central Business District  492408.4344   \n\n                   Y  \n17641   5.456972e+06  \n548849  5.456614e+06  \n433316  5.450727e+06  \n633633  5.458720e+06  \n723854  5.458643e+06  ",
      "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>TYPE</th>\n      <th>YEAR</th>\n      <th>MONTH</th>\n      <th>DAY</th>\n      <th>HOUR</th>\n      <th>MINUTE</th>\n      <th>HUNDRED_BLOCK</th>\n      <th>NEIGHBOURHOOD</th>\n      <th>X</th>\n      <th>Y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>17641</th>\n      <td>Break and Enter Commercial</td>\n      <td>2010</td>\n      <td>8</td>\n      <td>9</td>\n      <td>17</td>\n      <td>0</td>\n      <td>23XX BAYSWATER ST</td>\n      <td>Kitsilano</td>\n      <td>487540.4778</td>\n      <td>5.456972e+06</td>\n    </tr>\n    <tr>\n      <th>548849</th>\n      <td>Theft from Vehicle</td>\n      <td>2014</td>\n      <td>4</td>\n      <td>16</td>\n      <td>15</td>\n      <td>0</td>\n      <td>26XX LARCH ST</td>\n      <td>Kitsilano</td>\n      <td>488181.1463</td>\n      <td>5.456614e+06</td>\n    </tr>\n    <tr>\n      <th>433316</th>\n      <td>Other Theft</td>\n      <td>2004</td>\n      <td>3</td>\n      <td>24</td>\n      <td>12</td>\n      <td>25</td>\n      <td>83XX CHESTER ST</td>\n      <td>Sunset</td>\n      <td>493494.8305</td>\n      <td>5.450727e+06</td>\n    </tr>\n    <tr>\n      <th>633633</th>\n      <td>Theft from Vehicle</td>\n      <td>2008</td>\n      <td>1</td>\n      <td>7</td>\n      <td>9</td>\n      <td>0</td>\n      <td>7XX E HASTINGS ST</td>\n      <td>Strathcona</td>\n      <td>493497.2265</td>\n      <td>5.458720e+06</td>\n    </tr>\n    <tr>\n      <th>723854</th>\n      <td>Theft of Bicycle</td>\n      <td>2013</td>\n      <td>9</td>\n      <td>27</td>\n      <td>16</td>\n      <td>30</td>\n      <td>CARRALL ST / E PENDER ST</td>\n      <td>Central Business District</td>\n      <td>492408.4344</td>\n      <td>5.458643e+06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": "df.info()\n# 797 566 observations\n# some null",
   "metadata": {
    "tags": [],
    "cell_id": "00003-82b3011e-eba5-4065-9547-81965e88ccfa",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "de1e323c",
    "execution_start": 1628967195600,
    "execution_millis": 122,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 797566 entries, 0 to 797565\nData columns (total 10 columns):\n #   Column         Non-Null Count   Dtype  \n---  ------         --------------   -----  \n 0   TYPE           797566 non-null  object \n 1   YEAR           797566 non-null  int64  \n 2   MONTH          797566 non-null  int64  \n 3   DAY            797566 non-null  int64  \n 4   HOUR           797566 non-null  int64  \n 5   MINUTE         797566 non-null  int64  \n 6   HUNDRED_BLOCK  797554 non-null  object \n 7   NEIGHBOURHOOD  797442 non-null  object \n 8   X              797493 non-null  float64\n 9   Y              797493 non-null  float64\ndtypes: float64(2), int64(5), object(3)\nmemory usage: 60.8+ MB\n",
     "output_type": "stream"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "source": "N = len(df)\n(df.isnull().sum()) / N *100\n# small percentage of null on 4 variables",
   "metadata": {
    "tags": [],
    "cell_id": "00002-9ae54581-b52c-4643-9c67-dcd88a02871c",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9052a0f3",
    "execution_start": 1628968221638,
    "execution_millis": 102,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "TYPE             0.000000\nYEAR             0.000000\nMONTH            0.000000\nDAY              0.000000\nHOUR             0.000000\nMINUTE           0.000000\nHUNDRED_BLOCK    0.001505\nNEIGHBOURHOOD    0.015547\nX                0.009153\nY                0.009153\ndtype: float64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "source": "#df.describe()",
   "metadata": {
    "tags": [],
    "cell_id": "00003-81df5d2f-780d-48ad-8e94-dbf44a0ff3b2",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5ca36e8d",
    "execution_start": 1628968361846,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "source": "categorical = df.select_dtypes('object').columns\n\nprint('% of unique values:')\nfor cat in categorical:\n    unique = df[cat].nunique()\n    unique = round(unique/N*100,2)\n    print(cat, unique)\n# no significant cardinality in categoricals",
   "metadata": {
    "tags": [],
    "cell_id": "00004-6c41634f-925c-4db4-a7bb-fba3a6450821",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5713f13c",
    "execution_start": 1628968503644,
    "execution_millis": 283,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "text": "% of unique values:\nTYPE 0.0\nHUNDRED_BLOCK 2.88\nNEIGHBOURHOOD 0.0\n",
     "output_type": "stream"
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "source": "df.head()",
   "metadata": {
    "tags": [],
    "cell_id": "00005-5d69dc82-f997-4a8a-94aa-0d14c1c0bc96",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c085b6ba",
    "execution_start": 1628968563304,
    "execution_millis": 24,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 5,
       "column_count": 10,
       "columns": [
        {
         "name": "TYPE",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "Break and Enter Commercial",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "YEAR",
         "dtype": "int64",
         "stats": {
          "unique_count": 4,
          "nan_count": 0,
          "min": "2012",
          "max": "2021",
          "histogram": [
           {
            "bin_start": 2012,
            "bin_end": 2012.9,
            "count": 1
           },
           {
            "bin_start": 2012.9,
            "bin_end": 2013.8,
            "count": 0
           },
           {
            "bin_start": 2013.8,
            "bin_end": 2014.7,
            "count": 1
           },
           {
            "bin_start": 2014.7,
            "bin_end": 2015.6,
            "count": 0
           },
           {
            "bin_start": 2015.6,
            "bin_end": 2016.5,
            "count": 0
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      "text/plain": "                         TYPE  YEAR  MONTH  DAY  HOUR  MINUTE  \\\n0  Break and Enter Commercial  2012     12   14     8      52   \n1  Break and Enter Commercial  2019      3    7     2       6   \n2  Break and Enter Commercial  2019      8   27     4      12   \n3  Break and Enter Commercial  2021      4   26     4      44   \n4  Break and Enter Commercial  2014      8    8     5      13   \n\n     HUNDRED_BLOCK NEIGHBOURHOOD            X             Y  \n0              NaN      Oakridge  491285.0000  5.453433e+06  \n1    10XX SITKA SQ      Fairview  490612.9648  5.457110e+06  \n2  10XX ALBERNI ST      West End  491007.7798  5.459174e+06  \n3  10XX ALBERNI ST      West End  491007.7798  5.459174e+06  \n4  10XX ALBERNI ST      West End  491015.9434  5.459166e+06  ",
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     },
     "metadata": {}
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "source": "type = df.TYPE.value_counts()\ntype_perc = round((type/N)*100 ,2)\ntype_perc\n# 1/4 of crime are vehicle thefts,\n# another 1/4 are other forms of thefts\n# major personal injuries such are offense against a person and \n#    homicide comprise <10%",
   "metadata": {
    "tags": [],
    "cell_id": "00006-2c43032b-6f59-4b21-a170-19b374d79514",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b1636fdc",
    "execution_start": 1628970352057,
    "execution_millis": 93,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 44,
     "data": {
      "text/plain": "Theft from Vehicle                                        28.27\nOther Theft                                               24.26\nMischief                                                  11.73\nBreak and Enter Residential/Other                          8.73\nOffence Against a Person                                   8.51\nBreak and Enter Commercial                                 5.43\nTheft of Vehicle                                           5.38\nTheft of Bicycle                                           4.26\nVehicle Collision or Pedestrian Struck (with Injury)       3.36\nVehicle Collision or Pedestrian Struck (with Fatality)     0.04\nHomicide                                                   0.04\nName: TYPE, dtype: float64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 44
  },
  {
   "cell_type": "code",
   "source": "import matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set()\ntype_perc.plot(kind='bar', rot = 90)\nplt.xlabel('Crime type')\nplt.ylabel('%')\nplt.title('Percentage distribution of Types of Crime');",
   "metadata": {
    "tags": [],
    "cell_id": "00009-214a1bba-ee6e-46ea-84b3-5f51d32fcbd5",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9c276dc0",
    "execution_start": 1628970616865,
    "execution_millis": 784,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 389,
       "height": 559
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": 57
  },
  {
   "cell_type": "code",
   "source": "neigh = df.NEIGHBOURHOOD.value_counts()\nneigh_perc = round((neigh/N)*100 ,2)\nneigh_perc\n## with minimal null, not yet filled\n# 1/4 of crimes occur in the CBD",
   "metadata": {
    "tags": [],
    "cell_id": "00007-abef4943-6c6f-4d66-acd1-59372cfa01c7",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a25c3269",
    "execution_start": 1628970523994,
    "execution_millis": 10,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 54,
     "data": {
      "text/plain": "Central Business District    26.48\nWest End                      9.03\nStrathcona                    6.32\nMount Pleasant                6.27\nFairview                      6.25\nGrandview-Woodland            5.70\nRenfrew-Collingwood           5.23\nKitsilano                     5.02\nKensington-Cedar Cottage      4.78\nHastings-Sunrise              3.51\nSunset                        3.28\nMarpole                       2.49\nRiley Park                    2.44\nVictoria-Fraserview           2.03\nKillarney                     1.91\nOakridge                      1.55\nDunbar-Southlands             1.33\nKerrisdale                    1.33\nWest Point Grey               1.13\nArbutus Ridge                 1.09\nSouth Cambie                  1.01\nShaughnessy                   1.00\nStanley Park                  0.67\nMusqueam                      0.12\nName: NEIGHBOURHOOD, dtype: float64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 54
  },
  {
   "cell_type": "code",
   "source": "neigh_perc.plot(kind='bar', rot = 90)\nplt.xlabel('Neighbourhood')\nplt.ylabel('%')\nplt.title('Percentage distribution of Neighbourhood crime');",
   "metadata": {
    "tags": [],
    "cell_id": "00009-aedb23b1-8962-4926-adee-20e0bbfa5dfd",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e210a65e",
    "execution_start": 1628970632225,
    "execution_millis": 388,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 389,
       "height": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": 58
  },
  {
   "cell_type": "code",
   "source": "type_neigh = df.groupby('TYPE')['NEIGHBOURHOOD'].value_counts()\n(((type_neigh.sort_values(ascending=False))/N)*100).head()\n# the leading crimes are thefts and mischief that occur in the CBD, \n#    accounting for almost 20%\n\n# https://geodash.vpd.ca/opendata/\n# Other Theft: Theft of property that includes personal items (purse, wallet, cellphone, laptop, etc.), bicycle, etc.",
   "metadata": {
    "tags": [],
    "cell_id": "00008-92b9b49c-cd07-4fd5-8c2f-b747104833e6",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1e71104c",
    "execution_start": 1628969362634,
    "execution_millis": 135,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 39,
     "data": {
      "text/plain": "TYPE                NEIGHBOURHOOD            \nOther Theft         Central Business District    8.538478\nTheft from Vehicle  Central Business District    8.229664\nMischief            Central Business District    3.012791\nTheft from Vehicle  West End                     2.730808\nOther Theft         West End                     2.551764\nName: NEIGHBOURHOOD, dtype: float64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 39
  },
  {
   "cell_type": "code",
   "source": "year = df.YEAR.value_counts()\nyear_perc = ((year/N)*100).sort_index(ascending=False)\nyear_perc",
   "metadata": {
    "tags": [],
    "cell_id": "00009-f1238268-255a-47da-b0f0-9e5889159ffc",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "bab03a6",
    "execution_start": 1628970657330,
    "execution_millis": 6,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 59,
     "data": {
      "text/plain": "2021    1.839095\n2020    4.697668\n2019    6.054546\n2018    5.571577\n2017    5.423376\n2016    5.535717\n2015    5.050742\n2014    4.924107\n2013    4.496305\n2012    4.461700\n2011    4.319016\n2010    4.473862\n2009    4.761111\n2008    5.232921\n2007    5.566310\n2006    6.225692\n2005    6.700010\n2004    7.289177\n2003    7.377070\nName: YEAR, dtype: float64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 59
  },
  {
   "cell_type": "code",
   "source": "year_perc.plot(kind='bar', rot = 70)\nplt.xlabel('Year')\nplt.ylabel('%')\nplt.title('Annual percentage distribution of crime (2003-2021');\n# decreasing percentage from 2003 - 2012\n# increasing trend 2013-2019\n# decreased in 2020 --> ? pandemic effect\n# 2021 ongoing",
   "metadata": {
    "tags": [],
    "cell_id": "00010-f59a0f09-f798-4fa3-bf61-e0c14558aec6",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d1648129",
    "execution_start": 1628970722157,
    "execution_millis": 317,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 383,
       "height": 303
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": 62
  },
  {
   "cell_type": "code",
   "source": "year_type = df.groupby('YEAR')['TYPE'].value_counts()\n(((year_type.sort_values(ascending=False))/N)*100).head()",
   "metadata": {
    "tags": [],
    "cell_id": "00013-2071339c-312f-4ad0-932e-535ea9904525",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "210bf31f",
    "execution_start": 1628970941077,
    "execution_millis": 120,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 65,
     "data": {
      "text/plain": "YEAR  TYPE              \n2004  Theft from Vehicle    2.282820\n2003  Theft from Vehicle    2.224769\n2019  Theft from Vehicle    2.132990\n2005  Theft from Vehicle    2.075816\n2018  Theft from Vehicle    1.881224\nName: TYPE, dtype: float64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 65
  },
  {
   "cell_type": "code",
   "source": "veh = df[df.TYPE == 'Theft from Vehicle']\nveh.groupby('YEAR')['NEIGHBOURHOOD'].value_counts().sort_values(ascending=False).head(10)",
   "metadata": {
    "tags": [],
    "cell_id": "00014-ac70694e-3d02-42db-a991-941398b30aac",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "82d06a4b",
    "execution_start": 1628971584719,
    "execution_millis": 59,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 78,
     "data": {
      "text/plain": "YEAR  NEIGHBOURHOOD            \n2019  Central Business District    6375\n2003  Central Business District    5340\n2018  Central Business District    5300\n2004  Central Business District    4419\n2016  Central Business District    4384\n2017  Central Business District    4313\n2006  Central Business District    4084\n2007  Central Business District    3869\n2008  Central Business District    3831\n2005  Central Business District    3780\nName: NEIGHBOURHOOD, dtype: int64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 78
  },
  {
   "cell_type": "code",
   "source": "yr_2021 = df[df.YEAR==2021]\ncbd = df[df.NEIGHBOURHOOD == 'Central Business District']\n\nyr_2021_cbd = yr_2021[yr_2021.NEIGHBOURHOOD == 'Central Business District']\n\nyr_2021_cbd_type = yr_2021_cbd.groupby('TYPE')\nyr_2021_cbd_type_ct = yr_2021_cbd_type.count()\nyr_2021_cbd_type_ct['YEAR'].sort_values(ascending=False)",
   "metadata": {
    "tags": [],
    "cell_id": "00015-976f0d2d-ac5a-452d-a4d5-a9ea26626c9e",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8701bad9",
    "execution_start": 1628972504915,
    "execution_millis": 72,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 110,
     "data": {
      "text/plain": "TYPE\nOther Theft                                             1062\nMischief                                                 856\nTheft from Vehicle                                       772\nOffence Against a Person                                 565\nBreak and Enter Commercial                               319\nTheft of Bicycle                                         128\nBreak and Enter Residential/Other                         59\nVehicle Collision or Pedestrian Struck (with Injury)      58\nTheft of Vehicle                                          37\nHomicide                                                   4\nName: YEAR, dtype: int64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 110
  },
  {
   "cell_type": "code",
   "source": "(yr_2021_cbd_type_ct['YEAR'].sort_values(ascending=False)).plot(kind='bar')\nplt.xlabel('Crime type')\nplt.ylabel('Frequency of crime')\nplt.title('Frequency of Crime types in the Central Business District in 2021');",
   "metadata": {
    "tags": [],
    "cell_id": "00015-feae3673-012c-428f-9618-437004db7b01",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "943eb431",
    "execution_start": 1628972643253,
    "execution_millis": 234,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "image/png": {
       "width": 433,
       "height": 550
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": 114
  },
  {
   "cell_type": "code",
   "source": "df2 = pd.read_csv('/work/Amenity_Rooms_and_Assets.csv')\ndf2.sample(5)\n\n## would have helped to have neighbourhood name info, to correlate with statcan",
   "metadata": {
    "tags": [],
    "cell_id": "00017-9a02912d-44d8-497f-b5f3-285052449c13",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ef0e543b",
    "execution_start": 1628972869747,
    "execution_millis": 117,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 117,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 5,
       "column_count": 40,
       "columns": [
        {
         "name": "Nos. of Suites",
         "dtype": "int64",
         "stats": {
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          "nan_count": 0,
          "min": "16",
          "max": "79",
          "histogram": [
           {
            "bin_start": 16,
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           },
           {
            "bin_start": 22.3,
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           },
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            "bin_end": 34.9,
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           },
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            "bin_start": 53.8,
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            "bin_start": 72.69999999999999,
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            "count": 1
           }
          ]
         }
        },
        {
         "name": "Building Names",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "COLLINGWOOD TOWER",
            "count": 1
           },
           {
            "name": "BRIDGEVIEW PLACE",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Building Code",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "CT",
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           },
           {
            "name": "BVP",
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           },
           {
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          ]
         }
        },
        {
         "name": "Address",
         "dtype": "object",
         "stats": {
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          "nan_count": 0,
          "categories": [
           {
            "name": "5657 Harold St.",
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           },
           {
            "name": "238 Davie St.",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Postal Code",
         "dtype": "object",
         "stats": {
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          "nan_count": 0,
          "categories": [
           {
            "name": "V5R 5V6",
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           },
           {
            "name": "V6B 5X8",
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           },
           {
            "name": "3 others",
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           }
          ]
         }
        },
        {
         "name": "No. of Suite Type",
         "dtype": "object",
         "stats": {
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          "nan_count": 0,
          "categories": [
           {
            "name": "Bachelor (56), 1BDR (12)",
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           },
           {
            "name": "1BDR (56), 2BDR (12), Handicap (4)",
            "count": 1
           },
           {
            "name": "3 others",
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           }
          ]
         }
        },
        {
         "name": "Build Year",
         "dtype": "float64",
         "stats": {
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          "min": "1964.0",
          "max": "1993.0",
          "histogram": [
           {
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           },
           {
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           },
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            "bin_end": 1975.6,
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           },
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            "bin_end": 1990.1,
            "count": 0
           },
           {
            "bin_start": 1990.1,
            "bin_end": 1993,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Resident Profile",
         "dtype": "object",
         "stats": {
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          "nan_count": 0,
          "categories": [
           {
            "name": "Seniors",
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           },
           {
            "name": "Families, People with Disabilities, Seniors",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "BC Housing Registration (required)",
         "dtype": "bool",
         "stats": {
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          "categories": [
           {
            "name": "False",
            "count": 3
           },
           {
            "name": "True",
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           }
          ]
         }
        },
        {
         "name": "Pet Friendly ",
         "dtype": "bool",
         "stats": {
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          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Amenity room",
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         "stats": {
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          "categories": [
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         "Stove": "True",
         "Fridge": "True",
         "Television": "False",
         "Sink": "False",
         "Comments": "8th Floor Amenity Room.  Combined with laundry room. 2nd Floor Amenity Room.  Combined with laundry…",
         "Recommended Occupancy": "12-15",
         "Access": "OPEN",
         "Picture": "nan",
         "Type of Flooring": "nan",
         "Type of Flooring.1": "nan",
         "Bike Storage": "Y",
         "Location": "Door left of entrance",
         "Rent - Lowest Range": 320,
         "Rent - Highest Range": "1,284.00",
         "Average Rent": 623.78,
         "Median Rent": "529.00",
         "Unnamed: 36": "nan",
         "Unnamed: 37": "nan",
         "Unnamed: 38": "nan",
         "Unnamed: 39": "nan",
         "_deepnote_index_column": 17
        },
        {
         "Nos. of Suites": 16,
         "Building Names": "BURRARD MANOR",
         "Building Code": "BM",
         "Address": "2330 Balsam St",
         "Postal Code": "V6K 3M8",
         "No. of Suite Type": "Bachelor (15), 1BDR (1)",
         "Build Year": 1967,
         "Resident Profile": "Seniors",
         "BC Housing Registration (required)": "False",
         "Pet Friendly ": "False",
         "Amenity room": "No",
         "Lounge": "nan",
         "Other": "nan",
         "Static Chairs": "nan",
         "Movable Chairs": "nan",
         "Division": 1,
         "Tables": "nan",
         "Appliances ": "nan",
         "DVD Player": "False",
         "Microwave": "False",
         "Stove": "False",
         "Fridge": "False",
         "Television": "False",
         "Sink": "False",
         "Comments": "nan",
         "Recommended Occupancy": "nan",
         "Access": "nan",
         "Picture": "nan",
         "Type of Flooring": "nan",
         "Type of Flooring.1": "nan",
         "Bike Storage": "nan",
         "Location": "nan",
         "Rent - Lowest Range": 475,
         "Rent - Highest Range": "1,084.00",
         "Average Rent": 747.69,
         "Median Rent": "787.50",
         "Unnamed: 36": "nan",
         "Unnamed: 37": "nan",
         "Unnamed: 38": "nan",
         "Unnamed: 39": "nan",
         "_deepnote_index_column": 2
        },
        {
         "Nos. of Suites": 21,
         "Building Names": "ARBUTUS COURT",
         "Building Code": "AC",
         "Address": "2085 W. 5th Ave.",
         "Postal Code": "V6J 1P8",
         "No. of Suite Type": "1BDR (21)",
         "Build Year": 1964,
         "Resident Profile": "Families, People with Disabilities, Seniors",
         "BC Housing Registration (required)": "False",
         "Pet Friendly ": "False",
         "Amenity room": "No",
         "Lounge": "nan",
         "Other": "nan",
         "Static Chairs": "nan",
         "Movable Chairs": "nan",
         "Division": 1,
         "Tables": "nan",
         "Appliances ": "nan",
         "DVD Player": "False",
         "Microwave": "False",
         "Stove": "False",
         "Fridge": "False",
         "Television": "False",
         "Sink": "False",
         "Comments": "nan",
         "Recommended Occupancy": "nan",
         "Access": "nan",
         "Picture": "nan",
         "Type of Flooring": "nan",
         "Type of Flooring.1": "nan",
         "Bike Storage": "Y",
         "Location": "Parkade",
         "Rent - Lowest Range": 475,
         "Rent - Highest Range": "1,553.00",
         "Average Rent": 886.1,
         "Median Rent": "803.00",
         "Unnamed: 36": "nan",
         "Unnamed: 37": "nan",
         "Unnamed: 38": "nan",
         "Unnamed: 39": "nan",
         "_deepnote_index_column": 1
        },
        {
         "Nos. of Suites": 25,
         "Building Names": "SOROPTIMIST LIONS",
         "Building Code": "SL",
         "Address": "1444 E. 13th Ave.",
         "Postal Code": "V5N 2B6",
         "No. of Suite Type": "Bachelor (19), 1BDR (6)",
         "Build Year": 1971,
         "Resident Profile": "Seniors",
         "BC Housing Registration (required)": "False",
         "Pet Friendly ": "False",
         "Amenity room": "Yes",
         "Lounge": "nan",
         "Other": "nan",
         "Static Chairs": 4,
         "Movable Chairs": 3,
         "Division": 1,
         "Tables": 1,
         "Appliances ": "Sink / Television",
         "DVD Player": "False",
         "Microwave": "False",
         "Stove": "False",
         "Fridge": "False",
         "Television": "True",
         "Sink": "True",
         "Comments": "nan",
         "Recommended Occupancy": "8-10",
         "Access": "OPEN",
         "Picture": "Y",
         "Type of Flooring": "C",
         "Type of Flooring.1": "Carpeting",
         "Bike Storage": "Y",
         "Location": "Rear Parking Shed",
         "Rent - Lowest Range": 475,
         "Rent - Highest Range": "1,150.00",
         "Average Rent": 753.12,
         "Median Rent": "772.00",
         "Unnamed: 36": "nan",
         "Unnamed: 37": "nan",
         "Unnamed: 38": "nan",
         "Unnamed: 39": "nan",
         "_deepnote_index_column": 15
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "    Nos. of Suites     Building Names Building Code            Address  \\\n19              79  COLLINGWOOD TOWER            CT    5657 Harold St.   \n17              72   BRIDGEVIEW PLACE           BVP      238 Davie St.   \n2               16      BURRARD MANOR            BM     2330 Balsam St   \n1               21      ARBUTUS COURT            AC   2085 W. 5th Ave.   \n15              25  SOROPTIMIST LIONS            SL  1444 E. 13th Ave.   \n\n   Postal Code                   No. of Suite Type  Build Year  \\\n19     V5R 5V6            Bachelor (56), 1BDR (12)      1977.0   \n17     V6B 5X8  1BDR (56), 2BDR (12), Handicap (4)      1993.0   \n2      V6K 3M8             Bachelor (15), 1BDR (1)      1967.0   \n1      V6J 1P8                           1BDR (21)      1964.0   \n15     V5N 2B6             Bachelor (19), 1BDR (6)      1971.0   \n\n                               Resident Profile  \\\n19                                      Seniors   \n17  Families, People with Disabilities, Seniors   \n2                                       Seniors   \n1   Families, People with Disabilities, Seniors   \n15                                      Seniors   \n\n    BC Housing Registration (required)  Pet Friendly   ... Bike Storage  \\\n19                                True          False  ...          NaN   \n17                                True          False  ...            Y   \n2                                False          False  ...          NaN   \n1                                False          False  ...            Y   \n15                               False          False  ...            Y   \n\n                 Location Rent - Lowest Range  Rent - Highest Range  \\\n19                    NaN               220.0                772.00   \n17  Door left of entrance               320.0              1,284.00   \n2                     NaN               475.0              1,084.00   \n1                 Parkade               475.0              1,553.00   \n15      Rear Parking Shed               475.0              1,150.00   \n\n    Average Rent  Median Rent  Unnamed: 36 Unnamed: 37  Unnamed: 38  \\\n19        494.75       475.00          NaN         NaN          NaN   \n17        623.78       529.00          NaN         NaN          NaN   \n2         747.69       787.50          NaN         NaN          NaN   \n1         886.10       803.00          NaN         NaN          NaN   \n15        753.12       772.00          NaN         NaN          NaN   \n\n    Unnamed: 39  \n19          NaN  \n17          NaN  \n2           NaN  \n1           NaN  \n15          NaN  \n\n[5 rows x 40 columns]",
      "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>Nos. of Suites</th>\n      <th>Building Names</th>\n      <th>Building Code</th>\n      <th>Address</th>\n      <th>Postal Code</th>\n      <th>No. of Suite Type</th>\n      <th>Build Year</th>\n      <th>Resident Profile</th>\n      <th>BC Housing Registration (required)</th>\n      <th>Pet Friendly</th>\n      <th>...</th>\n      <th>Bike Storage</th>\n      <th>Location</th>\n      <th>Rent - Lowest Range</th>\n      <th>Rent - Highest Range</th>\n      <th>Average Rent</th>\n      <th>Median Rent</th>\n      <th>Unnamed: 36</th>\n      <th>Unnamed: 37</th>\n      <th>Unnamed: 38</th>\n      <th>Unnamed: 39</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>19</th>\n      <td>79</td>\n      <td>COLLINGWOOD TOWER</td>\n      <td>CT</td>\n      <td>5657 Harold St.</td>\n      <td>V5R 5V6</td>\n      <td>Bachelor (56), 1BDR (12)</td>\n      <td>1977.0</td>\n      <td>Seniors</td>\n      <td>True</td>\n      <td>False</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>220.0</td>\n      <td>772.00</td>\n      <td>494.75</td>\n      <td>475.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>72</td>\n      <td>BRIDGEVIEW PLACE</td>\n      <td>BVP</td>\n      <td>238 Davie St.</td>\n      <td>V6B 5X8</td>\n      <td>1BDR (56), 2BDR (12), Handicap (4)</td>\n      <td>1993.0</td>\n      <td>Families, People with Disabilities, Seniors</td>\n      <td>True</td>\n      <td>False</td>\n      <td>...</td>\n      <td>Y</td>\n      <td>Door left of entrance</td>\n      <td>320.0</td>\n      <td>1,284.00</td>\n      <td>623.78</td>\n      <td>529.00</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>16</td>\n      <td>BURRARD MANOR</td>\n      <td>BM</td>\n      <td>2330 Balsam St</td>\n      <td>V6K 3M8</td>\n      <td>Bachelor (15), 1BDR (1)</td>\n      <td>1967.0</td>\n      <td>Seniors</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>475.0</td>\n      <td>1,084.00</td>\n      <td>747.69</td>\n      <td>787.50</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>21</td>\n      <td>ARBUTUS COURT</td>\n      <td>AC</td>\n      <td>2085 W. 5th Ave.</td>\n      <td>V6J 1P8</td>\n      <td>1BDR (21)</td>\n      <td>1964.0</td>\n      <td>Families, People with Disabilities, Seniors</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>Y</td>\n      <td>Parkade</td>\n      <td>475.0</td>\n      <td>1,553.00</td>\n      <td>886.10</td>\n      <td>803.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>25</td>\n      <td>SOROPTIMIST LIONS</td>\n      <td>SL</td>\n      <td>1444 E. 13th Ave.</td>\n      <td>V5N 2B6</td>\n      <td>Bachelor (19), 1BDR (6)</td>\n      <td>1971.0</td>\n      <td>Seniors</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>Y</td>\n      <td>Rear Parking Shed</td>\n      <td>475.0</td>\n      <td>1,150.00</td>\n      <td>753.12</td>\n      <td>772.00</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>5 rows × 40 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": 117
  },
  {
   "cell_type": "code",
   "source": "df2['neighbourhood'] = df2['Postal Code']\ndf2.neighbourhood.replace({'V5R 5C6': 'Renfrew-Collingwood',\n                            'V5Z 1B3' : 'Fairview',\n                            'V6G 1K1' :  'Stanley Park',\n                            'V6K 1L9': 'Kitsilano',\n                            'V5T 1J9': 'Mount Pleasant',\n                            'V6B 5X8': 'Central Business District',\n                            'V5K 4X8': 'Hastings-Sunrise',\n                            'V6K 3M8': 'Kitsilano',\n                            'V5R 2K6': 'Renfrew-Collingwood',\n                            'V5R 5V6': 'Renfrew-Collingwood',\n                            'V5R 3N3': 'Rendrew-Collingwood',\n                            'V5N 5X1': 'Grandview-Woodland',\n                            'V6J 1P8': 'Kitsilano',\n                            'V5N 2A1': 'Grandview-Woodland',\n                            'V6E 1R2': 'West End',\n                            'V6E 1S2': 'West End',\n                            'V5N 1Y6': 'Kensington-Cedar Cottage',\n                            'V5R 4S2': 'Renfrew-Collingwood',\n                            'V6H 2S2': 'Fairview',\n                            'V6P 4W9': 'Marpole',\n                            'V5N 2B6': 'Kensington-Cedar Cottage',\n                            'V5P 1K5': 'Victoria-Fraserview',\n                            'V5N 1P3': 'Grandview-Woodland',\n                            'V6M 3Y1': 'Kerrisdale'\n\n                            }, inplace=True)\n",
   "metadata": {
    "tags": [],
    "cell_id": "00019-2eb0f780-40d1-40d6-965d-636ea17bbe53",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "dbf1876a",
    "execution_start": 1628981394182,
    "execution_millis": 0,
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": 182
  },
  {
   "cell_type": "code",
   "source": "df2.neighbourhood.unique()",
   "metadata": {
    "tags": [],
    "cell_id": "00020-6fd7c297-82c8-4a24-8b74-f5a1300bc1d0",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8b7679b0",
    "execution_start": 1628981395315,
    "execution_millis": 7,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 183,
     "data": {
      "text/plain": "array(['Hastings-Sunrise', 'Kitsilano', 'Grandview-Woodland', 'West End',\n       'Stanley Park', 'Kensington-Cedar Cottage', 'Renfrew-Collingwood',\n       'Kerrisdale', 'Fairview', 'Marpole', 'Mount Pleasant',\n       'Central Business District', 'Rendrew-Collingwood',\n       'Victoria-Fraserview'], dtype=object)"
     },
     "metadata": {}
    }
   ],
   "execution_count": 183
  },
  {
   "cell_type": "code",
   "source": "## need to relate postal codes with crime type neighbourhood/x/y\n## alternative: derive neighbourhoods from postal code\n### not promising, too much overlap\n#### alternative:  area code  https://www.zip-codes.com/canadian/postal-code.asp?postalcode=v6g+1k1\n# postal code info https://postalcodeinfo.ca/postal-code/v6g-1k1/\n# lacking info filled with google maps search",
   "metadata": {
    "tags": [],
    "cell_id": "00015-7364239e-1a57-4d91-9ea5-40610bb419c6",
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "source": "df.NEIGHBOURHOOD.unique()",
   "metadata": {
    "tags": [],
    "cell_id": "00020-7bbdfd1e-f630-48f6-92ec-9e9e5c7db13a",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "42b51368",
    "execution_start": 1628974183276,
    "execution_millis": 59,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 123,
     "data": {
      "text/plain": "array(['Oakridge', 'Fairview', 'West End', 'Central Business District',\n       'Hastings-Sunrise', 'Strathcona', 'Grandview-Woodland',\n       'Kitsilano', 'Kensington-Cedar Cottage', 'Sunset',\n       'Mount Pleasant', 'Stanley Park', 'Shaughnessy', 'Marpole',\n       'West Point Grey', 'Victoria-Fraserview', 'Kerrisdale',\n       'Riley Park', 'Arbutus Ridge', 'Renfrew-Collingwood', 'Killarney',\n       'Dunbar-Southlands', 'South Cambie', 'Musqueam', nan], dtype=object)"
     },
     "metadata": {}
    }
   ],
   "execution_count": 123
  },
  {
   "cell_type": "code",
   "source": "df.head()",
   "metadata": {
    "tags": [],
    "cell_id": "00023-4a3eef1f-6d2f-4d0e-9d34-84f5c65ffc9c",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c085b6ba",
    "execution_start": 1628976211920,
    "execution_millis": 31,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 130,
     "data": {
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      "text/plain": "                           TYPE  YEAR  MONTH  DAY  HOUR  MINUTE  \\\n3    Break and Enter Commercial  2021      4   26     4      44   \n165  Break and Enter Commercial  2021      6   15     4       0   \n288  Break and Enter Commercial  2021      3   23    17      14   \n326  Break and Enter Commercial  2021      2   14     7      35   \n413  Break and Enter Commercial  2021      5    6     3      27   \n\n       HUNDRED_BLOCK              NEIGHBOURHOOD            X             Y  \n3    10XX ALBERNI ST                   West End  491007.7798  5.459174e+06  \n165   10XX BEACH AVE  Central Business District  490258.4148  5.458155e+06  \n288  10XX BURRARD ST                   West End  490729.8291  5.458622e+06  \n326     10XX BUTE ST                   West End  490409.1263  5.458906e+06  \n413   10XX CANADA PL  Central Business District  491493.3778  5.459542e+06  ",
      "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>TYPE</th>\n      <th>YEAR</th>\n      <th>MONTH</th>\n      <th>DAY</th>\n      <th>HOUR</th>\n      <th>MINUTE</th>\n      <th>HUNDRED_BLOCK</th>\n      <th>NEIGHBOURHOOD</th>\n      <th>X</th>\n      <th>Y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>Break and Enter Commercial</td>\n      <td>2021</td>\n      <td>4</td>\n      <td>26</td>\n      <td>4</td>\n      <td>44</td>\n      <td>10XX ALBERNI ST</td>\n      <td>West End</td>\n      <td>491007.7798</td>\n      <td>5.459174e+06</td>\n    </tr>\n    <tr>\n      <th>165</th>\n      <td>Break and Enter Commercial</td>\n      <td>2021</td>\n      <td>6</td>\n      <td>15</td>\n      <td>4</td>\n      <td>0</td>\n      <td>10XX BEACH AVE</td>\n      <td>Central Business District</td>\n      <td>490258.4148</td>\n      <td>5.458155e+06</td>\n    </tr>\n    <tr>\n      <th>288</th>\n      <td>Break and Enter Commercial</td>\n      <td>2021</td>\n      <td>3</td>\n      <td>23</td>\n      <td>17</td>\n      <td>14</td>\n      <td>10XX BURRARD ST</td>\n      <td>West End</td>\n      <td>490729.8291</td>\n      <td>5.458622e+06</td>\n    </tr>\n    <tr>\n      <th>326</th>\n      <td>Break and Enter Commercial</td>\n      <td>2021</td>\n      <td>2</td>\n      <td>14</td>\n      <td>7</td>\n      <td>35</td>\n      <td>10XX BUTE ST</td>\n      <td>West End</td>\n      <td>490409.1263</td>\n      <td>5.458906e+06</td>\n    </tr>\n    <tr>\n      <th>413</th>\n      <td>Break and Enter Commercial</td>\n      <td>2021</td>\n      <td>5</td>\n      <td>6</td>\n      <td>3</td>\n      <td>27</td>\n      <td>10XX CANADA PL</td>\n      <td>Central Business District</td>\n      <td>491493.3778</td>\n      <td>5.459542e+06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": 187
  },
  {
   "cell_type": "code",
   "source": "# df crime\nyr_2021_neigh = yr_2021.groupby('NEIGHBOURHOOD')\nyr_2021_neigh = yr_2021_neigh['TYPE']\nyr_2021_neigh_ct = yr_2021_neigh.count()\nyr_2021_neigh_ct",
   "metadata": {
    "tags": [],
    "cell_id": "00026-0f335e5e-2845-4213-b317-fdc3c37accc4",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "90bed839",
    "execution_start": 1628982151390,
    "execution_millis": 15,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 194,
     "data": {
      "text/plain": "NEIGHBOURHOOD\nArbutus Ridge                 137\nCentral Business District    3860\nDunbar-Southlands             157\nFairview                      790\nGrandview-Woodland            726\nHastings-Sunrise              603\nKensington-Cedar Cottage      690\nKerrisdale                    215\nKillarney                     240\nKitsilano                     711\nMarpole                       435\nMount Pleasant               1032\nMusqueam                       14\nOakridge                      163\nRenfrew-Collingwood           966\nRiley Park                    368\nShaughnessy                   148\nSouth Cambie                  177\nStanley Park                   32\nStrathcona                   1145\nSunset                        445\nVictoria-Fraserview           249\nWest End                     1158\nWest Point Grey               201\nName: TYPE, dtype: int64"
     },
     "metadata": {}
    }
   ],
   "execution_count": 194
  },
  {
   "cell_type": "code",
   "source": "# df2 amenities\ndf2 = df2.rename(columns= {'neighbourhood': 'NEIGHBOURHOOD'})\ndf2.head()",
   "metadata": {
    "tags": [],
    "cell_id": "00026-e1282c0f-2b06-4192-a824-b516e43eb37f",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ea9899e8",
    "execution_start": 1628982450988,
    "execution_millis": 11,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 201,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 5,
       "column_count": 41,
       "columns": [
        {
         "name": "Nos. of Suites",
         "dtype": "int64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "15",
          "max": "64",
          "histogram": [
           {
            "bin_start": 15,
            "bin_end": 19.9,
            "count": 2
           },
           {
            "bin_start": 19.9,
            "bin_end": 24.8,
            "count": 1
           },
           {
            "bin_start": 24.8,
            "bin_end": 29.700000000000003,
            "count": 0
           },
           {
            "bin_start": 29.700000000000003,
            "bin_end": 34.6,
            "count": 0
           },
           {
            "bin_start": 34.6,
            "bin_end": 39.5,
            "count": 1
           },
           {
            "bin_start": 39.5,
            "bin_end": 44.400000000000006,
            "count": 0
           },
           {
            "bin_start": 44.400000000000006,
            "bin_end": 49.300000000000004,
            "count": 0
           },
           {
            "bin_start": 49.300000000000004,
            "bin_end": 54.2,
            "count": 0
           },
           {
            "bin_start": 54.2,
            "bin_end": 59.1,
            "count": 0
           },
           {
            "bin_start": 59.1,
            "bin_end": 64,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Building Names",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "ALICE SAUNDERS",
            "count": 1
           },
           {
            "name": "ARBUTUS COURT",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Building Code",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "AS",
            "count": 1
           },
           {
            "name": "AC",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Address",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "2924 Venables St. ",
            "count": 1
           },
           {
            "name": "2085 W. 5th Ave.",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Postal Code",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "V5K 4X8",
            "count": 1
           },
           {
            "name": "V6J 1P8",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "No. of Suite Type",
         "dtype": "object",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "categories": [
           {
            "name": "Bachelor (55), 1BDR (9)",
            "count": 1
           },
           {
            "name": "1BDR (21)",
            "count": 1
           },
           {
            "name": "3 others",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Build Year",
         "dtype": "float64",
         "stats": {
          "unique_count": 5,
          "nan_count": 0,
          "min": "1954.0",
          "max": "1977.0",
          "histogram": [
           {
            "bin_start": 1954,
            "bin_end": 1956.3,
            "count": 1
           },
           {
            "bin_start": 1956.3,
            "bin_end": 1958.6,
            "count": 0
           },
           {
            "bin_start": 1958.6,
            "bin_end": 1960.9,
            "count": 0
           },
           {
            "bin_start": 1960.9,
            "bin_end": 1963.2,
            "count": 1
           },
           {
            "bin_start": 1963.2,
            "bin_end": 1965.5,
            "count": 1
           },
           {
            "bin_start": 1965.5,
            "bin_end": 1967.8,
            "count": 1
           },
           {
            "bin_start": 1967.8,
            "bin_end": 1970.1,
            "count": 0
           },
           {
            "bin_start": 1970.1,
            "bin_end": 1972.4,
            "count": 0
           },
           {
            "bin_start": 1972.4,
            "bin_end": 1974.7,
            "count": 0
           },
           {
            "bin_start": 1974.7,
            "bin_end": 1977,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Resident Profile",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "categories": [
           {
            "name": "Seniors",
            "count": 3
           },
           {
            "name": "Families, People with Disabilities, Seniors",
            "count": 1
           },
           {
            "name": "Couples, People with Disabilities, Seniors",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "BC Housing Registration (required)",
         "dtype": "bool",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Pet Friendly ",
         "dtype": "bool",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Amenity room",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "No",
            "count": 3
           },
           {
            "name": "Yes",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "Lounge",
         "dtype": "object",
         "stats": {
          "unique_count": 0,
          "nan_count": 5,
          "categories": [
           {
            "name": "Missing",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Other",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 4,
          "categories": [
           {
            "name": "Games Room",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 4
           }
          ]
         }
        },
        {
         "name": "Static Chairs",
         "dtype": "float64",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "min": "3.0",
          "max": "9.0",
          "histogram": [
           {
            "bin_start": 3,
            "bin_end": 3.6,
            "count": 1
           },
           {
            "bin_start": 3.6,
            "bin_end": 4.2,
            "count": 0
           },
           {
            "bin_start": 4.2,
            "bin_end": 4.8,
            "count": 0
           },
           {
            "bin_start": 4.8,
            "bin_end": 5.4,
            "count": 0
           },
           {
            "bin_start": 5.4,
            "bin_end": 6,
            "count": 0
           },
           {
            "bin_start": 6,
            "bin_end": 6.6,
            "count": 0
           },
           {
            "bin_start": 6.6,
            "bin_end": 7.2,
            "count": 0
           },
           {
            "bin_start": 7.2,
            "bin_end": 7.8,
            "count": 0
           },
           {
            "bin_start": 7.8,
            "bin_end": 8.399999999999999,
            "count": 0
           },
           {
            "bin_start": 8.399999999999999,
            "bin_end": 9,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Movable Chairs",
         "dtype": "float64",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "min": "6.0",
          "max": "19.0",
          "histogram": [
           {
            "bin_start": 6,
            "bin_end": 7.3,
            "count": 1
           },
           {
            "bin_start": 7.3,
            "bin_end": 8.6,
            "count": 0
           },
           {
            "bin_start": 8.6,
            "bin_end": 9.9,
            "count": 0
           },
           {
            "bin_start": 9.9,
            "bin_end": 11.2,
            "count": 0
           },
           {
            "bin_start": 11.2,
            "bin_end": 12.5,
            "count": 0
           },
           {
            "bin_start": 12.5,
            "bin_end": 13.8,
            "count": 0
           },
           {
            "bin_start": 13.8,
            "bin_end": 15.1,
            "count": 0
           },
           {
            "bin_start": 15.1,
            "bin_end": 16.4,
            "count": 0
           },
           {
            "bin_start": 16.4,
            "bin_end": 17.700000000000003,
            "count": 0
           },
           {
            "bin_start": 17.700000000000003,
            "bin_end": 19,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Division",
         "dtype": "int64",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "min": "1",
          "max": "1",
          "histogram": [
           {
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            "count": 0
           },
           {
            "bin_start": 0.6,
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            "count": 0
           },
           {
            "bin_start": 0.7,
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            "count": 0
           },
           {
            "bin_start": 0.8,
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            "count": 0
           },
           {
            "bin_start": 0.9,
            "bin_end": 1,
            "count": 0
           },
           {
            "bin_start": 1,
            "bin_end": 1.1,
            "count": 5
           },
           {
            "bin_start": 1.1,
            "bin_end": 1.2000000000000002,
            "count": 0
           },
           {
            "bin_start": 1.2000000000000002,
            "bin_end": 1.3,
            "count": 0
           },
           {
            "bin_start": 1.3,
            "bin_end": 1.4,
            "count": 0
           },
           {
            "bin_start": 1.4,
            "bin_end": 1.5,
            "count": 0
           }
          ]
         }
        },
        {
         "name": "Tables",
         "dtype": "float64",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "min": "1.0",
          "max": "10.0",
          "histogram": [
           {
            "bin_start": 1,
            "bin_end": 1.9,
            "count": 1
           },
           {
            "bin_start": 1.9,
            "bin_end": 2.8,
            "count": 0
           },
           {
            "bin_start": 2.8,
            "bin_end": 3.7,
            "count": 0
           },
           {
            "bin_start": 3.7,
            "bin_end": 4.6,
            "count": 0
           },
           {
            "bin_start": 4.6,
            "bin_end": 5.5,
            "count": 0
           },
           {
            "bin_start": 5.5,
            "bin_end": 6.4,
            "count": 0
           },
           {
            "bin_start": 6.4,
            "bin_end": 7.3,
            "count": 0
           },
           {
            "bin_start": 7.3,
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            "count": 0
           },
           {
            "bin_start": 8.2,
            "bin_end": 9.1,
            "count": 0
           },
           {
            "bin_start": 9.1,
            "bin_end": 10,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Appliances ",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "categories": [
           {
            "name": "Sink, Television",
            "count": 1
           },
           {
            "name": "Television",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "DVD Player",
         "dtype": "bool",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Microwave",
         "dtype": "bool",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Stove",
         "dtype": "bool",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Fridge",
         "dtype": "bool",
         "stats": {
          "unique_count": 1,
          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 5
           }
          ]
         }
        },
        {
         "name": "Television",
         "dtype": "bool",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 3
           },
           {
            "name": "True",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "Sink",
         "dtype": "bool",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "False",
            "count": 4
           },
           {
            "name": "True",
            "count": 1
           }
          ]
         }
        },
        {
         "name": "Comments",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "categories": [
           {
            "name": ". Games room has billiards and shuffleboard table.  Stored in Room 119A are 14 stacking chairs, 3x6' tables and 7X3' tables.",
            "count": 1
           },
           {
            "name": "Round table.  ",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Recommended Occupancy",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 3,
          "categories": [
           {
            "name": "20-25",
            "count": 1
           },
           {
            "name": "7-10",
            "count": 1
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Access",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 3,
          "categories": [
           {
            "name": "OPEN",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Picture",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 3,
          "categories": [
           {
            "name": "Y",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Type of Flooring",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 3,
          "categories": [
           {
            "name": "C",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Type of Flooring.1",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 3,
          "categories": [
           {
            "name": "Carpeting",
            "count": 2
           },
           {
            "name": "Missing",
            "count": 3
           }
          ]
         }
        },
        {
         "name": "Bike Storage",
         "dtype": "object",
         "stats": {
          "unique_count": 1,
          "nan_count": 2,
          "categories": [
           {
            "name": "Y",
            "count": 3
           },
           {
            "name": "Missing",
            "count": 2
           }
          ]
         }
        },
        {
         "name": "Location",
         "dtype": "object",
         "stats": {
          "unique_count": 3,
          "nan_count": 2,
          "categories": [
           {
            "name": "119A",
            "count": 1
           },
           {
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         "Nos. of Suites": 64,
         "Building Names": "ALICE SAUNDERS",
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         "Postal Code": "V5K 4X8",
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         "Pet Friendly ": "False",
         "Amenity room": "Yes",
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         "Division": 1,
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         "Appliances ": "Sink, Television",
         "DVD Player": "False",
         "Microwave": "False",
         "Stove": "False",
         "Fridge": "False",
         "Television": "True",
         "Sink": "True",
         "Comments": ". Games room has billiards and shuffleboard table.  Stored in Room 119A are 14 stacking chairs, 3x6…",
         "Recommended Occupancy": "20-25",
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         "Unnamed: 36": "nan",
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         "Unnamed: 36": "nan",
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         "Unnamed: 36": "nan",
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         "Nos. of Suites": 15,
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         "Unnamed: 36": "nan",
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      "text/plain": "   Nos. of Suites  Building Names Building Code             Address  \\\n0              64  ALICE SAUNDERS            AS  2924 Venables St.    \n1              21   ARBUTUS COURT            AC    2085 W. 5th Ave.   \n2              16   BURRARD MANOR            BM      2330 Balsam St   \n3              36    EDWARD BYERS            EB    1451 E. 12th Ave   \n4              15  FLORENCE MANOR            FM     1325 Burnaby St   \n\n  Postal Code        No. of Suite Type  Build Year  \\\n0     V5K 4X8  Bachelor (55), 1BDR (9)      1977.0   \n1     V6J 1P8                1BDR (21)      1964.0   \n2     V6K 3M8  Bachelor (15), 1BDR (1)      1967.0   \n3     V5N 2A1  Bachelor (35), 1BDR (1)      1962.0   \n4     V6E 1R2  Bachelor (4), 1BDR (11)      1954.0   \n\n                              Resident Profile  \\\n0                                      Seniors   \n1  Families, People with Disabilities, Seniors   \n2                                      Seniors   \n3                                      Seniors   \n4   Couples, People with Disabilities, Seniors   \n\n   BC Housing Registration (required)  Pet Friendly   ...  \\\n0                               False          False  ...   \n1                               False          False  ...   \n2                               False          False  ...   \n3                               False          False  ...   \n4                               False          False  ...   \n\n                 Location Rent - Lowest Range Rent - Highest Range  \\\n0                    119A               475.0               779.00   \n1                 Parkade               475.0             1,553.00   \n2                     NaN               475.0             1,084.00   \n3  Room off rear entrance                 NaN                  NaN   \n4                     NaN               320.0             1,741.00   \n\n   Average Rent  Median Rent  Unnamed: 36  Unnamed: 37 Unnamed: 38  \\\n0        699.50       772.00          NaN          NaN         NaN   \n1        886.10       803.00          NaN          NaN         NaN   \n2        747.69       787.50          NaN          NaN         NaN   \n3           NaN          NaN          NaN          NaN         NaN   \n4        835.93       803.50          NaN          NaN         NaN   \n\n   Unnamed: 39       NEIGHBOURHOOD  \n0          NaN    Hastings-Sunrise  \n1          NaN           Kitsilano  \n2          NaN           Kitsilano  \n3          NaN  Grandview-Woodland  \n4          NaN            West End  \n\n[5 rows x 41 columns]",
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   ],
   "execution_count": 201
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  {
   "cell_type": "code",
   "source": "df3 = df2.merge(yr_2021_neigh_ct, on='NEIGHBOURHOOD')\ndf3.head(3)",
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      "text/plain": "   Nos. of Suites  Building Names Building Code             Address  \\\n0              64  ALICE SAUNDERS            AS  2924 Venables St.    \n1              21   ARBUTUS COURT            AC    2085 W. 5th Ave.   \n\n  Postal Code        No. of Suite Type  Build Year  \\\n0     V5K 4X8  Bachelor (55), 1BDR (9)      1977.0   \n1     V6J 1P8                1BDR (21)      1964.0   \n\n                              Resident Profile  \\\n0                                      Seniors   \n1  Families, People with Disabilities, Seniors   \n\n   BC Housing Registration (required)  Pet Friendly   ... Rent - Lowest Range  \\\n0                               False          False  ...               475.0   \n1                               False          False  ...               475.0   \n\n  Rent - Highest Range Average Rent  Median Rent  Unnamed: 36  Unnamed: 37  \\\n0               779.00        699.5       772.00          NaN          NaN   \n1             1,553.00        886.1       803.00          NaN          NaN   \n\n   Unnamed: 38 Unnamed: 39     NEIGHBOURHOOD  2021_crime_freq  \n0          NaN         NaN  Hastings-Sunrise              603  \n1          NaN         NaN         Kitsilano              711  \n\n[2 rows x 42 columns]",
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   "execution_count": 207
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  {
   "cell_type": "code",
   "source": "df3['2021_crime_freq'].sum()",
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  {
   "cell_type": "code",
   "source": "df3['2021_crime_rate'] = (df3['2021_crime_freq'] / df3['2021_crime_freq'].sum()) *100\ndf3['2021_crime_rate'] = round(df3['2021_crime_rate'], 2)\ndf3.head()",
   "metadata": {
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         "Type of Flooring.1": "nan",
         "Bike Storage": "nan",
         "Location": "nan",
         "Rent - Lowest Range": 475,
         "Rent - Highest Range": "1,084.00",
         "Average Rent": 747.69,
         "Median Rent": "787.50",
         "Unnamed: 36": "nan",
         "Unnamed: 37": "nan",
         "Unnamed: 38": "nan",
         "Unnamed: 39": "nan",
         "NEIGHBOURHOOD": "Kitsilano",
         "2021_crime_freq": 711,
         "2021_crime_rate": 3.3,
         "_deepnote_index_column": 2
        },
        {
         "Nos. of Suites": 31,
         "Building Names": "MORELAND KENNEDY",
         "Building Code": "MK",
         "Address": "2495 W. 3rd Ave.",
         "Postal Code": "V6K 1L9",
         "No. of Suite Type": "Bachelor (20), 1BDR (11)",
         "Build Year": 1974,
         "Resident Profile": "Seniors",
         "BC Housing Registration (required)": "False",
         "Pet Friendly ": "False",
         "Amenity room": "Yes",
         "Lounge": "nan",
         "Other": "nan",
         "Static Chairs": 6,
         "Movable Chairs": 8,
         "Division": 1,
         "Tables": 2,
         "Appliances ": "Sink / Television",
         "DVD Player": "False",
         "Microwave": "False",
         "Stove": "False",
         "Fridge": "False",
         "Television": "True",
         "Sink": "True",
         "Comments": "1x6' table, 1 dining room table",
         "Recommended Occupancy": "8-10",
         "Access": "OPEN",
         "Picture": "Y",
         "Type of Flooring": "C",
         "Type of Flooring.1": "Carpeting",
         "Bike Storage": "Y",
         "Location": "Rear Parking Storage Room",
         "Rent - Lowest Range": 500,
         "Rent - Highest Range": "1,442.00",
         "Average Rent": 860.26,
         "Median Rent": "812.00",
         "Unnamed: 36": "nan",
         "Unnamed: 37": "nan",
         "Unnamed: 38": "nan",
         "Unnamed: 39": "nan",
         "NEIGHBOURHOOD": "Kitsilano",
         "2021_crime_freq": 711,
         "2021_crime_rate": 3.3,
         "_deepnote_index_column": 3
        },
        {
         "Nos. of Suites": 36,
         "Building Names": "EDWARD BYERS",
         "Building Code": "EB",
         "Address": "1451 E. 12th Ave",
         "Postal Code": "V5N 2A1",
         "No. of Suite Type": "Bachelor (35), 1BDR (1)",
         "Build Year": 1962,
         "Resident Profile": "Seniors",
         "BC Housing Registration (required)": "False",
         "Pet Friendly ": "False",
         "Amenity room": "Yes",
         "Lounge": "nan",
         "Other": "nan",
         "Static Chairs": 3,
         "Movable Chairs": 6,
         "Division": 1,
         "Tables": 1,
         "Appliances ": "Television",
         "DVD Player": "False",
         "Microwave": "False",
         "Stove": "False",
         "Fridge": "False",
         "Television": "True",
         "Sink": "False",
         "Comments": "Round table.  ",
         "Recommended Occupancy": "7-10",
         "Access": "OPEN",
         "Picture": "Y",
         "Type of Flooring": "C",
         "Type of Flooring.1": "Carpeting",
         "Bike Storage": "Y",
         "Location": "Room off rear entrance",
         "Rent - Lowest Range": "nan",
         "Rent - Highest Range": "nan",
         "Average Rent": "nan",
         "Median Rent": "nan",
         "Unnamed: 36": "nan",
         "Unnamed: 37": "nan",
         "Unnamed: 38": "nan",
         "Unnamed: 39": "nan",
         "NEIGHBOURHOOD": "Grandview-Woodland",
         "2021_crime_freq": 726,
         "2021_crime_rate": 3.37,
         "_deepnote_index_column": 4
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "   Nos. of Suites    Building Names Building Code             Address  \\\n0              64    ALICE SAUNDERS            AS  2924 Venables St.    \n1              21     ARBUTUS COURT            AC    2085 W. 5th Ave.   \n2              16     BURRARD MANOR            BM      2330 Balsam St   \n3              31  MORELAND KENNEDY            MK    2495 W. 3rd Ave.   \n4              36      EDWARD BYERS            EB    1451 E. 12th Ave   \n\n  Postal Code         No. of Suite Type  Build Year  \\\n0     V5K 4X8   Bachelor (55), 1BDR (9)      1977.0   \n1     V6J 1P8                 1BDR (21)      1964.0   \n2     V6K 3M8   Bachelor (15), 1BDR (1)      1967.0   \n3     V6K 1L9  Bachelor (20), 1BDR (11)      1974.0   \n4     V5N 2A1   Bachelor (35), 1BDR (1)      1962.0   \n\n                              Resident Profile  \\\n0                                      Seniors   \n1  Families, People with Disabilities, Seniors   \n2                                      Seniors   \n3                                      Seniors   \n4                                      Seniors   \n\n   BC Housing Registration (required)  Pet Friendly   ...  \\\n0                               False          False  ...   \n1                               False          False  ...   \n2                               False          False  ...   \n3                               False          False  ...   \n4                               False          False  ...   \n\n  Rent - Highest Range Average Rent Median Rent  Unnamed: 36  Unnamed: 37  \\\n0               779.00       699.50      772.00          NaN          NaN   \n1             1,553.00       886.10      803.00          NaN          NaN   \n2             1,084.00       747.69      787.50          NaN          NaN   \n3             1,442.00       860.26      812.00          NaN          NaN   \n4                  NaN          NaN         NaN          NaN          NaN   \n\n   Unnamed: 38  Unnamed: 39       NEIGHBOURHOOD  2021_crime_freq  \\\n0          NaN          NaN    Hastings-Sunrise              603   \n1          NaN          NaN           Kitsilano              711   \n2          NaN          NaN           Kitsilano              711   \n3          NaN          NaN           Kitsilano              711   \n4          NaN          NaN  Grandview-Woodland              726   \n\n   2021_crime_rate  \n0             2.80  \n1             3.30  \n2             3.30  \n3             3.30  \n4             3.37  \n\n[5 rows x 43 columns]",
      "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>Nos. of Suites</th>\n      <th>Building Names</th>\n      <th>Building Code</th>\n      <th>Address</th>\n      <th>Postal Code</th>\n      <th>No. of Suite Type</th>\n      <th>Build Year</th>\n      <th>Resident Profile</th>\n      <th>BC Housing Registration (required)</th>\n      <th>Pet Friendly</th>\n      <th>...</th>\n      <th>Rent - Highest Range</th>\n      <th>Average Rent</th>\n      <th>Median Rent</th>\n      <th>Unnamed: 36</th>\n      <th>Unnamed: 37</th>\n      <th>Unnamed: 38</th>\n      <th>Unnamed: 39</th>\n      <th>NEIGHBOURHOOD</th>\n      <th>2021_crime_freq</th>\n      <th>2021_crime_rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>64</td>\n      <td>ALICE SAUNDERS</td>\n      <td>AS</td>\n      <td>2924 Venables St.</td>\n      <td>V5K 4X8</td>\n      <td>Bachelor (55), 1BDR (9)</td>\n      <td>1977.0</td>\n      <td>Seniors</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>779.00</td>\n      <td>699.50</td>\n      <td>772.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Hastings-Sunrise</td>\n      <td>603</td>\n      <td>2.80</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>21</td>\n      <td>ARBUTUS COURT</td>\n      <td>AC</td>\n      <td>2085 W. 5th Ave.</td>\n      <td>V6J 1P8</td>\n      <td>1BDR (21)</td>\n      <td>1964.0</td>\n      <td>Families, People with Disabilities, Seniors</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>1,553.00</td>\n      <td>886.10</td>\n      <td>803.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Kitsilano</td>\n      <td>711</td>\n      <td>3.30</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>16</td>\n      <td>BURRARD MANOR</td>\n      <td>BM</td>\n      <td>2330 Balsam St</td>\n      <td>V6K 3M8</td>\n      <td>Bachelor (15), 1BDR (1)</td>\n      <td>1967.0</td>\n      <td>Seniors</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>1,084.00</td>\n      <td>747.69</td>\n      <td>787.50</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Kitsilano</td>\n      <td>711</td>\n      <td>3.30</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31</td>\n      <td>MORELAND KENNEDY</td>\n      <td>MK</td>\n      <td>2495 W. 3rd Ave.</td>\n      <td>V6K 1L9</td>\n      <td>Bachelor (20), 1BDR (11)</td>\n      <td>1974.0</td>\n      <td>Seniors</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>1,442.00</td>\n      <td>860.26</td>\n      <td>812.00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Kitsilano</td>\n      <td>711</td>\n      <td>3.30</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>36</td>\n      <td>EDWARD BYERS</td>\n      <td>EB</td>\n      <td>1451 E. 12th Ave</td>\n      <td>V5N 2A1</td>\n      <td>Bachelor (35), 1BDR (1)</td>\n      <td>1962.0</td>\n      <td>Seniors</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</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      <td>NaN</td>\n      <td>Grandview-Woodland</td>\n      <td>726</td>\n      <td>3.37</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 43 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": 210
  },
  {
   "cell_type": "code",
   "source": "bldg_neigh_crime = df3[['Building Names', '2021_crime_rate']]\nbldg_neigh_crime.set_index('Building Names', inplace=True)\nbldg_neigh_crime.head()",
   "metadata": {
    "tags": [],
    "cell_id": "00031-e0eb15c3-577a-402e-8831-8c3a223754cb",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "80df1ffe",
    "execution_start": 1628984478427,
    "execution_millis": 8,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 253,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 5,
       "column_count": 1,
       "columns": [
        {
         "name": "2021_crime_rate",
         "dtype": "float64",
         "stats": {
          "unique_count": 3,
          "nan_count": 0,
          "min": "2.8",
          "max": "3.37",
          "histogram": [
           {
            "bin_start": 2.8,
            "bin_end": 2.8569999999999998,
            "count": 1
           },
           {
            "bin_start": 2.8569999999999998,
            "bin_end": 2.9139999999999997,
            "count": 0
           },
           {
            "bin_start": 2.9139999999999997,
            "bin_end": 2.971,
            "count": 0
           },
           {
            "bin_start": 2.971,
            "bin_end": 3.028,
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           },
           {
            "bin_start": 3.028,
            "bin_end": 3.085,
            "count": 0
           },
           {
            "bin_start": 3.085,
            "bin_end": 3.142,
            "count": 0
           },
           {
            "bin_start": 3.142,
            "bin_end": 3.199,
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           },
           {
            "bin_start": 3.199,
            "bin_end": 3.2560000000000002,
            "count": 0
           },
           {
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            "bin_end": 3.313,
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           },
           {
            "bin_start": 3.313,
            "bin_end": 3.37,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "object"
        }
       ],
       "rows_top": [
        {
         "2021_crime_rate": 2.8,
         "_deepnote_index_column": "ALICE SAUNDERS"
        },
        {
         "2021_crime_rate": 3.3,
         "_deepnote_index_column": "ARBUTUS COURT"
        },
        {
         "2021_crime_rate": 3.3,
         "_deepnote_index_column": "BURRARD MANOR"
        },
        {
         "2021_crime_rate": 3.3,
         "_deepnote_index_column": "MORELAND KENNEDY"
        },
        {
         "2021_crime_rate": 3.37,
         "_deepnote_index_column": "EDWARD BYERS"
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "                  2021_crime_rate\nBuilding Names                   \nALICE SAUNDERS               2.80\nARBUTUS COURT                3.30\nBURRARD MANOR                3.30\nMORELAND KENNEDY             3.30\nEDWARD BYERS                 3.37",
      "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>2021_crime_rate</th>\n    </tr>\n    <tr>\n      <th>Building Names</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ALICE SAUNDERS</th>\n      <td>2.80</td>\n    </tr>\n    <tr>\n      <th>ARBUTUS COURT</th>\n      <td>3.30</td>\n    </tr>\n    <tr>\n      <th>BURRARD MANOR</th>\n      <td>3.30</td>\n    </tr>\n    <tr>\n      <th>MORELAND KENNEDY</th>\n      <td>3.30</td>\n    </tr>\n    <tr>\n      <th>EDWARD BYERS</th>\n      <td>3.37</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": 253
  },
  {
   "cell_type": "code",
   "source": "plt.figure(figsize=(10,5))\nbldg_neigh_crime.plot(kind='bar');\nplt.ylabel('% crime rate')\nplt.title('Crime rates per Building, 2021');\n# high crime rates in Bridgeview Place neighbourhood\n# lowest in Gordon Fahrni neighbourhood",
   "metadata": {
    "tags": [],
    "cell_id": "00034-83359328-dde4-4f09-940e-87d4070a0422",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5dae27a4",
    "execution_start": 1628984591461,
    "execution_millis": 458,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x360 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 400,
       "height": 416
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": 258
  },
  {
   "cell_type": "code",
   "source": "",
   "metadata": {
    "tags": [],
    "cell_id": "00035-58be1ba3-8187-44c5-ac48-15c220eb2f5a",
    "deepnote_cell_type": "code"
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=2ed0a2ac-4145-4dd5-b1d7-ad53322853db' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>",
   "metadata": {
    "tags": [],
    "created_in_deepnote_cell": true,
    "deepnote_cell_type": "markdown"
   }
  }
 ],
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "orig_nbformat": 2,
  "deepnote": {
   "is_reactive": false
  },
  "deepnote_notebook_id": "61c28ba1-6a16-4a87-8456-c185990444e9",
  "deepnote_execution_queue": []
 }
}