2-data-inspection-cleaning-wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Data Inspection, Cleaning and Wrangling in Python\n",
    "## Data Analysis Using Python: A Beginner’s Guide Featuring NYC Open Data  \n",
    "\n",
    "\n",
    "Author: Mark Bauer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table of Contents\n",
    "=================\n",
    "\n",
    "   Getting Started\n",
    "          \n",
    "   1. Importing Libraries \n",
    "   2. Reading In Data as a pandas Dataframe      \n",
    "   3. Data Inspection\n",
    "       * 3.1 Previewing Data\n",
    "       * 3.2 Calculating Summary Statistics\n",
    "       * 3.3 Identifying Null Values \n",
    "   4. Data Cleaning & Wrangling\n",
    "       * 4.1 Previewing Column Values\n",
    "       * 4.2 Exploring Distribution of Values\n",
    "       * 4.3 Filtering Data\n",
    "       * 4.4 Create New Columns\n",
    "       * 4.5 Filling In or Dropping Null Values \n",
    "   5. Sample Analysis\n",
    "       * 5.1. Line Plot of Year Built\n",
    "       * 5.2. Identify Oldest Building in Dataset  \n",
    "   6. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Goal:** In this notebook, we will review various ways to to inspect, clean, wrangle, and detect outliers in your data.\n",
    "\n",
    "**Main Library:** [pandas](https://pandas.pydata.org/) is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started: Accessing the Building Footprints Dataset\n",
    "\n",
    "![building_footprints_csv](images/building-footprints-csv.png)\n",
    "\n",
    "Screenshot of link address."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dataset Link**: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh\n",
    "\n",
    "**Documentation**: https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing verions of Python modules and packages with **watermark** documention for installing watermark: https://github.com/rasbt/watermark. Performed for reproducibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.0\n",
      "IPython version      : 8.6.0\n",
      "\n",
      "numpy     : 1.23.4\n",
      "pandas    : 1.5.1\n",
      "matplotlib: 3.6.2\n",
      "seaborn   : 0.12.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# watermark\n",
    "%reload_ext watermark\n",
    "%watermark -v -p numpy,pandas,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Reading In Data as a pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>2100</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F5F8CDA5-69E2-46F8-8F69-BA95C025B520}</td>\n",
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       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746627</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>2100</td>\n",
       "      <td>51.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9F644794-F72C-4582-9E5E-B337E2B97068}</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MULTIPOLYGON (((-73.96113466505085 40.57743931...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3245111</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>786626</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3086910048</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F916B22D-E25B-44AE-9FA9-2A51191B9CDF}</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1950.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746409</td>\n",
       "      <td>18.015113</td>\n",
       "      <td>2100</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>4075020005</td>\n",
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       "                                            the_geom NAME      BIN  \\\n",
       "0  MULTIPOLYGON (((-73.96664570466969 40.62599676...  NaN  3170958   \n",
       "1  MULTIPOLYGON (((-74.16790202462265 40.63936048...  NaN  5028452   \n",
       "2  MULTIPOLYGON (((-74.19510813278613 40.55610681...  NaN  5078368   \n",
       "3  MULTIPOLYGON (((-73.96113466505085 40.57743931...  NaN  3245111   \n",
       "4  MULTIPOLYGON (((-73.75421559146166 40.75591276...  NaN  4161096   \n",
       "\n",
       "   CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "0      1925.0  08/22/2017 12:00:00 AM  Constructed     96807   29.749853   \n",
       "1      1965.0  08/22/2017 12:00:00 AM  Constructed    326368   22.630000   \n",
       "2      1970.0  08/22/2017 12:00:00 AM  Constructed    746627   35.760000   \n",
       "3      1928.0  08/22/2017 12:00:00 AM  Constructed    786626   37.500000   \n",
       "4      1950.0  08/22/2017 12:00:00 AM  Constructed    746409   18.015113   \n",
       "\n",
       "   FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "0       2100        40.0           0          0  3065220021  3065220021   \n",
       "1       2100        39.0           0          0  5012640036  5012640036   \n",
       "2       2100        51.0           0          0  5060190091  5060190091   \n",
       "3       2100         6.0           0          0  3086910048  3086910048   \n",
       "4       2100        93.0           0          0  4075020005  4075020005   \n",
       "\n",
       "   GEOMSOURCE                                GLOBALID  \n",
       "0  Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
       "1  Photogramm  {F5F8CDA5-69E2-46F8-8F69-BA95C025B520}  \n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading in data as a pandas dataframe\n",
    "path = 'https://data.cityofnewyork.us/api/views/qb5r-6dgf/rows.csv?accessType=DOWNLOAD'\n",
    "building_df = pd.read_csv(path)\n",
    "\n",
    "# preview data\n",
    "building_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Inspection\n",
    "## 3.1 Previewing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>Photogramm</td>\n",
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       "      <td>1970.0</td>\n",
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       "      <td>35.760000</td>\n",
       "      <td>2100</td>\n",
       "      <td>51.0</td>\n",
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       "      <td>5060190091</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9F644794-F72C-4582-9E5E-B337E2B97068}</td>\n",
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       "      <td>3245111</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>786626</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2100</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>1950.0</td>\n",
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       "      <td>93.0</td>\n",
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       "                                            the_geom NAME      BIN  \\\n",
       "0  MULTIPOLYGON (((-73.96664570466969 40.62599676...  NaN  3170958   \n",
       "1  MULTIPOLYGON (((-74.16790202462265 40.63936048...  NaN  5028452   \n",
       "2  MULTIPOLYGON (((-74.19510813278613 40.55610681...  NaN  5078368   \n",
       "3  MULTIPOLYGON (((-73.96113466505085 40.57743931...  NaN  3245111   \n",
       "4  MULTIPOLYGON (((-73.75421559146166 40.75591276...  NaN  4161096   \n",
       "\n",
       "   CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "0      1925.0  08/22/2017 12:00:00 AM  Constructed     96807   29.749853   \n",
       "1      1965.0  08/22/2017 12:00:00 AM  Constructed    326368   22.630000   \n",
       "2      1970.0  08/22/2017 12:00:00 AM  Constructed    746627   35.760000   \n",
       "3      1928.0  08/22/2017 12:00:00 AM  Constructed    786626   37.500000   \n",
       "4      1950.0  08/22/2017 12:00:00 AM  Constructed    746409   18.015113   \n",
       "\n",
       "   FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "0       2100        40.0           0          0  3065220021  3065220021   \n",
       "1       2100        39.0           0          0  5012640036  5012640036   \n",
       "2       2100        51.0           0          0  5060190091  5060190091   \n",
       "3       2100         6.0           0          0  3086910048  3086910048   \n",
       "4       2100        93.0           0          0  4075020005  4075020005   \n",
       "\n",
       "   GEOMSOURCE                                GLOBALID  \n",
       "0  Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
       "1  Photogramm  {F5F8CDA5-69E2-46F8-8F69-BA95C025B520}  \n",
       "2  Photogramm  {9F644794-F72C-4582-9E5E-B337E2B97068}  \n",
       "3  Photogramm  {F916B22D-E25B-44AE-9FA9-2A51191B9CDF}  \n",
       "4  Photogramm  {525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}  "
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     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "# return the first five rows\n",
    "building_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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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>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1082959</th>\n",
       "      <td>MULTIPOLYGON (((-73.99956501703511 40.72039743...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1091063</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>11/01/2024 12:00:00 AM</td>\n",
       "      <td>Alteration</td>\n",
       "      <td>1302671</td>\n",
       "      <td>94.660000</td>\n",
       "      <td>2100</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1004730051</td>\n",
       "      <td>1004730051</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{151309FC-B779-46CE-B1D9-1584AEA02FC6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082960</th>\n",
       "      <td>MULTIPOLYGON (((-74.0000149828318 40.688200830...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3394115</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1114346</td>\n",
       "      <td>81.127098</td>\n",
       "      <td>2100</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3003040018</td>\n",
       "      <td>3003047502</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{A200FA56-7C7C-42F7-AA99-2C9DD5BF9EF6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082961</th>\n",
       "      <td>MULTIPOLYGON (((-73.99984919403173 40.68862382...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3397444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01/22/2019 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1114440</td>\n",
       "      <td>73.179002</td>\n",
       "      <td>2100</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3003040018</td>\n",
       "      <td>3003047502</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9B137E5C-68B4-408A-B449-E5BF57F3B6E3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082962</th>\n",
       "      <td>MULTIPOLYGON (((-73.8343042812369 40.757419581...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4539469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01/22/2019 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1107493</td>\n",
       "      <td>194.084179</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4050660001</td>\n",
       "      <td>4050667503</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{B056D55A-FD91-4671-A57C-8523F26C8C61}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082963</th>\n",
       "      <td>MULTIPOLYGON (((-73.83440749536311 40.75742362...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4540380</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>04/11/2019 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1114668</td>\n",
       "      <td>185.779728</td>\n",
       "      <td>2100</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4050660001</td>\n",
       "      <td>4050667503</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{E549C8DC-0457-43CB-88F6-136472CE6946}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom NAME      BIN  \\\n",
       "1082959  MULTIPOLYGON (((-73.99956501703511 40.72039743...  NaN  1091063   \n",
       "1082960  MULTIPOLYGON (((-74.0000149828318 40.688200830...  NaN  3394115   \n",
       "1082961  MULTIPOLYGON (((-73.99984919403173 40.68862382...  NaN  3397444   \n",
       "1082962  MULTIPOLYGON (((-73.8343042812369 40.757419581...  NaN  4539469   \n",
       "1082963  MULTIPOLYGON (((-73.83440749536311 40.75742362...  NaN  4540380   \n",
       "\n",
       "         CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  \\\n",
       "1082959      1900.0  11/01/2024 12:00:00 AM   Alteration   1302671   \n",
       "1082960      2009.0  08/22/2017 12:00:00 AM  Constructed   1114346   \n",
       "1082961         NaN  01/22/2019 12:00:00 AM  Constructed   1114440   \n",
       "1082962         NaN  01/22/2019 12:00:00 AM  Constructed   1107493   \n",
       "1082963      2006.0  04/11/2019 12:00:00 AM  Constructed   1114668   \n",
       "\n",
       "         HEIGHTROOF  FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  \\\n",
       "1082959   94.660000       2100        24.0           0          0  1004730051   \n",
       "1082960   81.127098       2100        17.0           0          0  3003040018   \n",
       "1082961   73.179002       2100        16.0           0          0  3003040018   \n",
       "1082962  194.084179       2100         9.0           0          0  4050660001   \n",
       "1082963  185.779728       2100        22.0           0          0  4050660001   \n",
       "\n",
       "         MPLUTO_BBL  GEOMSOURCE                                GLOBALID  \n",
       "1082959  1004730051  Other (Man  {151309FC-B779-46CE-B1D9-1584AEA02FC6}  \n",
       "1082960  3003047502  Photogramm  {A200FA56-7C7C-42F7-AA99-2C9DD5BF9EF6}  \n",
       "1082961  3003047502  Photogramm  {9B137E5C-68B4-408A-B449-E5BF57F3B6E3}  \n",
       "1082962  4050667503  Photogramm  {B056D55A-FD91-4671-A57C-8523F26C8C61}  \n",
       "1082963  4050667503  Photogramm  {E549C8DC-0457-43CB-88F6-136472CE6946}  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return the last five rows\n",
    "building_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1082964, 16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the shape or dimensions of our dataframe (i.e. rows, columns)\n",
    "building_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset has 1,082,964 rows and 16 columns.\n"
     ]
    }
   ],
   "source": [
    "# more readable\n",
    "rows, columns = building_df.shape\n",
    "print(f'This dataset has {rows:,} rows and {columns:,} columns.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the object's type\n",
    "type(building_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the object's type of a column\n",
    "type(building_df['CNSTRCT_YR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# notice the difference of type from series to dataframe\n",
    "print(type(building_df[\"CNSTRCT_YR\"]))\n",
    "print(type(building_df[[\"CNSTRCT_YR\"]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['the_geom', 'NAME', 'BIN', 'CNSTRCT_YR', 'LSTMODDATE', 'LSTSTATYPE',\n",
       "       'DOITT_ID', 'HEIGHTROOF', 'FEAT_CODE', 'GROUNDELEV', 'SHAPE_AREA',\n",
       "       'SHAPE_LEN', 'BASE_BBL', 'MPLUTO_BBL', 'GEOMSOURCE', 'GLOBALID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# columns as attributes\n",
    "building_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "the_geom       object\n",
       "NAME           object\n",
       "BIN             int64\n",
       "CNSTRCT_YR    float64\n",
       "LSTMODDATE     object\n",
       "LSTSTATYPE     object\n",
       "DOITT_ID        int64\n",
       "HEIGHTROOF    float64\n",
       "FEAT_CODE       int64\n",
       "GROUNDELEV    float64\n",
       "SHAPE_AREA      int64\n",
       "SHAPE_LEN       int64\n",
       "BASE_BBL        int64\n",
       "MPLUTO_BBL      int64\n",
       "GEOMSOURCE     object\n",
       "GLOBALID       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the datatypes of columns\n",
    "building_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1082964 entries, 0 to 1082963\n",
      "Data columns (total 16 columns):\n",
      " #   Column      Non-Null Count    Dtype  \n",
      "---  ------      --------------    -----  \n",
      " 0   the_geom    1082964 non-null  object \n",
      " 1   NAME        2247 non-null     object \n",
      " 2   BIN         1082964 non-null  int64  \n",
      " 3   CNSTRCT_YR  1072721 non-null  float64\n",
      " 4   LSTMODDATE  1082964 non-null  object \n",
      " 5   LSTSTATYPE  1082633 non-null  object \n",
      " 6   DOITT_ID    1082964 non-null  int64  \n",
      " 7   HEIGHTROOF  1080385 non-null  float64\n",
      " 8   FEAT_CODE   1082964 non-null  int64  \n",
      " 9   GROUNDELEV  1082413 non-null  float64\n",
      " 10  SHAPE_AREA  1082964 non-null  int64  \n",
      " 11  SHAPE_LEN   1082964 non-null  int64  \n",
      " 12  BASE_BBL    1082964 non-null  int64  \n",
      " 13  MPLUTO_BBL  1082964 non-null  int64  \n",
      " 14  GEOMSOURCE  1082662 non-null  object \n",
      " 15  GLOBALID    1082964 non-null  object \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 132.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# return the names, non-null counts and datatypes of columns\n",
    "building_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1082964, step=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect RangeIndex\n",
    "building_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect True/False if column is unique on our unique key (DOITT_ID)\n",
    "building_df['DOITT_ID'].is_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Calculating Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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>CNSTRCT_YR</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.082964e+06</td>\n",
       "      <td>1.072721e+06</td>\n",
       "      <td>1.082964e+06</td>\n",
       "      <td>1.080385e+06</td>\n",
       "      <td>1.082964e+06</td>\n",
       "      <td>1.082413e+06</td>\n",
       "      <td>1082964.0</td>\n",
       "      <td>1082964.0</td>\n",
       "      <td>1.082964e+06</td>\n",
       "      <td>1.082964e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.713174e+06</td>\n",
       "      <td>1.939675e+03</td>\n",
       "      <td>5.951884e+05</td>\n",
       "      <td>2.771381e+01</td>\n",
       "      <td>2.693296e+03</td>\n",
       "      <td>5.512603e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.564311e+09</td>\n",
       "      <td>3.564311e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.026611e+06</td>\n",
       "      <td>2.952066e+01</td>\n",
       "      <td>3.624453e+05</td>\n",
       "      <td>2.183592e+01</td>\n",
       "      <td>1.198342e+03</td>\n",
       "      <td>4.155005e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.857978e+08</td>\n",
       "      <td>9.857978e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>1.652000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>-1.600000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000010e+09</td>\n",
       "      <td>1.000010e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.129808e+06</td>\n",
       "      <td>1.920000e+03</td>\n",
       "      <td>2.882088e+05</td>\n",
       "      <td>1.854000e+01</td>\n",
       "      <td>2.100000e+03</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.043430e+09</td>\n",
       "      <td>3.043430e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.067980e+06</td>\n",
       "      <td>1.930000e+03</td>\n",
       "      <td>5.766045e+05</td>\n",
       "      <td>2.613000e+01</td>\n",
       "      <td>2.100000e+03</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.019580e+09</td>\n",
       "      <td>4.019580e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.483554e+06</td>\n",
       "      <td>1.955000e+03</td>\n",
       "      <td>8.668562e+05</td>\n",
       "      <td>3.114000e+01</td>\n",
       "      <td>2.100000e+03</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.107013e+09</td>\n",
       "      <td>4.107013e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.799523e+06</td>\n",
       "      <td>2.025000e+03</td>\n",
       "      <td>1.302671e+06</td>\n",
       "      <td>1.550000e+03</td>\n",
       "      <td>5.110000e+03</td>\n",
       "      <td>1.335000e+03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000e+09</td>\n",
       "      <td>6.000000e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BIN    CNSTRCT_YR      DOITT_ID    HEIGHTROOF     FEAT_CODE  \\\n",
       "count  1.082964e+06  1.072721e+06  1.082964e+06  1.080385e+06  1.082964e+06   \n",
       "mean   3.713174e+06  1.939675e+03  5.951884e+05  2.771381e+01  2.693296e+03   \n",
       "std    1.026611e+06  2.952066e+01  3.624453e+05  2.183592e+01  1.198342e+03   \n",
       "min    1.000000e+06  1.652000e+03  1.000000e+00  0.000000e+00  1.000000e+03   \n",
       "25%    3.129808e+06  1.920000e+03  2.882088e+05  1.854000e+01  2.100000e+03   \n",
       "50%    4.067980e+06  1.930000e+03  5.766045e+05  2.613000e+01  2.100000e+03   \n",
       "75%    4.483554e+06  1.955000e+03  8.668562e+05  3.114000e+01  2.100000e+03   \n",
       "max    5.799523e+06  2.025000e+03  1.302671e+06  1.550000e+03  5.110000e+03   \n",
       "\n",
       "         GROUNDELEV  SHAPE_AREA  SHAPE_LEN      BASE_BBL    MPLUTO_BBL  \n",
       "count  1.082413e+06   1082964.0  1082964.0  1.082964e+06  1.082964e+06  \n",
       "mean   5.512603e+01         0.0        0.0  3.564311e+09  3.564311e+09  \n",
       "std    4.155005e+01         0.0        0.0  9.857978e+08  9.857978e+08  \n",
       "min   -1.600000e+01         0.0        0.0  1.000010e+09  1.000010e+09  \n",
       "25%    2.500000e+01         0.0        0.0  3.043430e+09  3.043430e+09  \n",
       "50%    4.600000e+01         0.0        0.0  4.019580e+09  4.019580e+09  \n",
       "75%    7.400000e+01         0.0        0.0  4.107013e+09  4.107013e+09  \n",
       "max    1.335000e+03         0.0        0.0  6.000000e+09  6.000000e+09  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics of numeric columns\n",
    "building_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>NAME</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1082964</td>\n",
       "      <td>2247</td>\n",
       "      <td>1082964</td>\n",
       "      <td>1082633</td>\n",
       "      <td>1082662</td>\n",
       "      <td>1082964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1082964</td>\n",
       "      <td>1888</td>\n",
       "      <td>1901</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1082964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>MULTIPOLYGON (((-73.96664570466969 40.62599676...</td>\n",
       "      <td>Adler Place Town Houses</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{31298F86-3088-4F53-B3DB-71A9EFA6FA1F}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>799638</td>\n",
       "      <td>1080270</td>\n",
       "      <td>1011168</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 the_geom  \\\n",
       "count                                             1082964   \n",
       "unique                                            1082964   \n",
       "top     MULTIPOLYGON (((-73.96664570466969 40.62599676...   \n",
       "freq                                                    1   \n",
       "\n",
       "                           NAME              LSTMODDATE   LSTSTATYPE  \\\n",
       "count                      2247                 1082964      1082633   \n",
       "unique                     1888                    1901           12   \n",
       "top     Adler Place Town Houses  08/22/2017 12:00:00 AM  Constructed   \n",
       "freq                         52                  799638      1080270   \n",
       "\n",
       "        GEOMSOURCE                                GLOBALID  \n",
       "count      1082662                                 1082964  \n",
       "unique           3                                 1082964  \n",
       "top     Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
       "freq       1011168                                       1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics of string columns\n",
    "building_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <td>1920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME</th>\n",
       "      <td>Adler Place Town Houses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               mode\n",
       "CNSTRCT_YR                   1920.0\n",
       "NAME        Adler Place Town Houses"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning the mode of CNSTRCT_YR and NAME\n",
    "(building_df\n",
    " .loc[:, ['CNSTRCT_YR', 'NAME']]\n",
    " .mode()\n",
    " .T\n",
    " .rename(columns={0: \"mode\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1920.0    129966\n",
       "1930.0    112408\n",
       "1925.0    108937\n",
       "1950.0     62598\n",
       "1910.0     52163\n",
       "1940.0     51179\n",
       "1960.0     40721\n",
       "1935.0     38502\n",
       "1931.0     36618\n",
       "1945.0     34011\n",
       "Name: CNSTRCT_YR, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the CNSTRCT_YR column - top 10\n",
    "(building_df['CNSTRCT_YR']\n",
    " .value_counts()\n",
    " .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adler Place Town Houses     52\n",
       "Cooper Place Town Houses    38\n",
       "Broun Place Town Houses     36\n",
       "Debs Place Town Houses      34\n",
       "Defoe Place Town Houses     26\n",
       "Asch Loop Town Houses       18\n",
       "Earhart Lane Town Houses    16\n",
       "NO NAME                      9\n",
       "Restrooms                    8\n",
       "Kimball Block                6\n",
       "Name: NAME, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the NAME column - top 10\n",
    "(building_df['NAME']\n",
    " .value_counts()\n",
    " .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I provide several examples of the pandas `groupby` method below, as it's very common in data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTSTATYPE\n",
       "Alteration                     1518\n",
       "Constructed                 1080270\n",
       "Correction                       69\n",
       "Demolition                       13\n",
       "Geometry                         17\n",
       "Initialization                  111\n",
       "Investigate Construction         50\n",
       "Investigate Demolition            5\n",
       "Marked for Construction          23\n",
       "Marked for Demolition           126\n",
       "Merged                           93\n",
       "Split                           338\n",
       "Name: DOITT_ID, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by 'LSTSTATYPE', count of rows\n",
    "(building_df\n",
    " .groupby(by='LSTSTATYPE')['DOITT_ID']\n",
    " .count()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTSTATYPE: Feature last status type (Demolition, Alteration, Geometry, Initialization, Correction, Marked for Construction, Marked For Demolition, Constructed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>HEIGHTROOF_avg</th>\n",
       "      <th>GROUNDELEV_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>14.853740</td>\n",
       "      <td>39.965517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>18.060071</td>\n",
       "      <td>55.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>166.333333</td>\n",
       "      <td>50.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>17.081468</td>\n",
       "      <td>31.788288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>25.180486</td>\n",
       "      <td>29.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>367.722222</td>\n",
       "      <td>38.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>31.635171</td>\n",
       "      <td>55.105681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>54.010564</td>\n",
       "      <td>37.617391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>96.022102</td>\n",
       "      <td>52.051546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>11.700554</td>\n",
       "      <td>55.255799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           HEIGHTROOF_avg  GROUNDELEV_avg\n",
       "FEAT_CODE                                \n",
       "1000            14.853740       39.965517\n",
       "1001            18.060071       55.975000\n",
       "1003           166.333333       50.629630\n",
       "1004            17.081468       31.788288\n",
       "1005            25.180486       29.217391\n",
       "1006           367.722222       38.333333\n",
       "2100            31.635171       55.105681\n",
       "2110            54.010564       37.617391\n",
       "5100            96.022102       52.051546\n",
       "5110            11.700554       55.255799"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by 'FEAT_CODE', mean value of 'HEIGHTROOF' and mean value of 'GROUNDELEV'\n",
    "(building_df\n",
    " .groupby(by='FEAT_CODE')[['HEIGHTROOF', 'GROUNDELEV']]\n",
    " .mean()\n",
    " .rename(columns={\n",
    "     'HEIGHTROOF':'HEIGHTROOF_avg',\n",
    "     'GROUNDELEV':'GROUNDELEV_avg'\n",
    " })\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FEAT_CODE:\n",
    "\n",
    "\n",
    "Type of Building. List of values:\n",
    "1000 = Parking\n",
    "1001 = Gas Station Canopy\n",
    "1002 = Storage Tank\n",
    "1003 = Placeholder (triangle for permitted bldg)\n",
    "1004 = Auxiliary Structure (non-addressable, not garage)\n",
    "1005 = Temporary Structure (e.g. construction trailer)\n",
    "1006 = Cantilevered Building\n",
    "2100 = Building\n",
    "2110 = Skybridge\n",
    "5100 = Building Under Construction\n",
    "5110 = Garage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Alteration</th>\n",
       "      <th>1004</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>1448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Constructed</th>\n",
       "      <th>1000</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>866256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>213289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Correction</th>\n",
       "      <th>2100</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Demolition</th>\n",
       "      <th>2100</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Geometry</th>\n",
       "      <th>1000</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Initialization</th>\n",
       "      <th>1003</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Investigate Construction</th>\n",
       "      <th>2100</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investigate Demolition</th>\n",
       "      <th>2100</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Marked for Construction</th>\n",
       "      <th>1003</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Marked for Demolition</th>\n",
       "      <th>1003</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Merged</th>\n",
       "      <th>2100</th>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Split</th>\n",
       "      <th>1000</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100</th>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     count\n",
       "LSTSTATYPE               FEAT_CODE        \n",
       "Alteration               1004            3\n",
       "                         2100         1448\n",
       "                         5100            2\n",
       "                         5110           65\n",
       "Constructed              1000           85\n",
       "                         1001          123\n",
       "                         1003            7\n",
       "                         1004          221\n",
       "                         1005           24\n",
       "                         1006           17\n",
       "                         2100       866256\n",
       "                         2110          123\n",
       "                         5100          125\n",
       "                         5110       213289\n",
       "Correction               2100           50\n",
       "                         5100            1\n",
       "                         5110           18\n",
       "Demolition               2100           10\n",
       "                         5110            3\n",
       "Geometry                 1000           10\n",
       "                         2100            7\n",
       "Initialization           1003            6\n",
       "                         1004            1\n",
       "                         1006            1\n",
       "                         2100           47\n",
       "                         5100           52\n",
       "                         5110            4\n",
       "Investigate Construction 2100           34\n",
       "                         5100           13\n",
       "                         5110            3\n",
       "Investigate Demolition   2100            5\n",
       "Marked for Construction  1003           14\n",
       "                         2100            6\n",
       "                         5100            3\n",
       "Marked for Demolition    1003            2\n",
       "                         2100          107\n",
       "                         5110           17\n",
       "Merged                   2100           92\n",
       "                         5110            1\n",
       "Split                    1000            1\n",
       "                         1004            2\n",
       "                         2100          330\n",
       "                         5110            5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by 'LSTSTATYPE' and 'FEAT_CODE', count of rows\n",
    "(building_df\n",
    " .groupby(by=['LSTSTATYPE', 'FEAT_CODE'])[['DOITT_ID']]\n",
    " .count()\n",
    " .rename(columns={'DOITT_ID':'count'})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>LSTSTATYPE</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alteration</td>\n",
       "      <td>1004</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alteration</td>\n",
       "      <td>2100</td>\n",
       "      <td>1448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alteration</td>\n",
       "      <td>5100</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alteration</td>\n",
       "      <td>5110</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>1000</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>1001</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>1003</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>1004</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>1005</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>1006</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>2100</td>\n",
       "      <td>866256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>2110</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>5100</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>5110</td>\n",
       "      <td>213289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Correction</td>\n",
       "      <td>2100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Correction</td>\n",
       "      <td>5100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Correction</td>\n",
       "      <td>5110</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Demolition</td>\n",
       "      <td>2100</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Demolition</td>\n",
       "      <td>5110</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Geometry</td>\n",
       "      <td>1000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Geometry</td>\n",
       "      <td>2100</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Initialization</td>\n",
       "      <td>1003</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Initialization</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Initialization</td>\n",
       "      <td>1006</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Initialization</td>\n",
       "      <td>2100</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Initialization</td>\n",
       "      <td>5100</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Initialization</td>\n",
       "      <td>5110</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Investigate Construction</td>\n",
       "      <td>2100</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Investigate Construction</td>\n",
       "      <td>5100</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Investigate Construction</td>\n",
       "      <td>5110</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Investigate Demolition</td>\n",
       "      <td>2100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Marked for Construction</td>\n",
       "      <td>1003</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Marked for Construction</td>\n",
       "      <td>2100</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Marked for Construction</td>\n",
       "      <td>5100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Marked for Demolition</td>\n",
       "      <td>1003</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Marked for Demolition</td>\n",
       "      <td>2100</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Marked for Demolition</td>\n",
       "      <td>5110</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Merged</td>\n",
       "      <td>2100</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Merged</td>\n",
       "      <td>5110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Split</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Split</td>\n",
       "      <td>1004</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Split</td>\n",
       "      <td>2100</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Split</td>\n",
       "      <td>5110</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LSTSTATYPE  FEAT_CODE   count\n",
       "0                 Alteration       1004       3\n",
       "1                 Alteration       2100    1448\n",
       "2                 Alteration       5100       2\n",
       "3                 Alteration       5110      65\n",
       "4                Constructed       1000      85\n",
       "5                Constructed       1001     123\n",
       "6                Constructed       1003       7\n",
       "7                Constructed       1004     221\n",
       "8                Constructed       1005      24\n",
       "9                Constructed       1006      17\n",
       "10               Constructed       2100  866256\n",
       "11               Constructed       2110     123\n",
       "12               Constructed       5100     125\n",
       "13               Constructed       5110  213289\n",
       "14                Correction       2100      50\n",
       "15                Correction       5100       1\n",
       "16                Correction       5110      18\n",
       "17                Demolition       2100      10\n",
       "18                Demolition       5110       3\n",
       "19                  Geometry       1000      10\n",
       "20                  Geometry       2100       7\n",
       "21            Initialization       1003       6\n",
       "22            Initialization       1004       1\n",
       "23            Initialization       1006       1\n",
       "24            Initialization       2100      47\n",
       "25            Initialization       5100      52\n",
       "26            Initialization       5110       4\n",
       "27  Investigate Construction       2100      34\n",
       "28  Investigate Construction       5100      13\n",
       "29  Investigate Construction       5110       3\n",
       "30    Investigate Demolition       2100       5\n",
       "31   Marked for Construction       1003      14\n",
       "32   Marked for Construction       2100       6\n",
       "33   Marked for Construction       5100       3\n",
       "34     Marked for Demolition       1003       2\n",
       "35     Marked for Demolition       2100     107\n",
       "36     Marked for Demolition       5110      17\n",
       "37                    Merged       2100      92\n",
       "38                    Merged       5110       1\n",
       "39                     Split       1000       1\n",
       "40                     Split       1004       2\n",
       "41                     Split       2100     330\n",
       "42                     Split       5110       5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by 'LSTSTATYPE' and 'FEAT_CODE', count of rows and explode the indexes\n",
    "(building_df\n",
    " .groupby(by=['LSTSTATYPE', 'FEAT_CODE'])[['DOITT_ID']]\n",
    " .count()\n",
    " .rename(columns={'DOITT_ID':'count'})\n",
    " .reset_index()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BIN</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290292</td>\n",
       "      <td>0.156156</td>\n",
       "      <td>-0.356978</td>\n",
       "      <td>0.190979</td>\n",
       "      <td>0.087077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988673</td>\n",
       "      <td>0.988673</td>\n",
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       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <td>0.290292</td>\n",
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       "      <td>0.027337</td>\n",
       "      <td>-0.132291</td>\n",
       "      <td>-0.003038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.283942</td>\n",
       "      <td>0.283942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOITT_ID</th>\n",
       "      <td>0.156156</td>\n",
       "      <td>0.205949</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.158092</td>\n",
       "      <td>0.539096</td>\n",
       "      <td>-0.017085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.075716</td>\n",
       "      <td>0.075716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <td>-0.356978</td>\n",
       "      <td>0.027337</td>\n",
       "      <td>-0.158092</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.362291</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.327759</td>\n",
       "      <td>-0.327759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <td>0.190979</td>\n",
       "      <td>-0.132291</td>\n",
       "      <td>0.539096</td>\n",
       "      <td>-0.362291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.090450</td>\n",
       "      <td>0.090450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <td>0.087077</td>\n",
       "      <td>-0.003038</td>\n",
       "      <td>-0.017085</td>\n",
       "      <td>-0.002693</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.106218</td>\n",
       "      <td>0.106218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE_BBL</th>\n",
       "      <td>0.988673</td>\n",
       "      <td>0.283942</td>\n",
       "      <td>0.075716</td>\n",
       "      <td>-0.327759</td>\n",
       "      <td>0.090450</td>\n",
       "      <td>0.106218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <td>0.988673</td>\n",
       "      <td>0.283942</td>\n",
       "      <td>0.075716</td>\n",
       "      <td>-0.327759</td>\n",
       "      <td>0.090450</td>\n",
       "      <td>0.106218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BIN  CNSTRCT_YR  DOITT_ID  HEIGHTROOF  FEAT_CODE  GROUNDELEV  \\\n",
       "BIN         1.000000    0.290292  0.156156   -0.356978   0.190979    0.087077   \n",
       "CNSTRCT_YR  0.290292    1.000000  0.205949    0.027337  -0.132291   -0.003038   \n",
       "DOITT_ID    0.156156    0.205949  1.000000   -0.158092   0.539096   -0.017085   \n",
       "HEIGHTROOF -0.356978    0.027337 -0.158092    1.000000  -0.362291   -0.002693   \n",
       "FEAT_CODE   0.190979   -0.132291  0.539096   -0.362291   1.000000    0.001664   \n",
       "GROUNDELEV  0.087077   -0.003038 -0.017085   -0.002693   0.001664    1.000000   \n",
       "SHAPE_AREA       NaN         NaN       NaN         NaN        NaN         NaN   \n",
       "SHAPE_LEN        NaN         NaN       NaN         NaN        NaN         NaN   \n",
       "BASE_BBL    0.988673    0.283942  0.075716   -0.327759   0.090450    0.106218   \n",
       "MPLUTO_BBL  0.988673    0.283942  0.075716   -0.327759   0.090450    0.106218   \n",
       "\n",
       "            SHAPE_AREA  SHAPE_LEN  BASE_BBL  MPLUTO_BBL  \n",
       "BIN                NaN        NaN  0.988673    0.988673  \n",
       "CNSTRCT_YR         NaN        NaN  0.283942    0.283942  \n",
       "DOITT_ID           NaN        NaN  0.075716    0.075716  \n",
       "HEIGHTROOF         NaN        NaN -0.327759   -0.327759  \n",
       "FEAT_CODE          NaN        NaN  0.090450    0.090450  \n",
       "GROUNDELEV         NaN        NaN  0.106218    0.106218  \n",
       "SHAPE_AREA         NaN        NaN       NaN         NaN  \n",
       "SHAPE_LEN          NaN        NaN       NaN         NaN  \n",
       "BASE_BBL           NaN        NaN  1.000000    1.000000  \n",
       "MPLUTO_BBL         NaN        NaN  1.000000    1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute pairwise correlation of columns, excluding null values\n",
    "building_df.corr(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation in the form of heat map\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "# cells will be NA if all values in column are NA\n",
    "sns.heatmap(\n",
    "    building_df.corr(numeric_only=True), \n",
    "    annot=True, \n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation in the form of heat map\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "# dropping columns of all nulls\n",
    "corr_df = building_df.drop(columns=['SHAPE_AREA', 'SHAPE_LEN'])\n",
    "\n",
    "sns.heatmap(\n",
    "    corr_df.corr(numeric_only=True), \n",
    "    annot=True, \n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Identifying Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total null values in dataframe: 1,094,723\n"
     ]
    }
   ],
   "source": [
    "# total nulls in dataframe\n",
    "null_count = (\n",
    "    building_df\n",
    "    .isnull()\n",
    "    .sum()\n",
    "    .sum()\n",
    ")\n",
    "\n",
    "print(f'total null values in dataframe: {null_count:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total non-null values in dataframe: 16,232,701\n"
     ]
    }
   ],
   "source": [
    "# total non-nulls in dataframe\n",
    "nonnull_count = (\n",
    "    building_df\n",
    "    .notnull()\n",
    "    .sum()\n",
    "    .sum()\n",
    ")\n",
    "\n",
    "print(f'total non-null values in dataframe: {nonnull_count:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME          1080717\n",
       "CNSTRCT_YR      10243\n",
       "HEIGHTROOF       2579\n",
       "GROUNDELEV        551\n",
       "LSTSTATYPE        331\n",
       "GEOMSOURCE        302\n",
       "the_geom            0\n",
       "BIN                 0\n",
       "LSTMODDATE          0\n",
       "DOITT_ID            0\n",
       "FEAT_CODE           0\n",
       "SHAPE_AREA          0\n",
       "SHAPE_LEN           0\n",
       "BASE_BBL            0\n",
       "MPLUTO_BBL          0\n",
       "GLOBALID            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count nulls per column sorted descending\n",
    "(building_df\n",
    " .isnull()\n",
    " .sum()\n",
    " .sort_values(ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME          99.792514\n",
       "CNSTRCT_YR     0.945830\n",
       "HEIGHTROOF     0.238143\n",
       "GROUNDELEV     0.050879\n",
       "LSTSTATYPE     0.030564\n",
       "GEOMSOURCE     0.027886\n",
       "the_geom       0.000000\n",
       "BIN            0.000000\n",
       "LSTMODDATE     0.000000\n",
       "DOITT_ID       0.000000\n",
       "FEAT_CODE      0.000000\n",
       "SHAPE_AREA     0.000000\n",
       "SHAPE_LEN      0.000000\n",
       "BASE_BBL       0.000000\n",
       "MPLUTO_BBL     0.000000\n",
       "GLOBALID       0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating a percentage of the number of nulls to total number of records of each column\n",
    "(building_df\n",
    " .isnull()\n",
    " .sum()\n",
    " .sort_values(ascending=False)\n",
    " .div(building_df.shape[0])\n",
    " .mul(100)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NAME</th>\n",
       "      <td>99.792514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <td>0.945830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <td>0.238143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <td>0.050879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <td>0.030564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <td>0.027886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the_geom</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIN</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOITT_ID</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE_BBL</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GLOBALID</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            perc_missing\n",
       "NAME           99.792514\n",
       "CNSTRCT_YR      0.945830\n",
       "HEIGHTROOF      0.238143\n",
       "GROUNDELEV      0.050879\n",
       "LSTSTATYPE      0.030564\n",
       "GEOMSOURCE      0.027886\n",
       "the_geom        0.000000\n",
       "BIN             0.000000\n",
       "LSTMODDATE      0.000000\n",
       "DOITT_ID        0.000000\n",
       "FEAT_CODE       0.000000\n",
       "SHAPE_AREA      0.000000\n",
       "SHAPE_LEN       0.000000\n",
       "BASE_BBL        0.000000\n",
       "MPLUTO_BBL      0.000000\n",
       "GLOBALID        0.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % null as a dataframe\n",
    "missing_data = (building_df.isnull().sum() / building_df.shape[0]) * 100\n",
    "\n",
    "# creating a dataframe\n",
    "missing_df = pd.DataFrame(\n",
    "    missing_data,\n",
    "    columns=['perc_missing']\n",
    ")\n",
    "\n",
    "# sort values\n",
    "missing_df.sort_values(by='perc_missing', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
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       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1082964 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         the_geom  NAME    BIN  CNSTRCT_YR  LSTMODDATE  LSTSTATYPE  DOITT_ID  \\\n",
       "0           False  True  False       False       False       False     False   \n",
       "1           False  True  False       False       False       False     False   \n",
       "2           False  True  False       False       False       False     False   \n",
       "3           False  True  False       False       False       False     False   \n",
       "4           False  True  False       False       False       False     False   \n",
       "...           ...   ...    ...         ...         ...         ...       ...   \n",
       "1082959     False  True  False       False       False       False     False   \n",
       "1082960     False  True  False       False       False       False     False   \n",
       "1082961     False  True  False        True       False       False     False   \n",
       "1082962     False  True  False        True       False       False     False   \n",
       "1082963     False  True  False       False       False       False     False   \n",
       "\n",
       "         HEIGHTROOF  FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN  BASE_BBL  \\\n",
       "0             False      False       False       False      False     False   \n",
       "1             False      False       False       False      False     False   \n",
       "2             False      False       False       False      False     False   \n",
       "3             False      False       False       False      False     False   \n",
       "4             False      False       False       False      False     False   \n",
       "...             ...        ...         ...         ...        ...       ...   \n",
       "1082959       False      False       False       False      False     False   \n",
       "1082960       False      False       False       False      False     False   \n",
       "1082961       False      False       False       False      False     False   \n",
       "1082962       False      False       False       False      False     False   \n",
       "1082963       False      False       False       False      False     False   \n",
       "\n",
       "         MPLUTO_BBL  GEOMSOURCE  GLOBALID  \n",
       "0             False       False     False  \n",
       "1             False       False     False  \n",
       "2             False       False     False  \n",
       "3             False       False     False  \n",
       "4             False       False     False  \n",
       "...             ...         ...       ...  \n",
       "1082959       False       False     False  \n",
       "1082960       False       False     False  \n",
       "1082961       False       False     False  \n",
       "1082962       False       False     False  \n",
       "1082963       False       False     False  \n",
       "\n",
       "[1082964 rows x 16 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return a boolean same-sized object of nulls\n",
    "building_df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "the_geom      False\n",
       "NAME           True\n",
       "BIN           False\n",
       "CNSTRCT_YR     True\n",
       "LSTMODDATE    False\n",
       "LSTSTATYPE     True\n",
       "DOITT_ID      False\n",
       "HEIGHTROOF     True\n",
       "FEAT_CODE     False\n",
       "GROUNDELEV     True\n",
       "SHAPE_AREA    False\n",
       "SHAPE_LEN     False\n",
       "BASE_BBL      False\n",
       "MPLUTO_BBL    False\n",
       "GEOMSOURCE     True\n",
       "GLOBALID      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return a boolean if any of the values are null in column\n",
    "(building_df\n",
    " .isnull()\n",
    " .any()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          False\n",
       "1          False\n",
       "2          False\n",
       "3          False\n",
       "4          False\n",
       "           ...  \n",
       "1082959    False\n",
       "1082960    False\n",
       "1082961    False\n",
       "1082962    False\n",
       "1082963    False\n",
       "Name: NAME, Length: 1082964, dtype: bool"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return a boolean same-sized object of nulls in NAME column\n",
    "building_df['NAME'].notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <td>{DC541EA1-0B7F-4E54-94A3-E8A2F61F8DFF}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081239</th>\n",
       "      <td>MULTIPOLYGON (((-73.95209408465293 40.78829631...</td>\n",
       "      <td>Building B</td>\n",
       "      <td>1091029</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>08/25/2023 12:00:00 AM</td>\n",
       "      <td>Split</td>\n",
       "      <td>1300335</td>\n",
       "      <td>328.810000</td>\n",
       "      <td>1000</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1016040023</td>\n",
       "      <td>1016040023</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{E1111641-540E-4E76-86BC-81A88443EC5B}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081372</th>\n",
       "      <td>MULTIPOLYGON (((-73.96861892021917 40.75798235...</td>\n",
       "      <td>Lipstick Building</td>\n",
       "      <td>1038549</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>99468</td>\n",
       "      <td>464.420000</td>\n",
       "      <td>2100</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1013270001</td>\n",
       "      <td>1013277504</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{C07FE8C7-FD69-4312-821B-5C792E05E17F}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081928</th>\n",
       "      <td>MULTIPOLYGON (((-73.97900398176294 40.57405788...</td>\n",
       "      <td>Wonder Wheel</td>\n",
       "      <td>3347224</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>10/28/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>304722</td>\n",
       "      <td>13.990000</td>\n",
       "      <td>2100</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3086960036</td>\n",
       "      <td>3086960036</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{1C7AFB0B-7C5D-4C3B-B9BE-4B6BB11C5C60}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2247 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom  \\\n",
       "3575     MULTIPOLYGON (((-73.95317216502298 40.75924424...   \n",
       "4307     MULTIPOLYGON (((-73.88526375915657 40.86258229...   \n",
       "5785     MULTIPOLYGON (((-73.88149603905721 40.84855265...   \n",
       "7974     MULTIPOLYGON (((-73.8312596282937 40.871147879...   \n",
       "8129     MULTIPOLYGON (((-73.90110014758918 40.73308422...   \n",
       "...                                                    ...   \n",
       "1080866  MULTIPOLYGON (((-74.01066784278217 40.71190680...   \n",
       "1081166  MULTIPOLYGON (((-73.79566736659639 40.65514293...   \n",
       "1081239  MULTIPOLYGON (((-73.95209408465293 40.78829631...   \n",
       "1081372  MULTIPOLYGON (((-73.96861892021917 40.75798235...   \n",
       "1081928  MULTIPOLYGON (((-73.97900398176294 40.57405788...   \n",
       "\n",
       "                                  NAME      BIN  CNSTRCT_YR  \\\n",
       "3575     Roosevelt Isld Subway Station  1089270      1975.0   \n",
       "4307                       Alpha House  2102033      1922.0   \n",
       "5785                   Pheasant Aviary  2123889      1905.0   \n",
       "7974             Asch Loop Town Houses  2128807      1969.0   \n",
       "8129                           NO NAME  4597316      1940.0   \n",
       "...                                ...      ...         ...   \n",
       "1080866        Tower 2 World Trade Ctr  1089308      2023.0   \n",
       "1081166                             83  4542282      1994.0   \n",
       "1081239                     Building B  1091029      1992.0   \n",
       "1081372              Lipstick Building  1038549      1986.0   \n",
       "1081928                   Wonder Wheel  3347224      1923.0   \n",
       "\n",
       "                     LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "3575     03/25/2008 12:00:00 AM  Constructed    996736   28.470000       2100   \n",
       "4307     08/17/2017 12:00:00 AM  Constructed    194515   22.560000       5110   \n",
       "5785     07/17/2014 12:00:00 AM  Constructed    925752   22.075648       2100   \n",
       "7974     08/22/2017 12:00:00 AM  Constructed    197782   26.450000       2100   \n",
       "8129     08/17/2017 12:00:00 AM  Constructed    786648   11.000000       5110   \n",
       "...                         ...          ...       ...         ...        ...   \n",
       "1080866  05/06/2024 12:00:00 AM  Constructed   1256146   41.883905       2100   \n",
       "1081166  02/22/2024 12:00:00 AM   Alteration   1260149   41.000000       2100   \n",
       "1081239  08/25/2023 12:00:00 AM        Split   1300335  328.810000       1000   \n",
       "1081372  08/22/2017 12:00:00 AM  Constructed     99468  464.420000       2100   \n",
       "1081928  10/28/2024 12:00:00 AM  Constructed    304722   13.990000       2100   \n",
       "\n",
       "         GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "3575           16.0           0          0  1013730001  1013730001   \n",
       "4307           98.0           0          0  2032730001  2032730001   \n",
       "5785           70.0           0          0  2031200020  2031200020   \n",
       "7974           18.0           0          0  2051410120  2051410120   \n",
       "8129           68.0           0          0  4023750180  4023750180   \n",
       "...             ...         ...        ...         ...         ...   \n",
       "1080866        20.0           0          0  1000580001  1000580001   \n",
       "1081166         9.0           0          0  4142600001  4142600001   \n",
       "1081239        53.0           0          0  1016040023  1016040023   \n",
       "1081372        36.0           0          0  1013270001  1013277504   \n",
       "1081928         7.0           0          0  3086960036  3086960036   \n",
       "\n",
       "         GEOMSOURCE                                GLOBALID  \n",
       "3575     Photogramm  {E49130B9-6088-4BB5-9BA5-ABA8BFC69BA7}  \n",
       "4307     Photogramm  {8A21945E-76A9-47A7-9355-2D500A91A949}  \n",
       "5785     Photogramm  {2BCDE23D-4DD7-4642-8FC1-B347EDE772CA}  \n",
       "7974     Photogramm  {E7006798-10CF-49AA-BE4E-A1039405D38A}  \n",
       "8129     Other (Man  {E2CADF8E-D955-4BD4-AEED-0EFC3A2149C4}  \n",
       "...             ...                                     ...  \n",
       "1080866  Other (Man  {25251D04-C615-4866-B45B-2885FACDDF0D}  \n",
       "1081166  Other (Man  {DC541EA1-0B7F-4E54-94A3-E8A2F61F8DFF}  \n",
       "1081239  Other (Man  {E1111641-540E-4E76-86BC-81A88443EC5B}  \n",
       "1081372  Photogramm  {C07FE8C7-FD69-4312-821B-5C792E05E17F}  \n",
       "1081928  Photogramm  {1C7AFB0B-7C5D-4C3B-B9BE-4B6BB11C5C60}  \n",
       "\n",
       "[2247 rows x 16 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning rows with non-null values in NAME column\n",
    "building_df.loc[building_df['NAME'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MULTIPOLYGON (((-73.96664570466969 40.62599676...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3170958</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>96807</td>\n",
       "      <td>29.749853</td>\n",
       "      <td>2100</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{31298F86-3088-4F53-B3DB-71A9EFA6FA1F}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MULTIPOLYGON (((-74.16790202462265 40.63936048...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5028452</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>326368</td>\n",
       "      <td>22.630000</td>\n",
       "      <td>2100</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F5F8CDA5-69E2-46F8-8F69-BA95C025B520}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MULTIPOLYGON (((-74.19510813278613 40.55610681...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5078368</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746627</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>2100</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9F644794-F72C-4582-9E5E-B337E2B97068}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MULTIPOLYGON (((-73.96113466505085 40.57743931...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3245111</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>786626</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F916B22D-E25B-44AE-9FA9-2A51191B9CDF}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MULTIPOLYGON (((-73.75421559146166 40.75591276...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4161096</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746409</td>\n",
       "      <td>18.015113</td>\n",
       "      <td>2100</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082959</th>\n",
       "      <td>MULTIPOLYGON (((-73.99956501703511 40.72039743...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1091063</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>11/01/2024 12:00:00 AM</td>\n",
       "      <td>Alteration</td>\n",
       "      <td>1302671</td>\n",
       "      <td>94.660000</td>\n",
       "      <td>2100</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1004730051</td>\n",
       "      <td>1004730051</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{151309FC-B779-46CE-B1D9-1584AEA02FC6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082960</th>\n",
       "      <td>MULTIPOLYGON (((-74.0000149828318 40.688200830...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3394115</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1114346</td>\n",
       "      <td>81.127098</td>\n",
       "      <td>2100</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3003040018</td>\n",
       "      <td>3003047502</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{A200FA56-7C7C-42F7-AA99-2C9DD5BF9EF6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082961</th>\n",
       "      <td>MULTIPOLYGON (((-73.99984919403173 40.68862382...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3397444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01/22/2019 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1114440</td>\n",
       "      <td>73.179002</td>\n",
       "      <td>2100</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3003040018</td>\n",
       "      <td>3003047502</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9B137E5C-68B4-408A-B449-E5BF57F3B6E3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082962</th>\n",
       "      <td>MULTIPOLYGON (((-73.8343042812369 40.757419581...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4539469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01/22/2019 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1107493</td>\n",
       "      <td>194.084179</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4050660001</td>\n",
       "      <td>4050667503</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{B056D55A-FD91-4671-A57C-8523F26C8C61}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082963</th>\n",
       "      <td>MULTIPOLYGON (((-73.83440749536311 40.75742362...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4540380</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>04/11/2019 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1114668</td>\n",
       "      <td>185.779728</td>\n",
       "      <td>2100</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4050660001</td>\n",
       "      <td>4050667503</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{E549C8DC-0457-43CB-88F6-136472CE6946}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1080717 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom NAME      BIN  \\\n",
       "0        MULTIPOLYGON (((-73.96664570466969 40.62599676...  NaN  3170958   \n",
       "1        MULTIPOLYGON (((-74.16790202462265 40.63936048...  NaN  5028452   \n",
       "2        MULTIPOLYGON (((-74.19510813278613 40.55610681...  NaN  5078368   \n",
       "3        MULTIPOLYGON (((-73.96113466505085 40.57743931...  NaN  3245111   \n",
       "4        MULTIPOLYGON (((-73.75421559146166 40.75591276...  NaN  4161096   \n",
       "...                                                    ...  ...      ...   \n",
       "1082959  MULTIPOLYGON (((-73.99956501703511 40.72039743...  NaN  1091063   \n",
       "1082960  MULTIPOLYGON (((-74.0000149828318 40.688200830...  NaN  3394115   \n",
       "1082961  MULTIPOLYGON (((-73.99984919403173 40.68862382...  NaN  3397444   \n",
       "1082962  MULTIPOLYGON (((-73.8343042812369 40.757419581...  NaN  4539469   \n",
       "1082963  MULTIPOLYGON (((-73.83440749536311 40.75742362...  NaN  4540380   \n",
       "\n",
       "         CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  \\\n",
       "0            1925.0  08/22/2017 12:00:00 AM  Constructed     96807   \n",
       "1            1965.0  08/22/2017 12:00:00 AM  Constructed    326368   \n",
       "2            1970.0  08/22/2017 12:00:00 AM  Constructed    746627   \n",
       "3            1928.0  08/22/2017 12:00:00 AM  Constructed    786626   \n",
       "4            1950.0  08/22/2017 12:00:00 AM  Constructed    746409   \n",
       "...             ...                     ...          ...       ...   \n",
       "1082959      1900.0  11/01/2024 12:00:00 AM   Alteration   1302671   \n",
       "1082960      2009.0  08/22/2017 12:00:00 AM  Constructed   1114346   \n",
       "1082961         NaN  01/22/2019 12:00:00 AM  Constructed   1114440   \n",
       "1082962         NaN  01/22/2019 12:00:00 AM  Constructed   1107493   \n",
       "1082963      2006.0  04/11/2019 12:00:00 AM  Constructed   1114668   \n",
       "\n",
       "         HEIGHTROOF  FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  \\\n",
       "0         29.749853       2100        40.0           0          0  3065220021   \n",
       "1         22.630000       2100        39.0           0          0  5012640036   \n",
       "2         35.760000       2100        51.0           0          0  5060190091   \n",
       "3         37.500000       2100         6.0           0          0  3086910048   \n",
       "4         18.015113       2100        93.0           0          0  4075020005   \n",
       "...             ...        ...         ...         ...        ...         ...   \n",
       "1082959   94.660000       2100        24.0           0          0  1004730051   \n",
       "1082960   81.127098       2100        17.0           0          0  3003040018   \n",
       "1082961   73.179002       2100        16.0           0          0  3003040018   \n",
       "1082962  194.084179       2100         9.0           0          0  4050660001   \n",
       "1082963  185.779728       2100        22.0           0          0  4050660001   \n",
       "\n",
       "         MPLUTO_BBL  GEOMSOURCE                                GLOBALID  \n",
       "0        3065220021  Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
       "1        5012640036  Photogramm  {F5F8CDA5-69E2-46F8-8F69-BA95C025B520}  \n",
       "2        5060190091  Photogramm  {9F644794-F72C-4582-9E5E-B337E2B97068}  \n",
       "3        3086910048  Photogramm  {F916B22D-E25B-44AE-9FA9-2A51191B9CDF}  \n",
       "4        4075020005  Photogramm  {525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}  \n",
       "...             ...         ...                                     ...  \n",
       "1082959  1004730051  Other (Man  {151309FC-B779-46CE-B1D9-1584AEA02FC6}  \n",
       "1082960  3003047502  Photogramm  {A200FA56-7C7C-42F7-AA99-2C9DD5BF9EF6}  \n",
       "1082961  3003047502  Photogramm  {9B137E5C-68B4-408A-B449-E5BF57F3B6E3}  \n",
       "1082962  4050667503  Photogramm  {B056D55A-FD91-4671-A57C-8523F26C8C61}  \n",
       "1082963  4050667503  Photogramm  {E549C8DC-0457-43CB-88F6-136472CE6946}  \n",
       "\n",
       "[1080717 rows x 16 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning rows with null values in NAME column\n",
    "building_df.loc[building_df['NAME'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data Cleaning & Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be clean the **Construction Year** (i.e. CNSTRCT_YR) column, as this is the column we will be using in our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Previewing Column Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1925.0\n",
       "1          1965.0\n",
       "2          1970.0\n",
       "3          1928.0\n",
       "4          1950.0\n",
       "            ...  \n",
       "1082959    1900.0\n",
       "1082960    2009.0\n",
       "1082961       NaN\n",
       "1082962       NaN\n",
       "1082963    2006.0\n",
       "Name: CNSTRCT_YR, Length: 1082964, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning a series of the 'CNSTRCT_YR' column\n",
    "building_df[\"CNSTRCT_YR\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>CNSTRCT_YR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082959</th>\n",
       "      <td>1900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082960</th>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082961</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082962</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082963</th>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082964 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CNSTRCT_YR\n",
       "0            1925.0\n",
       "1            1965.0\n",
       "2            1970.0\n",
       "3            1928.0\n",
       "4            1950.0\n",
       "...             ...\n",
       "1082959      1900.0\n",
       "1082960      2009.0\n",
       "1082961         NaN\n",
       "1082962         NaN\n",
       "1082963      2006.0\n",
       "\n",
       "[1082964 rows x 1 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning a dataframe of the 'CNSTRCT_YR' column double brackets with .loc method\n",
    "building_df.loc[:, [\"CNSTRCT_YR\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>955187</th>\n",
       "      <td>MULTIPOLYGON (((-73.92083460429942 40.64439765...</td>\n",
       "      <td>Pieter Claesen Wyckoff House</td>\n",
       "      <td>3221967</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>01/28/2020 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>550741</td>\n",
       "      <td>24.540000</td>\n",
       "      <td>2100</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3079170009</td>\n",
       "      <td>3079170009</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710089</th>\n",
       "      <td>MULTIPOLYGON (((-73.8247828135812 40.762948467...</td>\n",
       "      <td>Bowne House</td>\n",
       "      <td>4113403</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>776399</td>\n",
       "      <td>26.120000</td>\n",
       "      <td>2100</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4050130006</td>\n",
       "      <td>4050130006</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{71A760A6-6D2F-48F8-8C93-C8B66071B868}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430689</th>\n",
       "      <td>MULTIPOLYGON (((-73.82449114078894 40.76279389...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4610542</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>08/17/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1190137</td>\n",
       "      <td>14.846959</td>\n",
       "      <td>5110</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4050130006</td>\n",
       "      <td>4050130006</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{36678A28-E8EE-45E8-BAA2-9A3F5118E176}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52813</th>\n",
       "      <td>MULTIPOLYGON (((-74.25311647359251 40.50309522...</td>\n",
       "      <td>Conference House</td>\n",
       "      <td>5087656</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>04/13/2020 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>702698</td>\n",
       "      <td>38.640000</td>\n",
       "      <td>2100</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5078570090</td>\n",
       "      <td>5078570090</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{CB13BCBA-9DF3-4455-9206-824FE91F2EDE}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521346</th>\n",
       "      <td>MULTIPOLYGON (((-74.06293248238293 40.61512359...</td>\n",
       "      <td>Alice Austen House Museum</td>\n",
       "      <td>5042188</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>04/13/2020 12:00:00 AM</td>\n",
       "      <td>Alteration</td>\n",
       "      <td>806241</td>\n",
       "      <td>22.260000</td>\n",
       "      <td>2100</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5028300049</td>\n",
       "      <td>5028300049</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{93B1CB7B-BE72-40DB-9B66-01E92DF4AE6E}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 the_geom  \\\n",
       "955187  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "710089  MULTIPOLYGON (((-73.8247828135812 40.762948467...   \n",
       "430689  MULTIPOLYGON (((-73.82449114078894 40.76279389...   \n",
       "52813   MULTIPOLYGON (((-74.25311647359251 40.50309522...   \n",
       "521346  MULTIPOLYGON (((-74.06293248238293 40.61512359...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "955187  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "710089                   Bowne House  4113403      1661.0   \n",
       "430689                           NaN  4610542      1661.0   \n",
       "52813               Conference House  5087656      1680.0   \n",
       "521346     Alice Austen House Museum  5042188      1690.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "955187  01/28/2020 12:00:00 AM  Constructed    550741   24.540000       2100   \n",
       "710089  08/22/2017 12:00:00 AM  Constructed    776399   26.120000       2100   \n",
       "430689  08/17/2017 12:00:00 AM  Constructed   1190137   14.846959       5110   \n",
       "52813   04/13/2020 12:00:00 AM  Constructed    702698   38.640000       2100   \n",
       "521346  04/13/2020 12:00:00 AM   Alteration    806241   22.260000       2100   \n",
       "\n",
       "        GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  GEOMSOURCE  \\\n",
       "955187        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "710089        64.0           0          0  4050130006  4050130006  Photogramm   \n",
       "430689        63.0           0          0  4050130006  4050130006  Photogramm   \n",
       "52813         38.0           0          0  5078570090  5078570090  Photogramm   \n",
       "521346        21.0           0          0  5028300049  5028300049  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "955187  {5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}  \n",
       "710089  {71A760A6-6D2F-48F8-8C93-C8B66071B868}  \n",
       "430689  {36678A28-E8EE-45E8-BAA2-9A3F5118E176}  \n",
       "52813   {CB13BCBA-9DF3-4455-9206-824FE91F2EDE}  \n",
       "521346  {93B1CB7B-BE72-40DB-9B66-01E92DF4AE6E}  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first five rows sorted by the 'CNSTRCT_YR' column in ascending order\n",
    "building_df.sort_values('CNSTRCT_YR').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>1081852</th>\n",
       "      <td>MULTIPOLYGON (((-74.230067606122 40.5237650566...</td>\n",
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       "      <td>5164123</td>\n",
       "      <td>2025.0</td>\n",
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       "      <td>Constructed</td>\n",
       "      <td>1302640</td>\n",
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       "      <td>79.0</td>\n",
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       "      <th>1077594</th>\n",
       "      <td>MULTIPOLYGON (((-73.92623075729338 40.69897310...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3428954</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>06/17/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302149</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3032180001</td>\n",
       "      <td>3032187501</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{CBB2E0AD-B0BE-43C0-B397-F1A600553D8C}</td>\n",
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       "    <tr>\n",
       "      <th>1081810</th>\n",
       "      <td>MULTIPOLYGON (((-73.9590071614393 40.694111756...</td>\n",
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       "      <td>3054935</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>05/24/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302101</td>\n",
       "      <td>58.567634</td>\n",
       "      <td>2100</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3019120022</td>\n",
       "      <td>3019120022</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9F74FFFF-9240-491E-BBFB-9A14F9149672}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081811</th>\n",
       "      <td>MULTIPOLYGON (((-73.97152904331807 40.60617410...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3177165</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>05/24/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302103</td>\n",
       "      <td>32.525289</td>\n",
       "      <td>2100</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3066580016</td>\n",
       "      <td>3066580016</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{87D80FFB-C8E0-499C-8A5B-914FF66F549E}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081813</th>\n",
       "      <td>MULTIPOLYGON (((-74.10577480200087 40.56462199...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5170436</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>04/18/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1301924</td>\n",
       "      <td>10.283611</td>\n",
       "      <td>5110</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5040470051</td>\n",
       "      <td>5040470051</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{4ED78AF8-F697-44ED-9481-59618FE9343F}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom NAME      BIN  \\\n",
       "1081852  MULTIPOLYGON (((-74.230067606122 40.5237650566...  NaN  5164123   \n",
       "1077594  MULTIPOLYGON (((-73.92623075729338 40.69897310...  NaN  3428954   \n",
       "1081810  MULTIPOLYGON (((-73.9590071614393 40.694111756...  NaN  3054935   \n",
       "1081811  MULTIPOLYGON (((-73.97152904331807 40.60617410...  NaN  3177165   \n",
       "1081813  MULTIPOLYGON (((-74.10577480200087 40.56462199...  NaN  5170436   \n",
       "\n",
       "         CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  \\\n",
       "1081852      2025.0  10/18/2024 12:00:00 AM  Constructed   1302640   \n",
       "1077594      2024.0  06/17/2024 12:00:00 AM  Constructed   1302149   \n",
       "1081810      2024.0  05/24/2024 12:00:00 AM  Constructed   1302101   \n",
       "1081811      2024.0  05/24/2024 12:00:00 AM  Constructed   1302103   \n",
       "1081813      2024.0  04/18/2024 12:00:00 AM  Constructed   1301924   \n",
       "\n",
       "         HEIGHTROOF  FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  \\\n",
       "1081852   30.000000       2100        79.0           0          0  5075770089   \n",
       "1077594   53.000000       2100        50.0           0          0  3032180001   \n",
       "1081810   58.567634       2100        52.0           0          0  3019120022   \n",
       "1081811   32.525289       2100        26.0           0          0  3066580016   \n",
       "1081813   10.283611       5110         6.0           0          0  5040470051   \n",
       "\n",
       "         MPLUTO_BBL  GEOMSOURCE                                GLOBALID  \n",
       "1081852  5075770089  Other (Man  {5875684A-4004-4531-9CEC-026A01906C19}  \n",
       "1077594  3032187501  Other (Man  {CBB2E0AD-B0BE-43C0-B397-F1A600553D8C}  \n",
       "1081810  3019120022  Photogramm  {9F74FFFF-9240-491E-BBFB-9A14F9149672}  \n",
       "1081811  3066580016  Photogramm  {87D80FFB-C8E0-499C-8A5B-914FF66F549E}  \n",
       "1081813  5040470051  Photogramm  {4ED78AF8-F697-44ED-9481-59618FE9343F}  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first five rows sorted by the 'CNSTRCT_YR' column in descending order\n",
    "building_df.sort_values('CNSTRCT_YR', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>CNSTRCT_YR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1081852</th>\n",
       "      <td>2025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077594</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081810</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081811</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081813</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CNSTRCT_YR\n",
       "1081852      2025.0\n",
       "1077594      2024.0\n",
       "1081810      2024.0\n",
       "1081811      2024.0\n",
       "1081813      2024.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first five rows sorted by the 'CNSTRCT_YR' column in descending order\n",
    "(building_df\n",
    " .sort_values('CNSTRCT_YR', ascending=False)\n",
    " .loc[:, ['CNSTRCT_YR']]\n",
    " .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1072721.00\n",
       "mean        1939.67\n",
       "std           29.52\n",
       "min         1652.00\n",
       "25%         1920.00\n",
       "50%         1930.00\n",
       "75%         1955.00\n",
       "max         2025.00\n",
       "Name: CNSTRCT_YR, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "descriptive statistics include those that summarize the central tendency, \n",
    "dispersion and shape of a dataset's distribution, excluding ``NaN`` values\n",
    "\"\"\"\n",
    "\n",
    "building_df['CNSTRCT_YR'].describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Exploring Distribution of Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pCT169ECbNm0wefLkKm/zV6M+xhgAXFxcMGvWLKSlpSkFek10Op3FMVGpNp/7ls6uoTtAdD07OzskJCQgISEBRUVF2LhxI2bOnImIiAicOnUKjo6O1b638pdRXl6e1T98Z86cgbu7u0VcVc+Kyc/Pr3LWqapnsaxYsQIDBw7EkiVLLJbX9ZdpbVyb6/XOnDkDGxsbuLq61rrdiIgIpKSkICsrC3369LmpPlTu79p66qmn8NRTT+HSpUvYtm0bZs+ejcjISBw5cgS+vr43fH91z83R6/UwmUxWy68vXNq0aYPTp0/Xqe9VUXtc3mru7u5o3bp1tc9ycnZ2BgB8//33OHPmDLZs2aLMMgGo9vlLtX1OkYODA0aPHo2lS5ciLy8PH330EZydnfHoo48qMTfz2aoszk0mk1JAArCavazc7//+979veFz5+vpi2bJlAIAjR47gX//6FxITE1FWVob33nvvhn26lcaPH4+3334bzz//PMaPH1+nNmrzuW/pOONEjdodd9yBRx55BBMmTMD58+eVBx5W/jK8/q+zytMzK1assFienZ2NQ4cOYfDgwQCA3r17Q6/X44svvrCIy8rKqtVUuUajsfjFDAB79+5FZmam6jbqKiAgAG3btsXnn38OEVGWX7p0CatWrUJISEiNRWZ1pk6dCicnJ8THx6O4uNhqvYhg9erVAICQkBA4ODhY7e/Tp0/j+++/V/Z3XTk5OWHYsGF48cUXUVZWhgMHDgCofvxvpEOHDti7d6/Fsu+//x4XL160WDZs2DBs3rwZhw8frrat2vRB7XF5q0VGRuLcuXMoLy+vckY4ICAAwP8VQtcf2++//3699WXs2LEoLy/HG2+8gfXr1+Pxxx+3OF5v5rNV+YfP9WP97bffWryOiIiAnZ0dfv755yr3R8+ePats39/fH7NmzUJQUBB++uknNeneUjqdDq+++iqys7Px5Zdf1qmN2nzuWzrOOFGjExUVhcDAQPTs2RNt2rTBiRMnsGjRIvj6+irXPwQFBQEA3n77bYwZMwZarRYBAQEICAjAs88+i5SUFNjY2GDYsGE4fvw4XnrpJfj4+GDq1KkA/jg1lpCQgAULFsDV1RUPPfQQTp8+jTlz5sDb29vimqGaREZG4pVXXsHs2bMRGhqKw4cPY+7cufDz88PVq1dvzQ76/2xsbJCUlIQnnngCkZGRGDduHEwmE9544w0UFRVV+VRhNfz8/JCamorHHnsM99xzDyZOnIju3bsD+OOJ3R999BFEBA899BDuuOMOvPTSS5g5cyb+8pe/YPTo0Th37hzmzJkDe3t75Tqb2oiLi4ODgwP69esHb29v5OfnY8GCBTAYDLj33nsBAIGBgQCADz74AM7OzrC3t4efn1+Vpz+uFRMTg5deegkvv/wyQkNDcfDgQSxevNjqFPLcuXOxYcMGDBgwADNnzkRQUBCKioqQlpaGhIQE3H333bjzzjvh4OCAlStXolOnTmjVqhWMRiOMRqPVdtUel7fa448/jpUrV+KBBx7A3/72N/Tq1QtarRanT5/G5s2b8eCDD+Khhx5C37594erqir/+9a+YPXs2tFotVq5ciT179tRbX3r27ImuXbti0aJFEBGL03TAzX22HnjgAbi5uWHs2LGYO3cu7OzssHz5cpw6dcoirkOHDpg7dy5efPFF/PLLLxg6dChcXV1x9uxZ7Nq1C05OTpgzZw727t2LiRMn4tFHH0XHjh2h0+nw/fffY+/evXjhhRfqbZ/cjNGjR2PhwoXYsGFDnd5fm899i9eQV6ZT81Mfz3F68803pW/fvuLu7i46nU7at28vY8eOtXhWicgfjxMwGo1iY2NT5XOc/P39RavViru7uzz55JNVPsfp1VdflXbt2olOp5OuXbvK2rVrpVu3bhZ37dR0R5rJZJLp06dL27Ztxd7eXnr06CFr1qyRMWPGWORZeVfdG2+8YfH+6tq+0X681po1a6R3795ib28vTk5OMnjwYPnPf/6jajs1+fnnnyU+Pl7uuusu0ev14uDgIJ07d5aEhASruxk//PBD6dq1q+h0OjEYDPLggw9aPfKg8nk/17v+rqNPPvlE7r//fvH09BSdTidGo1FGjRpl9dyfRYsWiZ+fn9ja2lb5HKeqmEwmee6558THx0ccHBwkNDRUcnNzq3yO06lTp+Tpp58WLy8v0Wq1Sj+ufb7WP//5T7n77rtFq9Wqfo7TjY7L6vp//TF1I9WNudlsloULF0q3bt3E3t5eWrVqJXfffbeMGzdOjh49qsTt2LFDQkJCxNHRUdq0aSPPPPOM/PTTT1Z3ElY3rmq8/fbbAkA6d+5stU7tZ0uk6rsld+3aJX379hUnJydp27atzJ49Wz788MMq78Zds2aN3H///eLi4iJ6vV58fX3lkUcekY0bN4qIyNmzZyU2Nlbuvvtu5dliXbt2lbfeekuuXr1aY441Pcfpetc/Q646uOauumulp6crdy9X97ujum1Xqs3nvqXSiFwzx0/Uwh07dgx33303Zs+ejZkzZzZ0d4iIqJFh4UQt1p49e/DPf/4Tffv2hYuLCw4fPoykpCSUlJRg//791d5dR0RELRevcaIWy8nJCT/++COWLVuGoqIiGAwGDBw4EPPmzWPRREREVeKMExEREZFKfBwBERERkUosnIiIiIhUYuFEREREpBIvDm8AFRUVOHPmDJydnWv9VQVERERUv0QEFy5cgNFovOEDkFk4NYAzZ87Ax8enobtBRERE1zh16tQNv+CbhVMDqPwizVOnTtXqG8WrYjabkZ6ejvDwcGi12vroXqPWkvJlrs1TS8oVaFn5Mtemq6SkBD4+Psq/zzVh4dQAKk/Pubi41Evh5OjoCBcXl2Zx8N5IS8qXuTZPLSlXoGXly1ybPjWXz/DicCIiIiKVWDgRERERqcTCiYiIiEglFk5EREREKrFwIiIiIlKJhRMRERGRSiyciIiIiFRi4URERESkEgsnIiIiIpVYOBERERGpxMKJiIiISCUWTkREREQq8Ut+iYioSevwwrp6aef4a8PrpR1q3jjjRERERKQSCyciIiIilVg4EREREanEwomIiIhIJRZORERERCqxcCIiIiJSiYUTERERkUosnIiIiIhUYuFEREREpBILJyIiIiKVWDgRERERqcTCiYiIiEglFk5EREREKrFwIiIiIlKJhRMRERGRSg1eOC1ZsgRdu3aFi4sLXFxcEBISgg0bNijrRQSJiYkwGo1wcHDAwIEDceDAAYs2TCYTJk2aBHd3dzg5OWHEiBE4ffq0RUxhYSFiYmJgMBhgMBgQExODoqIii5iTJ08iKioKTk5OcHd3x+TJk1FWVmYRs2/fPoSGhsLBwQFt27bF3LlzISL1u1OIiIioUWrwwqldu3Z47bXX8OOPP+LHH3/EoEGD8OCDDyrFUVJSEpKTk7F48WJkZ2fDy8sLYWFhuHDhgtLGlClTsHr1aqSmpmL79u24ePEiIiMjUV5ersRER0cjNzcXaWlpSEtLQ25uLmJiYpT15eXlGD58OC5duoTt27cjNTUVq1atwrRp05SYkpIShIWFwWg0Ijs7GykpKVi4cCGSk5Nvw54iIiKihmbX0B2IioqyeD1v3jwsWbIEWVlZ6Ny5MxYtWoQXX3wRDz/8MADgk08+gaenJz7//HOMGzcOxcXFWLZsGT777DMMGTIEALBixQr4+Phg48aNiIiIwKFDh5CWloasrCz07t0bALB06VKEhITg8OHDCAgIQHp6Og4ePIhTp07BaDQCAN58803ExsZi3rx5cHFxwcqVK3HlyhUsX74cer0egYGBOHLkCJKTk5GQkACNRnMb9xwRERHdbg0+43St8vJypKam4tKlSwgJCcGxY8eQn5+P8PBwJUav1yM0NBQ7duwAAOTk5MBsNlvEGI1GBAYGKjGZmZkwGAxK0QQAffr0gcFgsIgJDAxUiiYAiIiIgMlkQk5OjhITGhoKvV5vEXPmzBkcP368/ncIERERNSoNPuME/HHdUEhICK5cuYJWrVph9erV6Ny5s1LUeHp6WsR7enrixIkTAID8/HzodDq4urpaxeTn5ysxHh4eVtv18PCwiLl+O66urtDpdBYxHTp0sNpO5To/P78q8zOZTDCZTMrrkpISAIDZbIbZbK5mr6hT+f6bbaepaEn5MtfmqSXlCtyefPW29XOdKX8fq9fccq1NHo2icAoICEBubi6KioqwatUqjBkzBlu3blXWX38KTERueFrs+piq4usjpvLC8Jr6s2DBAsyZM8dqeXp6OhwdHWvIQr2MjIx6aaepaEn5MtfmqSXlCtzafJN61U8769evr5d2WtLYNpdcL1++rDq2URROOp0Od911FwCgZ8+eyM7Oxttvv43nn38ewB+zOd7e3kp8QUGBMtPj5eWFsrIyFBYWWsw6FRQUoG/fvkrM2bNnrbb722+/WbSzc+dOi/WFhYUwm80WMZWzT9duB7CeFbvWjBkzkJCQoLwuKSmBj48PwsPD4eLiUtOuuSGz2YyMjAyEhYVBq9XeVFtNQUvKl7k2Ty0pV+D25BuY+F29tLM/MeKm3t+Sxra55Vp5JkiNRlE4XU9EYDKZ4OfnBy8vL2RkZKB79+4AgLKyMmzduhWvv/46ACA4OBharRYZGRkYNWoUACAvLw/79+9HUlISACAkJATFxcXYtWsXevX640+TnTt3ori4WCmuQkJCMG/ePOTl5SlFWnp6OvR6PYKDg5WYmTNnoqysDDqdTokxGo1Wp/CupdfrLa6LqqTVauvtgKvPtpqClpQvc22eWlKuwK3N11RePzfm8Pdx7TWXXGuTQ4NfHD5z5kz88MMPOH78OPbt24cXX3wRW7ZswRNPPAGNRoMpU6Zg/vz5WL16Nfbv34/Y2Fg4OjoiOjoaAGAwGDB27FhMmzYNmzZtwu7du/Hkk08iKChIucuuU6dOGDp0KOLi4pCVlYWsrCzExcUhMjISAQEBAIDw8HB07twZMTEx2L17NzZt2oTp06cjLi5OmRWKjo6GXq9HbGws9u/fj9WrV2P+/Pm8o46IiKiFaPAZp7NnzyImJgZ5eXkwGAzo2rUr0tLSEBYWBgB47rnnUFpaivj4eBQWFqJ3795IT0+Hs7Oz0sZbb70FOzs7jBo1CqWlpRg8eDCWL18OW1tbJWblypWYPHmycvfdiBEjsHjxYmW9ra0t1q1bh/j4ePTr1w8ODg6Ijo7GwoULlRiDwYCMjAxMmDABPXv2hKurKxISEixOwxEREVHz1eCF07Jly2pcr9FokJiYiMTExGpj7O3tkZKSgpSUlGpj3NzcsGLFihq31b59e6xdu7bGmKCgIGzbtq3GGCIiImqeGvxUHREREVFTwcKJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFKLJyIiIiIVGLhRERERKQSCyciIiIilVg4EREREanEwomIiIhIJRZORERERCqxcCIiIiJSiYUTERERkUosnIiIiIhUYuFEREREpBILJyIiIiKVWDgRERERqcTCiYiIiEglFk5EREREKrFwIiIiIlKJhRMRERGRSiyciIiIiFRi4URERESkEgsnIiIiIpVYOBERERGpxMKJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFKLJyIiIiIVGLhRERERKQSCyciIiIilVg4EREREanEwomIiIhIpQYvnBYsWIB7770Xzs7O8PDwwMiRI3H48GGLmNjYWGg0GoufPn36WMSYTCZMmjQJ7u7ucHJywogRI3D69GmLmMLCQsTExMBgMMBgMCAmJgZFRUUWMSdPnkRUVBScnJzg7u6OyZMno6yszCJm3759CA0NhYODA9q2bYu5c+dCROpvpxAREVGj1OCF09atWzFhwgRkZWUhIyMDV69eRXh4OC5dumQRN3ToUOTl5Sk/69evt1g/ZcoUrF69Gqmpqdi+fTsuXryIyMhIlJeXKzHR0dHIzc1FWloa0tLSkJubi5iYGGV9eXk5hg8fjkuXLmH79u1ITU3FqlWrMG3aNCWmpKQEYWFhMBqNyM7ORkpKChYuXIjk5ORbtIeIiIiosbBr6A6kpaVZvP7444/h4eGBnJwcDBgwQFmu1+vh5eVVZRvFxcVYtmwZPvvsMwwZMgQAsGLFCvj4+GDjxo2IiIjAoUOHkJaWhqysLPTu3RsAsHTpUoSEhODw4cMICAhAeno6Dh48iFOnTsFoNAIA3nzzTcTGxmLevHlwcXHBypUrceXKFSxfvhx6vR6BgYE4cuQIkpOTkZCQAI1Gcyt2ExERETUCDT7jdL3i4mIAgJubm8XyLVu2wMPDA/7+/oiLi0NBQYGyLicnB2azGeHh4coyo9GIwMBA7NixAwCQmZkJg8GgFE0A0KdPHxgMBouYwMBApWgCgIiICJhMJuTk5CgxoaGh0Ov1FjFnzpzB8ePH62kvEBERUWPU4DNO1xIRJCQkoH///ggMDFSWDxs2DI8++ih8fX1x7NgxvPTSSxg0aBBycnKg1+uRn58PnU4HV1dXi/Y8PT2Rn58PAMjPz4eHh4fVNj08PCxiPD09Lda7urpCp9NZxHTo0MFqO5Xr/Pz8rLZhMplgMpmU1yUlJQAAs9kMs9msat9Up/L9N9tOU9GS8mWuzVNLyhW4PfnqbevnGlP+PlavueVamzwaVeE0ceJE7N27F9u3b7dY/thjjyn/HxgYiJ49e8LX1xfr1q3Dww8/XG17ImJx6qyq02j1EVN5YXh1p+kWLFiAOXPmWC1PT0+Ho6Njtf2vjYyMjHppp6loSfky1+apJeUK3Np8k3rVTzvXXztbVy1pbJtLrpcvX1Yd22gKp0mTJuGbb77Btm3b0K5duxpjvb294evri6NHjwIAvLy8UFZWhsLCQotZp4KCAvTt21eJOXv2rFVbv/32mzJj5OXlhZ07d1qsLywshNlstoipnH26djsArGarKs2YMQMJCQnK65KSEvj4+CA8PBwuLi415nojZrMZGRkZCAsLg1arvam2moKWlC9zbZ5aUq7A7ck3MPG7emlnf2LETb2/JY1tc8u18kyQGg1eOIkIJk2ahNWrV2PLli1Vnuq63rlz53Dq1Cl4e3sDAIKDg6HVapGRkYFRo0YBAPLy8rB//34kJSUBAEJCQlBcXIxdu3ahV68//jzZuXMniouLleIqJCQE8+bNQ15entJ2eno69Ho9goODlZiZM2eirKwMOp1OiTEajVan8Crp9XqLa6IqabXaejvg6rOtpqAl5ctcm6eWlCtwa/M1ldfPTTn8fVx7zSXX2uTQ4BeHT5gwAStWrMDnn38OZ2dn5OfnIz8/H6WlpQCAixcvYvr06cjMzMTx48exZcsWREVFwd3dHQ899BAAwGAwYOzYsZg2bRo2bdqE3bt348knn0RQUJByl12nTp0wdOhQxMXFISsrC1lZWYiLi0NkZCQCAgIAAOHh4ejcuTNiYmKwe/dubNq0CdOnT0dcXJwyMxQdHQ29Xo/Y2Fjs378fq1evxvz583lHHRERUQvQ4IXTkiVLUFxcjIEDB8Lb21v5+eKLLwAAtra22LdvHx588EH4+/tjzJgx8Pf3R2ZmJpydnZV23nrrLYwcORKjRo1Cv3794OjoiG+//Ra2trZKzMqVKxEUFITw8HCEh4eja9eu+Oyzz5T1tra2WLduHezt7dGvXz+MGjUKI0eOxMKFC5UYg8GAjIwMnD59Gj179kR8fDwSEhIsTsURERFR89QoTtXVxMHBAd99d+Pz1/b29khJSUFKSkq1MW5ublixYkWN7bRv3x5r166tMSYoKAjbtm27YZ+IiIioeWnwGSciIiKipoKFExEREZFKLJyIiIiIVGLhRERERKQSCyciIiIilVg4EREREanEwomIiIhIJRZORERERCqxcCIiIiJSiYUTERERkUosnIiIiIhUYuFEREREpBILJyIiIiKVWDgRERERqcTCiYiIiEglFk5EREREKrFwIiIiIlKJhRMRERGRSiyciIiIiFRi4URERESkEgsnIiIiIpVYOBERERGpxMKJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFKLJyIiIiIVLJr6A4QEVHL1OGFdQ3dBaJa44wTERERkUosnIiIiIhUYuFEREREpBILJyIiIiKVWDgRERERqcTCiYiIiEilBi+cFixYgHvvvRfOzs7w8PDAyJEjcfjwYYsYEUFiYiKMRiMcHBwwcOBAHDhwwCLGZDJh0qRJcHd3h5OTE0aMGIHTp09bxBQWFiImJgYGgwEGgwExMTEoKiqyiDl58iSioqLg5OQEd3d3TJ48GWVlZRYx+/btQ2hoKBwcHNC2bVvMnTsXIlJ/O4WIiIgapQYvnLZu3YoJEyYgKysLGRkZuHr1KsLDw3Hp0iUlJikpCcnJyVi8eDGys7Ph5eWFsLAwXLhwQYmZMmUKVq9ejdTUVGzfvh0XL15EZGQkysvLlZjo6Gjk5uYiLS0NaWlpyM3NRUxMjLK+vLwcw4cPx6VLl7B9+3akpqZi1apVmDZtmhJTUlKCsLAwGI1GZGdnIyUlBQsXLkRycvIt3lNERETU0Br8AZhpaWkWrz/++GN4eHggJycHAwYMgIhg0aJFePHFF/Hwww8DAD755BN4enri888/x7hx41BcXIxly5bhs88+w5AhQwAAK1asgI+PDzZu3IiIiAgcOnQIaWlpyMrKQu/evQEAS5cuRUhICA4fPoyAgACkp6fj4MGDOHXqFIxGIwDgzTffRGxsLObNmwcXFxesXLkSV65cwfLly6HX6xEYGIgjR44gOTkZCQkJ0Gg0t3HvERER0e3U4DNO1ysuLgYAuLm5AQCOHTuG/Px8hIeHKzF6vR6hoaHYsWMHACAnJwdms9kixmg0IjAwUInJzMyEwWBQiiYA6NOnDwwGg0VMYGCgUjQBQEREBEwmE3JycpSY0NBQ6PV6i5gzZ87g+PHj9bkriIiIqJFp8Bmna4kIEhIS0L9/fwQGBgIA8vPzAQCenp4WsZ6enjhx4oQSo9Pp4OrqahVT+f78/Hx4eHhYbdPDw8Mi5vrtuLq6QqfTWcR06NDBajuV6/z8/Ky2YTKZYDKZlNclJSUAALPZDLPZXN3uUKXy/TfbTlPRkvJlrs1TS8oVqDlfvW3jujaUv4/Va2651iaPeiucTp06hQMHDuDee+9F69at69TGxIkTsXfvXmzfvt1q3fWnwETkhqfFro+pKr4+YiovDK+uPwsWLMCcOXOslqenp8PR0bHGHNTKyMiol3aaipaUL3NtnlpSrkDV+Sb1aoCO1GD9+vX10k5LGtvmkuvly5dVx9apcJo1axYuXbqEt956CwCwceNGREVFoaysDHfccQe2bduGLl261KrNSZMm4ZtvvsG2bdvQrl07ZbmXlxeAP2ZzvL29leUFBQXKTI+XlxfKyspQWFhoMetUUFCAvn37KjFnz5612u5vv/1m0c7OnTst1hcWFsJsNlvEVM4+XbsdwHpWrNKMGTOQkJCgvC4pKYGPjw/Cw8Ph4uJS0265IbPZjIyMDISFhUGr1d5UW01BS8qXuTZPLSlXoOZ8AxO/a6BeVW1/YsRNvb8ljW1zy7XyTJAadSqcVq1aZVEIzJo1C127dsWLL76IxMREvPrqq/jnP/+pqi0RwaRJk7B69Wps2bLF6lSXn58fvLy8kJGRge7duwMAysrKsHXrVrz++usAgODgYGi1WmRkZGDUqFEAgLy8POzfvx9JSUkAgJCQEBQXF2PXrl3o1euPP3N27tyJ4uJipbgKCQnBvHnzkJeXpxRp6enp0Ov1CA4OVmJmzpyJsrIy6HQ6JcZoNFqdwquk1+stromqpNVq6+2Aq8+2moKWlC9zbZ5aUq5A1fmayhvXzTT8fVx7zSXX2uRQp8Lp119/xV133QUAOHfuHLKzs7F+/XpERETgypUrFrfv38iECRPw+eef4+uvv4azs7Mym2MwGODg4ACNRoMpU6Zg/vz56NixIzp27Ij58+fD0dER0dHRSuzYsWMxbdo0tG7dGm5ubpg+fTqCgoKUu+w6deqEoUOHIi4uDu+//z4A4Nlnn0VkZCQCAgIAAOHh4ejcuTNiYmLwxhtv4Pz585g+fTri4uKUmaHo6GjMmTMHsbGxmDlzJo4ePYr58+fj5Zdf5h11REREzVydCicRQUVFBQDgP//5D2xtbTFgwAAAgLe3N37//XfVbS1ZsgQAMHDgQIvlH3/8MWJjYwEAzz33HEpLSxEfH4/CwkL07t0b6enpcHZ2VuLfeust2NnZYdSoUSgtLcXgwYOxfPly2NraKjErV67E5MmTlbvvRowYgcWLFyvrbW1tsW7dOsTHx6Nfv35wcHBAdHQ0Fi5cqMQYDAZkZGRgwoQJ6NmzJ1xdXZGQkGAxA0dERETNU50KpzvvvBNr167F4MGDkZqail69esHBwQHAH6fIrr+7rSZqnrit0WiQmJiIxMTEamPs7e2RkpKClJSUamPc3NywYsWKGrfVvn17rF27tsaYoKAgbNu2rcYYIiIian7qVDiNGzcOEyZMwKeffoqioiJ89NFHyrr//Oc/6Ny5c711kIiIiKixqFPhNH78eLi6umLHjh3o1asXnnzySWVdaWkpxowZU28dJCIiImos6vwcp8cffxyPP/641fIPPvjgpjpERERE1FjV6StXbG1tsWvXrirX5eTkWFyQTURERNRc1KlwqumC7sq77YiIiIiamzp/yW91zyzKycnBHXfcUddmiYiIiBot1dc4vf3223j77bcB/FE0jRw50upp2KWlpSgoKMAjjzxSv70kIiIiagRUF04eHh7K988dP34cf/rTn6xmlvR6PYKCgvC3v/2tXjtJRERE1BioLpxGjx6N0aNHAwDuv/9+LFmyBHffffct6xgRERFRY1OnxxFs3ry5vvtBRERE1OjV+TlOIoLs7GycOHECpaWlVuv/8pe/3FTHiIiIiBqbOhVOR44cwYgRI3D06NEqH02g0WhYOBEREVGzU6fCacKECbhy5Qq++OILdO3a1eruOiIiIqLmqE6F065du7B06VI+doCIiIhalDo9ALNVq1ZwcXGp774QERERNWp1KpyeeuopfP755/XdFyIiIqJGrU6n6gIDA/HPf/4TI0aMQFRUFFq3bm0V8/DDD99054iIiIgakzoVTtHR0QCAY8eOYe3atVbrNRoNysvLb65nRERERI0MH4BJREREpFKdCqfQ0ND67gcRERFRo1eni8OJiIiIWqI6zTgNGjSoxvUajQabNm2qU4eIiIiIGqs6FU4VFRXQaDQWy37//XccPnwYHh4e8Pf3r5fOERERETUmdSqctmzZUuXyI0eO4MEHH8Ts2bNvpk9EREREjVK9XuPk7++Pv//973juuefqs1kiIiKiRqHeLw7v0KED9u/fX9/NEhERETW4ei+cVq1aBaPRWN/NEhERETW4Ol3j9PTTT1stM5lM2Lt3Lw4ePIikpKSb7hgREdHt1OGFdTf1fr2tIKlXPXWGGq06FU7ff/+91V119vb26NChA2bMmKF8JQsRERFRc1Knwun48eP13A0iIiKixo9PDiciIiJSqU4zTgBw/vx5vPXWW9i0aRPOnTsHd3d3DBkyBFOmTIGrq2t99pGIiIioUajTjNOvv/6KHj16YN68eSguLkb79u1RVFSEV155BT169MCZM2fqu59EREREDa5OhdPMmTNRWlqKnTt34sCBA8jIyMCBAwewc+dOlJaWYubMmfXdTyIiIqIGV6fCKS0tDa+++iruvfdei+X33nsv5s6diw0bNqhua9u2bYiKioLRaIRGo8GaNWss1sfGxkKj0Vj89OnTxyLGZDJh0qRJcHd3h5OTE0aMGIHTp09bxBQWFiImJgYGgwEGgwExMTEoKiqyiDl58iSioqLg5OQEd3d3TJ48GWVlZRYx+/btQ2hoKBwcHNC2bVvMnTsXIqI6XyIiImq66lQ4FRcXo0OHDlWu8/PzQ3Fxseq2Ll26hG7dumHx4sXVxgwdOhR5eXnKz/r16y3WT5kyBatXr0Zqaiq2b9+OixcvIjIyEuXl5UpMdHQ0cnNzkZaWhrS0NOTm5iImJkZZX15ejuHDh+PSpUvYvn07UlNTsWrVKkybNk2JKSkpQVhYGIxGI7Kzs5GSkoKFCxciOTlZdb5ERETUdNXp4nA/Pz+sW7cOYWFhVus2bNgAPz8/1W0NGzYMw4YNqzFGr9fDy8urynXFxcVYtmwZPvvsMwwZMgQAsGLFCvj4+GDjxo2IiIjAoUOHkJaWhqysLPTu3RsAsHTpUoSEhODw4cMICAhAeno6Dh48iFOnTilPPn/zzTcRGxuLefPmwcXFBStXrsSVK1ewfPly6PV6BAYG4siRI0hOTkZCQoLVs62IiIioeanTjNNTTz2Ff/zjH5g8eTJycnJw5swZ5OTkYOrUqfjHP/6BsWPH1msnt2zZAg8PD/j7+yMuLg4FBQXKupycHJjNZoSHhyvLjEYjAgMDsWPHDgBAZmYmDAaDUjQBQJ8+fWAwGCxiAgMDLb4uJiIiAiaTCTk5OUpMaGgo9Hq9RcyZM2f4bCsiIqIWoE4zTn//+9/x888/Y/HixXjnnXeU5SKCZ599FtOnT6+3Dg4bNgyPPvoofH19cezYMbz00ksYNGgQcnJyoNfrkZ+fD51OZ/UIBE9PT+Tn5wMA8vPz4eHhYdW2h4eHRYynp6fFeldXV+h0OouY609RVr4nPz+/2pk2k8kEk8mkvC4pKQEAmM1mmM1mtbuiSpXvv9l2moqWlC9zbZ5aUq5AzfnqbZvX9aF6mz/yaQlj29yO49rkUafCSaPR4P3330dCQgI2b96Mc+fOoXXr1hg0aBD8/f3r0mS1HnvsMeX/AwMD0bNnT/j6+mLdunV4+OGHq32fiFicOqvqNFp9xFReGF7TaboFCxZgzpw5VsvT09Ph6OhY7ftqIyMjo17aaSpaUr7MtXlqSbkCVefbXL/XrSWNbXPJ9fLly6pjVRdOhYWFeOaZZ/DUU08hMjISABAQEICAgAAlZu3atZgxYwY++OADtG7duhZdVs/b2xu+vr44evQoAMDLywtlZWUoLCy0mHUqKChA3759lZizZ89atfXbb78pM0ZeXl7YuXOnxfrCwkKYzWaLmMrZp2u3A8BqtupaM2bMQEJCgvK6pKQEPj4+CA8Ph4uLi+rcq2I2m5GRkYGwsDBotdqbaqspaEn5MtfmqSXlCtScb2Didw3Uq1tDbyN4pWdFixjb5nYcV54JUkN14fThhx9iz549GDp0aLUxQ4cORUJCAt555x28/PLLqjtRG+fOncOpU6fg7e0NAAgODoZWq0VGRgZGjRoFAMjLy8P+/fuRlJQEAAgJCUFxcTF27dqFXr3++BNn586dKC4uVoqrkJAQzJs3D3l5eUrb6enp0Ov1CA4OVmJmzpyJsrIy6HQ6JcZoNFZ7lyHwx8Xt114XVUmr1dbbAVefbTUFLSlf5to8taRcgarzNZU3zxtqWtLYNpdca5OD6ovDU1NTERcXBzu76mstOzs7xMXF4ZtvvlHdgYsXLyI3Nxe5ubkAgGPHjiE3NxcnT57ExYsXMX36dGRmZuL48ePYsmULoqKi4O7ujoceeggAYDAYMHbsWEybNg2bNm3C7t278eSTTyIoKEi5y65Tp04YOnQo4uLikJWVhaysLMTFxSEyMlKZMQsPD0fnzp0RExOD3bt3Y9OmTZg+fTri4uKUWaHo6Gjo9XrExsZi//79WL16NebPn8876oiIiFoI1YXTkSNH0LNnzxvG9ejRA0eOHFHdgR9//BHdu3dH9+7dAQAJCQno3r07Xn75Zdja2mLfvn148MEH4e/vjzFjxsDf3x+ZmZlwdnZW2njrrbcwcuRIjBo1Cv369YOjoyO+/fZb2NraKjErV65EUFAQwsPDER4ejq5du+Kzzz5T1tva2mLdunWwt7dHv379MGrUKIwcORILFy5UYgwGAzIyMnD69Gn07NkT8fHxSEhIsDgNR0RERM2X6lN1V69eVTWVpdVqa3V1+sCBA2t88vZ33934HLi9vT1SUlKQkpJSbYybmxtWrFhRYzvt27fH2rVra4wJCgrCtm3bbtgnIiIian5Uzzh5e3vj4MGDN4w7cOBAtQ+rJCIiImrKVBdOoaGhePfdd2ucTTKbzViyZAnuv//+eukcERERUWOiunCaOnUq/vvf/+Khhx7CmTNnrNafOXMGI0eOxOHDhzF16tR67SQRERFRY6D6GqeuXbvinXfeQXx8PPz8/BAcHKw8KfvYsWPIyclBRUUFlixZgqCgoFvWYSIiIqKGUqsnh8fFxSEwMBDz58/H5s2bkZWVBQBwdHTE0KFDMWPGDPTp0+eWdJSIiIioodX6K1dCQkLw7bffoqKiAr///jsAwN3dHTY2dfq+YCIiIqImo07fVQcANjY2VX5xLhEREVFzxWkiIiIiIpVYOBERERGpxMKJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFKLJyIiIiIVGLhRERERKQSCyciIiIilVg4EREREanEwomIiIhIJRZORERERCqxcCIiIiJSiYUTERERkUosnIiIiIhUYuFEREREpBILJyIiIiKVWDgRERERqcTCiYiIiEglFk5EREREKrFwIiIiIlLJrqE7QERE1JwEJn4HU7nmpto4/trweuoN1TfOOBERERGpxMKJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFKDV44bdu2DVFRUTAajdBoNFizZo3FehFBYmIijEYjHBwcMHDgQBw4cMAixmQyYdKkSXB3d4eTkxNGjBiB06dPW8QUFhYiJiYGBoMBBoMBMTExKCoqsog5efIkoqKi4OTkBHd3d0yePBllZWUWMfv27UNoaCgcHBzQtm1bzJ07FyJSb/uDiIiIGq8GL5wuXbqEbt26YfHixVWuT0pKQnJyMhYvXozs7Gx4eXkhLCwMFy5cUGKmTJmC1atXIzU1Fdu3b8fFixcRGRmJ8vJyJSY6Ohq5ublIS0tDWloacnNzERMTo6wvLy/H8OHDcenSJWzfvh2pqalYtWoVpk2bpsSUlJQgLCwMRqMR2dnZSElJwcKFC5GcnHwL9gwRERE1Ng3+HKdhw4Zh2LBhVa4TESxatAgvvvgiHn74YQDAJ598Ak9PT3z++ecYN24ciouLsWzZMnz22WcYMmQIAGDFihXw8fHBxo0bERERgUOHDiEtLQ1ZWVno3bs3AGDp0qUICQnB4cOHERAQgPT0dBw8eBCnTp2C0WgEALz55puIjY3FvHnz4OLigpUrV+LKlStYvnw59Ho9AgMDceTIESQnJyMhIQEazc09t4OIiIgatwafcarJsWPHkJ+fj/DwcGWZXq9HaGgoduzYAQDIycmB2Wy2iDEajQgMDFRiMjMzYTAYlKIJAPr06QODwWARExgYqBRNABAREQGTyYScnBwlJjQ0FHq93iLmzJkzOH78eP3vACIiImpUGnzGqSb5+fkAAE9PT4vlnp6eOHHihBKj0+ng6upqFVP5/vz8fHh4eFi17+HhYRFz/XZcXV2h0+ksYjp06GC1ncp1fn5+VeZhMplgMpmU1yUlJQAAs9kMs9lcTfbqVL7/ZttpKlpSvsy1eWpJuQI156u3bV7Xh+ptxOK/N6OxHx/N7TiuTR6NunCqdP0pMBG54Wmx62Oqiq+PmMoLw2vqz4IFCzBnzhyr5enp6XB0dKwhC/UyMjLqpZ2moiXly1ybp5aUK1B1vkm9GqAjt8ErPStuuo3169fXQ09uveZyHF++fFl1bKMunLy8vAD8MZvj7e2tLC8oKFBmery8vFBWVobCwkKLWaeCggL07dtXiTl79qxV+7/99ptFOzt37rRYX1hYCLPZbBFTOft07XYA61mxa82YMQMJCQnK65KSEvj4+CA8PBwuLi432As1M5vNyMjIQFhYGLRa7U211RS0pHyZa/PUknIFas43MPG7BurVraG3EbzSswIv/WgDU8XNXfO6PzGinnp1azS347jyTJAajbpw8vPzg5eXFzIyMtC9e3cAQFlZGbZu3YrXX38dABAcHAytVouMjAyMGjUKAJCXl4f9+/cjKSkJABASEoLi4mLs2rULvXr98SfOzp07UVxcrBRXISEhmDdvHvLy8pQiLT09HXq9HsHBwUrMzJkzUVZWBp1Op8QYjUarU3jX0uv1FtdFVdJqtfV2wNVnW01BS8qXuTZPLSlXoOp8b/aLcBsrU4XmpnNrKsdGczmOa5NDg18cfvHiReTm5iI3NxfAHxeE5+bm4uTJk9BoNJgyZQrmz5+P1atXY//+/YiNjYWjoyOio6MBAAaDAWPHjsW0adOwadMm7N69G08++SSCgoKUu+w6deqEoUOHIi4uDllZWcjKykJcXBwiIyMREBAAAAgPD0fnzp0RExOD3bt3Y9OmTZg+fTri4uKUWaHo6Gjo9XrExsZi//79WL16NebPn8876oiIiFqIBp9x+vHHH3H//fcrrytPaY0ZMwbLly/Hc889h9LSUsTHx6OwsBC9e/dGeno6nJ2dlfe89dZbsLOzw6hRo1BaWorBgwdj+fLlsLW1VWJWrlyJyZMnK3ffjRgxwuLZUba2tli3bh3i4+PRr18/ODg4IDo6GgsXLlRiDAYDMjIyMGHCBPTs2ROurq5ISEiwOA1HREREzVeDF04DBw6s8cnbGo0GiYmJSExMrDbG3t4eKSkpSElJqTbGzc0NK1asqLEv7du3x9q1a2uMCQoKwrZt22qMISIiouapwU/VERERETUVLJyIiIiIVGLhRERERKQSCyciIiIilVg4EREREanEwomIiIhIJRZORERERCqxcCIiIiJSiYUTERERkUosnIiIiIhUYuFEREREpBILJyIiIiKVWDgRERERqcTCiYiIiEglFk5EREREKrFwIiIiIlKJhRMRERGRSiyciIiIiFRi4URERESkEgsnIiIiIpXsGroDRETU9HR4YZ2qOL2tIKkXEJj4HUzlmlvcK6JbjzNORERERCqxcCIiIiJSiYUTERERkUosnIiIiIhUYuFEREREpBILJyIiIiKV+DgCIiIiqtH1j5+oy2Mmjr82/FZ07bbjjBMRERGRSiyciIiIiFRi4URERESkEgsnIiIiIpVYOBERERGpxMKJiIiISKVGXzglJiZCo9FY/Hh5eSnrRQSJiYkwGo1wcHDAwIEDceDAAYs2TCYTJk2aBHd3dzg5OWHEiBE4ffq0RUxhYSFiYmJgMBhgMBgQExODoqIii5iTJ08iKioKTk5OcHd3x+TJk1FWVnbLciciIqLGpdEXTgDQpUsX5OXlKT/79u1T1iUlJSE5ORmLFy9GdnY2vLy8EBYWhgsXLigxU6ZMwerVq5Gamort27fj4sWLiIyMRHl5uRITHR2N3NxcpKWlIS0tDbm5uYiJiVHWl5eXY/jw4bh06RK2b9+O1NRUrFq1CtOmTbs9O4GIiIgaXJN4AKadnZ3FLFMlEcGiRYvw4osv4uGHHwYAfPLJJ/D09MTnn3+OcePGobi4GMuWLcNnn32GIUOGAABWrFgBHx8fbNy4ERERETh06BDS0tKQlZWF3r17AwCWLl2KkJAQHD58GAEBAUhPT8fBgwdx6tQpGI1GAMCbb76J2NhYzJs3Dy4uLrdpbxAREVFDaRIzTkePHoXRaISfnx8ef/xx/PLLLwCAY8eOIT8/H+Hh4UqsXq9HaGgoduzYAQDIycmB2Wy2iDEajQgMDFRiMjMzYTAYlKIJAPr06QODwWARExgYqBRNABAREQGTyYScnJxblzwRERE1Go1+xql379749NNP4e/vj7Nnz+LVV19F3759ceDAAeTn5wMAPD09Ld7j6emJEydOAADy8/Oh0+ng6upqFVP5/vz8fHh4eFht28PDwyLm+u24urpCp9MpMdUxmUwwmUzK65KSEgCA2WyG2Wy+4T6oSeX7b7adpqIl5ctcm6fmkqveVtTF2YjFf5uz+sy1sR0f1493XXJtbDldqzZ9a/SF07Bhw5T/DwoKQkhICO6880588skn6NOnDwBAo7H8nhwRsVp2vetjqoqvS0xVFixYgDlz5lgtT09Ph6OjY43vVSsjI6Ne2mkqWlK+zLV5auq5JvWqXfwrPStuTUcaofrIdf369fXQk/pT3XjXJtfGltO1Ll++rDq20RdO13NyckJQUBCOHj2KkSNHAvhjNsjb21uJKSgoUGaHvLy8UFZWhsLCQotZp4KCAvTt21eJOXv2rNW2fvvtN4t2du7cabG+sLAQZrPZaibqejNmzEBCQoLyuqSkBD4+PggPD7/pa6PMZjMyMjIQFhYGrVZ7U201BS0pX+baPDWXXAMTv1MVp7cRvNKzAi/9aANThbovg22q6jPX/YkR9dSr+nH9eNcl18aW07UqzwSp0eQKJ5PJhEOHDuG+++6Dn58fvLy8kJGRge7duwMAysrKsHXrVrz++usAgODgYGi1WmRkZGDUqFEAgLy8POzfvx9JSUkAgJCQEBQXF2PXrl3o1euPsnrnzp0oLi5WiquQkBDMmzcPeXl5SpGWnp4OvV6P4ODgGvus1+uh1+utlmu12nr7xVmfbTUFLSlf5to8NfVcTeW1KwxMFZpav6epqo9cG9uxUV0+tcm1seV0rdr0rdEXTtOnT0dUVBTat2+PgoICvPrqqygpKcGYMWOg0WgwZcoUzJ8/Hx07dkTHjh0xf/58ODo6Ijo6GgBgMBgwduxYTJs2Da1bt4abmxumT5+OoKAg5S67Tp06YejQoYiLi8P7778PAHj22WcRGRmJgIAAAEB4eDg6d+6MmJgYvPHGGzh//jymT5+OuLg43lFHRET1qsML6xq6C1SNRl84nT59GqNHj8bvv/+ONm3aoE+fPsjKyoKvry8A4LnnnkNpaSni4+NRWFiI3r17Iz09Hc7Ozkobb731Fuzs7DBq1CiUlpZi8ODBWL58OWxtbZWYlStXYvLkycrddyNGjMDixYuV9ba2tli3bh3i4+PRr18/ODg4IDo6GgsXLrxNe4KIiIgaWqMvnFJTU2tcr9FokJiYiMTExGpj7O3tkZKSgpSUlGpj3NzcsGLFihq31b59e6xdu7bGGCIiImq+msRznIiIiIgaAxZORERERCqxcCIiIiJSiYUTERERkUosnIiIiIhUYuFEREREpBILJyIiIiKVWDgRERERqcTCiYiIiEglFk5EREREKrFwIiIiIlKJhRMRERGRSiyciIiIiFRi4URERESkEgsnIiIiIpVYOBERERGpxMKJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFKLJyIiIiIVLJr6A4QERFR89fhhXX10s7x14bXSzt1xRknIiIiIpVYOBERERGpxMKJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFKLJyIiIiIVGLhRERERKQSCyciIiIilVg4EREREanEr1whImpB6utrL4haKs44EREREanEwqmO3n33Xfj5+cHe3h7BwcH44YcfGrpLREREdIuxcKqDL774AlOmTMGLL76I3bt347777sOwYcNw8uTJhu4aERER3UK8xqkOkpOTMXbsWDzzzDMAgEWLFuG7777DkiVLsGDBggbuHRE1NtdeV6S3FST1AgITv4OpXNOAvSKiuuCMUy2VlZUhJycH4eHhFsvDw8OxY8eOBuoVERER3Q6ccaql33//HeXl5fD09LRY7unpifz8/CrfYzKZYDKZlNfFxcUAgPPnz8NsNt9Uf8xmMy5fvoxz585Bq9XeVFtNQUvKl7nWXe8Fm+qhV/Xn2l+0dhWCy5crYGe2QXlF859xakn5Mtfb49y5c/Xe5oULFwAAInLDWBZOdaTRWB4oImK1rNKCBQswZ84cq+V+fn63pG9E1LhFN3QHbrOWlC9zvfXc37x1bV+4cAEGg6HGGBZOteTu7g5bW1ur2aWCggKrWahKM2bMQEJCgvK6oqIC58+fR+vWrastttQqKSmBj48PTp06BRcXl5tqqyloSfky1+apJeUKtKx8mWvTJSK4cOECjEbjDWNZONWSTqdDcHAwMjIy8NBDDynLMzIy8OCDD1b5Hr1eD71eb7HsjjvuqNd+ubi4NIuDV62WlC9zbZ5aUq5Ay8qXuTZNN5ppqsTCqQ4SEhIQExODnj17IiQkBB988AFOnjyJv/71rw3dNSIiIrqFWDjVwWOPPYZz585h7ty5yMvLQ2BgINavXw9fX9+G7hoRERHdQiyc6ig+Ph7x8fEN3Q3o9XrMnj3b6lRgc9WS8mWuzVNLyhVoWfky15ZBI2ruvSMiIiIiPgCTiIiISC0WTkREREQqsXAiIiIiUomFUyOwbds2REVFwWg0QqPRYM2aNVYxhw4dwogRI2AwGODs7Iw+ffrg5MmTyvqBAwdCo9FY/Dz++OMWbRQWFiImJgYGgwEGgwExMTEoKiq6xdlZulGu1+dQ+fPGG28oMSaTCZMmTYK7uzucnJwwYsQInD592qKdxpArUD/5NpexvXjxIiZOnIh27drBwcEBnTp1wpIlSyximsrY1keuzWVcz549i9jYWBiNRjg6OmLo0KE4evSoRUxTGVegfvJtCmO7YMEC3HvvvXB2doaHhwdGjhyJw4cPW8SICBITE2E0GuHg4ICBAwfiwIEDFjFNaWzrCwunRuDSpUvo1q0bFi9eXOX6n3/+Gf3798fdd9+NLVu2YM+ePXjppZdgb29vERcXF4e8vDzl5/3337dYHx0djdzcXKSlpSEtLQ25ubmIiYm5ZXlV5Ua5Xtv/vLw8fPTRR9BoNPjzn/+sxEyZMgWrV69Gamoqtm/fjosXLyIyMhLl5eVKTGPIFaiffIHmMbZTp05FWloaVqxYgUOHDmHq1KmYNGkSvv76ayWmqYxtfeQKNP1xFRGMHDkSv/zyC77++mvs3r0bvr6+GDJkCC5duqTENZVxBeonX6Dxj+3WrVsxYcIEZGVlISMjA1evXkV4eLhFHklJSUhOTsbixYuRnZ0NLy8vhIWFKd/rBjStsa03Qo0KAFm9erXFsscee0yefPLJGt8XGhoqf/vb36pdf/DgQQEgWVlZyrLMzEwBIP/9739vpst1VlWu13vwwQdl0KBByuuioiLRarWSmpqqLPv111/FxsZG0tLSRKRx5ipSt3xFms/YdunSRebOnWuxrEePHjJr1iwRabpjW5dcRZrHuB4+fFgAyP79+5VlV69eFTc3N1m6dKmINN1xFalbviJNc2wLCgoEgGzdulVERCoqKsTLy0tee+01JebKlStiMBjkvffeE5GmPbY3gzNOjVxFRQXWrVsHf39/REREwMPDA717967ydN7KlSvh7u6OLl26YPr06RZ/FWRmZsJgMKB3797Ksj59+sBgMGDHjh23I5VaO3v2LNatW4exY8cqy3JycmA2mxEeHq4sMxqNCAwMVPJoirkCVedbqTmMbf/+/fHNN9/g119/hYhg8+bNOHLkCCIiIgA0r7G9Ua6Vmvq4mkwmALCY/ba1tYVOp8P27dsBNK9xVZNvpaY2tsXFxQAANzc3AMCxY8eQn59vMW56vR6hoaFKH5vT2NYGH4DZyBUUFODixYt47bXX8Oqrr+L1119HWloaHn74YWzevBmhoaEAgCeeeAJ+fn7w8vLC/v37MWPGDOzZswcZGRkAgPz8fHh4eFi17+HhYfWFxY3FJ598AmdnZzz88MPKsvz8fOh0Ori6ulrEenp6Knk0xVyBqvMFms/Y/uMf/0BcXBzatWsHOzs72NjY4MMPP0T//v0BNK+xvVGuQPMY17vvvhu+vr6YMWMG3n//fTg5OSE5ORn5+fnIy8sD0LzGVU2+QNMbWxFBQkIC+vfvj8DAQABQ+nH9l9d7enrixIkTSkxzGdvaYOHUyFVUVAAAHnzwQUydOhUAcM8992DHjh147733lMIpLi5OeU9gYCA6duyInj174qeffkKPHj0A/HEh8vVEpMrljcFHH32EJ554wuparqpcn0dTyxWoPt/mMrb/+Mc/kJWVhW+++Qa+vr7Ytm0b4uPj4e3tjSFDhlT7vqY4tmpybQ7jqtVqsWrVKowdOxZubm6wtbXFkCFDMGzYsBu+tymOq9p8m9rYTpw4EXv37rWaNQOs+6mmj01xbGuDp+oaOXd3d9jZ2aFz584Wyzt16mRxV931evToAa1Wq9zt4eXlhbNnz1rF/fbbb1Z/UTQGP/zwAw4fPoxnnnnGYrmXlxfKyspQWFhosbygoEDJo6nlClSfb1Wa4tiWlpZi5syZSE5ORlRUFLp27YqJEyfisccew8KFCwE0n7FVk2tVmuK4AkBwcDByc3NRVFSEvLw8pKWl4dy5c/Dz8wPQfMa10o3yrUpjHttJkybhm2++webNm9GuXTtluZeXFwBYzQpdP27NaWzVYuHUyOl0Otx7771Wt4keOXKkxi8VPnDgAMxmM7y9vQEAISEhKC4uxq5du5SYnTt3ori4GH379r01nb8Jy5YtQ3BwMLp162axPDg4GFqtVpnyBv64M23//v1KHk0tV6D6fKvSFMfWbDbDbDbDxsbyV46tra0yq9pcxlZNrlVpiuN6LYPBgDZt2uDo0aP48ccf8eCDDwJoPuN6veryrUpjHFsRwcSJE/HVV1/h+++/tyr8Kk81XjtuZWVl2Lp1q9LH5jq2N3TbL0cnKxcuXJDdu3fL7t27BYAkJyfL7t275cSJEyIi8tVXX4lWq5UPPvhAjh49KikpKWJrays//PCDiIj873//kzlz5kh2drYcO3ZM1q1bJ3fffbd0795drl69qmxn6NCh0rVrV8nMzJTMzEwJCgqSyMjIRpWriEhxcbE4OjrKkiVLqmzjr3/9q7Rr1042btwoP/30kwwaNEi6devW6HIVufl8m9PYhoaGSpcuXWTz5s3yyy+/yMcffyz29vby7rvvKm00lbG92Vyb07j+61//ks2bN8vPP/8sa9asEV9fX3n44Yct2mgq4ypy8/k2lbEdP368GAwG2bJli+Tl5Sk/ly9fVmJee+01MRgM8tVXX8m+fftk9OjR4u3tLSUlJUpMUxrb+sLCqRHYvHmzALD6GTNmjBKzbNkyueuuu8Te3l66desma9asUdadPHlSBgwYIG5ubqLT6eTOO++UyZMny7lz5yy2c+7cOXniiSfE2dlZnJ2d5YknnpDCwsLblOUf1OT6/vvvi4ODgxQVFVXZRmlpqUycOFHc3NzEwcFBIiMj5eTJkxYxjSFXkZvPtzmNbV5ensTGxorRaBR7e3sJCAiQN998UyoqKpQ2msrY3myuzWlc3377bWnXrp1otVpp3769zJo1S0wmk0UbTWVcRW4+36YytlXlCEA+/vhjJaaiokJmz54tXl5eotfrZcCAAbJv3z6LdprS2NYXjYhI/c9jERERETU/vMaJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFKLJyIiIiIVGLhRERERKQSCyciIiIilVg4EREREanEwomIbmjv3r146qmn4OfnB3t7e7Rq1Qo9evRAUlISzp8/36B927FjBxITE1FUVNRgfTh48CASExNx/Phxq3WxsbHo0KHDbe3Pl19+CY1Gg5SUlCrXP/vss9Dr9di7d+9t7RdRc8AnhxNRjZYuXYr4+HgEBAQgPj4enTt3htlsxo8//oilS5eiW7duWL16dYP1b+HChfj73/+OY8eO3fYCpdK///1vPProo9i8eTMGDhxose7nn39GSUkJunfvflv79MQTT2DNmjXYs2cP7rrrLmV5eno6IiIisGDBArzwwgu3tU9EzYFdQ3eAiBqvzMxMjB8/HmFhYVizZg30er2yLiwsDNOmTUNaWloD9rD2SktL4eDgcNu2d+edd962bV1r8eLF2LJlC2JjY7Ft2zbY2NigpKQEzzzzDEJCQvD3v//9tvTj8uXLcHR0vC3bIrotGvar8oioMYuMjBQ7OzurL+2sTnl5ubz++usSEBAgOp1O2rRpIzExMXLq1CmLuNDQUOnSpYvs2rVL+vfvLw4ODuLn5ycLFiyQ8vJyi/ZeeeUV8ff3F3t7ezEYDBIUFCSLFi0SEZHZs2dX+UWlmzdvFhERX19fGT58uKxatUruuece0ev18vzzz8uxY8esvtC0EgCZPXu2xbJDhw7J448/Lh4eHqLT6cTHx0diYmLkypUr8vHHH9f4ZaljxowRX19fi/ZKS0vlhRdekA4dOohWqxWj0Sjx8fFWX3xa2f8NGzZI9+7dlS8MXrZsmarx2LBhgwCQhQsXiojI008/LY6OjnLkyBERESkuLpZp06ZZ9ONvf/ubXLx40aKdxYsXy3333Sdt2rQRR0dHCQwMlNdff13Kysos4irHdevWrRISEiIODg7y2GOPqeorUVPBGSciqlJ5eTm+//57BAcHw8fHR9V7xo8fjw8++AATJ05EZGQkjh8/jpdeeglbtmzBTz/9BHd3dyU2Pz8fTzzxBKZNm4bZs2dj9erVmDFjBoxGI/7yl78AAJKSkpCYmIhZs2ZhwIABMJvN+O9//6tcz/TMM8/g/PnzSElJwVdffQVvb28AQOfOnZXt/PTTTzh06BBmzZoFPz8/ODk51Wo/7NmzB/3794e7uzvmzp2Ljh07Ii8vD9988w3KysowfPhwzJ8/HzNnzsQ777yDHj16AKh+pklEMHLkSGzatAkzZszAfffdh71792L27NnIzMxEZmamxczenj17MG3aNLzwwgvw9PTEhx9+iLFjx+Kuu+7CgAEDauz70KFDMW7cOMyaNQs2Njb46KOPsHjxYnTs2BGXL19GaGgoTp8+jZkzZ6Jr1644cOAAXn75Zezbtw8bN26ERqMB8MfpxujoaPj5+UGn02HPnj2YN28e/vvf/+Kjjz6y2GZeXh6efPJJPPfcc5g/fz5sbHgpLTUzDV25EVHjlJ+fLwDk8ccfVxV/6NAhASDx8fEWy3fu3CkAZObMmcqy0NBQASA7d+60iO3cubNEREQoryMjI+Wee+6pcbtvvPGGAJBjx45ZrfP19RVbW1s5fPiwxfLazDgNGjRI7rjjDikoKKi2D19++aXFTNe1rp9xSktLEwCSlJRkEffFF18IAPnggw8s+m9vby8nTpxQlpWWloqbm5uMGzeu2v5c68KFC/KnP/1JAMiQIUOkoqJCREQWLFggNjY2kp2dbRH/73//WwDI+vXrq2yvvLxczGazfPrpp2Jrayvnz59X1lWO66ZNm1T1jagp4p8CRFQvNm/eDOCPu8iu1atXL3Tq1AmbNm2yWO7l5YVevXpZLOvatStOnDhh8d49e/YgPj4e3333HUpKSmrdr65du8Lf37/W7wP+uD5n69atGDVqFNq0aVOnNq73/fffA7DeT48++iicnJys9tM999yD9u3bK6/t7e3h7+9vsZ9q0qpVKzz33HMAgDlz5iizSGvXrkVgYCDuueceXL16VfmJiIiARqPBli1blDZ2796NESNGoHXr1rC1tYVWq8Vf/vIXlJeX48iRIxbbc3V1xaBBg1T1jagpYuFERFVyd3eHo6Mjjh07pir+3LlzAKCcLruW0WhU1ldq3bq1VZxer0dpaanyesaMGVi4cCGysrIwbNgwtG7dGoMHD8aPP/6oOo+q+qNWYWEhysvL0a5duzq3cb1z587Bzs7OqhDTaDTw8vKq0366kcpTfzqdTll29uxZ7N27F1qt1uLH2dkZIoLff/8dAHDy5Encd999+PXXX/H222/jhx9+QHZ2Nt555x0AsOrHzexvoqaA1zgRUZVsbW0xePBgbNiwAadPn75h8VD5D3xeXp5V7JkzZyyub1LLzs4OCQkJSEhIQFFRETZu3IiZM2ciIiICp06dUnW3VuUMy7Xs7e0BACaTyWL59UWLm5sbbG1tcfr06Vr3vTqtW7fG1atX8dtvv1kUTyKC/Px83HvvvfW2rZq4u7vDwcHB6hqla9cDwJo1a3Dp0iV89dVX8PX1Vdbn5uZW+b6q9jdRc8IZJyKq1owZMyAiiIuLQ1lZmdV6s9mMb7/9FgCU0zMrVqywiMnOzsahQ4cwePDgm+rLHXfcgUceeQQTJkzA+fPnlYdNVs6m1GYGxtPTE/b29lYPgPz6668tXjs4OCA0NBRffvmlMgNTldr0oXI/XL+fVq1ahUuXLt30flIrMjISP//8M1q3bo2ePXta/VQ+E6uyELr2gnURwdKlS29LP4kaG844EVG1QkJCsGTJEsTHxyM4OBjjx49Hly5dYDabsXv3bnzwwQcIDAxEVFQUAgIC8OyzzyIlJQU2NjYYNmyYcledj48Ppk6dWuvtR0VFITAwED179kSbNm1w4sQJLFq0CL6+vujYsSMAICgoCADw9ttvY8yYMdBqtQgICICzs3O17Wo0Gjz55JP46KOPcOedd6Jbt27YtWsXPv/8c6vY5ORk9O/fH71798YLL7yAu+66C2fPnsU333yD999/H87OzggMDAQAfPDBB3B2doa9vT38/PyqPM0WFhaGiIgIPP/88ygpKUG/fv2Uu+q6d++OmJiYWu+nupgyZQpWrVqFAQMGYOrUqejatSsqKipw8uRJpKenY9q0aejduzfCwsKg0+kwevRoPPfcc7hy5QqWLFmCwsLC29JPokanYa9NJ6KmIDc3V8aMGSPt27cXnU4nTk5O0r17d3n55Zct7jarfI6Tv7+/aLVacXd3lyeffLLa5zhd7/o70N58803p27evuLu7i06nk/bt28vYsWPl+PHjFu+bMWOGGI1GsbGxqfI5TlUpLi6WZ555Rjw9PcXJyUmioqLk+PHjVT7H6eDBg/Loo49K69atlX7ExsbKlStXlJhFixaJn5+f2NraqnqO0/PPPy++vr6i1WrF29tbxo8fX+1znK4XGhoqoaGhVeZVlcpnTV1/B93Fixdl1qxZynO3Kp+TNXXqVMnPz1fivv32W+nWrZvY29tL27Zt5e9//7vyjKhr7ySsblyJmhN+5QoRERGRSrzGiYiIiEglFk5EREREKrFwIiIiIlKJhRMRERGRSiyciIiIiFRi4URERESkEgsnIiIiIpVYOBERERGpxMKJiIiISCUWTkREREQqsXAiIiIiUomFExEREZFK/w/alXXMzVG+0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a histogram of our 'CNSTRCT_YR' column with 25 bins\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "building_df['CNSTRCT_YR'].hist(\n",
    "    ax=ax,\n",
    "    bins=25\n",
    ")\n",
    "\n",
    "# adding labels\n",
    "plt.xlabel('Construction Year', fontsize=12)\n",
    "plt.ylabel('Counts', fontsize=12) \n",
    "plt.title('Histogram of Construction Year Values in NYC', fontsize=12) \n",
    "\n",
    "# tight figure\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a histogram of our 'CNSTRCT_YR' column with 50 bins\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "building_df['CNSTRCT_YR'].hist(\n",
    "    ax=ax,\n",
    "    bins=50\n",
    ")\n",
    "\n",
    "plt.xlabel('Construction Year', fontsize=12)\n",
    "plt.ylabel('Counts', fontsize=12)\n",
    "plt.title('Histogram of Construction Year Values in NYC', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a box plot from our 'CNSTRCT_YR' column\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "building_df.boxplot(\n",
    "    column=['CNSTRCT_YR'], \n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.title('Box Plot of Construction Year Value', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Filtering Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**df.iloc**: Purely integer-location based indexing for selection by position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MULTIPOLYGON (((-73.96664570466969 40.62599676998366, -73.96684846176461 40.625977490862574, -73.96685938726297 40.62604419372411, -73.96661621040211 40.62606731716107, -73.96660638332114 40.626007324369795, -73.96664680403327 40.626003480977275, -73.96664570466969 40.62599676998366)))'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first row, first column\n",
    "building_df.iloc[0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME                                             NaN\n",
       "BIN                                          3170958\n",
       "CNSTRCT_YR                                    1925.0\n",
       "LSTMODDATE                    08/22/2017 12:00:00 AM\n",
       "LSTSTATYPE                               Constructed\n",
       "DOITT_ID                                       96807\n",
       "HEIGHTROOF                                 29.749853\n",
       "FEAT_CODE                                       2100\n",
       "GROUNDELEV                                      40.0\n",
       "SHAPE_AREA                                         0\n",
       "SHAPE_LEN                                          0\n",
       "BASE_BBL                                  3065220021\n",
       "MPLUTO_BBL                                3065220021\n",
       "GEOMSOURCE                                Photogramm\n",
       "GLOBALID      {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first row, start at second column return all columns\n",
    "building_df.iloc[0, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME                             NaN\n",
       "BIN                          3170958\n",
       "CNSTRCT_YR                    1925.0\n",
       "LSTMODDATE    08/22/2017 12:00:00 AM\n",
       "LSTSTATYPE               Constructed\n",
       "DOITT_ID                       96807\n",
       "HEIGHTROOF                 29.749853\n",
       "FEAT_CODE                       2100\n",
       "GROUNDELEV                      40.0\n",
       "SHAPE_AREA                         0\n",
       "SHAPE_LEN                          0\n",
       "BASE_BBL                  3065220021\n",
       "MPLUTO_BBL                3065220021\n",
       "GEOMSOURCE                Photogramm\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first row, start at second column up to the last\n",
    "building_df.iloc[0, 1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>LSTSTATYPE</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MULTIPOLYGON (((-73.96664570466969 40.62599676...</td>\n",
       "      <td>3170958</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MULTIPOLYGON (((-74.14644228489016 40.58885592...</td>\n",
       "      <td>5122427</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MULTIPOLYGON (((-74.1344861688728 40.600563532...</td>\n",
       "      <td>5034441</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             the_geom      BIN   LSTSTATYPE  \\\n",
       "0   MULTIPOLYGON (((-73.96664570466969 40.62599676...  3170958  Constructed   \n",
       "5   MULTIPOLYGON (((-74.14644228489016 40.58885592...  5122427  Constructed   \n",
       "10  MULTIPOLYGON (((-74.1344861688728 40.600563532...  5034441  Constructed   \n",
       "\n",
       "    FEAT_CODE  \n",
       "0        2100  \n",
       "5        2100  \n",
       "10       2100  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify row and column positions\n",
    "building_df.iloc[[0, 5, 10], [0, 2, 5, 8]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**df.loc**: Access a group of rows and columns by label(s) or a boolean array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
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   "outputs": [
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       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
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       "      <td>MULTIPOLYGON (((-74.221228292902 40.5303177260...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5133580</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
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       "      <td>0</td>\n",
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       "      <td>5073560090</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{57FDA52A-A7A6-4571-8B09-609930CE6308}</td>\n",
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       "      <td>MULTIPOLYGON (((-73.87129515296562 40.65717370...</td>\n",
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       "      <td>3394646</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1212853</td>\n",
       "      <td>21.608508</td>\n",
       "      <td>2100</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3044520815</td>\n",
       "      <td>3044520815</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{33E7F6CF-9E99-4317-A915-30ACA041102C}</td>\n",
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       "      <td>{12419B35-0CC1-4D78-B6C4-63B357ED71F5}</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3390096</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1049056</td>\n",
       "      <td>38.747306</td>\n",
       "      <td>2100</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>3017700054</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{A0589BC1-A408-4818-AADD-55772C4D2AAE}</td>\n",
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       "      <th>224</th>\n",
       "      <td>MULTIPOLYGON (((-73.70962745809653 40.73251740...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4555084</td>\n",
       "      <td>2004.0</td>\n",
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       "      <td>Constructed</td>\n",
       "      <td>1227251</td>\n",
       "      <td>12.010000</td>\n",
       "      <td>5110</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4088150039</td>\n",
       "      <td>4088150039</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{BA42A1C4-FD8B-4FF9-B768-2644D66E0D7B}</td>\n",
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      "text/plain": [
       "                                              the_geom NAME      BIN  \\\n",
       "6    MULTIPOLYGON (((-74.221228292902 40.5303177260...  NaN  5133580   \n",
       "50   MULTIPOLYGON (((-73.87129515296562 40.65717370...  NaN  3394646   \n",
       "80   MULTIPOLYGON (((-73.92554066446792 40.70095338...  NaN  3072927   \n",
       "98   MULTIPOLYGON (((-73.94885443008951 40.69275708...  NaN  3390096   \n",
       "224  MULTIPOLYGON (((-73.70962745809653 40.73251740...  NaN  4555084   \n",
       "\n",
       "     CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "6        2001.0  08/22/2017 12:00:00 AM  Constructed    937499   27.310000   \n",
       "50       2009.0  08/22/2017 12:00:00 AM  Constructed   1212853   21.608508   \n",
       "80       2003.0  08/22/2017 12:00:00 AM  Constructed    986709   25.110000   \n",
       "98       2004.0  08/22/2017 12:00:00 AM  Constructed   1049056   38.747306   \n",
       "224      2004.0  08/17/2017 12:00:00 AM  Constructed   1227251   12.010000   \n",
       "\n",
       "     FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "6         2100       105.0           0          0  5073560090  5073560090   \n",
       "50        2100        18.0           0          0  3044520815  3044520815   \n",
       "80        2100        52.0           0          0  3032090105  3032090105   \n",
       "98        2100        38.0           0          0  3017700054  3017700054   \n",
       "224       5110        93.0           0          0  4088150039  4088150039   \n",
       "\n",
       "     GEOMSOURCE                                GLOBALID  \n",
       "6    Photogramm  {57FDA52A-A7A6-4571-8B09-609930CE6308}  \n",
       "50   Photogramm  {33E7F6CF-9E99-4317-A915-30ACA041102C}  \n",
       "80   Photogramm  {12419B35-0CC1-4D78-B6C4-63B357ED71F5}  \n",
       "98   Photogramm  {A0589BC1-A408-4818-AADD-55772C4D2AAE}  \n",
       "224  Photogramm  {BA42A1C4-FD8B-4FF9-B768-2644D66E0D7B}  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# locate rows where construction year > 2000\n",
    "(building_df\n",
    " .loc[building_df['CNSTRCT_YR'] > 2000]\n",
    " .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
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      "text/plain": [
       "         BIN  CNSTRCT_YR\n",
       "6    5133580      2001.0\n",
       "50   3394646      2009.0\n",
       "80   3072927      2003.0\n",
       "98   3390096      2004.0\n",
       "224  4555084      2004.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# locate construction year > 2000, columns 'BIN', 'CNSTRCT_YR'\n",
    "(building_df\n",
    " .loc[building_df['CNSTRCT_YR'] > 2000, ['BIN', 'CNSTRCT_YR']]\n",
    " .head()\n",
    ")"
   ]
  },
  {
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   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <td>5073560090</td>\n",
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       "      <td>{57FDA52A-A7A6-4571-8B09-609930CE6308}</td>\n",
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       "    <tr>\n",
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       "      <td>3394646</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
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       "      <td>1212853</td>\n",
       "      <td>21.608508</td>\n",
       "      <td>2100</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3044520815</td>\n",
       "      <td>3044520815</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{33E7F6CF-9E99-4317-A915-30ACA041102C}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>MULTIPOLYGON (((-73.92554066446792 40.70095338...</td>\n",
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       "      <td>3072927</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>986709</td>\n",
       "      <td>25.110000</td>\n",
       "      <td>2100</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3032090105</td>\n",
       "      <td>3032090105</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{12419B35-0CC1-4D78-B6C4-63B357ED71F5}</td>\n",
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       "      <th>98</th>\n",
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       "      <td>3390096</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1049056</td>\n",
       "      <td>38.747306</td>\n",
       "      <td>2100</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3017700054</td>\n",
       "      <td>3017700054</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{A0589BC1-A408-4818-AADD-55772C4D2AAE}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>MULTIPOLYGON (((-73.70962745809653 40.73251740...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4555084</td>\n",
       "      <td>2004.0</td>\n",
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       "      <td>12.010000</td>\n",
       "      <td>5110</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4088150039</td>\n",
       "      <td>4088150039</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{BA42A1C4-FD8B-4FF9-B768-2644D66E0D7B}</td>\n",
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      "text/plain": [
       "                                              the_geom NAME      BIN  \\\n",
       "6    MULTIPOLYGON (((-74.221228292902 40.5303177260...  NaN  5133580   \n",
       "50   MULTIPOLYGON (((-73.87129515296562 40.65717370...  NaN  3394646   \n",
       "80   MULTIPOLYGON (((-73.92554066446792 40.70095338...  NaN  3072927   \n",
       "98   MULTIPOLYGON (((-73.94885443008951 40.69275708...  NaN  3390096   \n",
       "224  MULTIPOLYGON (((-73.70962745809653 40.73251740...  NaN  4555084   \n",
       "\n",
       "     CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "6        2001.0  08/22/2017 12:00:00 AM  Constructed    937499   27.310000   \n",
       "50       2009.0  08/22/2017 12:00:00 AM  Constructed   1212853   21.608508   \n",
       "80       2003.0  08/22/2017 12:00:00 AM  Constructed    986709   25.110000   \n",
       "98       2004.0  08/22/2017 12:00:00 AM  Constructed   1049056   38.747306   \n",
       "224      2004.0  08/17/2017 12:00:00 AM  Constructed   1227251   12.010000   \n",
       "\n",
       "     FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "6         2100       105.0           0          0  5073560090  5073560090   \n",
       "50        2100        18.0           0          0  3044520815  3044520815   \n",
       "80        2100        52.0           0          0  3032090105  3032090105   \n",
       "98        2100        38.0           0          0  3017700054  3017700054   \n",
       "224       5110        93.0           0          0  4088150039  4088150039   \n",
       "\n",
       "     GEOMSOURCE                                GLOBALID  \n",
       "6    Photogramm  {57FDA52A-A7A6-4571-8B09-609930CE6308}  \n",
       "50   Photogramm  {33E7F6CF-9E99-4317-A915-30ACA041102C}  \n",
       "80   Photogramm  {12419B35-0CC1-4D78-B6C4-63B357ED71F5}  \n",
       "98   Photogramm  {A0589BC1-A408-4818-AADD-55772C4D2AAE}  \n",
       "224  Photogramm  {BA42A1C4-FD8B-4FF9-B768-2644D66E0D7B}  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# locate construction year greater than 2000 but less than 2020\n",
    "(building_df\n",
    " .loc[(building_df['CNSTRCT_YR'] > 2000)\n",
    "      & (building_df['CNSTRCT_YR'] < 2020)]\n",
    " .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MULTIPOLYGON (((-74.221228292902 40.5303177260...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5133580</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>937499</td>\n",
       "      <td>27.310000</td>\n",
       "      <td>2100</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5073560090</td>\n",
       "      <td>5073560090</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{57FDA52A-A7A6-4571-8B09-609930CE6308}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>MULTIPOLYGON (((-74.15800726390819 40.62204540...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5129621</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>954205</td>\n",
       "      <td>30.040000</td>\n",
       "      <td>2100</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5016800094</td>\n",
       "      <td>5016800094</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{B157C284-BD54-4819-B872-FF307A76B62B}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>MULTIPOLYGON (((-73.87129515296562 40.65717370...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3394646</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1212853</td>\n",
       "      <td>21.608508</td>\n",
       "      <td>2100</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3044520815</td>\n",
       "      <td>3044520815</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{33E7F6CF-9E99-4317-A915-30ACA041102C}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>MULTIPOLYGON (((-73.92554066446792 40.70095338...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3072927</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>986709</td>\n",
       "      <td>25.110000</td>\n",
       "      <td>2100</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3032090105</td>\n",
       "      <td>3032090105</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{12419B35-0CC1-4D78-B6C4-63B357ED71F5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>MULTIPOLYGON (((-73.94885443008951 40.69275708...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3390096</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1049056</td>\n",
       "      <td>38.747306</td>\n",
       "      <td>2100</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3017700054</td>\n",
       "      <td>3017700054</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{A0589BC1-A408-4818-AADD-55772C4D2AAE}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             the_geom NAME      BIN  \\\n",
       "6   MULTIPOLYGON (((-74.221228292902 40.5303177260...  NaN  5133580   \n",
       "38  MULTIPOLYGON (((-74.15800726390819 40.62204540...  NaN  5129621   \n",
       "50  MULTIPOLYGON (((-73.87129515296562 40.65717370...  NaN  3394646   \n",
       "80  MULTIPOLYGON (((-73.92554066446792 40.70095338...  NaN  3072927   \n",
       "98  MULTIPOLYGON (((-73.94885443008951 40.69275708...  NaN  3390096   \n",
       "\n",
       "    CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "6       2001.0  08/22/2017 12:00:00 AM  Constructed    937499   27.310000   \n",
       "38      2000.0  08/22/2017 12:00:00 AM  Constructed    954205   30.040000   \n",
       "50      2009.0  08/22/2017 12:00:00 AM  Constructed   1212853   21.608508   \n",
       "80      2003.0  08/22/2017 12:00:00 AM  Constructed    986709   25.110000   \n",
       "98      2004.0  08/22/2017 12:00:00 AM  Constructed   1049056   38.747306   \n",
       "\n",
       "    FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "6        2100       105.0           0          0  5073560090  5073560090   \n",
       "38       2100        22.0           0          0  5016800094  5016800094   \n",
       "50       2100        18.0           0          0  3044520815  3044520815   \n",
       "80       2100        52.0           0          0  3032090105  3032090105   \n",
       "98       2100        38.0           0          0  3017700054  3017700054   \n",
       "\n",
       "    GEOMSOURCE                                GLOBALID  \n",
       "6   Photogramm  {57FDA52A-A7A6-4571-8B09-609930CE6308}  \n",
       "38  Photogramm  {B157C284-BD54-4819-B872-FF307A76B62B}  \n",
       "50  Photogramm  {33E7F6CF-9E99-4317-A915-30ACA041102C}  \n",
       "80  Photogramm  {12419B35-0CC1-4D78-B6C4-63B357ED71F5}  \n",
       "98  Photogramm  {A0589BC1-A408-4818-AADD-55772C4D2AAE}  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# locate construction year between 2000 and 2020 inclusive\n",
    "(building_df\n",
    " .loc[building_df['CNSTRCT_YR'].between(2000, 2020)]\n",
    " .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
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       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
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       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>MULTIPOLYGON (((-74.15800726390819 40.62204540...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5129621</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>954205</td>\n",
       "      <td>30.04</td>\n",
       "      <td>2100</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5016800094</td>\n",
       "      <td>5016800094</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{B157C284-BD54-4819-B872-FF307A76B62B}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>MULTIPOLYGON (((-74.13556237981483 40.64013483...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5132682</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>926272</td>\n",
       "      <td>29.08</td>\n",
       "      <td>2100</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5010750122</td>\n",
       "      <td>5010750122</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{6E88B8D1-E1FC-4421-BC53-42D3C4BCFE52}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>MULTIPOLYGON (((-73.87150759130621 40.75533109...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4538129</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>12/07/2018 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1108486</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2100</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4017110040</td>\n",
       "      <td>4017110040</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{E48501F8-F7F6-4B20-879D-20D8ED1F0DF3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>MULTIPOLYGON (((-74.0658246076789 40.592294919...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5132135</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>12/07/2018 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>922090</td>\n",
       "      <td>31.00</td>\n",
       "      <td>2100</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5031230005</td>\n",
       "      <td>5031230005</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{D33FC432-28B1-4B87-A9FE-327923A7B9D1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>MULTIPOLYGON (((-73.79809509137095 40.67312203...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4536134</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>960214</td>\n",
       "      <td>25.54</td>\n",
       "      <td>2100</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4120600066</td>\n",
       "      <td>4120600066</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{81BEA7A2-37AE-4BB3-8565-A43BCF9263C8}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               the_geom NAME      BIN  \\\n",
       "38    MULTIPOLYGON (((-74.15800726390819 40.62204540...  NaN  5129621   \n",
       "195   MULTIPOLYGON (((-74.13556237981483 40.64013483...  NaN  5132682   \n",
       "451   MULTIPOLYGON (((-73.87150759130621 40.75533109...  NaN  4538129   \n",
       "552   MULTIPOLYGON (((-74.0658246076789 40.592294919...  NaN  5132135   \n",
       "1268  MULTIPOLYGON (((-73.79809509137095 40.67312203...  NaN  4536134   \n",
       "\n",
       "      CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "38        2000.0  08/22/2017 12:00:00 AM  Constructed    954205       30.04   \n",
       "195       2000.0  08/22/2017 12:00:00 AM  Constructed    926272       29.08   \n",
       "451       2000.0  12/07/2018 12:00:00 AM  Constructed   1108486       38.00   \n",
       "552       2000.0  12/07/2018 12:00:00 AM  Constructed    922090       31.00   \n",
       "1268      2000.0  08/22/2017 12:00:00 AM  Constructed    960214       25.54   \n",
       "\n",
       "      FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "38         2100        22.0           0          0  5016800094  5016800094   \n",
       "195        2100        28.0           0          0  5010750122  5010750122   \n",
       "451        2100        78.0           0          0  4017110040  4017110040   \n",
       "552        2100        10.0           0          0  5031230005  5031230005   \n",
       "1268       2100        32.0           0          0  4120600066  4120600066   \n",
       "\n",
       "      GEOMSOURCE                                GLOBALID  \n",
       "38    Photogramm  {B157C284-BD54-4819-B872-FF307A76B62B}  \n",
       "195   Photogramm  {6E88B8D1-E1FC-4421-BC53-42D3C4BCFE52}  \n",
       "451   Other (Man  {E48501F8-F7F6-4B20-879D-20D8ED1F0DF3}  \n",
       "552   Other (Man  {D33FC432-28B1-4B87-A9FE-327923A7B9D1}  \n",
       "1268  Photogramm  {81BEA7A2-37AE-4BB3-8565-A43BCF9263C8}  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# locate construction year value is in list\n",
    "years = [2000, 2020]\n",
    "\n",
    "(building_df\n",
    " .loc[building_df['CNSTRCT_YR'].isin(years)]\n",
    " .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
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       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MULTIPOLYGON (((-73.96664570466969 40.62599676...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3170958</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>96807</td>\n",
       "      <td>29.749853</td>\n",
       "      <td>2100</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{31298F86-3088-4F53-B3DB-71A9EFA6FA1F}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MULTIPOLYGON (((-74.16790202462265 40.63936048...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5028452</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>326368</td>\n",
       "      <td>22.630000</td>\n",
       "      <td>2100</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F5F8CDA5-69E2-46F8-8F69-BA95C025B520}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MULTIPOLYGON (((-74.19510813278613 40.55610681...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5078368</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746627</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>2100</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9F644794-F72C-4582-9E5E-B337E2B97068}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MULTIPOLYGON (((-73.96113466505085 40.57743931...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3245111</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>786626</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F916B22D-E25B-44AE-9FA9-2A51191B9CDF}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MULTIPOLYGON (((-73.75421559146166 40.75591276...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4161096</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746409</td>\n",
       "      <td>18.015113</td>\n",
       "      <td>2100</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            the_geom NAME      BIN  \\\n",
       "0  MULTIPOLYGON (((-73.96664570466969 40.62599676...  NaN  3170958   \n",
       "1  MULTIPOLYGON (((-74.16790202462265 40.63936048...  NaN  5028452   \n",
       "2  MULTIPOLYGON (((-74.19510813278613 40.55610681...  NaN  5078368   \n",
       "3  MULTIPOLYGON (((-73.96113466505085 40.57743931...  NaN  3245111   \n",
       "4  MULTIPOLYGON (((-73.75421559146166 40.75591276...  NaN  4161096   \n",
       "\n",
       "   CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "0      1925.0  08/22/2017 12:00:00 AM  Constructed     96807   29.749853   \n",
       "1      1965.0  08/22/2017 12:00:00 AM  Constructed    326368   22.630000   \n",
       "2      1970.0  08/22/2017 12:00:00 AM  Constructed    746627   35.760000   \n",
       "3      1928.0  08/22/2017 12:00:00 AM  Constructed    786626   37.500000   \n",
       "4      1950.0  08/22/2017 12:00:00 AM  Constructed    746409   18.015113   \n",
       "\n",
       "   FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "0       2100        40.0           0          0  3065220021  3065220021   \n",
       "1       2100        39.0           0          0  5012640036  5012640036   \n",
       "2       2100        51.0           0          0  5060190091  5060190091   \n",
       "3       2100         6.0           0          0  3086910048  3086910048   \n",
       "4       2100        93.0           0          0  4075020005  4075020005   \n",
       "\n",
       "   GEOMSOURCE                                GLOBALID  \n",
       "0  Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
       "1  Photogramm  {F5F8CDA5-69E2-46F8-8F69-BA95C025B520}  \n",
       "2  Photogramm  {9F644794-F72C-4582-9E5E-B337E2B97068}  \n",
       "3  Photogramm  {F916B22D-E25B-44AE-9FA9-2A51191B9CDF}  \n",
       "4  Photogramm  {525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# locate construction year values not in list\n",
    "years = [2000, 2020]\n",
    "\n",
    "(building_df\n",
    " .loc[~building_df['CNSTRCT_YR'].isin(years)]\n",
    " .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Create New Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\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>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "      <th>after2020_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MULTIPOLYGON (((-73.96664570466969 40.62599676...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3170958</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>96807</td>\n",
       "      <td>29.749853</td>\n",
       "      <td>2100</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{31298F86-3088-4F53-B3DB-71A9EFA6FA1F}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MULTIPOLYGON (((-74.16790202462265 40.63936048...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5028452</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>326368</td>\n",
       "      <td>22.630000</td>\n",
       "      <td>2100</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F5F8CDA5-69E2-46F8-8F69-BA95C025B520}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MULTIPOLYGON (((-74.19510813278613 40.55610681...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5078368</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746627</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>2100</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9F644794-F72C-4582-9E5E-B337E2B97068}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MULTIPOLYGON (((-73.96113466505085 40.57743931...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3245111</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>786626</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F916B22D-E25B-44AE-9FA9-2A51191B9CDF}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MULTIPOLYGON (((-73.75421559146166 40.75591276...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4161096</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746409</td>\n",
       "      <td>18.015113</td>\n",
       "      <td>2100</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            the_geom NAME      BIN  \\\n",
       "0  MULTIPOLYGON (((-73.96664570466969 40.62599676...  NaN  3170958   \n",
       "1  MULTIPOLYGON (((-74.16790202462265 40.63936048...  NaN  5028452   \n",
       "2  MULTIPOLYGON (((-74.19510813278613 40.55610681...  NaN  5078368   \n",
       "3  MULTIPOLYGON (((-73.96113466505085 40.57743931...  NaN  3245111   \n",
       "4  MULTIPOLYGON (((-73.75421559146166 40.75591276...  NaN  4161096   \n",
       "\n",
       "   CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "0      1925.0  08/22/2017 12:00:00 AM  Constructed     96807   29.749853   \n",
       "1      1965.0  08/22/2017 12:00:00 AM  Constructed    326368   22.630000   \n",
       "2      1970.0  08/22/2017 12:00:00 AM  Constructed    746627   35.760000   \n",
       "3      1928.0  08/22/2017 12:00:00 AM  Constructed    786626   37.500000   \n",
       "4      1950.0  08/22/2017 12:00:00 AM  Constructed    746409   18.015113   \n",
       "\n",
       "   FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "0       2100        40.0           0          0  3065220021  3065220021   \n",
       "1       2100        39.0           0          0  5012640036  5012640036   \n",
       "2       2100        51.0           0          0  5060190091  5060190091   \n",
       "3       2100         6.0           0          0  3086910048  3086910048   \n",
       "4       2100        93.0           0          0  4075020005  4075020005   \n",
       "\n",
       "   GEOMSOURCE                                GLOBALID  after2020_flag  \n",
       "0  Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}               0  \n",
       "1  Photogramm  {F5F8CDA5-69E2-46F8-8F69-BA95C025B520}               0  \n",
       "2  Photogramm  {9F644794-F72C-4582-9E5E-B337E2B97068}               0  \n",
       "3  Photogramm  {F916B22D-E25B-44AE-9FA9-2A51191B9CDF}               0  \n",
       "4  Photogramm  {525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}               0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create flag for buildings built after 2020, 1 (True) and 0 (False)\n",
    "new_cols_df = building_df.copy()\n",
    "\n",
    "new_cols_df.loc[new_cols_df['CNSTRCT_YR'] > 2020, ['after2020_flag']] = 1\n",
    "new_cols_df['after2020_flag'] = new_cols_df['after2020_flag'].fillna(0).astype(int)\n",
    "\n",
    "# preview\n",
    "new_cols_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>HEIGHTROOF</th>\n",
       "      <th>HEIGHTROOF_round</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.749853</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.630000</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.760000</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.500000</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.015113</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HEIGHTROOF  HEIGHTROOF_round\n",
       "0   29.749853              30.0\n",
       "1   22.630000              23.0\n",
       "2   35.760000              36.0\n",
       "3   37.500000              38.0\n",
       "4   18.015113              18.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new column rounding HEIGHTROOF\n",
    "new_cols_df['HEIGHTROOF_round'] = new_cols_df['HEIGHTROOF'].round(0)\n",
    "\n",
    "new_cols_df.loc[:, ['HEIGHTROOF', 'HEIGHTROOF_round']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 Filling In or Dropping Null Values for CNSTRCT_YR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total nulls: 1,094,723\n",
      "total nulls after dropping nulls in CNSTRCT_YR: 1,073,301\n"
     ]
    }
   ],
   "source": [
    "nulls = building_df.isnull().sum().sum()\n",
    "print(f'total nulls: {nulls:,}')\n",
    "\n",
    "# dropping all records where 'CNSTRCT_YR' as null\n",
    "nulls = (\n",
    "    building_df\n",
    "    .dropna(subset=['CNSTRCT_YR'])\n",
    "    .isnull()\n",
    "    .sum()\n",
    "    .sum()\n",
    ")\n",
    "\n",
    "print(f'total nulls after dropping nulls in CNSTRCT_YR: {nulls:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of nulls for CNSTRCT_YR: 10,243\n"
     ]
    }
   ],
   "source": [
    "# saving a new dataframe to fill nans with the median value of 'CNSTRCT_YR'\n",
    "building_df_fillna = building_df.copy()\n",
    "counts = building_df_fillna['CNSTRCT_YR'].isnull().sum()\n",
    "\n",
    "print(f'number of nulls for CNSTRCT_YR: {counts:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median value of CNSTRCT_YR: 1930.0\n"
     ]
    }
   ],
   "source": [
    "median = building_df_fillna['CNSTRCT_YR'].median()\n",
    "\n",
    "print(f'median value of CNSTRCT_YR: {median}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\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>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MULTIPOLYGON (((-73.96664570466969 40.62599676...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3170958</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>96807</td>\n",
       "      <td>29.749853</td>\n",
       "      <td>2100</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{31298F86-3088-4F53-B3DB-71A9EFA6FA1F}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MULTIPOLYGON (((-74.16790202462265 40.63936048...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5028452</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>326368</td>\n",
       "      <td>22.630000</td>\n",
       "      <td>2100</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F5F8CDA5-69E2-46F8-8F69-BA95C025B520}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MULTIPOLYGON (((-74.19510813278613 40.55610681...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5078368</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746627</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>2100</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9F644794-F72C-4582-9E5E-B337E2B97068}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MULTIPOLYGON (((-73.96113466505085 40.57743931...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3245111</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>786626</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F916B22D-E25B-44AE-9FA9-2A51191B9CDF}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MULTIPOLYGON (((-73.75421559146166 40.75591276...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4161096</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746409</td>\n",
       "      <td>18.015113</td>\n",
       "      <td>2100</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            the_geom NAME      BIN  \\\n",
       "0  MULTIPOLYGON (((-73.96664570466969 40.62599676...  NaN  3170958   \n",
       "1  MULTIPOLYGON (((-74.16790202462265 40.63936048...  NaN  5028452   \n",
       "2  MULTIPOLYGON (((-74.19510813278613 40.55610681...  NaN  5078368   \n",
       "3  MULTIPOLYGON (((-73.96113466505085 40.57743931...  NaN  3245111   \n",
       "4  MULTIPOLYGON (((-73.75421559146166 40.75591276...  NaN  4161096   \n",
       "\n",
       "   CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "0      1925.0  08/22/2017 12:00:00 AM  Constructed     96807   29.749853   \n",
       "1      1965.0  08/22/2017 12:00:00 AM  Constructed    326368   22.630000   \n",
       "2      1970.0  08/22/2017 12:00:00 AM  Constructed    746627   35.760000   \n",
       "3      1928.0  08/22/2017 12:00:00 AM  Constructed    786626   37.500000   \n",
       "4      1950.0  08/22/2017 12:00:00 AM  Constructed    746409   18.015113   \n",
       "\n",
       "   FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "0       2100        40.0           0          0  3065220021  3065220021   \n",
       "1       2100        39.0           0          0  5012640036  5012640036   \n",
       "2       2100        51.0           0          0  5060190091  5060190091   \n",
       "3       2100         6.0           0          0  3086910048  3086910048   \n",
       "4       2100        93.0           0          0  4075020005  4075020005   \n",
       "\n",
       "   GEOMSOURCE                                GLOBALID  \n",
       "0  Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
       "1  Photogramm  {F5F8CDA5-69E2-46F8-8F69-BA95C025B520}  \n",
       "2  Photogramm  {9F644794-F72C-4582-9E5E-B337E2B97068}  \n",
       "3  Photogramm  {F916B22D-E25B-44AE-9FA9-2A51191B9CDF}  \n",
       "4  Photogramm  {525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# saving the median of our 'CNSTRCT_YR' as the name 'median'\n",
    "median = building_df_fillna['CNSTRCT_YR'].median()\n",
    "\n",
    "# filling na values in our'CNSTRCT_YR' column with the column's median value\n",
    "building_df_fillna[\"CNSTRCT_YR\"] = building_df_fillna[\"CNSTRCT_YR\"].fillna(value=median)\n",
    "\n",
    "building_df_fillna.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of nulls for CNSTRCT_YR: 0\n"
     ]
    }
   ],
   "source": [
    "# sanity check\n",
    "counts = building_df_fillna['CNSTRCT_YR'].isnull().sum()\n",
    "\n",
    "print(f'number of nulls for CNSTRCT_YR: {counts:,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Sample Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Line Plot of Year Built"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count\n",
       "CNSTRCT_YR       \n",
       "1652            1\n",
       "1661            2\n",
       "1680            1\n",
       "1690            1\n",
       "1694            1"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning number of buildings by year\n",
    "year_df = (\n",
    "    building_df\n",
    "    .dropna(subset=['CNSTRCT_YR'])\n",
    "    .astype({'CNSTRCT_YR':int})\n",
    "    .groupby('CNSTRCT_YR')[['DOITT_ID']]\n",
    "    .count()\n",
    "    .reset_index()\n",
    "    .rename({'DOITT_ID': 'count'}, axis=1)\n",
    "    .set_index('CNSTRCT_YR', drop=True)\n",
    ")\n",
    "\n",
    "# preview dataframe\n",
    "year_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "# plotting line chart of number of buildings by 'CNSTRCT_YR'\n",
    "ax.plot(year_df['count'], linewidth=2)\n",
    "\n",
    "plt.xlabel('Construction Year', fontsize=12)\n",
    "plt.ylabel('Number of Buildings', fontsize=12)\n",
    "plt.title('Number of Buildings by Construction Year in NYC', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>count</th>\n",
       "      <th>cumsum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count  cumsum\n",
       "CNSTRCT_YR               \n",
       "1652            1       1\n",
       "1661            2       3\n",
       "1680            1       4\n",
       "1690            1       5\n",
       "1694            1       6"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the cumulative sum of buildings by 'CNSTRCT_YR'\n",
    "year_df['cumsum'] = year_df['count'].cumsum()\n",
    "\n",
    "# previewing first five rows\n",
    "year_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "ax.plot(year_df['cumsum'], linewidth=2)\n",
    "\n",
    "plt.xlabel('Construction Year', fontsize=12)\n",
    "plt.ylabel('Cumulative Number of Buildings', fontsize=12)\n",
    "plt.title('Cumulative Number of Buildings by Construction Year in NYC', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Identify Oldest Building in Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by the 'CNSTRCT_YR' in ascending order and dropping records that don't have a building name (i.e. NAME)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>955187</th>\n",
       "      <td>MULTIPOLYGON (((-73.92083460429942 40.64439765...</td>\n",
       "      <td>Pieter Claesen Wyckoff House</td>\n",
       "      <td>3221967</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>01/28/2020 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>550741</td>\n",
       "      <td>24.54</td>\n",
       "      <td>2100</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3079170009</td>\n",
       "      <td>3079170009</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710089</th>\n",
       "      <td>MULTIPOLYGON (((-73.8247828135812 40.762948467...</td>\n",
       "      <td>Bowne House</td>\n",
       "      <td>4113403</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>776399</td>\n",
       "      <td>26.12</td>\n",
       "      <td>2100</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4050130006</td>\n",
       "      <td>4050130006</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{71A760A6-6D2F-48F8-8C93-C8B66071B868}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52813</th>\n",
       "      <td>MULTIPOLYGON (((-74.25311647359251 40.50309522...</td>\n",
       "      <td>Conference House</td>\n",
       "      <td>5087656</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>04/13/2020 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>702698</td>\n",
       "      <td>38.64</td>\n",
       "      <td>2100</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5078570090</td>\n",
       "      <td>5078570090</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{CB13BCBA-9DF3-4455-9206-824FE91F2EDE}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521346</th>\n",
       "      <td>MULTIPOLYGON (((-74.06293248238293 40.61512359...</td>\n",
       "      <td>Alice Austen House Museum</td>\n",
       "      <td>5042188</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>04/13/2020 12:00:00 AM</td>\n",
       "      <td>Alteration</td>\n",
       "      <td>806241</td>\n",
       "      <td>22.26</td>\n",
       "      <td>2100</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5028300049</td>\n",
       "      <td>5028300049</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{93B1CB7B-BE72-40DB-9B66-01E92DF4AE6E}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503563</th>\n",
       "      <td>MULTIPOLYGON (((-74.01121069434295 40.70334382...</td>\n",
       "      <td>Fraunces Tavern</td>\n",
       "      <td>1078965</td>\n",
       "      <td>1719.0</td>\n",
       "      <td>01/28/2020 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>299639</td>\n",
       "      <td>47.83</td>\n",
       "      <td>2100</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000070035</td>\n",
       "      <td>1000070035</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{4B8DF813-9EAD-402A-81D9-58ECCB4ED502}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 the_geom  \\\n",
       "955187  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "710089  MULTIPOLYGON (((-73.8247828135812 40.762948467...   \n",
       "52813   MULTIPOLYGON (((-74.25311647359251 40.50309522...   \n",
       "521346  MULTIPOLYGON (((-74.06293248238293 40.61512359...   \n",
       "503563  MULTIPOLYGON (((-74.01121069434295 40.70334382...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "955187  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "710089                   Bowne House  4113403      1661.0   \n",
       "52813               Conference House  5087656      1680.0   \n",
       "521346     Alice Austen House Museum  5042188      1690.0   \n",
       "503563               Fraunces Tavern  1078965      1719.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "955187  01/28/2020 12:00:00 AM  Constructed    550741       24.54       2100   \n",
       "710089  08/22/2017 12:00:00 AM  Constructed    776399       26.12       2100   \n",
       "52813   04/13/2020 12:00:00 AM  Constructed    702698       38.64       2100   \n",
       "521346  04/13/2020 12:00:00 AM   Alteration    806241       22.26       2100   \n",
       "503563  01/28/2020 12:00:00 AM  Constructed    299639       47.83       2100   \n",
       "\n",
       "        GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  GEOMSOURCE  \\\n",
       "955187        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "710089        64.0           0          0  4050130006  4050130006  Photogramm   \n",
       "52813         38.0           0          0  5078570090  5078570090  Photogramm   \n",
       "521346        21.0           0          0  5028300049  5028300049  Photogramm   \n",
       "503563         7.0           0          0  1000070035  1000070035  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "955187  {5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}  \n",
       "710089  {71A760A6-6D2F-48F8-8C93-C8B66071B868}  \n",
       "52813   {CB13BCBA-9DF3-4455-9206-824FE91F2EDE}  \n",
       "521346  {93B1CB7B-BE72-40DB-9B66-01E92DF4AE6E}  \n",
       "503563  {4B8DF813-9EAD-402A-81D9-58ECCB4ED502}  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(building_df\n",
    " .sort_values(by='CNSTRCT_YR')\n",
    " .dropna(subset=['NAME'])\n",
    " .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>955187</th>\n",
       "      <td>MULTIPOLYGON (((-73.92083460429942 40.64439765...</td>\n",
       "      <td>Pieter Claesen Wyckoff House</td>\n",
       "      <td>3221967</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>01/28/2020 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>550741</td>\n",
       "      <td>24.54</td>\n",
       "      <td>2100</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3079170009</td>\n",
       "      <td>3079170009</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 the_geom  \\\n",
       "955187  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "955187  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "955187  01/28/2020 12:00:00 AM  Constructed    550741       24.54       2100   \n",
       "\n",
       "        GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  GEOMSOURCE  \\\n",
       "955187        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "955187  {5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identifying the oldest building with a building name in this dataset and\n",
    "oldest = (\n",
    "    building_df\n",
    "    .sort_values(by='CNSTRCT_YR')\n",
    "    .dropna(subset=['NAME'])\n",
    "    .head(1)\n",
    ")\n",
    "\n",
    "oldest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will use the Pieter Claesen Wyckoff House as the oldest building in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Screenshot of Pieter Claesen Wyckoff House from Wikipedia:\n",
    "\n",
    "https://en.wikipedia.org/wiki/Wyckoff_House"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Pieter-Claesen-Wyckoff-House](images/Pieter-Claesen-Wyckoff-House.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we reviewed various ways to inspect, clean, wrangle, and detect outliers in your data.  Specifically, we focused on ways to inspect the shape (number of rows, columns) of your data, fill in or replace null values with the median, and detect outliers with box plots. This step is crucial if you are interested in modeling or predicting values. In Part 3, we will focus on data visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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