exploratory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d56f523",
   "metadata": {},
   "source": [
    "# Exploring the Building Elevation and Subgrade (BES) Dataset in New York City in Python\n",
    "Mark Bauer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55b4ccdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import urllib\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "plt.rcParams['savefig.facecolor'] = 'white'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c14469e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.13\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "numpy     : 1.23.1\n",
      "pandas    : 1.4.3\n",
      "matplotlib: 3.5.2\n",
      "seaborn   : 0.11.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Printing verions of Python modules and packages with **watermark** - the IPython magic extension.\n",
    "%reload_ext watermark\n",
    "%watermark -v -p numpy,pandas,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6dd7631",
   "metadata": {},
   "source": [
    "![cover-photo](images/dataset-cover-photo.png)\n",
    "Screenshot of dataset on NYC Open Data.\n",
    "\n",
    "Link to dataset website: https://data.cityofnewyork.us/City-Government/Building-Elevation-and-Subgrade-BES-/bsin-59hv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "124c952a",
   "metadata": {},
   "source": [
    "# Download Data Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a78e78f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building Elevation and Subgrade Dataset Guidance.pdf\r\n",
      "Building_Elevation_and_Subgrade-Data_Dictionary.xlsx\r\n",
      "README.md\r\n",
      "exploratory.ipynb\r\n",
      "\u001b[34mfigures\u001b[m\u001b[m/\r\n",
      "\u001b[34mimages\u001b[m\u001b[m/\r\n"
     ]
    }
   ],
   "source": [
    "# url of data dictionary\n",
    "url = 'https://data.cityofnewyork.us/api/views/bsin-59hv/files/03455783-2880-43f9-9e53-718ecb353c47\\\n",
    "?download=true&filename=Building_Elevation_and_Subgrade-Data_Dictionary.xlsx'\n",
    "\n",
    "file_name = 'Building_Elevation_and_Subgrade-Data_Dictionary.xlsx'\n",
    "urllib.request.urlretrieve(url, file_name)\n",
    "\n",
    "# sanity check to confirm download\n",
    "%ls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3977f990",
   "metadata": {},
   "source": [
    "# Download Dataset Guidance PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b9ae674",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building Elevation and Subgrade Dataset Guidance.pdf\r\n",
      "Building_Elevation_and_Subgrade-Data_Dictionary.xlsx\r\n",
      "README.md\r\n",
      "exploratory.ipynb\r\n",
      "\u001b[34mfigures\u001b[m\u001b[m/\r\n",
      "\u001b[34mimages\u001b[m\u001b[m/\r\n"
     ]
    }
   ],
   "source": [
    "# url of dataset guidance\n",
    "url = 'https://data.cityofnewyork.us/api/views/bsin-59hv/files/ae1b369c-6ce1-4805-9ad0-aca9f62f3182\\\n",
    "?download=true&filename=Building%20Elevation%20and%20Subgrade%20Dataset%20Guidance.pdf'\n",
    "\n",
    "file_name = 'Building Elevation and Subgrade Dataset Guidance.pdf'\n",
    "urllib.request.urlretrieve(url, file_name)\n",
    "\n",
    "# sanity check to confirm download\n",
    "%ls"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ac22ab",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "Link to dataset website: https://data.cityofnewyork.us/City-Government/Building-Elevation-and-Subgrade-BES-/bsin-59hv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b989c0f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows: 861,876\n",
      "columns: 26\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>bin</th>\n",
       "      <th>bbl</th>\n",
       "      <th>borough</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>address</th>\n",
       "      <th>z_grade</th>\n",
       "      <th>z_floor</th>\n",
       "      <th>subgrade</th>\n",
       "      <th>...</th>\n",
       "      <th>longitude</th>\n",
       "      <th>pluto_bbl</th>\n",
       "      <th>Council</th>\n",
       "      <th>BoroCD</th>\n",
       "      <th>CTLabel</th>\n",
       "      <th>BoroCT2020</th>\n",
       "      <th>NTA2020</th>\n",
       "      <th>NTAName</th>\n",
       "      <th>CDTA2020</th>\n",
       "      <th>CDTAName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (-74.22274561187417 40.52134422844183)</td>\n",
       "      <td>5128004</td>\n",
       "      <td>5075340353</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>353</td>\n",
       "      <td>78 SAVO LOOP</td>\n",
       "      <td>29.428</td>\n",
       "      <td>32.332</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>-74.222746</td>\n",
       "      <td>5075340353</td>\n",
       "      <td>51</td>\n",
       "      <td>503</td>\n",
       "      <td>226.01</td>\n",
       "      <td>5022601</td>\n",
       "      <td>SI0304</td>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Woodrow</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (-74.24179250549321 40.52875316810818)</td>\n",
       "      <td>5155392</td>\n",
       "      <td>5075960125</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7596.0</td>\n",
       "      <td>125</td>\n",
       "      <td>72 CHART LOOP</td>\n",
       "      <td>25.366</td>\n",
       "      <td>26.703</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>-74.241793</td>\n",
       "      <td>5075960125</td>\n",
       "      <td>51</td>\n",
       "      <td>503</td>\n",
       "      <td>226.02</td>\n",
       "      <td>5022602</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (-74.24109012652094 40.528883822921635)</td>\n",
       "      <td>5148808</td>\n",
       "      <td>5075960131</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7596.0</td>\n",
       "      <td>131</td>\n",
       "      <td>40 TIDES LANE</td>\n",
       "      <td>36.172</td>\n",
       "      <td>38.902</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>-74.241090</td>\n",
       "      <td>5075960131</td>\n",
       "      <td>51</td>\n",
       "      <td>503</td>\n",
       "      <td>226.02</td>\n",
       "      <td>5022602</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (-74.24649640323727 40.507045708438184)</td>\n",
       "      <td>5088274</td>\n",
       "      <td>5079150042</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7915.0</td>\n",
       "      <td>42</td>\n",
       "      <td>328 MAIN STREET</td>\n",
       "      <td>69.897</td>\n",
       "      <td>72.459</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>-74.246496</td>\n",
       "      <td>5079150042</td>\n",
       "      <td>51</td>\n",
       "      <td>503</td>\n",
       "      <td>244.01</td>\n",
       "      <td>5024401</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (-74.24198032271697 40.51005666426356)</td>\n",
       "      <td>5087850</td>\n",
       "      <td>5078680123</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7868.0</td>\n",
       "      <td>123</td>\n",
       "      <td>309 SLEIGHT AVENUE</td>\n",
       "      <td>74.907</td>\n",
       "      <td>79.122</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>-74.241980</td>\n",
       "      <td>5078680123</td>\n",
       "      <td>51</td>\n",
       "      <td>503</td>\n",
       "      <td>244.01</td>\n",
       "      <td>5024401</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        the_geom      bin         bbl  \\\n",
       "0   POINT (-74.22274561187417 40.52134422844183)  5128004  5075340353   \n",
       "1   POINT (-74.24179250549321 40.52875316810818)  5155392  5075960125   \n",
       "2  POINT (-74.24109012652094 40.528883822921635)  5148808  5075960131   \n",
       "3  POINT (-74.24649640323727 40.507045708438184)  5088274  5079150042   \n",
       "4   POINT (-74.24198032271697 40.51005666426356)  5087850  5078680123   \n",
       "\n",
       "   borough   block  lot             address  z_grade  z_floor subgrade  ...  \\\n",
       "0      5.0  7534.0  353        78 SAVO LOOP   29.428   32.332        N  ...   \n",
       "1      5.0  7596.0  125       72 CHART LOOP   25.366   26.703        N  ...   \n",
       "2      5.0  7596.0  131       40 TIDES LANE   36.172   38.902        N  ...   \n",
       "3      5.0  7915.0   42     328 MAIN STREET   69.897   72.459        N  ...   \n",
       "4      5.0  7868.0  123  309 SLEIGHT AVENUE   74.907   79.122        N  ...   \n",
       "\n",
       "   longitude   pluto_bbl Council  BoroCD  CTLabel  BoroCT2020  NTA2020  \\\n",
       "0 -74.222746  5075340353      51     503   226.01     5022601   SI0304   \n",
       "1 -74.241793  5075960125      51     503   226.02     5022602   SI0305   \n",
       "2 -74.241090  5075960131      51     503   226.02     5022602   SI0305   \n",
       "3 -74.246496  5079150042      51     503   244.01     5024401   SI0305   \n",
       "4 -74.241980  5078680123      51     503   244.01     5024401   SI0305   \n",
       "\n",
       "                                  NTAName  CDTA2020  \\\n",
       "0  Annadale-Huguenot-Prince's Bay-Woodrow      SI03   \n",
       "1                  Tottenville-Charleston      SI03   \n",
       "2                  Tottenville-Charleston      SI03   \n",
       "3                  Tottenville-Charleston      SI03   \n",
       "4                  Tottenville-Charleston      SI03   \n",
       "\n",
       "                                CDTAName  \n",
       "0  SI03 South Shore (CD 3 Approximation)  \n",
       "1  SI03 South Shore (CD 3 Approximation)  \n",
       "2  SI03 South Shore (CD 3 Approximation)  \n",
       "3  SI03 South Shore (CD 3 Approximation)  \n",
       "4  SI03 South Shore (CD 3 Approximation)  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data from NYC Open Data\n",
    "url = 'https://data.cityofnewyork.us/api/views/bsin-59hv/rows.csv?accessType=DOWNLOAD'\n",
    "df = pd.read_csv(url)\n",
    "\n",
    "rows, cols = df.shape\n",
    "print('rows: {:,}\\ncolumns: {}'.format(rows, cols))\n",
    "\n",
    "# preview data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b3f5392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin field is unique: True\n"
     ]
    }
   ],
   "source": [
    "# is building identification number unique\n",
    "is_unique = df['bin'].is_unique\n",
    "print('bin field is unique: {}'.format(is_unique))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ceae2875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 861876 entries, 0 to 861875\n",
      "Data columns (total 26 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   the_geom    861876 non-null  object \n",
      " 1   bin         861876 non-null  int64  \n",
      " 2   bbl         861876 non-null  int64  \n",
      " 3   borough     861875 non-null  float64\n",
      " 4   block       861875 non-null  float64\n",
      " 5   lot         861876 non-null  int64  \n",
      " 6   address     844875 non-null  object \n",
      " 7   z_grade     861876 non-null  float64\n",
      " 8   z_floor     861876 non-null  float64\n",
      " 9   subgrade    861876 non-null  object \n",
      " 10  notes1      861876 non-null  object \n",
      " 11  notes2      202458 non-null  object \n",
      " 12  notes3      472003 non-null  object \n",
      " 13  x           861876 non-null  float64\n",
      " 14  y           861876 non-null  float64\n",
      " 15  latitude    861876 non-null  float64\n",
      " 16  longitude   861876 non-null  float64\n",
      " 17  pluto_bbl   861876 non-null  int64  \n",
      " 18  Council     861876 non-null  int64  \n",
      " 19  BoroCD      861876 non-null  int64  \n",
      " 20  CTLabel     861876 non-null  float64\n",
      " 21  BoroCT2020  861876 non-null  int64  \n",
      " 22  NTA2020     861876 non-null  object \n",
      " 23  NTAName     861876 non-null  object \n",
      " 24  CDTA2020    861876 non-null  object \n",
      " 25  CDTAName    861876 non-null  object \n",
      "dtypes: float64(9), int64(7), object(10)\n",
      "memory usage: 171.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# preview datatypes and non-null counts\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b08fbca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "notes2        659418\n",
       "notes3        389873\n",
       "address        17001\n",
       "borough            1\n",
       "block              1\n",
       "the_geom           0\n",
       "longitude          0\n",
       "CDTA2020           0\n",
       "NTAName            0\n",
       "NTA2020            0\n",
       "BoroCT2020         0\n",
       "CTLabel            0\n",
       "BoroCD             0\n",
       "Council            0\n",
       "pluto_bbl          0\n",
       "x                  0\n",
       "latitude           0\n",
       "y                  0\n",
       "bin                0\n",
       "notes1             0\n",
       "subgrade           0\n",
       "z_floor            0\n",
       "z_grade            0\n",
       "lot                0\n",
       "bbl                0\n",
       "CDTAName           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview null counts per column\n",
    "(df\n",
    " .isnull()\n",
    " .sum()\n",
    " .sort_values(ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc695c9",
   "metadata": {},
   "source": [
    "Note: there is one building with null borough and block value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6dcaf1cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin</th>\n",
       "      <th>z_grade</th>\n",
       "      <th>z_floor</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.618760e+05</td>\n",
       "      <td>861876.000000</td>\n",
       "      <td>861876.000000</td>\n",
       "      <td>8.618760e+05</td>\n",
       "      <td>861876.000000</td>\n",
       "      <td>861876.000000</td>\n",
       "      <td>861876.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.610723e+06</td>\n",
       "      <td>53.915012</td>\n",
       "      <td>58.113704</td>\n",
       "      <td>1.006345e+06</td>\n",
       "      <td>191685.085757</td>\n",
       "      <td>40.692719</td>\n",
       "      <td>-73.920238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.053307e+06</td>\n",
       "      <td>42.192895</td>\n",
       "      <td>42.560673</td>\n",
       "      <td>3.288964e+04</td>\n",
       "      <td>30587.626141</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.118567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000003e+06</td>\n",
       "      <td>-13.096000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.132653e+05</td>\n",
       "      <td>120977.993002</td>\n",
       "      <td>40.498478</td>\n",
       "      <td>-74.255266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.089156e+06</td>\n",
       "      <td>23.304000</td>\n",
       "      <td>27.438000</td>\n",
       "      <td>9.893564e+05</td>\n",
       "      <td>168135.316338</td>\n",
       "      <td>40.628135</td>\n",
       "      <td>-73.981596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.026428e+06</td>\n",
       "      <td>44.328000</td>\n",
       "      <td>48.370000</td>\n",
       "      <td>1.009009e+06</td>\n",
       "      <td>189533.841782</td>\n",
       "      <td>40.686807</td>\n",
       "      <td>-73.910686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.259005e+06</td>\n",
       "      <td>72.709250</td>\n",
       "      <td>77.063000</td>\n",
       "      <td>1.030179e+06</td>\n",
       "      <td>211378.936954</td>\n",
       "      <td>40.746741</td>\n",
       "      <td>-73.834218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.799523e+06</td>\n",
       "      <td>402.839000</td>\n",
       "      <td>404.397000</td>\n",
       "      <td>1.067283e+06</td>\n",
       "      <td>272622.861956</td>\n",
       "      <td>40.914923</td>\n",
       "      <td>-73.700369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                bin        z_grade        z_floor             x  \\\n",
       "count  8.618760e+05  861876.000000  861876.000000  8.618760e+05   \n",
       "mean   3.610723e+06      53.915012      58.113704  1.006345e+06   \n",
       "std    1.053307e+06      42.192895      42.560673  3.288964e+04   \n",
       "min    1.000003e+06     -13.096000       0.000000  9.132653e+05   \n",
       "25%    3.089156e+06      23.304000      27.438000  9.893564e+05   \n",
       "50%    4.026428e+06      44.328000      48.370000  1.009009e+06   \n",
       "75%    4.259005e+06      72.709250      77.063000  1.030179e+06   \n",
       "max    5.799523e+06     402.839000     404.397000  1.067283e+06   \n",
       "\n",
       "                   y       latitude      longitude  \n",
       "count  861876.000000  861876.000000  861876.000000  \n",
       "mean   191685.085757      40.692719     -73.920238  \n",
       "std     30587.626141       0.083946       0.118567  \n",
       "min    120977.993002      40.498478     -74.255266  \n",
       "25%    168135.316338      40.628135     -73.981596  \n",
       "50%    189533.841782      40.686807     -73.910686  \n",
       "75%    211378.936954      40.746741     -73.834218  \n",
       "max    272622.861956      40.914923     -73.700369  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics of selected numeric columns\n",
    "cols = [\n",
    "    'bin',\n",
    "    'z_grade', 'z_floor', \n",
    "    'x', 'y', 'latitude', 'longitude'\n",
    "]\n",
    "\n",
    "df.loc[:, cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3c03ef78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>the_geom</th>\n",
       "      <th>address</th>\n",
       "      <th>subgrade</th>\n",
       "      <th>notes1</th>\n",
       "      <th>notes2</th>\n",
       "      <th>notes3</th>\n",
       "      <th>NTA2020</th>\n",
       "      <th>NTAName</th>\n",
       "      <th>CDTA2020</th>\n",
       "      <th>CDTAName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>861876</td>\n",
       "      <td>844875</td>\n",
       "      <td>861876</td>\n",
       "      <td>861876</td>\n",
       "      <td>202458</td>\n",
       "      <td>472003</td>\n",
       "      <td>861876</td>\n",
       "      <td>861876</td>\n",
       "      <td>861876</td>\n",
       "      <td>861876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>861876</td>\n",
       "      <td>796756</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>POINT (-74.22274561187417 40.52134422844183)</td>\n",
       "      <td>FINGERBOARD ROAD</td>\n",
       "      <td>Y</td>\n",
       "      <td>Property was Successfully Measured</td>\n",
       "      <td>Commercial, Industrial or other Non-Residentia...</td>\n",
       "      <td>Visible Door or Window in subgrade space</td>\n",
       "      <td>SI0302</td>\n",
       "      <td>Great Kills-Eltingville</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>423</td>\n",
       "      <td>472003</td>\n",
       "      <td>646782</td>\n",
       "      <td>112354</td>\n",
       "      <td>310268</td>\n",
       "      <td>16194</td>\n",
       "      <td>16194</td>\n",
       "      <td>48095</td>\n",
       "      <td>48095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            the_geom           address  \\\n",
       "count                                         861876            844875   \n",
       "unique                                        861876            796756   \n",
       "top     POINT (-74.22274561187417 40.52134422844183)  FINGERBOARD ROAD   \n",
       "freq                                               1               423   \n",
       "\n",
       "       subgrade                              notes1  \\\n",
       "count    861876                              861876   \n",
       "unique        2                                  12   \n",
       "top           Y  Property was Successfully Measured   \n",
       "freq     472003                              646782   \n",
       "\n",
       "                                                   notes2  \\\n",
       "count                                              202458   \n",
       "unique                                                  9   \n",
       "top     Commercial, Industrial or other Non-Residentia...   \n",
       "freq                                               112354   \n",
       "\n",
       "                                          notes3 NTA2020  \\\n",
       "count                                     472003  861876   \n",
       "unique                                         2     256   \n",
       "top     Visible Door or Window in subgrade space  SI0302   \n",
       "freq                                      310268   16194   \n",
       "\n",
       "                        NTAName CDTA2020  \\\n",
       "count                    861876   861876   \n",
       "unique                      256       71   \n",
       "top     Great Kills-Eltingville     SI03   \n",
       "freq                      16194    48095   \n",
       "\n",
       "                                     CDTAName  \n",
       "count                                  861876  \n",
       "unique                                     72  \n",
       "top     SI03 South Shore (CD 3 Approximation)  \n",
       "freq                                    48095  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics string columns\n",
    "df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26fa441a",
   "metadata": {},
   "source": [
    "# Adding Borough Column\n",
    "It's useful to have a full Neighborhood Tabulation Areas (NTA) and Borough column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7ff306f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (262, 12)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>borocode</th>\n",
       "      <th>boroname</th>\n",
       "      <th>countyfips</th>\n",
       "      <th>nta2020</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>ntaabbrev</th>\n",
       "      <th>ntatype</th>\n",
       "      <th>cdta2020</th>\n",
       "      <th>cdtaname</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN6491</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>CntrlPk</td>\n",
       "      <td>9</td>\n",
       "      <td>MN64</td>\n",
       "      <td>MN64 Central Park (JIA 64 Equivalent)</td>\n",
       "      <td>32721.097429</td>\n",
       "      <td>3.831238e+07</td>\n",
       "      <td>POLYGON ((-73.95246 40.79824, -73.95237 40.798...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN0401</td>\n",
       "      <td>Chelsea-Hudson Yards</td>\n",
       "      <td>Chls_HdsYd</td>\n",
       "      <td>0</td>\n",
       "      <td>MN04</td>\n",
       "      <td>MN04 Chelsea-Hell's Kitchen (CD 4 Approximation)</td>\n",
       "      <td>40095.235570</td>\n",
       "      <td>2.967175e+07</td>\n",
       "      <td>POLYGON ((-74.00440 40.76267, -74.00220 40.761...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN0301</td>\n",
       "      <td>Chinatown-Two Bridges</td>\n",
       "      <td>Chntwn</td>\n",
       "      <td>0</td>\n",
       "      <td>MN03</td>\n",
       "      <td>MN03 Lower East Side-Chinatown (CD 3 Equivalent)</td>\n",
       "      <td>16464.371337</td>\n",
       "      <td>1.154263e+07</td>\n",
       "      <td>POLYGON ((-73.98915 40.71671, -73.98975 40.715...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN1102</td>\n",
       "      <td>East Harlem (North)</td>\n",
       "      <td>EstHrlm_N</td>\n",
       "      <td>0</td>\n",
       "      <td>MN11</td>\n",
       "      <td>MN11 East Harlem (CD 11 Equivalent)</td>\n",
       "      <td>28710.999462</td>\n",
       "      <td>2.610451e+07</td>\n",
       "      <td>MULTIPOLYGON (((-73.92719 40.79735, -73.92735 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN1101</td>\n",
       "      <td>East Harlem (South)</td>\n",
       "      <td>EstHrlm_S</td>\n",
       "      <td>0</td>\n",
       "      <td>MN11</td>\n",
       "      <td>MN11 East Harlem (CD 11 Equivalent)</td>\n",
       "      <td>18682.264948</td>\n",
       "      <td>1.665394e+07</td>\n",
       "      <td>POLYGON ((-73.94103 40.79255, -73.93865 40.791...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   borocode   boroname countyfips nta2020                ntaname   ntaabbrev  \\\n",
       "0       1.0  Manhattan        061  MN6491           Central Park     CntrlPk   \n",
       "1       1.0  Manhattan        061  MN0401   Chelsea-Hudson Yards  Chls_HdsYd   \n",
       "2       1.0  Manhattan        061  MN0301  Chinatown-Two Bridges      Chntwn   \n",
       "3       1.0  Manhattan        061  MN1102    East Harlem (North)   EstHrlm_N   \n",
       "4       1.0  Manhattan        061  MN1101    East Harlem (South)   EstHrlm_S   \n",
       "\n",
       "  ntatype cdta2020                                          cdtaname  \\\n",
       "0       9     MN64             MN64 Central Park (JIA 64 Equivalent)   \n",
       "1       0     MN04  MN04 Chelsea-Hell's Kitchen (CD 4 Approximation)   \n",
       "2       0     MN03  MN03 Lower East Side-Chinatown (CD 3 Equivalent)   \n",
       "3       0     MN11               MN11 East Harlem (CD 11 Equivalent)   \n",
       "4       0     MN11               MN11 East Harlem (CD 11 Equivalent)   \n",
       "\n",
       "     shape_leng    shape_area  \\\n",
       "0  32721.097429  3.831238e+07   \n",
       "1  40095.235570  2.967175e+07   \n",
       "2  16464.371337  1.154263e+07   \n",
       "3  28710.999462  2.610451e+07   \n",
       "4  18682.264948  1.665394e+07   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-73.95246 40.79824, -73.95237 40.798...  \n",
       "1  POLYGON ((-74.00440 40.76267, -74.00220 40.761...  \n",
       "2  POLYGON ((-73.98915 40.71671, -73.98975 40.715...  \n",
       "3  MULTIPOLYGON (((-73.92719 40.79735, -73.92735 ...  \n",
       "4  POLYGON ((-73.94103 40.79255, -73.93865 40.791...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading in NTA shape, only being used as a df and not gdf\n",
    "url = 'https://data.cityofnewyork.us/api/geospatial/9nt8-h7nd?accessType=DOWNLOAD&method=export&format=Shapefile'\n",
    "nta_df = gpd.read_file(url)\n",
    "\n",
    "print('shape of data: {}'.format(nta_df.shape))\n",
    "nta_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e056910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (861876, 29)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>bin</th>\n",
       "      <th>bbl</th>\n",
       "      <th>borough</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (-74.22274561187417 40.52134422844183)</td>\n",
       "      <td>5128004</td>\n",
       "      <td>5075340353</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>353</td>\n",
       "      <td>78 SAVO LOOP</td>\n",
       "      <td>29.428</td>\n",
       "      <td>32.332</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>226.01</td>\n",
       "      <td>5022601</td>\n",
       "      <td>SI0304</td>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Woodrow</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0304</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Woodrow, Staten...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (-74.24179250549321 40.52875316810818)</td>\n",
       "      <td>5155392</td>\n",
       "      <td>5075960125</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7596.0</td>\n",
       "      <td>125</td>\n",
       "      <td>72 CHART LOOP</td>\n",
       "      <td>25.366</td>\n",
       "      <td>26.703</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>226.02</td>\n",
       "      <td>5022602</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Tottenville-Charleston, Staten Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (-74.24109012652094 40.528883822921635)</td>\n",
       "      <td>5148808</td>\n",
       "      <td>5075960131</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7596.0</td>\n",
       "      <td>131</td>\n",
       "      <td>40 TIDES LANE</td>\n",
       "      <td>36.172</td>\n",
       "      <td>38.902</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>226.02</td>\n",
       "      <td>5022602</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Tottenville-Charleston, Staten Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (-74.24649640323727 40.507045708438184)</td>\n",
       "      <td>5088274</td>\n",
       "      <td>5079150042</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7915.0</td>\n",
       "      <td>42</td>\n",
       "      <td>328 MAIN STREET</td>\n",
       "      <td>69.897</td>\n",
       "      <td>72.459</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>244.01</td>\n",
       "      <td>5024401</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Tottenville-Charleston, Staten Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (-74.24198032271697 40.51005666426356)</td>\n",
       "      <td>5087850</td>\n",
       "      <td>5078680123</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7868.0</td>\n",
       "      <td>123</td>\n",
       "      <td>309 SLEIGHT AVENUE</td>\n",
       "      <td>74.907</td>\n",
       "      <td>79.122</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>244.01</td>\n",
       "      <td>5024401</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Tottenville-Charleston, Staten Island</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        the_geom      bin         bbl  \\\n",
       "0   POINT (-74.22274561187417 40.52134422844183)  5128004  5075340353   \n",
       "1   POINT (-74.24179250549321 40.52875316810818)  5155392  5075960125   \n",
       "2  POINT (-74.24109012652094 40.528883822921635)  5148808  5075960131   \n",
       "3  POINT (-74.24649640323727 40.507045708438184)  5088274  5079150042   \n",
       "4   POINT (-74.24198032271697 40.51005666426356)  5087850  5078680123   \n",
       "\n",
       "   borough   block  lot             address  z_grade  z_floor subgrade  ...  \\\n",
       "0      5.0  7534.0  353        78 SAVO LOOP   29.428   32.332        N  ...   \n",
       "1      5.0  7596.0  125       72 CHART LOOP   25.366   26.703        N  ...   \n",
       "2      5.0  7596.0  131       40 TIDES LANE   36.172   38.902        N  ...   \n",
       "3      5.0  7915.0   42     328 MAIN STREET   69.897   72.459        N  ...   \n",
       "4      5.0  7868.0  123  309 SLEIGHT AVENUE   74.907   79.122        N  ...   \n",
       "\n",
       "  BoroCD CTLabel BoroCT2020  NTA2020                                 NTAName  \\\n",
       "0    503  226.01    5022601   SI0304  Annadale-Huguenot-Prince's Bay-Woodrow   \n",
       "1    503  226.02    5022602   SI0305                  Tottenville-Charleston   \n",
       "2    503  226.02    5022602   SI0305                  Tottenville-Charleston   \n",
       "3    503  244.01    5024401   SI0305                  Tottenville-Charleston   \n",
       "4    503  244.01    5024401   SI0305                  Tottenville-Charleston   \n",
       "\n",
       "   CDTA2020                               CDTAName  nta2020       boroname  \\\n",
       "0      SI03  SI03 South Shore (CD 3 Approximation)   SI0304  Staten Island   \n",
       "1      SI03  SI03 South Shore (CD 3 Approximation)   SI0305  Staten Island   \n",
       "2      SI03  SI03 South Shore (CD 3 Approximation)   SI0305  Staten Island   \n",
       "3      SI03  SI03 South Shore (CD 3 Approximation)   SI0305  Staten Island   \n",
       "4      SI03  SI03 South Shore (CD 3 Approximation)   SI0305  Staten Island   \n",
       "\n",
       "                                        ntaname_boro  \n",
       "0  Annadale-Huguenot-Prince's Bay-Woodrow, Staten...  \n",
       "1              Tottenville-Charleston, Staten Island  \n",
       "2              Tottenville-Charleston, Staten Island  \n",
       "3              Tottenville-Charleston, Staten Island  \n",
       "4              Tottenville-Charleston, Staten Island  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge and retrieve nta and boro name data\n",
    "df = df.merge(\n",
    "    nta_df.loc[:, ['nta2020', 'boroname']],\n",
    "    left_on='NTA2020',\n",
    "    right_on='nta2020',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "df['ntaname_boro'] = df['NTAName'] + ', ' + df['boroname']\n",
    "\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8ebd16e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "notes2          659418\n",
       "notes3          389873\n",
       "address          17001\n",
       "borough              1\n",
       "block                1\n",
       "the_geom             0\n",
       "NTA2020              0\n",
       "BoroCD               0\n",
       "CTLabel              0\n",
       "BoroCT2020           0\n",
       "CDTA2020             0\n",
       "NTAName              0\n",
       "pluto_bbl            0\n",
       "CDTAName             0\n",
       "nta2020              0\n",
       "boroname             0\n",
       "Council              0\n",
       "y                    0\n",
       "longitude            0\n",
       "latitude             0\n",
       "bin                  0\n",
       "x                    0\n",
       "notes1               0\n",
       "subgrade             0\n",
       "z_floor              0\n",
       "z_grade              0\n",
       "lot                  0\n",
       "bbl                  0\n",
       "ntaname_boro         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check for nulls\n",
    "(df\n",
    " .isnull()\n",
    " .sum()\n",
    " .sort_values(ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e219fb6f",
   "metadata": {},
   "source": [
    "# Examining the Measurement Quality Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ea66ae",
   "metadata": {},
   "source": [
    "![images](images/building-elevation-fields.png)\n",
    "\n",
    "Screenshot of elevation columns from data dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7472303",
   "metadata": {},
   "source": [
    "![images](images/notes1-descriptions.png)\n",
    "\n",
    "Screenshot of **notes_1** column data dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69dcf937",
   "metadata": {},
   "source": [
    "The `notes_1` column seems to tell us interesting information about the obstacles and quality of the measurement. Let's examine further and see what we can find."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd7dbadb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Property was Successfully Measured                                                                                                      646782\n",
       "Property not Visible Due to Construction or Obstruction                                                                                 115281\n",
       "Property was Successfully Measured; No Address Plate or Identification on Building                                                       61567\n",
       "Property not Visible Due to Construction or Obstruction; No Address Plate or Identification on Building                                  17132\n",
       "Property not Visible from Street; No Address Plate or Identification on Building                                                         12080\n",
       "Property was Successfully Measured; Property Under Construction                                                                           2946\n",
       "Vacant Lot; No Address Plate or Identification on Building                                                                                1957\n",
       "Property not Visible Due to Construction or Obstruction; No Address Plate or Identification on Building; Property Under Construction      1676\n",
       "Property not Visible Due to Construction or Obstruction; Property Under Construction                                                      1414\n",
       "Property was Successfully Measured; No Address Plate or Identification on Building; Property Under Construction                           1039\n",
       "Property not Visible from Street; No Address Plate or Identification on Building; Property Under Construction                                1\n",
       "Property not Visible from Street                                                                                                             1\n",
       "Name: notes1, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview building elevation measurements notes and corresponding count of unique values\n",
    "df['notes1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "661beae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of properties with 'Property was Successfully Measured' and no additional notes: 75.04%\n"
     ]
    }
   ],
   "source": [
    "# how many properties were successfully measured with no other notes?\n",
    "count_measured = (\n",
    "    df\n",
    "    .loc[df['notes1'] == 'Property was Successfully Measured']\n",
    "    .shape[0]\n",
    ")\n",
    "\n",
    "measured_perc = count_measured / df.shape[0] * 100\n",
    "\n",
    "print(\"Percent of properties with 'Property was Successfully Measured' \\\n",
    "and no additional notes: {:.2f}%\".format(measured_perc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097f9b1c",
   "metadata": {},
   "source": [
    "Most of the properties were successfully measured. Now let's examine the quality of measurements by Neighborhood Tablulation Area (NTA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf8f243d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (237, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>count_measured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allerton, Bronx</td>\n",
       "      <td>1662</td>\n",
       "      <td>1335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alley Pond Park, Queens</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Woodrow, Staten...</td>\n",
       "      <td>11386</td>\n",
       "      <td>9492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arden Heights-Rossville, Staten Island</td>\n",
       "      <td>9250</td>\n",
       "      <td>7848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Astoria (Central), Queens</td>\n",
       "      <td>4283</td>\n",
       "      <td>2885.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        ntaname_boro  count  count_measured\n",
       "0                                    Allerton, Bronx   1662          1335.0\n",
       "1                            Alley Pond Park, Queens      7             NaN\n",
       "2  Annadale-Huguenot-Prince's Bay-Woodrow, Staten...  11386          9492.0\n",
       "3             Arden Heights-Rossville, Staten Island   9250          7848.0\n",
       "4                          Astoria (Central), Queens   4283          2885.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total counts by NTA\n",
    "nta_counts = (\n",
    "    df\n",
    "    .groupby(by='ntaname_boro')['bin']\n",
    "    .count()\n",
    "    .rename('count')\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# counts that were measured successfully by NTA\n",
    "nta_counts_measured = (\n",
    "    df\n",
    "    .loc[df['notes1'] == 'Property was Successfully Measured']\n",
    "    .groupby(by='ntaname_boro')['bin']\n",
    "    .count()\n",
    "    .rename('count_measured')\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# merge both counts and count measured dataframes\n",
    "nta_counts_merged = nta_counts.merge(\n",
    "    nta_counts_measured,\n",
    "    on='ntaname_boro',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "# examine data\n",
    "print('shape of data: {}'.format(nta_counts_measured.shape))\n",
    "nta_counts_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c2eaff56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>count_measured</th>\n",
       "      <th>perc_measured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allerton, Bronx</td>\n",
       "      <td>1662</td>\n",
       "      <td>1335</td>\n",
       "      <td>80.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alley Pond Park, Queens</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Woodrow, Staten...</td>\n",
       "      <td>11386</td>\n",
       "      <td>9492</td>\n",
       "      <td>83.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arden Heights-Rossville, Staten Island</td>\n",
       "      <td>9250</td>\n",
       "      <td>7848</td>\n",
       "      <td>84.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Astoria (Central), Queens</td>\n",
       "      <td>4283</td>\n",
       "      <td>2885</td>\n",
       "      <td>67.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        ntaname_boro  count  count_measured  \\\n",
       "0                                    Allerton, Bronx   1662            1335   \n",
       "1                            Alley Pond Park, Queens      7               0   \n",
       "2  Annadale-Huguenot-Prince's Bay-Woodrow, Staten...  11386            9492   \n",
       "3             Arden Heights-Rossville, Staten Island   9250            7848   \n",
       "4                          Astoria (Central), Queens   4283            2885   \n",
       "\n",
       "   perc_measured  \n",
       "0          80.32  \n",
       "1           0.00  \n",
       "2          83.37  \n",
       "3          84.84  \n",
       "4          67.36  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fix nulls and datatypes\n",
    "nta_counts_merged['count_measured'] = nta_counts_merged['count_measured'].fillna(0).astype(int)\n",
    "\n",
    "nta_counts_merged['perc_measured'] = round(\n",
    "    nta_counts_merged['count_measured'] / nta_counts_merged['count'] * 100, 2\n",
    ")\n",
    "\n",
    "nta_counts_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6f487a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>count_measured</th>\n",
       "      <th>perc_measured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Forest Park, Queens</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Woodlawn Cemetery, Bronx</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Green-Wood Cemetery, Brooklyn</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mount Olivet &amp; All Faiths Cemeteries, Queens</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Great Kills Park, Staten Island</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>United Nations, Manhattan</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jamaica Bay (East), Queens</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alley Pond Park, Queens</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Claremont Park, Bronx</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kissena Park, Queens</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Dyker Beach Park, Brooklyn</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Astoria Park, Queens</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Shirley Chisholm State Park, Brooklyn</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Montefiore Cemetery, Queens</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mount Hebron &amp; Cedar Grove Cemeteries, Queens</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Calvert Vaux Park, Brooklyn</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rockaway Community Park, Queens</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Lincoln Terrace Park, Brooklyn</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Evergreens Cemetery, Brooklyn</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     ntaname_boro  count  count_measured  \\\n",
       "0                             Forest Park, Queens     30               0   \n",
       "1                        Woodlawn Cemetery, Bronx     23               0   \n",
       "2                   Green-Wood Cemetery, Brooklyn     20               0   \n",
       "3    Mount Olivet & All Faiths Cemeteries, Queens     19               0   \n",
       "4                 Great Kills Park, Staten Island      9               0   \n",
       "5                       United Nations, Manhattan      7               0   \n",
       "6                      Jamaica Bay (East), Queens      7               0   \n",
       "7                         Alley Pond Park, Queens      7               0   \n",
       "8                           Claremont Park, Bronx      7               0   \n",
       "9                            Kissena Park, Queens      7               0   \n",
       "10                     Dyker Beach Park, Brooklyn      6               0   \n",
       "11                           Astoria Park, Queens      6               0   \n",
       "12          Shirley Chisholm State Park, Brooklyn      6               0   \n",
       "13                    Montefiore Cemetery, Queens      4               0   \n",
       "14  Mount Hebron & Cedar Grove Cemeteries, Queens      3               0   \n",
       "15                    Calvert Vaux Park, Brooklyn      3               0   \n",
       "16                Rockaway Community Park, Queens      2               0   \n",
       "17                 Lincoln Terrace Park, Brooklyn      1               0   \n",
       "18              The Evergreens Cemetery, Brooklyn      1               0   \n",
       "\n",
       "    perc_measured  \n",
       "0             0.0  \n",
       "1             0.0  \n",
       "2             0.0  \n",
       "3             0.0  \n",
       "4             0.0  \n",
       "5             0.0  \n",
       "6             0.0  \n",
       "7             0.0  \n",
       "8             0.0  \n",
       "9             0.0  \n",
       "10            0.0  \n",
       "11            0.0  \n",
       "12            0.0  \n",
       "13            0.0  \n",
       "14            0.0  \n",
       "15            0.0  \n",
       "16            0.0  \n",
       "17            0.0  \n",
       "18            0.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check ntas with no successfully measured buildings \n",
    "(nta_counts_merged\n",
    " .loc[(nta_counts_merged['count'] > 0)\n",
    "      & (nta_counts_merged['count_measured'] == 0)]\n",
    " .sort_values(by='count', ascending=False)\n",
    " .reset_index(drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e8fef2",
   "metadata": {},
   "source": [
    "**Table xx: Neighborhood Tabulation Area (NTA) that have buildings but none were measured successfully.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5dd519b",
   "metadata": {},
   "source": [
    "There are neighborhoods that didn't have any buildings measured, as well as some with small values. Let's filter buildings measured > 0. Most of the above are parks and cemeteries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a193df3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>count_measured</th>\n",
       "      <th>perc_measured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pelham Bay Park, Bronx</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bronx Park, Bronx</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Central Park, Manhattan</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Van Cortlandt Park, Bronx</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>2.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ferry Point Park-St. Raymond Cemetery, Bronx</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Randall's Island, Manhattan</td>\n",
       "      <td>148</td>\n",
       "      <td>4</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Prospect Park, Brooklyn</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The Battery-Governors Island-Ellis Island-Libe...</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cunningham Park, Queens</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>7.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yankee Stadium-Macombs Dam Park, Bronx</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>7.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Inwood Hill Park, Manhattan</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>7.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Flushing Meadows-Corona Park, Queens</td>\n",
       "      <td>122</td>\n",
       "      <td>11</td>\n",
       "      <td>9.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Highland Park-Cypress Hills Cemeteries (North)...</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>11.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Marine Park-Plumb Island, Brooklyn</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>11.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>St. John Cemetery, Queens</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>11.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>John F. Kennedy International Airport, Queens</td>\n",
       "      <td>242</td>\n",
       "      <td>37</td>\n",
       "      <td>15.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LaGuardia Airport, Queens</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>15.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Middle Village Cemetery, Queens</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>McGuire Fields, Brooklyn</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Soundview Park, Bronx</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         ntaname_boro  count  count_measured  \\\n",
       "0                              Pelham Bay Park, Bronx     92               1   \n",
       "1                                   Bronx Park, Bronx     60               1   \n",
       "2                             Central Park, Manhattan     52               1   \n",
       "3                           Van Cortlandt Park, Bronx     47               1   \n",
       "4        Ferry Point Park-St. Raymond Cemetery, Bronx     44               1   \n",
       "5                         Randall's Island, Manhattan    148               4   \n",
       "6                             Prospect Park, Brooklyn     48               2   \n",
       "7   The Battery-Governors Island-Ellis Island-Libe...     15               1   \n",
       "8                             Cunningham Park, Queens     14               1   \n",
       "9              Yankee Stadium-Macombs Dam Park, Bronx     13               1   \n",
       "10                        Inwood Hill Park, Manhattan     13               1   \n",
       "11               Flushing Meadows-Corona Park, Queens    122              11   \n",
       "12  Highland Park-Cypress Hills Cemeteries (North)...     35               4   \n",
       "13                 Marine Park-Plumb Island, Brooklyn     26               3   \n",
       "14                          St. John Cemetery, Queens     17               2   \n",
       "15      John F. Kennedy International Airport, Queens    242              37   \n",
       "16                          LaGuardia Airport, Queens     26               4   \n",
       "17                    Middle Village Cemetery, Queens      8               2   \n",
       "18                           McGuire Fields, Brooklyn      4               1   \n",
       "19                              Soundview Park, Bronx      4               1   \n",
       "\n",
       "    perc_measured  \n",
       "0            1.09  \n",
       "1            1.67  \n",
       "2            1.92  \n",
       "3            2.13  \n",
       "4            2.27  \n",
       "5            2.70  \n",
       "6            4.17  \n",
       "7            6.67  \n",
       "8            7.14  \n",
       "9            7.69  \n",
       "10           7.69  \n",
       "11           9.02  \n",
       "12          11.43  \n",
       "13          11.54  \n",
       "14          11.76  \n",
       "15          15.29  \n",
       "16          15.38  \n",
       "17          25.00  \n",
       "18          25.00  \n",
       "19          25.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table of percent measured in ascending order by NTA\n",
    "(nta_counts_merged\n",
    " .loc[nta_counts_merged['count_measured'] > 0]\n",
    " .sort_values(by='perc_measured')\n",
    " .head(20)\n",
    " .reset_index(drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245ed756",
   "metadata": {},
   "source": [
    "**Table xx:** Top 20 percent of properties that were successfully measured in ascending order by Neighborhood Tabulation Area (NTA)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef5c17b",
   "metadata": {},
   "source": [
    "Most of these neighborhoods continue to be parks and cemeteries. Now let's filter neigborhoods NTAs with count of buildings >= 300."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "346eab13",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>count_measured</th>\n",
       "      <th>perc_measured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Clinton Hill, Brooklyn</td>\n",
       "      <td>2262</td>\n",
       "      <td>770</td>\n",
       "      <td>34.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Park Slope, Brooklyn</td>\n",
       "      <td>6723</td>\n",
       "      <td>2381</td>\n",
       "      <td>35.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fort Greene, Brooklyn</td>\n",
       "      <td>2085</td>\n",
       "      <td>742</td>\n",
       "      <td>35.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brooklyn Heights, Brooklyn</td>\n",
       "      <td>1457</td>\n",
       "      <td>553</td>\n",
       "      <td>37.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hamilton Heights-Sugar Hill, Manhattan</td>\n",
       "      <td>1623</td>\n",
       "      <td>636</td>\n",
       "      <td>39.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Morningside Heights, Manhattan</td>\n",
       "      <td>509</td>\n",
       "      <td>200</td>\n",
       "      <td>39.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Manhattanville-West Harlem, Manhattan</td>\n",
       "      <td>442</td>\n",
       "      <td>175</td>\n",
       "      <td>39.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Upper West Side (Central), Manhattan</td>\n",
       "      <td>2755</td>\n",
       "      <td>1102</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Co-op City, Bronx</td>\n",
       "      <td>485</td>\n",
       "      <td>196</td>\n",
       "      <td>40.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sunset Park (Central), Brooklyn</td>\n",
       "      <td>4205</td>\n",
       "      <td>1708</td>\n",
       "      <td>40.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Carroll Gardens-Cobble Hill-Gowanus-Red Hook, ...</td>\n",
       "      <td>7196</td>\n",
       "      <td>3156</td>\n",
       "      <td>43.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prospect Heights, Brooklyn</td>\n",
       "      <td>1798</td>\n",
       "      <td>797</td>\n",
       "      <td>44.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bedford-Stuyvesant (East), Brooklyn</td>\n",
       "      <td>8940</td>\n",
       "      <td>3989</td>\n",
       "      <td>44.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Upper West Side-Manhattan Valley, Manhattan</td>\n",
       "      <td>981</td>\n",
       "      <td>438</td>\n",
       "      <td>44.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>West Village, Manhattan</td>\n",
       "      <td>2259</td>\n",
       "      <td>1038</td>\n",
       "      <td>45.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bedford-Stuyvesant (West), Brooklyn</td>\n",
       "      <td>7330</td>\n",
       "      <td>3398</td>\n",
       "      <td>46.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Harlem (South), Manhattan</td>\n",
       "      <td>1972</td>\n",
       "      <td>917</td>\n",
       "      <td>46.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Washington Heights (South), Manhattan</td>\n",
       "      <td>1216</td>\n",
       "      <td>569</td>\n",
       "      <td>46.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Upper West Side-Lincoln Square, Manhattan</td>\n",
       "      <td>955</td>\n",
       "      <td>452</td>\n",
       "      <td>47.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Downtown Brooklyn-DUMBO-Boerum Hill, Brooklyn</td>\n",
       "      <td>1974</td>\n",
       "      <td>936</td>\n",
       "      <td>47.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         ntaname_boro  count  count_measured  \\\n",
       "0                              Clinton Hill, Brooklyn   2262             770   \n",
       "1                                Park Slope, Brooklyn   6723            2381   \n",
       "2                               Fort Greene, Brooklyn   2085             742   \n",
       "3                          Brooklyn Heights, Brooklyn   1457             553   \n",
       "4              Hamilton Heights-Sugar Hill, Manhattan   1623             636   \n",
       "5                      Morningside Heights, Manhattan    509             200   \n",
       "6               Manhattanville-West Harlem, Manhattan    442             175   \n",
       "7                Upper West Side (Central), Manhattan   2755            1102   \n",
       "8                                   Co-op City, Bronx    485             196   \n",
       "9                     Sunset Park (Central), Brooklyn   4205            1708   \n",
       "10  Carroll Gardens-Cobble Hill-Gowanus-Red Hook, ...   7196            3156   \n",
       "11                         Prospect Heights, Brooklyn   1798             797   \n",
       "12                Bedford-Stuyvesant (East), Brooklyn   8940            3989   \n",
       "13        Upper West Side-Manhattan Valley, Manhattan    981             438   \n",
       "14                            West Village, Manhattan   2259            1038   \n",
       "15                Bedford-Stuyvesant (West), Brooklyn   7330            3398   \n",
       "16                          Harlem (South), Manhattan   1972             917   \n",
       "17              Washington Heights (South), Manhattan   1216             569   \n",
       "18          Upper West Side-Lincoln Square, Manhattan    955             452   \n",
       "19      Downtown Brooklyn-DUMBO-Boerum Hill, Brooklyn   1974             936   \n",
       "\n",
       "    perc_measured  \n",
       "0           34.04  \n",
       "1           35.42  \n",
       "2           35.59  \n",
       "3           37.95  \n",
       "4           39.19  \n",
       "5           39.29  \n",
       "6           39.59  \n",
       "7           40.00  \n",
       "8           40.41  \n",
       "9           40.62  \n",
       "10          43.86  \n",
       "11          44.33  \n",
       "12          44.62  \n",
       "13          44.65  \n",
       "14          45.95  \n",
       "15          46.36  \n",
       "16          46.50  \n",
       "17          46.79  \n",
       "18          47.33  \n",
       "19          47.42  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table of percent measured in ascending order where count of buildings >= 300 by NTA\n",
    "(nta_counts_merged\n",
    " .loc[nta_counts_merged['count'] >= 300]\n",
    " .sort_values(by='perc_measured')\n",
    " .head(20)\n",
    " .reset_index(drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faebe31a",
   "metadata": {},
   "source": [
    "**Table xx**: Top 20 percent of properties that were successfully measured in ascending order where Neighborhood Tabulation Area (NTA) includes at least 300 buildings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443ca71d",
   "metadata": {},
   "source": [
    "Now let's examine percent of buildings measured in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ebde6041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>count_measured</th>\n",
       "      <th>perc_measured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Holy Cross Cemetery, Brooklyn</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hollis, Queens</td>\n",
       "      <td>4126</td>\n",
       "      <td>3711</td>\n",
       "      <td>89.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cambria Heights, Queens</td>\n",
       "      <td>5311</td>\n",
       "      <td>4773</td>\n",
       "      <td>89.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Queens Village, Queens</td>\n",
       "      <td>11852</td>\n",
       "      <td>10650</td>\n",
       "      <td>89.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Westerleigh-Castleton Corners, Staten Island</td>\n",
       "      <td>9101</td>\n",
       "      <td>8124</td>\n",
       "      <td>89.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bayside, Queens</td>\n",
       "      <td>8615</td>\n",
       "      <td>7678</td>\n",
       "      <td>89.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oakwood-Richmondtown, Staten Island</td>\n",
       "      <td>6106</td>\n",
       "      <td>5408</td>\n",
       "      <td>88.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Murray Hill-Broadway Flushing, Queens</td>\n",
       "      <td>7693</td>\n",
       "      <td>6809</td>\n",
       "      <td>88.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Richmond Hill, Queens</td>\n",
       "      <td>3606</td>\n",
       "      <td>3174</td>\n",
       "      <td>88.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>South Ozone Park, Queens</td>\n",
       "      <td>14474</td>\n",
       "      <td>12708</td>\n",
       "      <td>87.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Baisley Park, Queens</td>\n",
       "      <td>8482</td>\n",
       "      <td>7434</td>\n",
       "      <td>87.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rosedale, Queens</td>\n",
       "      <td>5375</td>\n",
       "      <td>4708</td>\n",
       "      <td>87.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Howard Beach-Lindenwood, Queens</td>\n",
       "      <td>5784</td>\n",
       "      <td>5062</td>\n",
       "      <td>87.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pelham Gardens, Bronx</td>\n",
       "      <td>5132</td>\n",
       "      <td>4478</td>\n",
       "      <td>87.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ozone Park, Queens</td>\n",
       "      <td>4363</td>\n",
       "      <td>3795</td>\n",
       "      <td>86.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Whitestone-Beechhurst, Queens</td>\n",
       "      <td>6754</td>\n",
       "      <td>5874</td>\n",
       "      <td>86.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bellerose, Queens</td>\n",
       "      <td>6493</td>\n",
       "      <td>5644</td>\n",
       "      <td>86.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jamaica Hills-Briarwood, Queens</td>\n",
       "      <td>3610</td>\n",
       "      <td>3137</td>\n",
       "      <td>86.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Fresh Meadows-Utopia, Queens</td>\n",
       "      <td>4350</td>\n",
       "      <td>3777</td>\n",
       "      <td>86.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Laurelton, Queens</td>\n",
       "      <td>6583</td>\n",
       "      <td>5710</td>\n",
       "      <td>86.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    ntaname_boro  count  count_measured  \\\n",
       "0                  Holy Cross Cemetery, Brooklyn      1               1   \n",
       "1                                 Hollis, Queens   4126            3711   \n",
       "2                        Cambria Heights, Queens   5311            4773   \n",
       "3                         Queens Village, Queens  11852           10650   \n",
       "4   Westerleigh-Castleton Corners, Staten Island   9101            8124   \n",
       "5                                Bayside, Queens   8615            7678   \n",
       "6            Oakwood-Richmondtown, Staten Island   6106            5408   \n",
       "7          Murray Hill-Broadway Flushing, Queens   7693            6809   \n",
       "8                    South Richmond Hill, Queens   3606            3174   \n",
       "9                       South Ozone Park, Queens  14474           12708   \n",
       "10                          Baisley Park, Queens   8482            7434   \n",
       "11                              Rosedale, Queens   5375            4708   \n",
       "12               Howard Beach-Lindenwood, Queens   5784            5062   \n",
       "13                         Pelham Gardens, Bronx   5132            4478   \n",
       "14                            Ozone Park, Queens   4363            3795   \n",
       "15                 Whitestone-Beechhurst, Queens   6754            5874   \n",
       "16                             Bellerose, Queens   6493            5644   \n",
       "17               Jamaica Hills-Briarwood, Queens   3610            3137   \n",
       "18                  Fresh Meadows-Utopia, Queens   4350            3777   \n",
       "19                             Laurelton, Queens   6583            5710   \n",
       "\n",
       "    perc_measured  \n",
       "0          100.00  \n",
       "1           89.94  \n",
       "2           89.87  \n",
       "3           89.86  \n",
       "4           89.26  \n",
       "5           89.12  \n",
       "6           88.57  \n",
       "7           88.51  \n",
       "8           88.02  \n",
       "9           87.80  \n",
       "10          87.64  \n",
       "11          87.59  \n",
       "12          87.52  \n",
       "13          87.26  \n",
       "14          86.98  \n",
       "15          86.97  \n",
       "16          86.92  \n",
       "17          86.90  \n",
       "18          86.83  \n",
       "19          86.74  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table of percent measured in descending order by NTA\n",
    "(nta_counts_merged\n",
    " .sort_values(by='perc_measured', ascending=False)\n",
    " .head(20)\n",
    " .reset_index(drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb7ec35",
   "metadata": {},
   "source": [
    "**Table xx:** Top 20 percent of properties that were successfully measured in descending order by Neighborhood Tabulation Area (NTA)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac6ba5e",
   "metadata": {},
   "source": [
    "Map and visualize these percentages by NTA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7608d2b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (262, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>borocode</th>\n",
       "      <th>boroname</th>\n",
       "      <th>countyfips</th>\n",
       "      <th>nta2020</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>ntaabbrev</th>\n",
       "      <th>ntatype</th>\n",
       "      <th>cdta2020</th>\n",
       "      <th>cdtaname</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>ntaname_boro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN6491</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>CntrlPk</td>\n",
       "      <td>9</td>\n",
       "      <td>MN64</td>\n",
       "      <td>MN64 Central Park (JIA 64 Equivalent)</td>\n",
       "      <td>32721.097429</td>\n",
       "      <td>3.831238e+07</td>\n",
       "      <td>POLYGON ((997412.248 230101.816, 997436.269 23...</td>\n",
       "      <td>Central Park, Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN0401</td>\n",
       "      <td>Chelsea-Hudson Yards</td>\n",
       "      <td>Chls_HdsYd</td>\n",
       "      <td>0</td>\n",
       "      <td>MN04</td>\n",
       "      <td>MN04 Chelsea-Hell's Kitchen (CD 4 Approximation)</td>\n",
       "      <td>40095.235570</td>\n",
       "      <td>2.967175e+07</td>\n",
       "      <td>POLYGON ((983031.177 217138.506, 983640.320 21...</td>\n",
       "      <td>Chelsea-Hudson Yards, Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN0301</td>\n",
       "      <td>Chinatown-Two Bridges</td>\n",
       "      <td>Chntwn</td>\n",
       "      <td>0</td>\n",
       "      <td>MN03</td>\n",
       "      <td>MN03 Lower East Side-Chinatown (CD 3 Equivalent)</td>\n",
       "      <td>16464.371337</td>\n",
       "      <td>1.154263e+07</td>\n",
       "      <td>POLYGON ((987256.457 200392.989, 987090.306 19...</td>\n",
       "      <td>Chinatown-Two Bridges, Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN1102</td>\n",
       "      <td>East Harlem (North)</td>\n",
       "      <td>EstHrlm_N</td>\n",
       "      <td>0</td>\n",
       "      <td>MN11</td>\n",
       "      <td>MN11 East Harlem (CD 11 Equivalent)</td>\n",
       "      <td>28710.999462</td>\n",
       "      <td>2.610451e+07</td>\n",
       "      <td>MULTIPOLYGON (((1004410.678 229781.419, 100436...</td>\n",
       "      <td>East Harlem (North), Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN1101</td>\n",
       "      <td>East Harlem (South)</td>\n",
       "      <td>EstHrlm_S</td>\n",
       "      <td>0</td>\n",
       "      <td>MN11</td>\n",
       "      <td>MN11 East Harlem (CD 11 Equivalent)</td>\n",
       "      <td>18682.264948</td>\n",
       "      <td>1.665394e+07</td>\n",
       "      <td>POLYGON ((1000577.175 228028.553, 1001238.138 ...</td>\n",
       "      <td>East Harlem (South), Manhattan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   borocode   boroname countyfips nta2020                ntaname   ntaabbrev  \\\n",
       "0       1.0  Manhattan        061  MN6491           Central Park     CntrlPk   \n",
       "1       1.0  Manhattan        061  MN0401   Chelsea-Hudson Yards  Chls_HdsYd   \n",
       "2       1.0  Manhattan        061  MN0301  Chinatown-Two Bridges      Chntwn   \n",
       "3       1.0  Manhattan        061  MN1102    East Harlem (North)   EstHrlm_N   \n",
       "4       1.0  Manhattan        061  MN1101    East Harlem (South)   EstHrlm_S   \n",
       "\n",
       "  ntatype cdta2020                                          cdtaname  \\\n",
       "0       9     MN64             MN64 Central Park (JIA 64 Equivalent)   \n",
       "1       0     MN04  MN04 Chelsea-Hell's Kitchen (CD 4 Approximation)   \n",
       "2       0     MN03  MN03 Lower East Side-Chinatown (CD 3 Equivalent)   \n",
       "3       0     MN11               MN11 East Harlem (CD 11 Equivalent)   \n",
       "4       0     MN11               MN11 East Harlem (CD 11 Equivalent)   \n",
       "\n",
       "     shape_leng    shape_area  \\\n",
       "0  32721.097429  3.831238e+07   \n",
       "1  40095.235570  2.967175e+07   \n",
       "2  16464.371337  1.154263e+07   \n",
       "3  28710.999462  2.610451e+07   \n",
       "4  18682.264948  1.665394e+07   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  POLYGON ((997412.248 230101.816, 997436.269 23...   \n",
       "1  POLYGON ((983031.177 217138.506, 983640.320 21...   \n",
       "2  POLYGON ((987256.457 200392.989, 987090.306 19...   \n",
       "3  MULTIPOLYGON (((1004410.678 229781.419, 100436...   \n",
       "4  POLYGON ((1000577.175 228028.553, 1001238.138 ...   \n",
       "\n",
       "                       ntaname_boro  \n",
       "0           Central Park, Manhattan  \n",
       "1   Chelsea-Hudson Yards, Manhattan  \n",
       "2  Chinatown-Two Bridges, Manhattan  \n",
       "3    East Harlem (North), Manhattan  \n",
       "4    East Harlem (South), Manhattan  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading in NTA shape\n",
    "url = 'https://data.cityofnewyork.us/api/geospatial/9nt8-h7nd?accessType=DOWNLOAD&method=export&format=Shapefile'\n",
    "gdf = gpd.read_file(url)\n",
    "\n",
    "# convert crs to 2263\n",
    "gdf = gdf.to_crs(2263)\n",
    "\n",
    "gdf['ntaname_boro'] = gdf['ntaname'] + ', ' + gdf['boroname']\n",
    "\n",
    "print('shape of data:', gdf.shape)\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33188acc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (256, 16)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>borocode</th>\n",
       "      <th>boroname</th>\n",
       "      <th>countyfips</th>\n",
       "      <th>nta2020</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>ntaabbrev</th>\n",
       "      <th>ntatype</th>\n",
       "      <th>cdta2020</th>\n",
       "      <th>cdtaname</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>count_measured</th>\n",
       "      <th>perc_measured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN6491</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>CntrlPk</td>\n",
       "      <td>9</td>\n",
       "      <td>MN64</td>\n",
       "      <td>MN64 Central Park (JIA 64 Equivalent)</td>\n",
       "      <td>32721.097429</td>\n",
       "      <td>3.831238e+07</td>\n",
       "      <td>POLYGON ((997412.248 230101.816, 997436.269 23...</td>\n",
       "      <td>Central Park, Manhattan</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN0401</td>\n",
       "      <td>Chelsea-Hudson Yards</td>\n",
       "      <td>Chls_HdsYd</td>\n",
       "      <td>0</td>\n",
       "      <td>MN04</td>\n",
       "      <td>MN04 Chelsea-Hell's Kitchen (CD 4 Approximation)</td>\n",
       "      <td>40095.235570</td>\n",
       "      <td>2.967175e+07</td>\n",
       "      <td>POLYGON ((983031.177 217138.506, 983640.320 21...</td>\n",
       "      <td>Chelsea-Hudson Yards, Manhattan</td>\n",
       "      <td>2042</td>\n",
       "      <td>1095</td>\n",
       "      <td>53.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN0301</td>\n",
       "      <td>Chinatown-Two Bridges</td>\n",
       "      <td>Chntwn</td>\n",
       "      <td>0</td>\n",
       "      <td>MN03</td>\n",
       "      <td>MN03 Lower East Side-Chinatown (CD 3 Equivalent)</td>\n",
       "      <td>16464.371337</td>\n",
       "      <td>1.154263e+07</td>\n",
       "      <td>POLYGON ((987256.457 200392.989, 987090.306 19...</td>\n",
       "      <td>Chinatown-Two Bridges, Manhattan</td>\n",
       "      <td>1106</td>\n",
       "      <td>654</td>\n",
       "      <td>59.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN1102</td>\n",
       "      <td>East Harlem (North)</td>\n",
       "      <td>EstHrlm_N</td>\n",
       "      <td>0</td>\n",
       "      <td>MN11</td>\n",
       "      <td>MN11 East Harlem (CD 11 Equivalent)</td>\n",
       "      <td>28710.999462</td>\n",
       "      <td>2.610451e+07</td>\n",
       "      <td>MULTIPOLYGON (((1004410.678 229781.419, 100436...</td>\n",
       "      <td>East Harlem (North), Manhattan</td>\n",
       "      <td>1972</td>\n",
       "      <td>1318</td>\n",
       "      <td>66.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>061</td>\n",
       "      <td>MN1101</td>\n",
       "      <td>East Harlem (South)</td>\n",
       "      <td>EstHrlm_S</td>\n",
       "      <td>0</td>\n",
       "      <td>MN11</td>\n",
       "      <td>MN11 East Harlem (CD 11 Equivalent)</td>\n",
       "      <td>18682.264948</td>\n",
       "      <td>1.665394e+07</td>\n",
       "      <td>POLYGON ((1000577.175 228028.553, 1001238.138 ...</td>\n",
       "      <td>East Harlem (South), Manhattan</td>\n",
       "      <td>1019</td>\n",
       "      <td>577</td>\n",
       "      <td>56.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   borocode   boroname countyfips nta2020                ntaname   ntaabbrev  \\\n",
       "0       1.0  Manhattan        061  MN6491           Central Park     CntrlPk   \n",
       "1       1.0  Manhattan        061  MN0401   Chelsea-Hudson Yards  Chls_HdsYd   \n",
       "2       1.0  Manhattan        061  MN0301  Chinatown-Two Bridges      Chntwn   \n",
       "3       1.0  Manhattan        061  MN1102    East Harlem (North)   EstHrlm_N   \n",
       "4       1.0  Manhattan        061  MN1101    East Harlem (South)   EstHrlm_S   \n",
       "\n",
       "  ntatype cdta2020                                          cdtaname  \\\n",
       "0       9     MN64             MN64 Central Park (JIA 64 Equivalent)   \n",
       "1       0     MN04  MN04 Chelsea-Hell's Kitchen (CD 4 Approximation)   \n",
       "2       0     MN03  MN03 Lower East Side-Chinatown (CD 3 Equivalent)   \n",
       "3       0     MN11               MN11 East Harlem (CD 11 Equivalent)   \n",
       "4       0     MN11               MN11 East Harlem (CD 11 Equivalent)   \n",
       "\n",
       "     shape_leng    shape_area  \\\n",
       "0  32721.097429  3.831238e+07   \n",
       "1  40095.235570  2.967175e+07   \n",
       "2  16464.371337  1.154263e+07   \n",
       "3  28710.999462  2.610451e+07   \n",
       "4  18682.264948  1.665394e+07   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  POLYGON ((997412.248 230101.816, 997436.269 23...   \n",
       "1  POLYGON ((983031.177 217138.506, 983640.320 21...   \n",
       "2  POLYGON ((987256.457 200392.989, 987090.306 19...   \n",
       "3  MULTIPOLYGON (((1004410.678 229781.419, 100436...   \n",
       "4  POLYGON ((1000577.175 228028.553, 1001238.138 ...   \n",
       "\n",
       "                       ntaname_boro  count  count_measured  perc_measured  \n",
       "0           Central Park, Manhattan     52               1           1.92  \n",
       "1   Chelsea-Hudson Yards, Manhattan   2042            1095          53.62  \n",
       "2  Chinatown-Two Bridges, Manhattan   1106             654          59.13  \n",
       "3    East Harlem (North), Manhattan   1972            1318          66.84  \n",
       "4    East Harlem (South), Manhattan   1019             577          56.62  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge counts dataframe to geodataframe\n",
    "gdf_merged = gdf.merge(\n",
    "    nta_counts_merged,\n",
    "    on='ntaname_boro',\n",
    ")\n",
    "\n",
    "print('shape of data:', gdf_merged.shape)\n",
    "gdf_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae6c439",
   "metadata": {},
   "source": [
    "# Exclude some NTAs do to parks or cemeteries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f9059da4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MN6491', 'MN1291', 'MN1292', 'MN1191', 'MN0191', 'MN0661',\n",
       "       'BX2791', 'BX0492', 'BX0391', 'BX1091', 'BX1161', 'BX2891',\n",
       "       'BX0991', 'BX2691', 'BX1271', 'BX0491', 'BK5691', 'BK0261',\n",
       "       'BK1391', 'BK1893', 'BK1091', 'BK0771', 'BK0571', 'BK1771',\n",
       "       'BK0891', 'BK1891', 'BK1892', 'BK5591', 'BK5693', 'BK0471',\n",
       "       'QN1191', 'QN0191', 'QN0271', 'QN0891', 'QN8191', 'QN8291',\n",
       "       'QN0761', 'QN0574', 'QN8492', 'QN8491', 'QN8381', 'QN0791',\n",
       "       'QN8081', 'QN0572', 'QN1371', 'QN0871', 'QN0571', 'QN1491',\n",
       "       'QN0573', 'QN0171', 'QN0161', 'QN0261', 'SI9561', 'SI0291',\n",
       "       'SI0391', 'SI9593', 'SI9592', 'SI0191'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stuyvesant Town-Peter Cooper Village, Manhattan - MN0601\n",
    "# Fort Hamilton, Brooklyn - BK1061\n",
    "\n",
    "nta_exclude = (\n",
    "    gdf_merged\n",
    "    .loc[(gdf_merged['count'] < 300)\n",
    "            & (~gdf_merged['nta2020'].isin(['MN0601', 'BK1061'])),\n",
    "         'nta2020']\n",
    "    .values\n",
    ")\n",
    "\n",
    "nta_exclude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2e6434b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>perc_measured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>70.381869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.519697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>60.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>73.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>82.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.940000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       perc_measured\n",
       "count     198.000000\n",
       "mean       70.381869\n",
       "std        14.519697\n",
       "min        30.770000\n",
       "25%        60.440000\n",
       "50%        73.240000\n",
       "75%        82.750000\n",
       "max        89.940000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(gdf_merged\n",
    " .loc[~gdf_merged[\"nta2020\"].isin(nta_exclude), ['perc_measured']]\n",
    " .describe()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8d3f0898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1)\n",
    "cmap = plt.cm.viridis\n",
    "norm = mpl.colors.BoundaryNorm([0, 50, 60, 70, 80, 90], cmap.N)\n",
    "\n",
    "(gdf_merged\n",
    ".loc[~gdf_merged[\"nta2020\"].isin(nta_exclude)]\n",
    ".plot(\n",
    "    column='perc_measured',\n",
    "    legend=True,\n",
    "    cmap=cmap,\n",
    "    cax=cax,\n",
    "    norm=norm,\n",
    "    ax=ax)\n",
    ")\n",
    "\n",
    "gdf.plot(ax=ax, facecolor='None', linewidth=.4)\n",
    "\n",
    "ax.set_title('Percent of Properties Successfully Measured with \\\n",
    "No Additional Notes by NTA', fontsize=12)\n",
    "ax.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/successfully-measured.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa630a66",
   "metadata": {},
   "source": [
    "**Figure xx:** Percent of properties that were successfully measured by Neighborhood Tabulation Area (NTA).\n",
    "\n",
    "Caption this for later."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184e6831",
   "metadata": {},
   "source": [
    "# Examining Building Elevation Measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73c5e31",
   "metadata": {},
   "source": [
    "`z_grade`: The elevation of the building at it's lowest adjacent grade - the lowest point where the building touches the ground.\n",
    "\n",
    "`z_floor`: The elevation of what is estimated to be the lowest actively used floor.\n",
    "\n",
    "Definitions from the data dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "77341f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>z_grade</th>\n",
       "      <th>z_floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>861876.000000</td>\n",
       "      <td>861876.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.915012</td>\n",
       "      <td>58.113704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.192895</td>\n",
       "      <td>42.560673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.096000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.304000</td>\n",
       "      <td>27.438000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.328000</td>\n",
       "      <td>48.370000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.709250</td>\n",
       "      <td>77.063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>402.839000</td>\n",
       "      <td>404.397000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             z_grade        z_floor\n",
       "count  861876.000000  861876.000000\n",
       "mean       53.915012      58.113704\n",
       "std        42.192895      42.560673\n",
       "min       -13.096000       0.000000\n",
       "25%        23.304000      27.438000\n",
       "50%        44.328000      48.370000\n",
       "75%        72.709250      77.063000\n",
       "max       402.839000     404.397000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics\n",
    "(df\n",
    " .loc[:, ['z_grade', 'z_floor']]\n",
    " .describe()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3653f6bb",
   "metadata": {},
   "source": [
    "Check for values below zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "afe3c03b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of properties where z_grade value < 0: 11\n"
     ]
    }
   ],
   "source": [
    "counts = (\n",
    "    df\n",
    "    .loc[df['z_grade'] < 0]\n",
    "    .shape[0]\n",
    ")\n",
    "\n",
    "print('count of properties where z_grade value < 0: {:,}'.format(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b133bb1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of properties where z_floor value < 0: 0\n"
     ]
    }
   ],
   "source": [
    "counts = (\n",
    "    df\n",
    "    .loc[df['z_floor'] < 0]\n",
    "    .shape[0]\n",
    ")\n",
    "\n",
    "print('count of properties where z_floor value < 0: {:,}'.format(counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59771bd7",
   "metadata": {},
   "source": [
    "Check for values equal to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "494f31a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of properties where z_grade is 0 (no measurement): 14,039\n"
     ]
    }
   ],
   "source": [
    "counts = (\n",
    "    df\n",
    "    .loc[df['z_grade'] == 0]\n",
    "    .shape[0]\n",
    ")\n",
    "\n",
    "print('count of properties where z_grade is 0 (no measurement): {:,}'.format(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7a592a3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of properties where z_floor is 0 (no measurement): 14,038\n"
     ]
    }
   ],
   "source": [
    "counts = (\n",
    "    df\n",
    "    .loc[df['z_floor'] == 0]\n",
    "    .shape[0]\n",
    ")\n",
    "\n",
    "print('count of properties where z_floor is 0 (no measurement): {:,}'.format(counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd09d76",
   "metadata": {},
   "source": [
    "Interesting that there is one building that doesn't reconcile. Let's examine the measurement notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e852527d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bin</th>\n",
       "      <th>z_grade</th>\n",
       "      <th>z_floor</th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>notes1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>293514</th>\n",
       "      <td>3109155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.929</td>\n",
       "      <td>East Flatbush-Rugby, Brooklyn</td>\n",
       "      <td>Property was Successfully Measured</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            bin  z_grade  z_floor                   ntaname_boro  \\\n",
       "293514  3109155      0.0   42.929  East Flatbush-Rugby, Brooklyn   \n",
       "\n",
       "                                    notes1  \n",
       "293514  Property was Successfully Measured  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_id = (\n",
    "    df\n",
    "    .loc[(df['z_grade'] == 0) & (df['z_floor'] != 0), 'bin']\n",
    "    .values[0]\n",
    ")\n",
    "\n",
    "df.loc[df['bin'] == bin_id, ['bin', 'z_grade', 'z_floor', 'ntaname_boro', 'notes1']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a322554e",
   "metadata": {},
   "source": [
    "We can leave this record in for now even with the presense of zero, but it might be worth examining in the future. The notes1 confirms that this property was successfully measured. \n",
    "\n",
    "Let's drop all records where `z_floor` is zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2decd4f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>notes1</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Property not Visible from Street; No Address P...</td>\n",
       "      <td>12079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vacant Lot; No Address Plate or Identification...</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Property not Visible from Street; No Address P...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Property not Visible Due to Construction or Ob...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              notes1  count\n",
       "0  Property not Visible from Street; No Address P...  12079\n",
       "1  Vacant Lot; No Address Plate or Identification...   1957\n",
       "2  Property not Visible from Street; No Address P...      1\n",
       "3  Property not Visible Due to Construction or Ob...      1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check before dropping z_floor is zero\n",
    "(df\n",
    " .loc[df['z_floor'] == 0, 'notes1']\n",
    " .value_counts()\n",
    " .reset_index()\n",
    " .rename(columns={'index':'notes1', 'notes1':'count'})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0079748c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new shape of data: (847838, 29)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>the_geom</th>\n",
       "      <th>bin</th>\n",
       "      <th>bbl</th>\n",
       "      <th>borough</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>address</th>\n",
       "      <th>z_grade</th>\n",
       "      <th>z_floor</th>\n",
       "      <th>subgrade</th>\n",
       "      <th>...</th>\n",
       "      <th>BoroCD</th>\n",
       "      <th>CTLabel</th>\n",
       "      <th>BoroCT2020</th>\n",
       "      <th>NTA2020</th>\n",
       "      <th>NTAName</th>\n",
       "      <th>CDTA2020</th>\n",
       "      <th>CDTAName</th>\n",
       "      <th>nta2020</th>\n",
       "      <th>boroname</th>\n",
       "      <th>ntaname_boro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT (-74.22274561187417 40.52134422844183)</td>\n",
       "      <td>5128004</td>\n",
       "      <td>5075340353</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>353</td>\n",
       "      <td>78 SAVO LOOP</td>\n",
       "      <td>29.428</td>\n",
       "      <td>32.332</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>226.01</td>\n",
       "      <td>5022601</td>\n",
       "      <td>SI0304</td>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Woodrow</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0304</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Woodrow, Staten...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (-74.24179250549321 40.52875316810818)</td>\n",
       "      <td>5155392</td>\n",
       "      <td>5075960125</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7596.0</td>\n",
       "      <td>125</td>\n",
       "      <td>72 CHART LOOP</td>\n",
       "      <td>25.366</td>\n",
       "      <td>26.703</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>226.02</td>\n",
       "      <td>5022602</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Tottenville-Charleston, Staten Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (-74.24109012652094 40.528883822921635)</td>\n",
       "      <td>5148808</td>\n",
       "      <td>5075960131</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7596.0</td>\n",
       "      <td>131</td>\n",
       "      <td>40 TIDES LANE</td>\n",
       "      <td>36.172</td>\n",
       "      <td>38.902</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>226.02</td>\n",
       "      <td>5022602</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Tottenville-Charleston, Staten Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POINT (-74.24649640323727 40.507045708438184)</td>\n",
       "      <td>5088274</td>\n",
       "      <td>5079150042</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7915.0</td>\n",
       "      <td>42</td>\n",
       "      <td>328 MAIN STREET</td>\n",
       "      <td>69.897</td>\n",
       "      <td>72.459</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>244.01</td>\n",
       "      <td>5024401</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Tottenville-Charleston, Staten Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POINT (-74.24198032271697 40.51005666426356)</td>\n",
       "      <td>5087850</td>\n",
       "      <td>5078680123</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7868.0</td>\n",
       "      <td>123</td>\n",
       "      <td>309 SLEIGHT AVENUE</td>\n",
       "      <td>74.907</td>\n",
       "      <td>79.122</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>503</td>\n",
       "      <td>244.01</td>\n",
       "      <td>5024401</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Tottenville-Charleston</td>\n",
       "      <td>SI03</td>\n",
       "      <td>SI03 South Shore (CD 3 Approximation)</td>\n",
       "      <td>SI0305</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>Tottenville-Charleston, Staten Island</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        the_geom      bin         bbl  \\\n",
       "0   POINT (-74.22274561187417 40.52134422844183)  5128004  5075340353   \n",
       "1   POINT (-74.24179250549321 40.52875316810818)  5155392  5075960125   \n",
       "2  POINT (-74.24109012652094 40.528883822921635)  5148808  5075960131   \n",
       "3  POINT (-74.24649640323727 40.507045708438184)  5088274  5079150042   \n",
       "4   POINT (-74.24198032271697 40.51005666426356)  5087850  5078680123   \n",
       "\n",
       "   borough   block  lot             address  z_grade  z_floor subgrade  ...  \\\n",
       "0      5.0  7534.0  353        78 SAVO LOOP   29.428   32.332        N  ...   \n",
       "1      5.0  7596.0  125       72 CHART LOOP   25.366   26.703        N  ...   \n",
       "2      5.0  7596.0  131       40 TIDES LANE   36.172   38.902        N  ...   \n",
       "3      5.0  7915.0   42     328 MAIN STREET   69.897   72.459        N  ...   \n",
       "4      5.0  7868.0  123  309 SLEIGHT AVENUE   74.907   79.122        N  ...   \n",
       "\n",
       "  BoroCD CTLabel BoroCT2020  NTA2020                                 NTAName  \\\n",
       "0    503  226.01    5022601   SI0304  Annadale-Huguenot-Prince's Bay-Woodrow   \n",
       "1    503  226.02    5022602   SI0305                  Tottenville-Charleston   \n",
       "2    503  226.02    5022602   SI0305                  Tottenville-Charleston   \n",
       "3    503  244.01    5024401   SI0305                  Tottenville-Charleston   \n",
       "4    503  244.01    5024401   SI0305                  Tottenville-Charleston   \n",
       "\n",
       "   CDTA2020                               CDTAName  nta2020       boroname  \\\n",
       "0      SI03  SI03 South Shore (CD 3 Approximation)   SI0304  Staten Island   \n",
       "1      SI03  SI03 South Shore (CD 3 Approximation)   SI0305  Staten Island   \n",
       "2      SI03  SI03 South Shore (CD 3 Approximation)   SI0305  Staten Island   \n",
       "3      SI03  SI03 South Shore (CD 3 Approximation)   SI0305  Staten Island   \n",
       "4      SI03  SI03 South Shore (CD 3 Approximation)   SI0305  Staten Island   \n",
       "\n",
       "                                        ntaname_boro  \n",
       "0  Annadale-Huguenot-Prince's Bay-Woodrow, Staten...  \n",
       "1              Tottenville-Charleston, Staten Island  \n",
       "2              Tottenville-Charleston, Staten Island  \n",
       "3              Tottenville-Charleston, Staten Island  \n",
       "4              Tottenville-Charleston, Staten Island  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop all records where z_floor is zero\n",
    "df = (\n",
    "    df\n",
    "    .loc[df['z_floor'] != 0]\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "print('new shape of data:', df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8f0e36a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>z_grade</th>\n",
       "      <th>z_floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>847838.000000</td>\n",
       "      <td>847838.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.807705</td>\n",
       "      <td>59.075916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41.961769</td>\n",
       "      <td>42.244047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.096000</td>\n",
       "      <td>0.703000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.227250</td>\n",
       "      <td>28.361000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.095000</td>\n",
       "      <td>49.224000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>73.342750</td>\n",
       "      <td>77.704750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>402.839000</td>\n",
       "      <td>404.397000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             z_grade        z_floor\n",
       "count  847838.000000  847838.000000\n",
       "mean       54.807705      59.075916\n",
       "std        41.961769      42.244047\n",
       "min       -13.096000       0.703000\n",
       "25%        24.227250      28.361000\n",
       "50%        45.095000      49.224000\n",
       "75%        73.342750      77.704750\n",
       "max       402.839000     404.397000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics\n",
    "(df\n",
    " .loc[:, ['z_grade', 'z_floor']]\n",
    " .describe()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b78dbf3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y9gm8klB9gOIC5S3AMxTTy4fXH56ZNZreDKzqcCfwJkl/JmmkpPMobnC4PV3X96/A30p6jaS3ALPwtTZNQxFOQG14SLoc+K2IqHU3lZnZfiTNBz5HMeCp+OeIeF83258QER9Oz2cCH4uId6bn7wS+ApwAbAY+FRH3pnXjgb8D3g7sBP5PRPxdrf1a43GCY0MmnZZ6NbCB4i6nOyk6mn8YznaZmVl+fJGxDShJG9n/nDbAxylONS2juHhvO3AFsGLoWmdmZgcKz+CYmVlT625gFRG+ZuYA5QTHzMzMspPdKaqjjjoqJkyY0K99/OpXv+Lggw8emAYNoxziyCEGyCOO3sbwwAMPPBMRRw9ik4ZVvX1NDr/7CsfSmA70WLrqa7JLcCZMmMD69ev7tY+2tjZaW1sHpkHDKIc4cogB8oijtzFIqv4CtazU29fk8LuvcCyN6UCPpau+xt+DY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2YNIf3H5nWSfixpo6TPp/L5kp6Q9GBa3l+qM0/SZkmPSZpSKj9N0oa07mpJSuWjJN2aytdKmlCqM0PSprT4H8CaNbnsvgfHzJrWi8C7I2K3pIOAeyWtSuuuiogvlTeWdCIwHTiJ4v+b/UDSmyJiD3AtMBu4j+Kfuk4FVgGzgJ0RcYKk6cDlwHmSxgCXAJOAAB6QtDIidg5yzGY2SJzgDKAJc+8Y8H22LzxrwPdp1oii+L8xu9PTg9LS3f+SmQbcEhEvAlskbQYmS2oHDouINQCSbgDOpkhwpgHzU/3lwFfT7M4UYHVE7Eh1VlMkRcv6G9eGJ55j5gD3De4XzHrWY4Ij6TXAPcCotP3yiLhE0nzgz4Gn06afjYg7U515FCOlPcAnI+KuVH4asAQYTTGq+lREhKRRwA3AacAvgfMioj3VmQF8Lh3jbyJiaT9jNrMGJWkE8ABwAvC1iFgr6X3ARZIuANYDc9LMyjiKGZqKjlT2UnpcXU76uRUgIjolPQccWS6vUafcvtkUM0O0tLTQ1tbWY0wto2HOKZ09btcb9Rx3MOzevXvYjj3QHEtjGshY6pnB8bSxmQ2J1E+cKul1wHclnUzRb1xK0QdcClwBfBRQrV10U04f65TbtwhYBDBp0qSo5yvlr7lpBVdsGNjJ8vbzez7uYDjQ/yVAo3IstfV4kXEU+jRtHBFbgMq08VjStHGaiq5MG1fqVGZmlgNnVE8bp6SmMm1sZhmLiGeBNmBqRDwVEXsi4mXgm8DktFkHcFyp2njgyVQ+vkb5PnUkjQQOB3Z0sy8za1J1DStynDbuTl+nyAZ6Ghr6NxWdw7RlDjFAHnEMdgySjgZeiohnJY0G3gNcLmlsRGxLm30QeDg9XgncLOlKitniicC6iNgjaZek04G1wAXANaU6M4A1wDnAD9Np8ruAyyQdkbY7E5g3aMGa2aCrK8HJcdq4O32dIhvoCwmhf1PROUxb5hAD5BHHEMQwFliaBlSvAm6LiNsl3SjpVIrPfjvwcYCI2CjpNuARoBO4MPVVAJ9g7/V+q9ICcD1wY7ogeQfF6XQiYoekS4H703ZfqFxwbGbNqVcnhtPIqo1i2viVa28kfRO4PT3tz7RxR41p49aqOm29abOZNYeIeAh4a43yj3RTZwGwoEb5euDkGuUvAOd2sa/FwOJeNNnMGliP1+BIOjrN3FCaNv5JuqamonraeHr6Qq3j2TttvA3YJen0dH3NBcCKUp3KF2u9Mm0M3AWcKemINHV8ZiozMzMz61I9MzieNjYzM7Om0mOC42ljMzMzazb+X1RmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZtYQJL1G0jpJP5a0UdLnU/kYSaslbUo/jyjVmSdps6THJE0plZ8maUNad7UkpfJRkm5N5WslTSjVmZGOsUnSjCEM3cwGgRMcM2sULwLvjojfBU4Fpko6HZgL3B0RE4G703MknQhMB04CpgJflzQi7etaYDYwMS1TU/ksYGdEnABcBVye9jUGuAR4GzAZuKScSJlZ8xk53A0wMwOIiAB2p6cHpSWAaUBrKl8KtAEXp/JbIuJFYIukzcBkSe3AYRGxBkDSDcDZwKpUZ37a13Lgq2l2ZwqwOiJ2pDqrKZKiZYMSbD9NmHvHgO+zfeFZA75Ps+HU4wyOp43NbKhIGiHpQWA7RcKxFmiJiG0A6ecxafNxwNZS9Y5UNi49ri7fp05EdALPAUd2sy8za1L1zOBUpo13SzoIuFfSKuCPKaaNF0qaSzFtfHHVtPGxwA8kvSki9rB32vg+4E6KEdIqStPGkqZTTBufV5o2nkQxkntA0sqI2Dlgr4CZNYzUT5wq6XXAdyWd3M3mqrWLbsr7WmfvAaXZFH0YLS0ttLW1ddO8QstomHNKZ4/bDbd6Ytm9e3dd2zUDx9KYBjKWHhMcTxub2VCLiGcltVF83p+SNDYitkkaSzG7A8Usy3GlauOBJ1P5+Brl5TodkkYChwM7UnlrVZ22Gu1aBCwCmDRpUrS2tlZvsp9rblrBFRsa/2qA9vNbe9ymra2NemJuBo6lMQ1kLHV96tKFew8AJwBfi4i1kvaZNpZUnja+r1S9MtX7EnVOG0vq1bRxX0ZV3elrBjkYo7T+xJJDVp9DDJBHHIMdg6SjgZdScjMaeA/FbO5KYAawMP1ckaqsBG6WdCXFbPFEYF1E7JG0K12gvBa4ALimVGcGsAY4B/hhRISku4DLSqfazwTmDVqwZjbo6kpwGn3auC+jqu70NYOcORgX/tUxqupKDll9DjFAHnEMQQxjgaVpQPUq4LaIuF3SGuA2SbOAnwPnAkTERkm3AY8AncCFqa8C+ASwBBhNMUu8KpVfD9yYZpZ3UJxOJyJ2SLoUuD9t94XKzLGZNadezZs26rSxmTW/iHgIeGuN8l8CZ3RRZwGwoEb5emC/gVhEvEBKkGqsWwws7l2rzaxR1XMX1dFp5obStPFP2DvVC/tPG09Pd0Ydz95p423ALkmnp+trLqiqU9nXK9PGwF3AmZKOSFPHZ6YyMzMzsy7VM4PjaWMzMzNrKvXcReVpYzMzM2sq/lcNZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlh0nOGZmZpYdJzhmZmaWHSc4ZmZmlp2Rw92A4TRh7h01y+ec0snMLtaZmZlZ4/MMjpk1BEnHSfonSY9K2ijpU6l8vqQnJD2YlveX6syTtFnSY5KmlMpPk7QhrbtaklL5KEm3pvK1kiaU6syQtCktM4YwdDMbBAf0DI6ZNZROYE5E/EjSocADklandVdFxJfKG0s6EZgOnAQcC/xA0psiYg9wLTAbuA+4E5gKrAJmATsj4gRJ04HLgfMkjQEuASYBkY69MiJ2DnLMZjZIepzB8ajKzIZCRGyLiB+lx7uAR4Fx3VSZBtwSES9GxBZgMzBZ0ljgsIhYExEB3ACcXaqzND1eDpyR+qEpwOqI2JGSmtUUSZGZNal6ZnA8qjKzIZUGOW8F1gLvAC6SdAGwnqI/2kmR/NxXqtaRyl5Kj6vLST+3AkREp6TngCPL5TXqlNs1m6IPo6Wlhba2th5jaRldXNfX6OqJZffu3XVt1wwcS2MayFh6THAiYhuwLT3eJanuURWwRVJlVNVOGlUBSKqMqlalOvNT/eXAV6tHValOZVS1rHdhmlmzkHQI8G3g0xHxvKRrgUspBjmXAlcAHwVUo3p0U04f6+wtiFgELAKYNGlStLa2dhsLwDU3reCKDY1/NUD7+a09btPW1kY9MTcDx9KYBjKWXn3qchpVQdejqkYacfUnk80hq88hBsgjjqGIQdJBFMnNTRHxHYCIeKq0/pvA7elpB3Bcqfp44MlUPr5GeblOh6SRwOHAjlTeWlWnbSBiMrPhUXeCk9uoCujyVvA5p3Q2zIirnlFVV3LI6nOIAfKIY7BjSLO21wOPRsSVpfKxaSYZ4IPAw+nxSuBmSVdSnA6fCKyLiD2Sdkk6nWIwdgFwTanODGANcA7ww4gISXcBl0k6Im13JjBvsGI1s8FX119xj6rMbAi8A/gIsEHSg6nss8CHJJ1KMbhpBz4OEBEbJd0GPEJxreCF6Vo/gE8AS4DRFKfBV6Xy64Eb06nzHRTXCxIROyRdCtyftvtC5dS4mTWnHhMcj6rMbChExL3UnrW9s5s6C4AFNcrXAyfXKH8BOLeLfS0GFtfbXjNrbPXM4HhUZWZmZk2lnruoPKoyMzOzpuJ/1WBmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2YNQdJxkv5J0qOSNkr6VCofI2m1pE3p5xGlOvMkbZb0mKQppfLTJG1I666WpFQ+StKtqXytpAmlOjPSMTZJmjGEoZvZIHCCY2aNohOYExG/A5wOXCjpRGAucHdETATuTs9J66YDJwFTga9LGpH2dS0wG5iYlqmpfBawMyJOAK4CLk/7GgNcArwNmAxcUk6kzKz59JjgeFRlZkMhIrZFxI/S413Ao8A4YBqwNG22FDg7PZ4G3BIRL0bEFmAzMFnSWOCwiFgTEQHcUFWnsq/lwBmpH5oCrI6IHRGxE1jN3qTIzJrQyDq2qYyqfiTpUOABSauBmRSjqoWS5lKMqi6uGlUdC/xA0psiYg97R1X3AXdSdCCrKI2qJE2nGFWdVxpVTQIiHXtl6oDMLFNpkPNWYC3QEhHboEiCJB2TNhtH0ZdUdKSyl9Lj6vJKna1pX52SngOOLJfXqFNu12yKPoyWlhba2tp6jKVlNMw5pbPH7YZbPbHs3r27ru2agWNpTAMZS48JTupYKp3LLknlUVVr2mwp0AZcTGlUBWyRVBlVtZNGVQCSKqOqVanO/LSv5cBXq0dVqU5lVLWsHzGbWQOTdAjwbeDTEfF8muituWmNsuimvK919hZELAIWAUyaNClaW1u7atsrrrlpBVdsqGcsObzaz2/tcZu2tjbqibkZOJbGNJCx9OpTl9OoCroeVTXSiKs/mWwOWX0OMUAecQxFDJIOokhuboqI76TipySNTf3MWGB7Ku8AjitVHw88mcrH1ygv1+mQNBI4HNiRylur6rQNUFhmNgzqTnByG1UBzJx7R83yOad0NsyIq55RVVdyyOpziAHyiGOwY0izttcDj0bElaVVK4EZwML0c0Wp/GZJV1KcDp8IrIuIPZJ2STqdYjB2AXBN1b7WAOcAP4yIkHQXcFnpWsIzgXmDFKqZDYG6/op7VGVmQ+AdwEeADZIeTGWfpUhsbpM0C/g5cC5ARGyUdBvwCMW1ghema/0APgEsAUZTnAZflcqvB25Mp853UFwvSETskHQpcH/a7guVU+Nm1px6THA8qjKzoRAR91J71hbgjC7qLAAW1ChfD5xco/wFUoJUY91iYHG97TWzxlbPDI5HVWZmZtZU6rmLyqMqMzMzayr+JmMzMzPLjhMcMzMzy44THDMzM8uOExwzMzPLjhMcMzMzy44THDMzM8uOExwzMzPLjhMcMzMzy44THDMzM8uOExwzMzPLjhMcMzMzy44THDMzM8uOExwzMzPLjhMcMzMzy44THDMzM8uOExwzMzPLjhMcMzMzy44THDMzM8uOExwzawiSFkvaLunhUtl8SU9IejAt7y+tmydps6THJE0plZ8maUNad7UkpfJRkm5N5WslTSjVmSFpU1pmDFHIZjaIRva0gaTFwB8B2yPi5FQ2H/hz4Om02Wcj4s60bh4wC9gDfDIi7krlpwFLgNHAncCnIiIkjQJuAE4DfgmcFxHtqc4M4HPpGH8TEUv7GW/TmTD3jj7XnXNKJzNr1G9feFZ/mmQ2WJYAX6XoD8quiogvlQsknQhMB04CjgV+IOlNEbEHuBaYDdxH0ddMBVZR9Es7I+IESdOBy4HzJI0BLgEmAQE8IGllROwcnDDNbCjUM4OzhKKDqHZVRJyalkpyU+50pgJflzQibV/pdCampbLPVzod4CqKTodSp/M2YDJwiaQjeh2hmTWFiLgH2FHn5tOAWyLixYjYAmwGJksaCxwWEWsiIiiSpbNLdSqDpOXAGWl2ZwqwOiJ2pKRmNbX7PDNrIj3O4ETEPeWp3B680ukAWyRVOp12UqcDIKnS6axKdean+suBr1Z3OqlOpdNZVmdbzCwPF0m6AFgPzElJyDiKGZqKjlT2UnpcXU76uRUgIjolPQccWS6vUWcfkmZTDNRoaWmhra2tx8a3jC5mUxtdPbHs3r27ru2agWNpTAMZS48JTjeautOBrjudZumQetJVHM30Qcjlg5tDHMMUw7XApRSnji4FrgA+CqjGttFNOX2ss29hxCJgEcCkSZOitbW1m6YXrrlpBVds6E9XOzTaz2/tcZu2tjbqibkZOJbGNJCx9PVT1/SdDlDz+hQokoJm6JB60lUc9XRkjSKXD24OcQxHDBHxVOWxpG8Ct6enHcBxpU3HA0+m8vE1yst1OiSNBA6nOCXWAbRW1WkbqBjMbHj06S6qiHgqIvZExMvANymukYH+dTrU6HRq7cvMDhDpmpqKDwKVO6xWAtPTnVHHU1zXty4itgG7JJ2eTnVfAKwo1ancIXUO8MN0nc5dwJmSjkjX+Z2ZysysifUpwXGnY2YDTdIyYA3wZkkdkmYBX0y3fD8E/CHwvwAiYiNwG/AI8H3gwnQHFcAngOsoLjx+nOJaP4DrgSPTtYGfAeamfe2gmIm+Py1fqFz7Z2bNq57bxJdRTN8eJamD4s6mVkmnUpwyagc+DkWnI6nS6XSyf6ezhOI28VXs2+ncmDqdHRR3YREROyRVOh1wp2OWtYj4UI3i67vZfgGwoEb5euDkGuUvAOd2sa/FwOK6G2tmDa+eu6jc6ZiZmVlT8TcZm5mZWXac4JiZmVl2nOCYmZlZdpzgmJmZWXac4JiZmVl2mv/res3MrN8mdPHN7mVzTuns8hvgq7UvPKu/TTLrF8/gmJmZWXac4JiZmVl2nOCYmZlZdpzgmJmZWXac4JiZmVl2nOCYmZlZdpzgmJmZWXac4JiZmVl2nOCYmZlZdpzgmJmZWXac4JiZmVl2nOCYmZlZdpzgmFlDkLRY0nZJD5fKxkhaLWlT+nlEad08SZslPSZpSqn8NEkb0rqrJSmVj5J0aypfK2lCqc6MdIxNkmYMUchmNoh6THDc6ZjZEFkCTK0qmwvcHRETgbvTcySdCEwHTkp1vi5pRKpzLTAbmJiWyj5nATsj4gTgKuDytK8xwCXA24DJwCXlPs3MmlM9MzhLcKdjZoMsIu4BdlQVTwOWpsdLgbNL5bdExIsRsQXYDEyWNBY4LCLWREQAN1TVqexrOXBGGmhNAVZHxI6I2AmsZv8+z8yazMieNoiIe8qzKsk0oDU9Xgq0ARdT6nSALZIqnU47qdMBkFTpdFalOvPTvpYDX63udFKdSqezrPdhmlmTaomIbQARsU3SMal8HHBfabuOVPZSelxdXqmzNe2rU9JzwJHl8hp19iFpNsVAjZaWFtra2noOYDTMOaWzx+2aQW9iqee1GU67d+9u+DbWy7HU1mOC04Wm73Sg6w9qLh1SV3E00wchlw9uDnE0WAyqURbdlPe1zr6FEYuARQCTJk2K1tbWHht6zU0ruGJDX7vaxjLnlM66Y2k/v3VwG9NPbW1t1PP7awaOpbaB/tQ1TacDMHPuHTXLe/MhbmRdxdHoHU9ZLh/cHOIYphiekjQ2DaTGAttTeQdwXGm78cCTqXx8jfJynQ5JI4HDKU6JdbB3RrpSp21gwzCzodbXu6ieSp0NA9jpUKPTqbUvMztwrAQqNxjMAFaUyqenmxSOp7iub12aWd4l6fR0qvuCqjqVfZ0D/DBdp3MXcKakI9J1fmemMjNrYn1NcNzpmNmAkrQMWAO8WVKHpFnAQuC9kjYB703PiYiNwG3AI8D3gQsjYk/a1SeA6yguPH6c4lo/gOuBI9O1gZ8h3RyRrvO7FLg/LV+oXPtnZs2rx/MwqdNpBY6S1EFxZ9NC4LbUAf0cOBeKTkdSpdPpZP9OZwkwmqLDKXc6N6ZOZwfFXVhExA5JlU4H3OmYZS0iPtTFqjO62H4BsKBG+Xrg5BrlL5D6qhrrFgOL626smTW8eu6icqdjZmZmTcXfZGxmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmZmbZcYJjZmZm2XGCY2ZmZtlxgmNmDU9Su6QNkh6UtD6VjZG0WtKm9POI0vbzJG2W9JikKaXy09J+Nku6WpJS+ShJt6bytZImDHmQZjag+pXguNMxsyH0hxFxakRMSs/nAndHxETg7vQcSScC04GTgKnA1yWNSHWuBWYDE9MyNZXPAnZGxAnAVcDlQxCPmQ2igZjBcadjZsNhGrA0PV4KnF0qvyUiXoyILcBmYLKkscBhEbEmIgK4oapOZV/LgTMqAy0za04jB2Gf04DW9Hgp0AZcTKnTAbZIqnQ67aROB0BSpdNZlerMT/taDnxVklLnZGYHjgD+UVIA34iIRUBLRGwDiIhtko5J244D7ivV7UhlL6XH1eWVOlvTvjolPQccCTxTboSk2RSDMVpaWmhra+ux4S2jYc4pnfVH2sB6E0s9r81w2r17d8O3sV6Opbb+JjhN2+lA1x/UXDqkruK45qYVA3qcU8YdPqD7K8vlg5tDHMMcwzsi4snUn6yW9JNutq018xLdlHdXZ9+Coo9bBDBp0qRobW3tttFQfN6u2DAYY8mhN+eUzrpjaT+/dXAb009tbW3U8/trBo6ltv5+6pq20wGYOfeOmuW9+RA3sqGKYzA7slw+uDnEMZwxRMST6ed2Sd8FJgNPSRqbBlJjge1p8w7guFL18cCTqXx8jfJynQ5JI4HDgR2DFY+ZDb5+XYNT7nSAfTodgAHsdHCnY3ZgknSwpEMrj4EzgYeBlcCMtNkMoDI1uRKYnm5SOJ7iur51aWZ5l6TT0/U1F1TVqezrHOCHPhVu1tz6nOC40zGzIdIC3Cvpx8A64I6I+D6wEHivpE3Ae9NzImIjcBvwCPB94MKI2JP29QngOooLjx+nuNYP4HrgyHRt4GdIN0eYWfPqz/mLFuC76UaDkcDNEfF9SfcDt0maBfwcOBeKTkdSpdPpZP9OZwkwmqLDKXc6N6ZOZwfFXVhmdgCJiJ8Bv1uj/JfAGV3UWQAsqFG+Hji5RvkLpL7KBsaELi4B6I/2hWcN+D4tX31OcNzpmJmZWaPyNxmbmZlZdpzgmJmZWXac4JiZmVl2nOCYmZlZdpzgmJmZWXac4JiZmVl2nOCYmZlZdpzgmJmZWXac4JiZmVl2nOCYmZlZdpzgmJmZWXac4JiZmVl2nOCYmZlZdvr838TNKibMvWPA99m+8KwB36eZNbeB7GvmnNJJ64DtzRqRZ3DMzMwsO05wzMzMLDtOcMzMzCw7TnDMzMwsO05wzMzMLDtNcReVpKnAV4ARwHURsXCYm2SDrHK3xJxTOpk5AHdO+K4s64n7mQOP7wDNW8PP4EgaAXwNeB9wIvAhSScOb6vMLCfuZ8zy0wwzOJOBzRHxMwBJtwDTgEeGtVXWVAZjpNYbAzUT1VseTdbN/YwNiOHoa4arfylrxL5GETHcbeiWpHOAqRHxsfT8I8DbIuKi0jazgdnp6ZuBx/p52KOAZ/q5j0aQQxw5xAB5xNHbGN4QEUcPVmMGUj39TCrvS1+Tw+++wrE0pgM9lpp9TTPM4KhG2T5ZWUQsAhYN2AGl9RExaaD2N1xyiCOHGCCPOHKIoRs99jPQt74mp9fNsTQmx1Jbw1+DA3QAx5WejweeHKa2mFme3M+YZaYZEpz7gYmSjpf0amA6sHKY22RmeXE/Y5aZhj9FFRGdki4C7qK4fXNxRGwc5MMO2OmuYZZDHDnEAHnEkUMMNQ1yP5PT6+ZYGpNjqaHhLzI2MzMz661mOEVlZmZm1itOcMzMzCw7TnCqSJoq6TFJmyXNHe72dEXSYknbJT1cKhsjabWkTennEaV181JMj0maMjyt3pek4yT9k6RHJW2U9KlU3mxxvEbSOkk/TnF8PpU3VRxQfKOvpH+TdHt63nQxNIpm6UvKcuhXIJ++BfLqXyqGrJ+JCC9pobi48HHgjcCrgR8DJw53u7po6x8Avwc8XCr7IjA3PZ4LXJ4en5hiGQUcn2Ic0QAxjAV+Lz0+FPhpamuzxSHgkPT4IGAtcHqzxZHa9hngZuD2ZnxPNcrSTH1JVbubvl9Jbcuib0lty6Z/KcU0JP2MZ3D29crXtUfEfwCVr2tvOBFxD7CjqngasDQ9XgqcXSq/JSJejIgtwGaKWIdVRGyLiB+lx7uAR4FxNF8cERG709OD0hI0WRySxgNnAdeVipsqhgbSNH1JWQ79CuTTt0A+/UvFUPYzTnD2NQ7YWnrekcqaRUtEbIPiAw4ck8obPi5JE4C3UoxOmi6ONOX6ILAdWB0RzRjHl4G/BF4ulTVbDI0ip9enqd8Dzd63QDb9S8WXGaJ+xgnOvur6uvYm1NBxSToE+Dbw6Yh4vrtNa5Q1RBwRsSciTqX4BtzJkk7uZvOGi0PSHwHbI+KBeqvUKGuI30WDOBBen4aPMYe+BZq/f6kY6n7GCc6+mv3r2p+SNBYg/dyeyhs2LkkHUXRAN0XEd1Jx08VRERHPAm3AVJorjncA/1VSO8XplHdL+hbNFUMjyen1acr3QG59CzR1/1IxpP2ME5x9NfvXta8EZqTHM4AVpfLpkkZJOh6YCKwbhvbtQ5KA64FHI+LK0qpmi+NoSa9Lj0cD7wF+QhPFERHzImJ8REygeN//MCI+TBPF0GCavS8pa7r3QC59C+TRv1QMeT8z3FdTN9oCvJ/iivvHgb8a7vZ0085lwDbgJYosdxZwJHA3sCn9HFPa/q9STI8B7xvu9qc2vZNiuvEh4MG0vL8J43gL8G8pjoeBv07lTRVHqW2t7L27oSljaISlWfqSqjY3fb+S2pVF35LalVX/UmrjoPcz/lcNZmZmlh2fojIzM7PsOMExMzOz7DjBMTMzs+w4wTEzM7PsOMExMzOz7DjBMTMzs+w4wTEzM7Ps/H8eEnZlS9egxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "(df\n",
    " .loc[:, ['z_grade', 'z_floor']]\n",
    " .hist(ax=ax)\n",
    ")\n",
    "\n",
    "plt.suptitle('Histograms of Grade and Floor Heights (ft.)', fontsize=13)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd0dd5e",
   "metadata": {},
   "source": [
    "**Figure xx:** Histograms of z_grade and z_floor values (ft.).\n",
    "\n",
    "Caption this for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4c75f64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cp1bVC/ZvXFYBpcWRZEdVTY66DulQ2ZdHyyk+SVKTDChJUpMMKA3D5lEXIA2IfXmE/A5KktQkR1CSpCYZUJKkJhlQkqQmGVAamSTTSfyNiUYuyXlJ7k6yPclUkutGXZMMKA1YksNHXYO0ABcAv1NVZw7qgP4tHDrfQJHkncA7u4fHAPfP9Iea5ALgYuBB4F5gX1W9K8kngL3AK4AvJfl74IPASuAJ4Ner6p4kK4GPA6cDd3frnz72G4D3A0cCX+v2eWzwr1bLyXz6dpL3Aa8B1ibZBlzft24VcBVwGvA9YGNV3TFH+2XAScAaepdIeuvwXt0yUFXevFFVAEcA/wL88gzrTgLuB1b1bXdlt+4TwHXAYd3j5wOHd8uvBz7VLb8HuKpb/mngSWCS3vXOvgAc1a27GHjfqN8Pb+Nzm6tvd+ungclueQq4rlv+S+DSbvm1wO0HaL8M2AmsHPVrHoebIyj1+wvg5qr6zAzrzgA+X1V7AZJ8EnhR3/pPVtVT3fIxwJYk64Ci948DwM8BHwKo3qfNO7r2V9EbVf1rEoDnAF8c2KuS5u7bc3kN8BaAqro5yXFJjpmjHWBbVT0xqMKXMwNKACR5B3Aq8K7ZNjnAIR7vW/5jYHtV/UqSNfQ+nT5tpl+GB7ixqs6fV7HSQZhH355z9xnaao52eObfgg6BJ0mIJK8E/gB4W1X93yyb3Qb8fJJjuy9/3zLHIY8BvtEtv6Ov/QvAr3XP+VJ603wAtwCvTvLCbt1zk/SPzqQFmWffnkt/n50CvlVV35mjXQPkCErQ+2S5CtjeTbHtqKrf6N+gqr6R5E+AW+mdJHEX8Ogsx/sAvSm+9wA397X/FfDxbmrvdnqhR1V9s/uUe3WSI7tt/wj4r0N/aVrmDti3D+AyftRnvwdsOEC7Bshr8WnekhxdVY91I6hP0zvh4dOjrkvSeHKKTwfjsiS3A18F7gP+caTVSBprjqD0LElupfd7pH5vr6qvjKIeaVDs20uLASVJapJTfJKkJhlQkqQmGVCSpCYZUJKkJv0/1fAFebzvgq0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "df.boxplot(column=['z_grade', 'z_floor'])\n",
    "\n",
    "plt.ylabel('\\nfeet')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73cf343a",
   "metadata": {},
   "source": [
    "**Figure xx:** Boxplots of z_grade and z_floor values (ft.).\n",
    "\n",
    "Caption this for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "71626544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "sns.scatterplot(\n",
    "    x=df['z_grade'],\n",
    "    y=df['z_floor'],\n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('z_floor vs. z_grade (ft.)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88160f70",
   "metadata": {},
   "source": [
    "**Figure xx:** Histograms of z_grade and z_floor values (ft.).\n",
    "\n",
    "Caption this for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cd7effb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>bin</th>\n",
       "      <th>bbl</th>\n",
       "      <th>borough</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>address</th>\n",
       "      <th>z_grade</th>\n",
       "      <th>z_floor</th>\n",
       "      <th>subgrade</th>\n",
       "      <th>notes1</th>\n",
       "      <th>notes2</th>\n",
       "      <th>notes3</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>pluto_bbl</th>\n",
       "      <th>Council</th>\n",
       "      <th>BoroCD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>581783</th>\n",
       "      <td>POINT (-73.90546567085973 40.708900200415925)</td>\n",
       "      <td>4434111</td>\n",
       "      <td>4033730056</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3373.0</td>\n",
       "      <td>56</td>\n",
       "      <td>2115 GROVE STREET</td>\n",
       "      <td>113.93</td>\n",
       "      <td>63.443</td>\n",
       "      <td>N</td>\n",
       "      <td>Property was Successfully Measured; No Address...</td>\n",
       "      <td>Commercial, Industrial or other Non-Residentia...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.010459e+06</td>\n",
       "      <td>197562.090818</td>\n",
       "      <td>40.7089</td>\n",
       "      <td>-73.905466</td>\n",
       "      <td>4033730056</td>\n",
       "      <td>30</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             the_geom      bin         bbl  \\\n",
       "581783  POINT (-73.90546567085973 40.708900200415925)  4434111  4033730056   \n",
       "\n",
       "        borough   block  lot            address  z_grade  z_floor subgrade  \\\n",
       "581783      4.0  3373.0   56  2115 GROVE STREET   113.93   63.443        N   \n",
       "\n",
       "                                                   notes1  \\\n",
       "581783  Property was Successfully Measured; No Address...   \n",
       "\n",
       "                                                   notes2 notes3  \\\n",
       "581783  Commercial, Industrial or other Non-Residentia...    NaN   \n",
       "\n",
       "                   x              y  latitude  longitude   pluto_bbl  Council  \\\n",
       "581783  1.010459e+06  197562.090818   40.7089 -73.905466  4033730056       30   \n",
       "\n",
       "        BoroCD  \n",
       "581783     405  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    " .loc[\n",
    "     (df['z_grade'] > 100)\n",
    "     & (df['z_floor'] < 70)]\n",
    " .iloc[:, :-9]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b63f5d",
   "metadata": {},
   "source": [
    "More review is required, but this might be a measurement error. We can leave this in for now."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bfd6e08",
   "metadata": {},
   "source": [
    "Examine `z_grade` > `z_floor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9c048f2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of properties where z_floor is less than z_grade and z_floor is not zero: 316\n"
     ]
    }
   ],
   "source": [
    "counts = (\n",
    "    df\n",
    "    .loc[df['z_floor'] < df['z_grade']]\n",
    "    .shape[0]\n",
    ")\n",
    "\n",
    "print('count of properties where z_floor is less than z_grade and z_floor is not zero: {:,}'.format(counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7023e6df",
   "metadata": {},
   "source": [
    "Looks like there are 316 properties that seem to not be considered a first *occupied* floor. Let's check the notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4238614a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "notes1                                                                                                                              \n",
       "Property was Successfully Measured                                                                                                      246\n",
       "Property was Successfully Measured; No Address Plate or Identification on Building                                                       34\n",
       "Property not Visible Due to Construction or Obstruction                                                                                  13\n",
       "Property not Visible Due to Construction or Obstruction; No Address Plate or Identification on Building                                  10\n",
       "Property not Visible Due to Construction or Obstruction; No Address Plate or Identification on Building; Property Under Construction      5\n",
       "Property not Visible Due to Construction or Obstruction; Property Under Construction                                                      3\n",
       "Property was Successfully Measured; Property Under Construction                                                                           3\n",
       "Property was Successfully Measured; No Address Plate or Identification on Building; Property Under Construction                           2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine scenario\n",
    "(df\n",
    " .loc[df['z_grade'] > df['z_floor'], ['notes1']]\n",
    " .value_counts()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc97cbe",
   "metadata": {},
   "source": [
    "We can leave these in, but they might not be considered a first *occupied* floor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0c05dc",
   "metadata": {},
   "source": [
    "## Calculating Height from Building Adjacent Grade to First Floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a6dc8873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    847838.000000\n",
       "mean          4.268211\n",
       "std           3.087679\n",
       "min         -50.487000\n",
       "25%           2.015000\n",
       "50%           3.627000\n",
       "75%           6.571000\n",
       "max          94.440000\n",
       "Name: z_floor_grade, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# height from building floor to grade\n",
    "df['z_floor_grade'] = df['z_floor'] - df['z_grade']\n",
    "\n",
    "# summary stats\n",
    "df['z_floor_grade'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ce1c34ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean value of height from building adjacent grade to floor: 4.27 ft.\n"
     ]
    }
   ],
   "source": [
    "mean = df['z_floor_grade'].mean()\n",
    "\n",
    "print('mean value of height from building adjacent grade to floor: {:.2f} ft.'.format(mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b776b05c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean value of mean NTA height from building adjacent grade to floor: 3.82 ft.\n"
     ]
    }
   ],
   "source": [
    "# average of average neighborhood grade to floor height\n",
    "mean_nta = (\n",
    "    df\n",
    "    .groupby(by=['NTAName'])['z_floor_grade']\n",
    "    .mean()\n",
    "    .mean()\n",
    ")\n",
    "\n",
    "print('mean value of mean NTA height from building adjacent grade to floor: {:.2f} ft.'.format(mean_nta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a5443220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "df['z_floor_grade'].hist(ax=ax, bins=20)\n",
    "\n",
    "plt.title('Histogram of Building Adjacent Grade to First Floor (ft.)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c20a344",
   "metadata": {},
   "source": [
    "**Figure xx:** Histogram of building adjacent grade to first floor (ft.).\n",
    "\n",
    "Caption this for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "70b4804b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "sns.boxplot(x=df[\"z_floor_grade\"], ax=ax)\n",
    "\n",
    "plt.title('Building Adjacent Grade to First Floor (ft.)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a32a3bfc",
   "metadata": {},
   "source": [
    "Examine outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a633d7c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>bin</th>\n",
       "      <th>bbl</th>\n",
       "      <th>borough</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>address</th>\n",
       "      <th>z_grade</th>\n",
       "      <th>z_floor</th>\n",
       "      <th>subgrade</th>\n",
       "      <th>...</th>\n",
       "      <th>CTLabel</th>\n",
       "      <th>BoroCT2020</th>\n",
       "      <th>NTA2020</th>\n",
       "      <th>NTAName</th>\n",
       "      <th>CDTA2020</th>\n",
       "      <th>CDTAName</th>\n",
       "      <th>nta2020</th>\n",
       "      <th>boroname</th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>z_floor_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>581783</th>\n",
       "      <td>POINT (-73.90546567085973 40.708900200415925)</td>\n",
       "      <td>4434111</td>\n",
       "      <td>4033730056</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3373.0</td>\n",
       "      <td>56</td>\n",
       "      <td>2115 GROVE STREET</td>\n",
       "      <td>113.93</td>\n",
       "      <td>63.443</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>593.0</td>\n",
       "      <td>4059300</td>\n",
       "      <td>QN0502</td>\n",
       "      <td>Ridgewood</td>\n",
       "      <td>QN05</td>\n",
       "      <td>QN05 Ridgewood-Maspeth-Middle Village (CD 5 Ap...</td>\n",
       "      <td>QN0502</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Ridgewood, Queens</td>\n",
       "      <td>-50.487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             the_geom      bin         bbl  \\\n",
       "581783  POINT (-73.90546567085973 40.708900200415925)  4434111  4033730056   \n",
       "\n",
       "        borough   block  lot            address  z_grade  z_floor subgrade  \\\n",
       "581783      4.0  3373.0   56  2115 GROVE STREET   113.93   63.443        N   \n",
       "\n",
       "        ... CTLabel BoroCT2020 NTA2020    NTAName  CDTA2020  \\\n",
       "581783  ...   593.0    4059300  QN0502  Ridgewood      QN05   \n",
       "\n",
       "                                                 CDTAName  nta2020  boroname  \\\n",
       "581783  QN05 Ridgewood-Maspeth-Middle Village (CD 5 Ap...   QN0502    Queens   \n",
       "\n",
       "             ntaname_boro  z_floor_grade  \n",
       "581783  Ridgewood, Queens        -50.487  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['z_floor_grade'] < -40]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b398cc7c",
   "metadata": {},
   "source": [
    "Same outlier building as the previous inquiry. We can leave in for now, but probably good to examine further at some point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8d203547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\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>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allerton, Bronx</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>5.098075</td>\n",
       "      <td>3.770589</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.01925</td>\n",
       "      <td>5.0685</td>\n",
       "      <td>8.530</td>\n",
       "      <td>18.505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alley Pond Park, Queens</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.664000</td>\n",
       "      <td>3.176324</td>\n",
       "      <td>0.418</td>\n",
       "      <td>1.54100</td>\n",
       "      <td>2.6640</td>\n",
       "      <td>3.787</td>\n",
       "      <td>4.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Annadale-Huguenot-Prince's Bay-Woodrow, Staten...</td>\n",
       "      <td>11167.0</td>\n",
       "      <td>4.735034</td>\n",
       "      <td>3.072637</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2.36050</td>\n",
       "      <td>4.0560</td>\n",
       "      <td>7.778</td>\n",
       "      <td>18.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arden Heights-Rossville, Staten Island</td>\n",
       "      <td>9024.0</td>\n",
       "      <td>3.823133</td>\n",
       "      <td>3.187088</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.11700</td>\n",
       "      <td>2.9030</td>\n",
       "      <td>6.165</td>\n",
       "      <td>20.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Astoria (Central), Queens</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>4.041902</td>\n",
       "      <td>3.150262</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.85950</td>\n",
       "      <td>3.6930</td>\n",
       "      <td>6.572</td>\n",
       "      <td>15.774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        ntaname_boro    count      mean  \\\n",
       "0                                    Allerton, Bronx   1648.0  5.098075   \n",
       "1                            Alley Pond Park, Queens      2.0  2.664000   \n",
       "2  Annadale-Huguenot-Prince's Bay-Woodrow, Staten...  11167.0  4.735034   \n",
       "3             Arden Heights-Rossville, Staten Island   9024.0  3.823133   \n",
       "4                          Astoria (Central), Queens   4250.0  4.041902   \n",
       "\n",
       "        std    min      25%     50%    75%     max  \n",
       "0  3.770589  0.010  1.01925  5.0685  8.530  18.505  \n",
       "1  3.176324  0.418  1.54100  2.6640  3.787   4.910  \n",
       "2  3.072637  0.001  2.36050  4.0560  7.778  18.400  \n",
       "3  3.187088  0.000  1.11700  2.9030  6.165  20.415  \n",
       "4  3.150262  0.001  0.85950  3.6930  6.572  15.774  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean grade to floor height by NTA\n",
    "nta_mean = (\n",
    "    df\n",
    "    .groupby(by='ntaname_boro')['z_floor_grade']\n",
    "    .describe()\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# preview count measured dataframe\n",
    "nta_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "da5a8667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central Park, Manhattan</td>\n",
       "      <td>16</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.132537</td>\n",
       "      <td>0.645</td>\n",
       "      <td>1.16125</td>\n",
       "      <td>1.9235</td>\n",
       "      <td>2.20075</td>\n",
       "      <td>9.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chelsea-Hudson Yards, Manhattan</td>\n",
       "      <td>1996</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.630993</td>\n",
       "      <td>-0.993</td>\n",
       "      <td>0.35200</td>\n",
       "      <td>1.0970</td>\n",
       "      <td>6.68175</td>\n",
       "      <td>18.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chinatown-Two Bridges, Manhattan</td>\n",
       "      <td>1052</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.015440</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.42850</td>\n",
       "      <td>0.9275</td>\n",
       "      <td>3.70950</td>\n",
       "      <td>17.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>East Harlem (North), Manhattan</td>\n",
       "      <td>1928</td>\n",
       "      <td>3.61</td>\n",
       "      <td>4.002212</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.41300</td>\n",
       "      <td>1.2990</td>\n",
       "      <td>7.59650</td>\n",
       "      <td>26.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East Harlem (South), Manhattan</td>\n",
       "      <td>999</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.552444</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.52700</td>\n",
       "      <td>2.0170</td>\n",
       "      <td>6.17400</td>\n",
       "      <td>19.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ntaname_boro  count  mean       std    min      25%  \\\n",
       "0           Central Park, Manhattan     16  2.35  2.132537  0.645  1.16125   \n",
       "1   Chelsea-Hudson Yards, Manhattan   1996  3.34  3.630993 -0.993  0.35200   \n",
       "2  Chinatown-Two Bridges, Manhattan   1052  2.50  3.015440 -0.026  0.42850   \n",
       "3    East Harlem (North), Manhattan   1928  3.61  4.002212  0.000  0.41300   \n",
       "4    East Harlem (South), Manhattan    999  3.48  3.552444  0.004  0.52700   \n",
       "\n",
       "      50%      75%     max  \n",
       "0  1.9235  2.20075   9.144  \n",
       "1  1.0970  6.68175  18.299  \n",
       "2  0.9275  3.70950  17.029  \n",
       "3  1.2990  7.59650  26.196  \n",
       "4  2.0170  6.17400  19.001  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge dataframes to map\n",
    "nta_mean_merged = gdf.merge(\n",
    "    nta_mean,\n",
    "    on='ntaname_boro',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "# fill na with merge\n",
    "nta_mean_merged['count'] = nta_mean_merged['count'].fillna(0).astype(int)\n",
    "nta_mean_merged['mean'] = nta_mean_merged['mean'].round(2)\n",
    "\n",
    "# preview\n",
    "nta_mean_merged.iloc[:, -9:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9a7ccb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Midtown-Times Square, Manhattan</td>\n",
       "      <td>1434</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Midtown South-Flatiron-Union Square, Manhattan</td>\n",
       "      <td>1281</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tribeca-Civic Center, Manhattan</td>\n",
       "      <td>875</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SoHo-Little Italy-Hudson Square, Manhattan</td>\n",
       "      <td>1606</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lower East Side, Manhattan</td>\n",
       "      <td>977</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Financial District-Battery Park City, Manhattan</td>\n",
       "      <td>571</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>East Williamsburg, Brooklyn</td>\n",
       "      <td>4422</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Williamsburg, Brooklyn</td>\n",
       "      <td>3387</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chinatown-Two Bridges, Manhattan</td>\n",
       "      <td>1052</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Coney Island-Sea Gate, Brooklyn</td>\n",
       "      <td>2727</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Claremont Village-Claremont (East), Bronx</td>\n",
       "      <td>435</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Long Island City-Hunters Point, Queens</td>\n",
       "      <td>1202</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hunts Point, Bronx</td>\n",
       "      <td>1161</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Melrose, Bronx</td>\n",
       "      <td>1633</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bushwick (West), Brooklyn</td>\n",
       "      <td>4438</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>East Village, Manhattan</td>\n",
       "      <td>2342</td>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hell's Kitchen, Manhattan</td>\n",
       "      <td>1601</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jamaica, Queens</td>\n",
       "      <td>5064</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Spring Creek-Starrett City, Brooklyn</td>\n",
       "      <td>655</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Queensbridge-Ravenswood-Dutch Kills, Queens</td>\n",
       "      <td>2071</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       ntaname_boro  count  mean\n",
       "0                   Midtown-Times Square, Manhattan   1434  1.20\n",
       "1    Midtown South-Flatiron-Union Square, Manhattan   1281  1.21\n",
       "2                   Tribeca-Civic Center, Manhattan    875  1.45\n",
       "3        SoHo-Little Italy-Hudson Square, Manhattan   1606  1.81\n",
       "4                        Lower East Side, Manhattan    977  1.92\n",
       "5   Financial District-Battery Park City, Manhattan    571  1.96\n",
       "6                       East Williamsburg, Brooklyn   4422  2.34\n",
       "7                            Williamsburg, Brooklyn   3387  2.45\n",
       "8                  Chinatown-Two Bridges, Manhattan   1052  2.50\n",
       "9                   Coney Island-Sea Gate, Brooklyn   2727  2.61\n",
       "10        Claremont Village-Claremont (East), Bronx    435  2.62\n",
       "11           Long Island City-Hunters Point, Queens   1202  2.66\n",
       "12                               Hunts Point, Bronx   1161  2.78\n",
       "13                                   Melrose, Bronx   1633  2.83\n",
       "14                        Bushwick (West), Brooklyn   4438  2.90\n",
       "15                          East Village, Manhattan   2342  2.98\n",
       "16                        Hell's Kitchen, Manhattan   1601  3.01\n",
       "17                                  Jamaica, Queens   5064  3.01\n",
       "18             Spring Creek-Starrett City, Brooklyn    655  3.01\n",
       "19      Queensbridge-Ravenswood-Dutch Kills, Queens   2071  3.03"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table of percent measured in ascending order by NTA\n",
    "(nta_mean_merged\n",
    " .sort_values(by='mean')\n",
    " .loc[~nta_mean_merged['nta2020'].isin(nta_exclude), ['ntaname_boro', 'count', 'mean']]\n",
    " .head(20)\n",
    " .reset_index(drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff43ef5",
   "metadata": {},
   "source": [
    "**Table xx:** Average building grade to floor height in ascending order by NTA (Top 20)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f296e864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ntaname_boro</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hamilton Heights-Sugar Hill, Manhattan</td>\n",
       "      <td>1603</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Upper West Side (Central), Manhattan</td>\n",
       "      <td>2720</td>\n",
       "      <td>6.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kingsbridge Heights-Van Cortlandt Village, Bronx</td>\n",
       "      <td>995</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Park Slope, Brooklyn</td>\n",
       "      <td>6704</td>\n",
       "      <td>6.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Riverdale-Spuyten Duyvil, Bronx</td>\n",
       "      <td>2691</td>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brooklyn Heights, Brooklyn</td>\n",
       "      <td>1432</td>\n",
       "      <td>6.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Harlem (South), Manhattan</td>\n",
       "      <td>1945</td>\n",
       "      <td>6.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Washington Heights (South), Manhattan</td>\n",
       "      <td>1203</td>\n",
       "      <td>6.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Washington Heights (North), Manhattan</td>\n",
       "      <td>957</td>\n",
       "      <td>6.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bedford-Stuyvesant (East), Brooklyn</td>\n",
       "      <td>8905</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Upper West Side-Lincoln Square, Manhattan</td>\n",
       "      <td>946</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sunset Park (Central), Brooklyn</td>\n",
       "      <td>4174</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Clinton Hill, Brooklyn</td>\n",
       "      <td>2221</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Morris Park, Bronx</td>\n",
       "      <td>3865</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>East Flatbush-Remsen Village, Brooklyn</td>\n",
       "      <td>3496</td>\n",
       "      <td>5.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Harlem (North), Manhattan</td>\n",
       "      <td>2477</td>\n",
       "      <td>5.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kingsbridge-Marble Hill, Bronx</td>\n",
       "      <td>843</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pelham Gardens, Bronx</td>\n",
       "      <td>5098</td>\n",
       "      <td>5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bedford-Stuyvesant (West), Brooklyn</td>\n",
       "      <td>7251</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Fort Greene, Brooklyn</td>\n",
       "      <td>2044</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        ntaname_boro  count  mean\n",
       "0             Hamilton Heights-Sugar Hill, Manhattan   1603  7.00\n",
       "1               Upper West Side (Central), Manhattan   2720  6.66\n",
       "2   Kingsbridge Heights-Van Cortlandt Village, Bronx    995  6.58\n",
       "3                               Park Slope, Brooklyn   6704  6.33\n",
       "4                    Riverdale-Spuyten Duyvil, Bronx   2691  6.20\n",
       "5                         Brooklyn Heights, Brooklyn   1432  6.18\n",
       "6                          Harlem (South), Manhattan   1945  6.18\n",
       "7              Washington Heights (South), Manhattan   1203  6.15\n",
       "8              Washington Heights (North), Manhattan    957  6.08\n",
       "9                Bedford-Stuyvesant (East), Brooklyn   8905  6.02\n",
       "10         Upper West Side-Lincoln Square, Manhattan    946  5.90\n",
       "11                   Sunset Park (Central), Brooklyn   4174  5.84\n",
       "12                            Clinton Hill, Brooklyn   2221  5.84\n",
       "13                                Morris Park, Bronx   3865  5.84\n",
       "14            East Flatbush-Remsen Village, Brooklyn   3496  5.84\n",
       "15                         Harlem (North), Manhattan   2477  5.81\n",
       "16                    Kingsbridge-Marble Hill, Bronx    843  5.75\n",
       "17                             Pelham Gardens, Bronx   5098  5.66\n",
       "18               Bedford-Stuyvesant (West), Brooklyn   7251  5.56\n",
       "19                             Fort Greene, Brooklyn   2044  5.55"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table of percent measured in descending order by NTA\n",
    "(nta_mean_merged\n",
    " .sort_values(by='mean', ascending=False)\n",
    " .loc[~nta_mean_merged['nta2020'].isin(nta_exclude), ['ntaname_boro', 'count', 'mean']]\n",
    " .head(20)\n",
    " .reset_index(drop=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f3b0ae",
   "metadata": {},
   "source": [
    "**Table xx:** Average building adjacent grade to floor height in descending order by NTA (Top 20)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d32bf99b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1)\n",
    "cmap = plt.cm.viridis\n",
    "norm = mpl.colors.BoundaryNorm([0, 3, 4, 5, 6, 7], cmap.N)\n",
    "\n",
    "(nta_mean_merged\n",
    ".loc[~nta_mean_merged['nta2020'].isin(nta_exclude)]\n",
    ".plot(\n",
    "    column='mean',\n",
    "    legend=True,\n",
    "    cmap=cmap,\n",
    "    cax=cax,\n",
    "    norm=norm,\n",
    "    ax=ax)\n",
    ")\n",
    "\n",
    "gdf.plot(ax=ax, facecolor='None', linewidth=.4)\n",
    "\n",
    "ax.set_title('Average Building Adjacent Grade to Floor Height by NTA (ft.)', fontsize=12)\n",
    "ax.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/grade-floor-mean.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ff41a4",
   "metadata": {},
   "source": [
    "**Figure xx:** Average Building Adjacent Grade to Floor Height by Neighborhood Tabulation Area (NTA).\n",
    "\n",
    "Caption this for later."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "759755c3",
   "metadata": {},
   "source": [
    "# Examining Subgrade Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d3c3c0",
   "metadata": {},
   "source": [
    "Subgrade spaces  \n",
    "> As noted in the data dictionary, a subgrade space does not need to be habitable or show residential use to be recorded – this dataset cannot speak to whether recorded subgrade spaces are being used as residences. The z_grade measurement in buildings with subgrade space is not the same as the depth of the subgrade space – there is no way of knowing that measurement from outside of the building. The methodology relies on there being external clues that indicate subgrade space. It does not represent subgrade spaces that may only be internally accessible, such as cellars that may be present in large commercial or apartment buildings. **This dataset should not be treated as an inventory of subgrade spaces in New York City.**\n",
    "\n",
    "Source: Building_Elevation_and_Subgrade_Dataset_Guidance.pdf\n",
    "\n",
    "This dataset should not be treated as an inventory of subgrade spaces in New York City. The analysis below is purely exploratory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6968a11a",
   "metadata": {},
   "source": [
    "Examine Notes 2 and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a3f4981c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 847838 entries, 0 to 847837\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   notes2  188421 non-null  object\n",
      " 1   notes3  472002 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 12.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.loc[:, ['notes2', 'notes3']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1c0d7bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>notes2</th>\n",
       "      <th>z_floor_grade_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Attached Garage to Living Space; Partially Enclosed or Unenclosed Ground Floor for Parking or Access</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Partially Enclosed or Unenclosed Ground Floor for Parking or Access</td>\n",
       "      <td>6.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Attached Garage to Living Space; Fully Enclosed Ground floor for Apartment Lobby, Parking, or Access (includes sites with visible flood vents)</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fully Enclosed Ground floor for Apartment Lobby, Parking, or Access (includes sites with visible flood vents)</td>\n",
       "      <td>5.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Attached Garage to Living Space</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Commercial, Industrial or other Non-Residential Lowest Floor Active Use; Partially Enclosed or Unenclosed Ground Floor for Parking or Access</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Commercial, Industrial or other Non-Residential Lowest Floor Active Use; Fully Enclosed Ground floor for Apartment Lobby, Parking, or Access (includes sites with visible flood vents)</td>\n",
       "      <td>2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Commercial, Industrial or other Non-Residential Lowest Floor Active Use</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Commercial, Industrial or other Non-Residential Lowest Floor Active Use; Attached Garage to Living Space</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# notes2\n",
    "\n",
    "with pd.option_context('display.max_colwidth', 220):\n",
    "\n",
    "    notes2_df = (\n",
    "        df\n",
    "        .groupby(by='notes2')['z_floor_grade']\n",
    "        .mean()\n",
    "        .rename('z_floor_grade_avg')\n",
    "        .round(2)\n",
    "        .reset_index()\n",
    "        .sort_values(by='z_floor_grade_avg', ascending=False)\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "    \n",
    "    display(HTML(notes2_df.to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66837d80",
   "metadata": {},
   "source": [
    "**Table xx:** Mean Adjacent Grade to Floor Height by Floor Attributes (Notes 2).\n",
    "\n",
    "Caption this for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eac9a09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>notes3</th>\n",
       "      <th>z_floor_grade_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Visible Door or Window in subgrade space; Negative grade driveway walkway leading down to subgrade space</td>\n",
       "      <td>7.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Visible Door or Window in subgrade space</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# notes 3\n",
    "\n",
    "with pd.option_context('display.max_colwidth', 220):\n",
    "    \n",
    "    notes3_df = (\n",
    "        df\n",
    "        .groupby(by='notes3')['z_floor_grade']\n",
    "        .mean()\n",
    "        .rename('z_floor_grade_avg')\n",
    "        .round(2)\n",
    "        .reset_index()\n",
    "        .sort_values(by='z_floor_grade_avg', ascending=False)\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "\n",
    "    display(HTML(notes3_df.to_html()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67563175",
   "metadata": {},
   "source": [
    "**Table xx:** Mean Adjacent Grade to Floor Height by Subgrade Space Attributes (Notes 3).\n",
    "\n",
    "Caption this for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6faee2b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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