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",
    "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\n",
    "\n",
    "# to plot matplotlib figures inline on the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "printing verions of Python modules and packages with **watermark** documention for installing watermark: https://github.com/rasbt/watermark."
   ]
  },
  {
   "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": [
    "%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": [
    {
     "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading in data as a pandas dataframe assigned to building_df\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": [
    {
     "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previewing the first five rows\n",
    "building_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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>1083101</th>\n",
       "      <td>MULTIPOLYGON (((-73.93970312052463 40.70024464...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427437</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>32656</td>\n",
       "      <td>25.040000</td>\n",
       "      <td>2100</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3031320113</td>\n",
       "      <td>3031320113</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{0F7C5E77-0A77-4AAE-94C9-64CDF52ADD9D}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083102</th>\n",
       "      <td>MULTIPOLYGON (((-73.98928170927698 40.68173770...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427438</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302450</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3004160053</td>\n",
       "      <td>3004160053</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{29E84B32-8E32-408E-96AA-DB0F60DF91F2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083103</th>\n",
       "      <td>MULTIPOLYGON (((-73.99190407584891 40.69302272...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427442</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302454</td>\n",
       "      <td>39.539176</td>\n",
       "      <td>2100</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3002550012</td>\n",
       "      <td>3002550012</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{9B2DD7D1-F0A1-41D9-9240-44B9A15A8038}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083104</th>\n",
       "      <td>MULTIPOLYGON (((-73.99213857947052 40.69277855...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427441</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302453</td>\n",
       "      <td>39.539176</td>\n",
       "      <td>2100</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3002550014</td>\n",
       "      <td>3002550014</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{70CF0B39-B799-4DB1-A571-7E440FF19361}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083105</th>\n",
       "      <td>MULTIPOLYGON (((-73.90607854429192 40.69343575...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427439</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1262925</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3034190028</td>\n",
       "      <td>3034190028</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{2705C7E6-6178-48ED-8552-972D56718B52}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom NAME      BIN  \\\n",
       "1083101  MULTIPOLYGON (((-73.93970312052463 40.70024464...  NaN  3427437   \n",
       "1083102  MULTIPOLYGON (((-73.98928170927698 40.68173770...  NaN  3427438   \n",
       "1083103  MULTIPOLYGON (((-73.99190407584891 40.69302272...  NaN  3427442   \n",
       "1083104  MULTIPOLYGON (((-73.99213857947052 40.69277855...  NaN  3427441   \n",
       "1083105  MULTIPOLYGON (((-73.90607854429192 40.69343575...  NaN  3427439   \n",
       "\n",
       "         CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  \\\n",
       "1083101      1931.0  08/30/2024 12:00:00 AM  Constructed     32656   \n",
       "1083102      2016.0  08/30/2024 12:00:00 AM  Constructed   1302450   \n",
       "1083103      1914.0  08/30/2024 12:00:00 AM  Constructed   1302454   \n",
       "1083104      1914.0  08/30/2024 12:00:00 AM  Constructed   1302453   \n",
       "1083105      2013.0  08/30/2024 12:00:00 AM  Constructed   1262925   \n",
       "\n",
       "         HEIGHTROOF  FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  \\\n",
       "1083101   25.040000       2100        38.0           0          0  3031320113   \n",
       "1083102   51.000000       2100        13.0           0          0  3004160053   \n",
       "1083103   39.539176       2100        64.0           0          0  3002550012   \n",
       "1083104   39.539176       2100        64.0           0          0  3002550014   \n",
       "1083105   43.000000       2100        72.0           0          0  3034190028   \n",
       "\n",
       "         MPLUTO_BBL  GEOMSOURCE                                GLOBALID  \n",
       "1083101  3031320113  Other (Man  {0F7C5E77-0A77-4AAE-94C9-64CDF52ADD9D}  \n",
       "1083102  3004160053  Other (Man  {29E84B32-8E32-408E-96AA-DB0F60DF91F2}  \n",
       "1083103  3002550012  Other (Man  {9B2DD7D1-F0A1-41D9-9240-44B9A15A8038}  \n",
       "1083104  3002550014  Other (Man  {70CF0B39-B799-4DB1-A571-7E440FF19361}  \n",
       "1083105  3034190028  Other (Man  {2705C7E6-6178-48ED-8552-972D56718B52}  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previewing the last five rows\n",
    "building_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1083106, 16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing 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 1,083,106 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": [
    "# printing 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": [
    "# print list of columns\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": [
    "# printing 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: 1083106 entries, 0 to 1083105\n",
      "Data columns (total 16 columns):\n",
      " #   Column      Non-Null Count    Dtype  \n",
      "---  ------      --------------    -----  \n",
      " 0   the_geom    1083106 non-null  object \n",
      " 1   NAME        2247 non-null     object \n",
      " 2   BIN         1083106 non-null  int64  \n",
      " 3   CNSTRCT_YR  1072859 non-null  float64\n",
      " 4   LSTMODDATE  1083106 non-null  object \n",
      " 5   LSTSTATYPE  1082777 non-null  object \n",
      " 6   DOITT_ID    1083106 non-null  int64  \n",
      " 7   HEIGHTROOF  1080528 non-null  float64\n",
      " 8   FEAT_CODE   1083106 non-null  int64  \n",
      " 9   GROUNDELEV  1082554 non-null  float64\n",
      " 10  SHAPE_AREA  1083106 non-null  int64  \n",
      " 11  SHAPE_LEN   1083106 non-null  int64  \n",
      " 12  BASE_BBL    1083106 non-null  int64  \n",
      " 13  MPLUTO_BBL  1083106 non-null  int64  \n",
      " 14  GEOMSOURCE  1082810 non-null  object \n",
      " 15  GLOBALID    1083106 non-null  object \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 132.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# printing 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=1083106, step=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing 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": [
    "# printing 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>1.083106e+06</td>\n",
       "      <td>1.072859e+06</td>\n",
       "      <td>1.083106e+06</td>\n",
       "      <td>1.080528e+06</td>\n",
       "      <td>1.083106e+06</td>\n",
       "      <td>1.082554e+06</td>\n",
       "      <td>1083106.0</td>\n",
       "      <td>1083106.0</td>\n",
       "      <td>1.083106e+06</td>\n",
       "      <td>1.083106e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.713041e+06</td>\n",
       "      <td>1.939661e+03</td>\n",
       "      <td>5.950948e+05</td>\n",
       "      <td>2.770927e+01</td>\n",
       "      <td>2.693287e+03</td>\n",
       "      <td>5.512671e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.564203e+09</td>\n",
       "      <td>3.564203e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.026655e+06</td>\n",
       "      <td>2.950607e+01</td>\n",
       "      <td>3.623746e+05</td>\n",
       "      <td>2.182553e+01</td>\n",
       "      <td>1.198332e+03</td>\n",
       "      <td>4.154991e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.858404e+08</td>\n",
       "      <td>9.858404e+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>3.129718e+06</td>\n",
       "      <td>1.920000e+03</td>\n",
       "      <td>2.881752e+05</td>\n",
       "      <td>1.853000e+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>3.043390e+09</td>\n",
       "      <td>3.043390e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.067922e+06</td>\n",
       "      <td>1.930000e+03</td>\n",
       "      <td>5.765265e+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>4.019570e+09</td>\n",
       "      <td>4.019570e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.483494e+06</td>\n",
       "      <td>1.955000e+03</td>\n",
       "      <td>8.667342e+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>4.107010e+09</td>\n",
       "      <td>4.107010e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.799523e+06</td>\n",
       "      <td>2.024000e+03</td>\n",
       "      <td>1.302455e+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  1.083106e+06  1.072859e+06  1.083106e+06  1.080528e+06  1.083106e+06   \n",
       "mean   3.713041e+06  1.939661e+03  5.950948e+05  2.770927e+01  2.693287e+03   \n",
       "std    1.026655e+06  2.950607e+01  3.623746e+05  2.182553e+01  1.198332e+03   \n",
       "min    1.000000e+06  1.652000e+03  1.000000e+00  0.000000e+00  1.000000e+03   \n",
       "25%    3.129718e+06  1.920000e+03  2.881752e+05  1.853000e+01  2.100000e+03   \n",
       "50%    4.067922e+06  1.930000e+03  5.765265e+05  2.613000e+01  2.100000e+03   \n",
       "75%    4.483494e+06  1.955000e+03  8.667342e+05  3.114000e+01  2.100000e+03   \n",
       "max    5.799523e+06  2.024000e+03  1.302455e+06  1.550000e+03  5.110000e+03   \n",
       "\n",
       "         GROUNDELEV  SHAPE_AREA  SHAPE_LEN      BASE_BBL    MPLUTO_BBL  \n",
       "count  1.082554e+06   1083106.0  1083106.0  1.083106e+06  1.083106e+06  \n",
       "mean   5.512671e+01         0.0        0.0  3.564203e+09  3.564203e+09  \n",
       "std    4.154991e+01         0.0        0.0  9.858404e+08  9.858404e+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  3.043390e+09  3.043390e+09  \n",
       "50%    4.600000e+01         0.0        0.0  4.019570e+09  4.019570e+09  \n",
       "75%    7.400000e+01         0.0        0.0  4.107010e+09  4.107010e+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": [
    "# printing descriptive statistics of our 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>1083106</td>\n",
       "      <td>2247</td>\n",
       "      <td>1083106</td>\n",
       "      <td>1082777</td>\n",
       "      <td>1082810</td>\n",
       "      <td>1083106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1083106</td>\n",
       "      <td>1889</td>\n",
       "      <td>1866</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1083106</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>799886</td>\n",
       "      <td>1080435</td>\n",
       "      <td>1011437</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 the_geom  \\\n",
       "count                                             1083106   \n",
       "unique                                            1083106   \n",
       "top     MULTIPOLYGON (((-73.96664570466969 40.62599676...   \n",
       "freq                                                    1   \n",
       "\n",
       "                           NAME              LSTMODDATE   LSTSTATYPE  \\\n",
       "count                      2247                 1083106      1082777   \n",
       "unique                     1889                    1866           12   \n",
       "top     Adler Place Town Houses  08/22/2017 12:00:00 AM  Constructed   \n",
       "freq                         52                  799886      1080435   \n",
       "\n",
       "        GEOMSOURCE                                GLOBALID  \n",
       "count      1082810                                 1083106  \n",
       "unique           3                                 1083106  \n",
       "top     Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
       "freq       1011437                                       1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing descriptive statistics of our non-numeric columns (e.g. strings)\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>CNSTRCT_YR</th>\n",
       "      <th>NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <td>1920.0</td>\n",
       "      <td>Adler Place Town Houses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CNSTRCT_YR                     NAME\n",
       "mode      1920.0  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",
    " .rename(index={0: \"mode\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1920.0    130020\n",
       "1930.0    112438\n",
       "1925.0    108964\n",
       "1950.0     62611\n",
       "1910.0     52183\n",
       "1940.0     51191\n",
       "1960.0     40723\n",
       "1935.0     38509\n",
       "1931.0     36640\n",
       "1945.0     34015\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": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTSTATYPE\n",
       "Alteration                     1499\n",
       "Constructed                 1080435\n",
       "Correction                       68\n",
       "Demolition                       13\n",
       "Geometry                         17\n",
       "Initialization                  111\n",
       "Investigate Construction         50\n",
       "Investigate Demolition            5\n",
       "Marked for Construction          21\n",
       "Marked for Demolition           129\n",
       "Merged                           92\n",
       "Split                           337\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": "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>212.500000</td>\n",
       "      <td>50.880000</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>31.631185</td>\n",
       "      <td>55.105993</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>98.196353</td>\n",
       "      <td>52.203488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>11.700729</td>\n",
       "      <td>55.257440</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           212.500000       50.880000\n",
       "1004            17.081468       31.788288\n",
       "1005            25.180486       29.217391\n",
       "1006           367.722222       38.333333\n",
       "2100            31.631185       55.105993\n",
       "2110            54.010564       37.617391\n",
       "5100            98.196353       52.203488\n",
       "5110            11.700729       55.257440"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by 'FEAT_CODE', mean value of 'HEIGHTROOF' and 'GROUNDELEV'\n",
    "(building_df\n",
    " .groupby(by='FEAT_CODE')[['HEIGHTROOF', 'GROUNDELEV']]\n",
    " .mean()\n",
    " .rename(columns={'HEIGHTROOF':'HEIGHTROOF_avg', 'GROUNDELEV':'GROUNDELEV_avg'})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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></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>1429</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>866398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5100</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>213334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Correction</th>\n",
       "      <th>2100</th>\n",
       "      <td>49</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>12</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>110</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>91</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>329</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         1429\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       866398\n",
       "                         2110          123\n",
       "                         5100          103\n",
       "                         5110       213334\n",
       "Correction               2100           49\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           12\n",
       "                         2100            6\n",
       "                         5100            3\n",
       "Marked for Demolition    1003            2\n",
       "                         2100          110\n",
       "                         5110           17\n",
       "Merged                   2100           91\n",
       "                         5110            1\n",
       "Split                    1000            1\n",
       "                         1004            2\n",
       "                         2100          329\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",
       "    }\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>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>1429</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>866398</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>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Constructed</td>\n",
       "      <td>5110</td>\n",
       "      <td>213334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Correction</td>\n",
       "      <td>2100</td>\n",
       "      <td>49</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>12</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>110</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>91</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>329</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    1429\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  866398\n",
       "11               Constructed       2110     123\n",
       "12               Constructed       5100     103\n",
       "13               Constructed       5110  213334\n",
       "14                Correction       2100      49\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      12\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     110\n",
       "36     Marked for Demolition       5110      17\n",
       "37                    Merged       2100      91\n",
       "38                    Merged       5110       1\n",
       "39                     Split       1000       1\n",
       "40                     Split       1004       2\n",
       "41                     Split       2100     329\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": {
      "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>BIN</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.290448</td>\n",
       "      <td>0.156182</td>\n",
       "      <td>-0.357052</td>\n",
       "      <td>0.190987</td>\n",
       "      <td>0.087047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988677</td>\n",
       "      <td>0.988677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <td>0.290448</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.205393</td>\n",
       "      <td>0.026888</td>\n",
       "      <td>-0.132434</td>\n",
       "      <td>-0.002974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.284157</td>\n",
       "      <td>0.284157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOITT_ID</th>\n",
       "      <td>0.156182</td>\n",
       "      <td>0.205393</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.158557</td>\n",
       "      <td>0.539215</td>\n",
       "      <td>-0.017022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.075763</td>\n",
       "      <td>0.075763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <td>-0.357052</td>\n",
       "      <td>0.026888</td>\n",
       "      <td>-0.158557</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.362512</td>\n",
       "      <td>-0.002643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.327788</td>\n",
       "      <td>-0.327788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <td>0.190987</td>\n",
       "      <td>-0.132434</td>\n",
       "      <td>0.539215</td>\n",
       "      <td>-0.362512</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.090460</td>\n",
       "      <td>0.090460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <td>0.087047</td>\n",
       "      <td>-0.002974</td>\n",
       "      <td>-0.017022</td>\n",
       "      <td>-0.002643</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.106184</td>\n",
       "      <td>0.106184</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>0.988677</td>\n",
       "      <td>0.284157</td>\n",
       "      <td>0.075763</td>\n",
       "      <td>-0.327788</td>\n",
       "      <td>0.090460</td>\n",
       "      <td>0.106184</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>0.988677</td>\n",
       "      <td>0.284157</td>\n",
       "      <td>0.075763</td>\n",
       "      <td>-0.327788</td>\n",
       "      <td>0.090460</td>\n",
       "      <td>0.106184</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    0.290448  0.156182   -0.357052   0.190987    0.087047   \n",
       "CNSTRCT_YR  0.290448    1.000000  0.205393    0.026888  -0.132434   -0.002974   \n",
       "DOITT_ID    0.156182    0.205393  1.000000   -0.158557   0.539215   -0.017022   \n",
       "HEIGHTROOF -0.357052    0.026888 -0.158557    1.000000  -0.362512   -0.002643   \n",
       "FEAT_CODE   0.190987   -0.132434  0.539215   -0.362512   1.000000    0.001680   \n",
       "GROUNDELEV  0.087047   -0.002974 -0.017022   -0.002643   0.001680    1.000000   \n",
       "SHAPE_AREA       NaN         NaN       NaN         NaN        NaN         NaN   \n",
       "SHAPE_LEN        NaN         NaN       NaN         NaN        NaN         NaN   \n",
       "BASE_BBL    0.988677    0.284157  0.075763   -0.327788   0.090460    0.106184   \n",
       "MPLUTO_BBL  0.988677    0.284157  0.075763   -0.327788   0.090460    0.106184   \n",
       "\n",
       "            SHAPE_AREA  SHAPE_LEN  BASE_BBL  MPLUTO_BBL  \n",
       "BIN                NaN        NaN  0.988677    0.988677  \n",
       "CNSTRCT_YR         NaN        NaN  0.284157    0.284157  \n",
       "DOITT_ID           NaN        NaN  0.075763    0.075763  \n",
       "HEIGHTROOF         NaN        NaN -0.327788   -0.327788  \n",
       "FEAT_CODE          NaN        NaN  0.090460    0.090460  \n",
       "GROUNDELEV         NaN        NaN  0.106184    0.106184  \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",
    "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 nulls in dataframe: 1,094,861\n"
     ]
    }
   ],
   "source": [
    "# total nulls in dataframe\n",
    "null_count = (\n",
    "    building_df\n",
    "    .isnull()\n",
    "    .sum()\n",
    "    .sum()\n",
    ")\n",
    "\n",
    "print(f'total nulls in dataframe: {null_count:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total non-nulls in dataframe: 16,234,835\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-nulls in dataframe: {nonnull_count:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME          1080859\n",
       "CNSTRCT_YR      10247\n",
       "HEIGHTROOF       2578\n",
       "GROUNDELEV        552\n",
       "LSTSTATYPE        329\n",
       "GEOMSOURCE        296\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": [
    "# 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          99.792541\n",
       "CNSTRCT_YR     0.946075\n",
       "HEIGHTROOF     0.238019\n",
       "GROUNDELEV     0.050965\n",
       "LSTSTATYPE     0.030376\n",
       "GEOMSOURCE     0.027329\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>99.792541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <td>0.946075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <td>0.238019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <td>0.050965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <td>0.030376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <td>0.027329</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           99.792541\n",
       "CNSTRCT_YR      0.946075\n",
       "HEIGHTROOF      0.238019\n",
       "GROUNDELEV      0.050965\n",
       "LSTSTATYPE      0.030376\n",
       "GEOMSOURCE      0.027329\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, columns=['perc_missing']\n",
    ")\n",
    "\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",
       "    .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>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>1083101</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083102</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083103</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083104</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083105</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1083106 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",
       "1083101     False  True  False       False       False       False     False   \n",
       "1083102     False  True  False       False       False       False     False   \n",
       "1083103     False  True  False       False       False       False     False   \n",
       "1083104     False  True  False       False       False       False     False   \n",
       "1083105     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",
       "1083101       False      False       False       False      False     False   \n",
       "1083102       False      False       False       False      False     False   \n",
       "1083103       False      False       False       False      False     False   \n",
       "1083104       False      False       False       False      False     False   \n",
       "1083105       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",
       "1083101       False       False     False  \n",
       "1083102       False       False     False  \n",
       "1083103       False       False     False  \n",
       "1083104       False       False     False  \n",
       "1083105       False       False     False  \n",
       "\n",
       "[1083106 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",
       "1083101    False\n",
       "1083102    False\n",
       "1083103    False\n",
       "1083104    False\n",
       "1083105    False\n",
       "Name: NAME, Length: 1083106, 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": [
    {
     "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>3576</th>\n",
       "      <td>MULTIPOLYGON (((-73.95317216502298 40.75924424...</td>\n",
       "      <td>Roosevelt Isld Subway Station</td>\n",
       "      <td>1089270</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>03/25/2008 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>996736</td>\n",
       "      <td>28.470000</td>\n",
       "      <td>2100</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1013730001</td>\n",
       "      <td>1013730001</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{E49130B9-6088-4BB5-9BA5-ABA8BFC69BA7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4308</th>\n",
       "      <td>MULTIPOLYGON (((-73.88526375915657 40.86258229...</td>\n",
       "      <td>Alpha House</td>\n",
       "      <td>2102033</td>\n",
       "      <td>1922.0</td>\n",
       "      <td>08/17/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>194515</td>\n",
       "      <td>22.560000</td>\n",
       "      <td>5110</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2032730001</td>\n",
       "      <td>2032730001</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{8A21945E-76A9-47A7-9355-2D500A91A949}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5786</th>\n",
       "      <td>MULTIPOLYGON (((-73.88149603905721 40.84855265...</td>\n",
       "      <td>Pheasant Aviary</td>\n",
       "      <td>2123889</td>\n",
       "      <td>1905.0</td>\n",
       "      <td>07/17/2014 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>925752</td>\n",
       "      <td>22.075648</td>\n",
       "      <td>2100</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2031200020</td>\n",
       "      <td>2031200020</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{2BCDE23D-4DD7-4642-8FC1-B347EDE772CA}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7976</th>\n",
       "      <td>MULTIPOLYGON (((-73.8312596282937 40.871147879...</td>\n",
       "      <td>Asch Loop Town Houses</td>\n",
       "      <td>2128807</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>197782</td>\n",
       "      <td>26.450000</td>\n",
       "      <td>2100</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2051410120</td>\n",
       "      <td>2051410120</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{E7006798-10CF-49AA-BE4E-A1039405D38A}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8131</th>\n",
       "      <td>MULTIPOLYGON (((-73.90110014758918 40.73308422...</td>\n",
       "      <td>NO NAME</td>\n",
       "      <td>4597316</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>08/17/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>786648</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5110</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4023750180</td>\n",
       "      <td>4023750180</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{E2CADF8E-D955-4BD4-AEED-0EFC3A2149C4}</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>1080837</th>\n",
       "      <td>MULTIPOLYGON (((-73.85265384411393 40.86979051...</td>\n",
       "      <td>Eastchester Playground-Comfort Station</td>\n",
       "      <td>2125424</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>04/16/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>455210</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2045810002</td>\n",
       "      <td>2045810002</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{9D2E3969-E778-4A78-B689-ABAA7CD9C84C}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080989</th>\n",
       "      <td>MULTIPOLYGON (((-74.01066784278217 40.71190680...</td>\n",
       "      <td>Tower 2 World Trade Ctr</td>\n",
       "      <td>1089308</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>05/06/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1256146</td>\n",
       "      <td>41.883905</td>\n",
       "      <td>2100</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1000580001</td>\n",
       "      <td>1000580001</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{25251D04-C615-4866-B45B-2885FACDDF0D}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081326</th>\n",
       "      <td>MULTIPOLYGON (((-73.79566736659639 40.65514293...</td>\n",
       "      <td>83</td>\n",
       "      <td>4542282</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>02/22/2024 12:00:00 AM</td>\n",
       "      <td>Alteration</td>\n",
       "      <td>1260149</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4142600001</td>\n",
       "      <td>4142600001</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{DC541EA1-0B7F-4E54-94A3-E8A2F61F8DFF}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081416</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>1081570</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2247 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom  \\\n",
       "3576     MULTIPOLYGON (((-73.95317216502298 40.75924424...   \n",
       "4308     MULTIPOLYGON (((-73.88526375915657 40.86258229...   \n",
       "5786     MULTIPOLYGON (((-73.88149603905721 40.84855265...   \n",
       "7976     MULTIPOLYGON (((-73.8312596282937 40.871147879...   \n",
       "8131     MULTIPOLYGON (((-73.90110014758918 40.73308422...   \n",
       "...                                                    ...   \n",
       "1080837  MULTIPOLYGON (((-73.85265384411393 40.86979051...   \n",
       "1080989  MULTIPOLYGON (((-74.01066784278217 40.71190680...   \n",
       "1081326  MULTIPOLYGON (((-73.79566736659639 40.65514293...   \n",
       "1081416  MULTIPOLYGON (((-73.95209408465293 40.78829631...   \n",
       "1081570  MULTIPOLYGON (((-73.96861892021917 40.75798235...   \n",
       "\n",
       "                                           NAME      BIN  CNSTRCT_YR  \\\n",
       "3576              Roosevelt Isld Subway Station  1089270      1975.0   \n",
       "4308                                Alpha House  2102033      1922.0   \n",
       "5786                            Pheasant Aviary  2123889      1905.0   \n",
       "7976                      Asch Loop Town Houses  2128807      1969.0   \n",
       "8131                                    NO NAME  4597316      1940.0   \n",
       "...                                         ...      ...         ...   \n",
       "1080837  Eastchester Playground-Comfort Station  2125424      1949.0   \n",
       "1080989                 Tower 2 World Trade Ctr  1089308      2023.0   \n",
       "1081326                                      83  4542282      1994.0   \n",
       "1081416                              Building B  1091029      1992.0   \n",
       "1081570                       Lipstick Building  1038549      1986.0   \n",
       "\n",
       "                     LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "3576     03/25/2008 12:00:00 AM  Constructed    996736   28.470000       2100   \n",
       "4308     08/17/2017 12:00:00 AM  Constructed    194515   22.560000       5110   \n",
       "5786     07/17/2014 12:00:00 AM  Constructed    925752   22.075648       2100   \n",
       "7976     08/22/2017 12:00:00 AM  Constructed    197782   26.450000       2100   \n",
       "8131     08/17/2017 12:00:00 AM  Constructed    786648   11.000000       5110   \n",
       "...                         ...          ...       ...         ...        ...   \n",
       "1080837  04/16/2024 12:00:00 AM  Constructed    455210   14.000000       2100   \n",
       "1080989  05/06/2024 12:00:00 AM  Constructed   1256146   41.883905       2100   \n",
       "1081326  02/22/2024 12:00:00 AM   Alteration   1260149   41.000000       2100   \n",
       "1081416  08/25/2023 12:00:00 AM        Split   1300335  328.810000       1000   \n",
       "1081570  08/22/2017 12:00:00 AM  Constructed     99468  464.420000       2100   \n",
       "\n",
       "         GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "3576           16.0           0          0  1013730001  1013730001   \n",
       "4308           98.0           0          0  2032730001  2032730001   \n",
       "5786           70.0           0          0  2031200020  2031200020   \n",
       "7976           18.0           0          0  2051410120  2051410120   \n",
       "8131           68.0           0          0  4023750180  4023750180   \n",
       "...             ...         ...        ...         ...         ...   \n",
       "1080837        77.0           0          0  2045810002  2045810002   \n",
       "1080989        20.0           0          0  1000580001  1000580001   \n",
       "1081326         9.0           0          0  4142600001  4142600001   \n",
       "1081416        53.0           0          0  1016040023  1016040023   \n",
       "1081570        36.0           0          0  1013270001  1013277504   \n",
       "\n",
       "         GEOMSOURCE                                GLOBALID  \n",
       "3576     Photogramm  {E49130B9-6088-4BB5-9BA5-ABA8BFC69BA7}  \n",
       "4308     Photogramm  {8A21945E-76A9-47A7-9355-2D500A91A949}  \n",
       "5786     Photogramm  {2BCDE23D-4DD7-4642-8FC1-B347EDE772CA}  \n",
       "7976     Photogramm  {E7006798-10CF-49AA-BE4E-A1039405D38A}  \n",
       "8131     Other (Man  {E2CADF8E-D955-4BD4-AEED-0EFC3A2149C4}  \n",
       "...             ...                                     ...  \n",
       "1080837  Other (Man  {9D2E3969-E778-4A78-B689-ABAA7CD9C84C}  \n",
       "1080989  Other (Man  {25251D04-C615-4866-B45B-2885FACDDF0D}  \n",
       "1081326  Other (Man  {DC541EA1-0B7F-4E54-94A3-E8A2F61F8DFF}  \n",
       "1081416  Other (Man  {E1111641-540E-4E76-86BC-81A88443EC5B}  \n",
       "1081570  Photogramm  {C07FE8C7-FD69-4312-821B-5C792E05E17F}  \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",
       "    }\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>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>1083101</th>\n",
       "      <td>MULTIPOLYGON (((-73.93970312052463 40.70024464...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427437</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>32656</td>\n",
       "      <td>25.040000</td>\n",
       "      <td>2100</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3031320113</td>\n",
       "      <td>3031320113</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{0F7C5E77-0A77-4AAE-94C9-64CDF52ADD9D}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083102</th>\n",
       "      <td>MULTIPOLYGON (((-73.98928170927698 40.68173770...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427438</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302450</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3004160053</td>\n",
       "      <td>3004160053</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{29E84B32-8E32-408E-96AA-DB0F60DF91F2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083103</th>\n",
       "      <td>MULTIPOLYGON (((-73.99190407584891 40.69302272...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427442</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302454</td>\n",
       "      <td>39.539176</td>\n",
       "      <td>2100</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3002550012</td>\n",
       "      <td>3002550012</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{9B2DD7D1-F0A1-41D9-9240-44B9A15A8038}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083104</th>\n",
       "      <td>MULTIPOLYGON (((-73.99213857947052 40.69277855...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427441</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302453</td>\n",
       "      <td>39.539176</td>\n",
       "      <td>2100</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3002550014</td>\n",
       "      <td>3002550014</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{70CF0B39-B799-4DB1-A571-7E440FF19361}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083105</th>\n",
       "      <td>MULTIPOLYGON (((-73.90607854429192 40.69343575...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3427439</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>08/30/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1262925</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3034190028</td>\n",
       "      <td>3034190028</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{2705C7E6-6178-48ED-8552-972D56718B52}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1080859 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",
       "1083101  MULTIPOLYGON (((-73.93970312052463 40.70024464...  NaN  3427437   \n",
       "1083102  MULTIPOLYGON (((-73.98928170927698 40.68173770...  NaN  3427438   \n",
       "1083103  MULTIPOLYGON (((-73.99190407584891 40.69302272...  NaN  3427442   \n",
       "1083104  MULTIPOLYGON (((-73.99213857947052 40.69277855...  NaN  3427441   \n",
       "1083105  MULTIPOLYGON (((-73.90607854429192 40.69343575...  NaN  3427439   \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",
       "1083101      1931.0  08/30/2024 12:00:00 AM  Constructed     32656   \n",
       "1083102      2016.0  08/30/2024 12:00:00 AM  Constructed   1302450   \n",
       "1083103      1914.0  08/30/2024 12:00:00 AM  Constructed   1302454   \n",
       "1083104      1914.0  08/30/2024 12:00:00 AM  Constructed   1302453   \n",
       "1083105      2013.0  08/30/2024 12:00:00 AM  Constructed   1262925   \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",
       "1083101   25.040000       2100        38.0           0          0  3031320113   \n",
       "1083102   51.000000       2100        13.0           0          0  3004160053   \n",
       "1083103   39.539176       2100        64.0           0          0  3002550012   \n",
       "1083104   39.539176       2100        64.0           0          0  3002550014   \n",
       "1083105   43.000000       2100        72.0           0          0  3034190028   \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",
       "1083101  3031320113  Other (Man  {0F7C5E77-0A77-4AAE-94C9-64CDF52ADD9D}  \n",
       "1083102  3004160053  Other (Man  {29E84B32-8E32-408E-96AA-DB0F60DF91F2}  \n",
       "1083103  3002550012  Other (Man  {9B2DD7D1-F0A1-41D9-9240-44B9A15A8038}  \n",
       "1083104  3002550014  Other (Man  {70CF0B39-B799-4DB1-A571-7E440FF19361}  \n",
       "1083105  3034190028  Other (Man  {2705C7E6-6178-48ED-8552-972D56718B52}  \n",
       "\n",
       "[1080859 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 will be cleaning 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",
       "1083101    1931.0\n",
       "1083102    2016.0\n",
       "1083103    1914.0\n",
       "1083104    1914.0\n",
       "1083105    2013.0\n",
       "Name: CNSTRCT_YR, Length: 1083106, 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",
       "<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>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>1083101</th>\n",
       "      <td>1931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083102</th>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083103</th>\n",
       "      <td>1914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083104</th>\n",
       "      <td>1914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083105</th>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1083106 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",
       "1083101      1931.0\n",
       "1083102      2016.0\n",
       "1083103      1914.0\n",
       "1083104      1914.0\n",
       "1083105      2013.0\n",
       "\n",
       "[1083106 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",
       "<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>955513</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>430835</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>710320</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>52827</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>521520</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",
       "955513  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "430835  MULTIPOLYGON (((-73.82449114078894 40.76279389...   \n",
       "710320  MULTIPOLYGON (((-73.8247828135812 40.762948467...   \n",
       "52827   MULTIPOLYGON (((-74.25311647359251 40.50309522...   \n",
       "521520  MULTIPOLYGON (((-74.06293248238293 40.61512359...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "955513  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "430835                           NaN  4610542      1661.0   \n",
       "710320                   Bowne House  4113403      1661.0   \n",
       "52827               Conference House  5087656      1680.0   \n",
       "521520     Alice Austen House Museum  5042188      1690.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "955513  01/28/2020 12:00:00 AM  Constructed    550741   24.540000       2100   \n",
       "430835  08/17/2017 12:00:00 AM  Constructed   1190137   14.846959       5110   \n",
       "710320  08/22/2017 12:00:00 AM  Constructed    776399   26.120000       2100   \n",
       "52827   04/13/2020 12:00:00 AM  Constructed    702698   38.640000       2100   \n",
       "521520  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",
       "955513        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "430835        63.0           0          0  4050130006  4050130006  Photogramm   \n",
       "710320        64.0           0          0  4050130006  4050130006  Photogramm   \n",
       "52827         38.0           0          0  5078570090  5078570090  Photogramm   \n",
       "521520        21.0           0          0  5028300049  5028300049  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "955513  {5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}  \n",
       "430835  {36678A28-E8EE-45E8-BAA2-9A3F5118E176}  \n",
       "710320  {71A760A6-6D2F-48F8-8C93-C8B66071B868}  \n",
       "52827   {CB13BCBA-9DF3-4455-9206-824FE91F2EDE}  \n",
       "521520  {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": {
      "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>1078167</th>\n",
       "      <td>MULTIPOLYGON (((-74.14990289776875 40.58884084...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5175386</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>08/12/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302348</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5023750134</td>\n",
       "      <td>5023750134</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{D5A45DB3-D0F1-4522-A85F-82F3FA21A9C4}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081317</th>\n",
       "      <td>MULTIPOLYGON (((-73.87248318649782 40.76183155...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4607934</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>08/01/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302312</td>\n",
       "      <td>28.830000</td>\n",
       "      <td>2100</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4013760018</td>\n",
       "      <td>4013760018</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{9A2BE175-6856-4C0A-9C59-D91F336319B3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081335</th>\n",
       "      <td>MULTIPOLYGON (((-73.75873562524143 40.65792348...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4625015</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>05/23/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302096</td>\n",
       "      <td>14.008325</td>\n",
       "      <td>5110</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4134360028</td>\n",
       "      <td>4134360028</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{09E12932-17C2-4D9B-9B47-3FF9DD741DFD}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081334</th>\n",
       "      <td>MULTIPOLYGON (((-73.75873562524143 40.65792348...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4625014</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>05/23/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302095</td>\n",
       "      <td>14.008325</td>\n",
       "      <td>5110</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4134360029</td>\n",
       "      <td>4134360029</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{73BF6EDA-1554-4B2C-854B-A3E8B6CE0AD7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078414</th>\n",
       "      <td>MULTIPOLYGON (((-74.15015170157127 40.58867942...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5175392</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>08/12/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1302358</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5023730041</td>\n",
       "      <td>5023730041</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{3E7D31D0-4CE8-4150-A2EA-D111363D4794}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom NAME      BIN  \\\n",
       "1078167  MULTIPOLYGON (((-74.14990289776875 40.58884084...  NaN  5175386   \n",
       "1081317  MULTIPOLYGON (((-73.87248318649782 40.76183155...  NaN  4607934   \n",
       "1081335  MULTIPOLYGON (((-73.75873562524143 40.65792348...  NaN  4625015   \n",
       "1081334  MULTIPOLYGON (((-73.75873562524143 40.65792348...  NaN  4625014   \n",
       "1078414  MULTIPOLYGON (((-74.15015170157127 40.58867942...  NaN  5175392   \n",
       "\n",
       "         CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  \\\n",
       "1078167      2024.0  08/12/2024 12:00:00 AM  Constructed   1302348   \n",
       "1081317      2024.0  08/01/2024 12:00:00 AM  Constructed   1302312   \n",
       "1081335      2024.0  05/23/2024 12:00:00 AM  Constructed   1302096   \n",
       "1081334      2024.0  05/23/2024 12:00:00 AM  Constructed   1302095   \n",
       "1078414      2024.0  08/12/2024 12:00:00 AM  Constructed   1302358   \n",
       "\n",
       "         HEIGHTROOF  FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  \\\n",
       "1078167   29.000000       2100       137.0           0          0  5023750134   \n",
       "1081317   28.830000       2100        53.0           0          0  4013760018   \n",
       "1081335   14.008325       5110         7.0           0          0  4134360028   \n",
       "1081334   14.008325       5110         7.0           0          0  4134360029   \n",
       "1078414   30.000000       2100       135.0           0          0  5023730041   \n",
       "\n",
       "         MPLUTO_BBL  GEOMSOURCE                                GLOBALID  \n",
       "1078167  5023750134  Other (Man  {D5A45DB3-D0F1-4522-A85F-82F3FA21A9C4}  \n",
       "1081317  4013760018  Other (Man  {9A2BE175-6856-4C0A-9C59-D91F336319B3}  \n",
       "1081335  4134360028  Photogramm  {09E12932-17C2-4D9B-9B47-3FF9DD741DFD}  \n",
       "1081334  4134360029  Photogramm  {73BF6EDA-1554-4B2C-854B-A3E8B6CE0AD7}  \n",
       "1078414  5023730041  Other (Man  {3E7D31D0-4CE8-4150-A2EA-D111363D4794}  "
      ]
     },
     "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>1078167</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081317</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081335</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081334</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078414</th>\n",
       "      <td>2024.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CNSTRCT_YR\n",
       "1078167      2024.0\n",
       "1081317      2024.0\n",
       "1081335      2024.0\n",
       "1081334      2024.0\n",
       "1078414      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    1072859.00\n",
       "mean        1939.66\n",
       "std           29.51\n",
       "min         1652.00\n",
       "25%         1920.00\n",
       "50%         1930.00\n",
       "75%         1955.00\n",
       "max         2024.00\n",
       "Name: CNSTRCT_YR, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics include those that summarize the central tendency, \n",
    "# dispersion and shape of a dataset's distribution, excluding ``NaN`` values.\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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\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', fontsize=12) \n",
    "\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', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk4AAAGGCAYAAACNCg6xAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABRGklEQVR4nO3deVhU9f4H8PcAw7CIk6iAKALuJLhvqCluqAhKalYaeW+Ger25AWnYNbNuel2yzTQqlVJcUhHXuJCaooCmhom4hzugKQ6yCAN8f3945/wcWTyj6Mjwfj3PPHLO+cyZzxmc8e1ZvkchhBAgIiIiokcyM3YDRERERNUFgxMRERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTVTuRkZFQKBR6j/r168PHxwc7dux45v38+uuver2Ym5vD0dERr7zyCk6dOiXVXbx4EQqFApGRkQa/RlpaGj788ENcvHix6hr/n927d6NTp06wtbWFQqFATExMpfVZWVl477334OXlhVq1asHKygrNmzfH1KlTce7cuSrvzxDXr1/Hhx9+iJSUFKP1kJ+fjw8//BC//vprmWW6v7tP4/dYkfT0dNjZ2WHEiBHlLl+7di0UCgUiIiKeWU8Pmz59OhQKBU6fPl1hzfvvvw+FQoFjx47JXq+bmxv+9re/VUGHRP+PwYmqrVWrViEpKQmJiYn49ttvYW5ujoCAAGzfvt0o/cybNw9JSUnYu3cvZs6cifj4ePTo0QPXrl174nWnpaVh7ty5Vf4PrhACo0aNglKpxLZt25CUlITevXtXWH/48GF4eXlhxYoVGDlyJKKjoxEbG4uwsDAcO3YMXbp0qdL+DHX9+nXMnTvX6MFp7ty55QanIUOGICkpCQ0aNHhm/bi7u2PJkiWIjo7G2rVr9ZZlZmZi8uTJGDhwICZMmPDMenrYuHHjAAArV64sd3lpaSl+/PFHtGvXDh06dHiWrRGVYWHsBogel6enJzp16iRNDxo0CHXq1MG6desQEBDwzPtp3rw5unXrBgDo1asXXnjhBYwbNw6RkZF4//33n3k/cly/fh23b9/Gyy+/jH79+lVam5OTg2HDhsHKygqJiYlo1KiRtMzHxwcTJkzApk2bnnbLVSo/Px82NjbP7PXq16+P+vXrP7PX0wkODsaWLVswefJk9OnTRwpuEyZMgBACK1aseCZ9VPR+e3p6okuXLli9ejXmzZsHCwv9f5ri4uJw9epVzJw585n0SVQZ7nEik2FlZQVLS0solUq9+bdv38akSZPQsGFDWFpaokmTJnj//fdRWFgIALh37x7at2+PZs2aQaPRSM/LzMyEk5MTfHx8UFJSYnA/uhB16dKlSusOHDiAfv36wc7ODjY2NujevTt27twpLY+MjMQrr7wCAOjTp490SPBRh/wetd4PP/xQCj8zZ86EQqGAm5tbhev77rvvkJmZiYULF+qFpgeNHDlSb3rbtm3w9vaGjY0N7OzsMGDAACQlJenVfPjhh1AoFDh58iRef/11qNVqODo64q233tL7fQDAxo0b0bVrV6jVatjY2KBJkyZ46623ANw/ZNq5c2cAwN///nfpffrwww8BAH/7299Qq1YtnDhxAr6+vrCzs5PCYkWHdHx8fODj46M3786dOwgNDUWTJk2gUqng4OAAPz8/nD59GhcvXpSC0dy5c6UedOuu6FDdypUr0bZtW1hZWcHe3h4vv/yy3mHeB/s/f/48/Pz8UKtWLbi4uCA0NFT6u1wZXTgaP348AGD16tXYtm0bli5dioYNG0IIgWXLlqFdu3awtrZGnTp1MHLkSPz5559664mPj8ewYcPQqFEjWFlZoVmzZpgwYQL++usvvTrd7/XYsWMYOXIk6tSpg6ZNm1bY37hx45CZmYmff/65zLJVq1ZBpVJhzJgxuHfvHkJDQ9GuXTuo1WrY29vD29sbW7dufeR7UNH7rzvc/vBewl9++QX9+vVD7dq1YWNjgx49emD37t2PfB0ybQxOVG2VlJSguLgYWq0WV69exbRp05CXl4fRo0dLNffu3UOfPn3w448/IiQkBDt37sQbb7yBhQsXYvjw4QDuB66ffvoJN27ckP4RLi0txZgxYyCEwLp162Bubm5wf+fPnweASvcw7Nu3D3379oVGo8GKFSuwbt062NnZISAgABs2bABw//DOvHnzAABff/01kpKSkJSUhCFDhjzRet9++21ER0cDACZPnoykpCRs2bKlwnXGxcVJh0PlWLt2LYYNG4batWtj3bp1WLFiBbKzs+Hj44MDBw6UqR8xYgRatGiBzZs347333sPatWsxffp0aXlSUhJeffVVNGnSBOvXr8fOnTvxwQcfoLi4GADQoUMHrFq1CgDwr3/9S3qf3n77bWkdRUVFGDp0KPr27YutW7di7ty5srZF5+7du+jZsyciIiLw97//Hdu3b8c333yDFi1aICMjAw0aNEBsbCyA+0FA18Ps2bMrXOf8+fMxbtw4tG7dGtHR0fjiiy/wxx9/wNvbu8w5Y1qtFkOHDkW/fv2wdetWvPXWW/jss8+wYMGCR/beoEEDfP3119ixYwfmz5+PqVOnYsSIEdLnZcKECZg2bRr69++PmJgYLFu2DCdPnkT37t2RlZUlrefChQvw9vbG8uXLERcXhw8++ACHDh1Cz549odVqy7zu8OHD0axZM2zcuBHffPNNhf29/vrrsLGxKXO4Ljs7G1u3bsXLL7+MOnXqoLCwELdv30ZYWBhiYmKwbt069OzZE8OHD8ePP/74yPdBrjVr1sDX1xe1a9fGDz/8gJ9++gn29vYYOHAgw1NNJ4iqmVWrVgkAZR4qlUosW7ZMr/abb74RAMRPP/2kN3/BggUCgIiLi5PmbdiwQQAQn3/+ufjggw+EmZmZ3vKK7N27VwAQGzZsEFqtVuTn54v9+/eLZs2aCXNzc3H8+HEhhBDp6ekCgFi1apX03G7dugkHBwdx9+5daV5xcbHw9PQUjRo1EqWlpUIIITZu3CgAiL1798p6j+SuV9fTokWLHrnOVq1aCScnJ1mvX1JSIpydnYWXl5coKSmR5t+9e1c4ODiI7t27S/PmzJkjAIiFCxfqrWPSpEnCyspK6nXx4sUCgLhz506Fr/vbb7+VeY91xo4dKwCIlStXllnm6uoqxo4dW2Z+7969Re/evaXpjz76SAAQ8fHxFfZw8+ZNAUDMmTOnzDLd39309HQhhBDZ2dnC2tpa+Pn56dVdvnxZqFQqMXr06DL9P/x32c/PT7Rs2bLCfh42atQoAUA4OjqKmzdvCiGESEpKEgDEp59+qld75coVYW1tLWbMmFHuukpLS4VWqxWXLl0SAMTWrVulZbrf6wcffCC7t7FjxwqlUimysrKkeV999VWl73lxcbHQarVi3Lhxon379nrLHv69Pvz+6+g+w7rPV15enrC3txcBAQF6dSUlJaJt27aiS5cusreJTA/3OFG19eOPP+K3337Db7/9hp9//hljx47FP//5TyxdulSq2bNnD2xtbcscQtIdOnnwf46jRo3CP/7xD7z77rv497//jVmzZmHAgAGy+3n11VehVCphY2ODXr16oaSkBJs2bUKbNm3Krc/Ly8OhQ4cwcuRI1KpVS5pvbm6OoKAgXL16FWfOnJH9+k97vYY4c+YMrl+/jqCgIJiZ/f/XTK1atTBixAgkJycjPz9f7zlDhw7Vm27Tpg3u3buHGzduAIB0GG7UqFH46aefHvuk+4quLpPj559/RosWLdC/f//HXseDkpKSUFBQUOYwoYuLC/r27Vtmz4ZCoSizx69NmzaPPBz8oI8++ggAMGXKFNSrVw8AsGPHDigUCrzxxhsoLi6WHk5OTmjbtq3eIawbN25g4sSJcHFxgYWFBZRKJVxdXQGgzOFFwLD3e9y4cdBqtVi9erU0b9WqVXB1ddU7B2/jxo3o0aMHatWqJfWwYsWKcl//cSQmJuL27dsYO3as3vtRWlqKQYMG4bfffkNeXl6VvBZVPwxOVG15eHigU6dO6NSpEwYNGoSIiAj4+vpixowZuHPnDgDg1q1bcHJygkKh0Huug4MDLCwscOvWLb35b731FrRaLSwsLDBlyhSD+lmwYAF+++03HDt2DJcvX8aff/6JwMDACuuzs7MhhCj3CitnZ2epf0M9rfU2btwYN2/elPUPhm79FfVQWlqK7Oxsvfl169bVm1apVACAgoICAPdPuI+JiUFxcTHefPNNNGrUCJ6enli3bp3sbbCxsUHt2rVl1z/s5s2bFZ7f9Tge9T49/HuysbGBlZWV3jyVSoV79+7Jfk3d+2ppaSnNy8rKghACjo6OUCqVeo/k5GTp/KXS0lL4+voiOjoaM2bMwO7du3H48GEkJycD+P/f1YMMuYLwpZdeQosWLaRDrn/88QeOHTsmnbMGANHR0Rg1ahQaNmyINWvWICkpCb/99hveeustg96HyugOTY4cObLM+7FgwQIIIXD79u0qeS2qfnhVHZmUNm3a4L///S/Onj2LLl26oG7dujh06BCEEHrh6caNGyguLpb+xw3c31MTFBSEFi1aICsrC2+//basE051mjRponeV36PUqVMHZmZmyMjIKLPs+vXrAKDXn7HXO3DgQMTFxWH79u147bXXKq3VhaCKejAzM0OdOnUM7mHYsGEYNmwYCgsLkZycjPnz52P06NFwc3ODt7f3I5//cIDWsbKyKvcE67/++kvvvapfvz6uXr1qcN8VedT79Di/p8dRr149KBQKJCQkSMHqQbp5qampOH78OCIjIzF27Fhpue58vvJU9J5X5K233sJ7772Hw4cPY+3atTAzM9PbI7dmzRq4u7tjw4YNeuuWc4K8LnQ+XPvwie269/2rr76SLvJ4mKOjo6ztIdPDPU5kUnTj9+hOyO7Xrx9yc3PLDOqoO4n0wd3/EydOxOXLlxEdHY0VK1Zg27Zt+Oyzz55ar7a2tujatSuio6P1/qdeWlqKNWvWoFGjRmjRogWAsntfqmq9hhg3bhycnJwwY8aMCg+T6U42b9myJRo2bIi1a9dCCCEtz8vLw+bNm6Ur7R6XSqVC7969pZOif//9d2k+IO99epCbmxv++OMPvXlnz54tc0hz8ODBOHv2LPbs2VNpb3J78Pb2hrW1NdasWaM3/+rVq9izZ88jh4ioKv7+/hBC4Nq1a9Je3AcfXl5eAP4/BD0crqpy8MyxY8fCwsICERERiIqKQr9+/aRDgboeLC0t9UJTZmamrP/k6K4affh3vW3bNr3pHj164IUXXkBaWlq570enTp309thRzcI9TlRtpaamSldU3bp1C9HR0YiPj8fLL78Md3d3AMCbb76Jr7/+GmPHjsXFixfh5eWFAwcOYN68efDz85POVfn++++xZs0arFq1Cq1bt0br1q3xzjvvYObMmejRo8dTG9hx/vz5GDBgAPr06YOwsDBYWlpi2bJlSE1Nxbp166R/HDw9PQEA3377Lezs7GBlZQV3d/cyh7cMXa8h1Go1tm7dCn9/f7Rv3x7vvPMOvL29YWlpiXPnzmHNmjU4fvw4hg8fDjMzMyxcuBBjxoyBv78/JkyYgMLCQixatAh37tzBf/7zH4Nf/4MPPsDVq1fRr18/NGrUCHfu3MEXX3wBpVIpDdrZtGlTWFtbIyoqCh4eHqhVqxacnZ2lQ5QVCQoKwhtvvIFJkyZhxIgRuHTpEhYuXFjmishp06Zhw4YNGDZsGN577z106dIFBQUF2LdvH/z9/dGnTx/Y2dnB1dUVW7duRb9+/WBvb4969eqVO9TDCy+8gNmzZ2PWrFl488038frrr+PWrVuYO3curKysMGfOHIPfp8fRo0cPjB8/Hn//+99x5MgR9OrVC7a2tsjIyMCBAwfg5eWFf/zjH2jVqhWaNm2K9957D0II2NvbY/v27YiPj6+yXpycnODn54dVq1ZBCCENjqnj7++P6OhoTJo0CSNHjsSVK1fw8ccfo0GDBo8cub5z585o2bIlwsLCUFxcjDp16mDLli1lrvKsVasWvvrqK4wdOxa3b9/GyJEj4eDggJs3b+L48eO4efMmli9fXmXbTNWM0U5LJ3pM5V1Vp1arRbt27cSSJUvEvXv39Opv3bolJk6cKBo0aCAsLCyEq6urCA8Pl+r++OMPYW1tXeaqqnv37omOHTsKNzc3kZ2dXWE/uityNm7cWGnf5V1VJ4QQCQkJom/fvsLW1lZYW1uLbt26ie3bt5d5/ueffy7c3d2Fubl5hVeOGbpeQ66q08nMzBQzZ84UrVu3FjY2NkKlUolmzZqJCRMmiBMnTujVxsTEiK5duworKytha2sr+vXrJw4ePKhXo7v6SneFl87DV0Dt2LFDDB48WDRs2FBYWloKBwcH4efnJxISEvSet27dOtGqVSuhVCr1rm4bO3assLW1LXebSktLxcKFC0WTJk2ElZWV6NSpk9izZ0+Zq+qEuH8l3NSpU0Xjxo2FUqkUDg4OYsiQIeL06dNSzS+//CLat28vVCqVACD93aroqq7vv/9etGnTRlhaWgq1Wi2GDRsmTp48qVdTUf+690+uyn7nK1euFF27dpX+zjRt2lS8+eab4siRI1JNWlqaGDBggLCzsxN16tQRr7zyirh8+XKZKwkr+r3KsXXrVgFA2Nvbl/k8CyHEf/7zH+Hm5iZUKpXw8PAQ3333XbnvQ3lXS549e1b4+vqK2rVri/r164vJkyeLnTt3lnvV6r59+8SQIUOEvb29UCqVomHDhmLIkCGP/KyTaVMI8cB+dCIiIiKqEM9xIiIiIpKJwYmIiIhIJgYnIiIiIpkYnIiIiIhkYnAiIiIikonBiYiIiEgmkx0As7S0FNevX4ednd1jDfZHRERENYMQAnfv3oWzs7PejcnLY7LB6fr163BxcTF2G0RERFRNXLly5ZE38jbZ4GRnZwfg/pvwJHdDJyLTotVqERcXB19fXyiVSmO3Q0TPgZycHLi4uEjZoTImG5x0h+dq167N4EREEq1WCxsbG9SuXZvBiYj0yDm1hyeHExEREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTCY7jhMR0cPKG6NFCGGEToiouuIeJyKqESoa2I73siQiQzA4EZHJe1Q4YngiIrkYnIjIpMkNRQxPRCQHgxMRERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTAxORERERDJZGLsBIqLK5Ofn4/Tp08/ktY4dO/ZYz2vVqhVsbGyquBsieh4xOBHRc+306dPo2LHjM3mtx32do0ePokOHDlXcDRE9jwwKTvPnz0d0dDROnz4Na2trdO/eHQsWLEDLli2lGiEE5s6di2+//RbZ2dno2rUrvv76a7Ru3VqqKSwsRFhYGNatW4eCggL069cPy5YtQ6NGjaSa7OxsTJkyBdu2bQMADB06FF999RVeeOGFJ9xkIqpOWrVqhaNHjz728w0JQ4/7Oq1atXqs5xFR9aMQQgi5xYMGDcJrr72Gzp07o7i4GO+//z5OnDiBtLQ02NraAgAWLFiATz75BJGRkWjRogX+/e9/Y//+/Thz5gzs7OwAAP/4xz+wfft2REZGom7duggNDcXt27dx9OhRmJubAwAGDx6Mq1ev4ttvvwUAjB8/Hm5ubti+fbusXnNycqBWq6HRaFC7dm2D3hQiMi0KheKRNQZ8FRKRiTEoM4gncOPGDQFA7Nu3TwghRGlpqXBychL/+c9/pJp79+4JtVotvvnmGyGEEHfu3BFKpVKsX79eqrl27ZowMzMTsbGxQggh0tLSBACRnJws1SQlJQkA4vTp07J602g0AoDQaDRPsolEZCIAVPggoprNkMzwROc4aTQaAIC9vT0AID09HZmZmfD19ZVqVCoVevfujcTEREyYMAFHjx6FVqvVq3F2doanpycSExMxcOBAJCUlQa1Wo2vXrlJNt27doFarkZiYqHdoUKewsBCFhYXSdE5ODgBAq9VCq9U+yWYSkQkoKiqCpaVlufP5HUFUsxnyHfDYwUkIgZCQEPTs2ROenp4AgMzMTACAo6OjXq2joyMuXbok1VhaWqJOnTplanTPz8zMhIODQ5nXdHBwkGoeNn/+fMydO7fM/Li4OF7tQkQAgJiYGFzJBRafsECYVzFcagG7du0ydltEZGT5+fmyax87OL3zzjv4448/cODAgTLLHj6fQAjxyHMMHq4pr76y9YSHhyMkJESazsnJgYuLC3x9fXmOExFJjl++DZw4gm7duqFtY3tjt0NEzwHdUSo5His4TZ48Gdu2bcP+/fv1roRzcnICcH+PUYMGDaT5N27ckPZCOTk5oaioCNnZ2Xp7nW7cuIHu3btLNVlZWWVe9+bNm2X2ZumoVCqoVKoy85VKJZRK5WNsJRGZIgsLC+lPfjcQEQCDvgsMGjlcCIF33nkH0dHR2LNnD9zd3fWWu7u7w8nJCfHx8dK8oqIi7Nu3TwpFHTt2hFKp1KvJyMhAamqqVOPt7Q2NRoPDhw9LNYcOHYJGo5FqiIiIiJ41g/Y4/fOf/8TatWuxdetW2NnZSecbqdVqWFtbQ6FQYNq0aZg3bx6aN2+O5s2bY968ebCxscHo0aOl2nHjxiE0NBR169aFvb09wsLC4OXlhf79+wMAPDw8MGjQIAQHByMiIgLA/eEI/P39yz0xnIiIiOhZMCg4LV++HADg4+OjN3/VqlX429/+BgCYMWMGCgoKMGnSJGkAzLi4OGkMJwD47LPPYGFhgVGjRkkDYEZGRkpjOAFAVFQUpkyZIl19N3ToUCxduvRxtpGIiIioShg0AGZ1wgEwiag8KZduIXB5MmL+0Q3tXOsaux0ieg4YkhkMOseJiIiIqCZjcCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKJwYmIiIhIJgYnIiIiIpkYnIiIiIhkYnAiIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKJwYmIiIhIJgYnIiIiIpkYnIiIiIhkYnAiIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZLIzdABGZnvS/8pBXWGzsNsp14Wae9KeFxfP3FWirsoB7PVtjt0FEFTD4W2P//v1YtGgRjh49ioyMDGzZsgWBgYHS8qysLMycORNxcXG4c+cOevXqha+++grNmzeXanx8fLBv3z699b766qtYv369NJ2dnY0pU6Zg27ZtAIChQ4fiq6++wgsvvGBoy0T0DKX/lYc+i381dhuPFLrphLFbqNDeMB+GJ6LnlMHBKS8vD23btsXf//53jBgxQm+ZEAKBgYFQKpXYunUrateujSVLlqB///5IS0uDre3/fxEEBwfjo48+kqatra311jV69GhcvXoVsbGxAIDx48cjKCgI27dvN7RlInqGdHuaPn+1HZo51DJyN2XlFRRix69J8Pfxhq21ytjt6Dl/IxfTNqQ8t3vriOgxgtPgwYMxePDgcpedO3cOycnJSE1NRevWrQEAy5Ytg4ODA9atW4e3335bqrWxsYGTk1O56zl16hRiY2ORnJyMrl27AgC+++47eHt748yZM2jZsqWhbRPRM9bMoRY8G6qN3UYZWq0WmfWBDq51oFQqjd0OEVUzVXqAv7CwEABgZWUlzTM3N4elpSUOHDigF5yioqKwZs0aODo6YvDgwZgzZw7s7OwAAElJSVCr1VJoAoBu3bpBrVYjMTGx3OBUWFgovT4A5OTkALj/JanVaqtyM4moEsXFxdKfz+NnT9fT89jb8/7eEZkqQz5vVRqcWrVqBVdXV4SHhyMiIgK2trZYsmQJMjMzkZGRIdWNGTMG7u7ucHJyQmpqKsLDw3H8+HHEx8cDADIzM+Hg4FBm/Q4ODsjMzCz3tefPn4+5c+eWmR8XFwcbG5sq2kIiepQruQBggQMHDuDS83ekTqL7vnmeVJf3jsjU5Ofny66t0uCkVCqxefNmjBs3Dvb29jA3N0f//v3LHNoLDg6Wfvb09ETz5s3RqVMnHDt2DB06dAAAKBSKMusXQpQ7HwDCw8MREhIiTefk5MDFxQW+vr6oXbt2VWweEclw8noOFp9IRs+ePdHa+fn77Gm1WsTHx2PAgAHP3aG65/29IzJVuqNUclT5tbgdO3ZESkoKNBoNioqKUL9+fXTt2hWdOnWq8DkdOnSAUqnEuXPn0KFDBzg5OSErK6tM3c2bN+Ho6FjuOlQqFVSqsid6KpXK5+7LkciU6S7xt7CweK4/e8/jd0N1ee+ITI0hn7enNgCmWq1G/fr1ce7cORw5cgTDhg2rsPbkyZPQarVo0KABAMDb2xsajQaHDx+Wag4dOgSNRoPu3bs/rZaJiIiIKmXwHqfc3FycP39emk5PT0dKSgrs7e3RuHFjbNy4EfXr10fjxo1x4sQJTJ06FYGBgfD19QUAXLhwAVFRUfDz80O9evWQlpaG0NBQtG/fHj169AAAeHh4YNCgQQgODkZERASA+8MR+Pv784o6IiIiMhqDg9ORI0fQp08faVp3XtHYsWMRGRmJjIwMhISEICsrCw0aNMCbb76J2bNnS/WWlpbYvXs3vvjiC+Tm5sLFxQVDhgzBnDlzYG5uLtVFRUVhypQpUuAaOnQoli5d+tgbSkRERPSkDA5OPj4+EEJUuHzKlCmYMmVKhctdXFzKjBpeHnt7e6xZs8bQ9oiIiIieGt7kl4iIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKpym/yS0Q1W2HJPZhZXUN6zhmYWdUydjtlFBcX43rxdZy6fUq6qe7zIj0nF2ZW11BYcg+A2tjtEFE5nq9vDSKq9q7nXYKt+1eYdfjRtca0LHaZsVsol607cD2vHTrC0ditEFE5GJyIqEo527oiL30yvni1HZo6PJ97nA4eOIgePXs8d3ucLtzIxdQNKXDu42rsVoioAs/XtwYRVXsqcyuU3msI99ot8WLd5+9wk1arRbpFOjzsPaBUKo3djp7SexqU3rsJlbmVsVshogrw5HAiIiIimRiciIiIiGRicCIiIiKSicGJiIiISCaeHE5EVapAWwIASL2mMXIn5csrKMSRm4DTpWzYWquM3Y6e8zdyjd0CET0CgxMRVakL//vH/73oE0bupDIWWH3+N2M3USFbFb+aiZ5X/HQSUZXybe0EAGjqUAvWSnMjd1PWmQwNQjedwKcjvdCywfM3XIKtygLu9WyN3QYRVYDBiYiqlL2tJV7r0tjYbVSouLgYANC0vi08Gz5/wYmInm88OZyIiIhIJgYnIiIiIpkYnIiIiIhkYnAiIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSyeDgtH//fgQEBMDZ2RkKhQIxMTF6y7OysvC3v/0Nzs7OsLGxwaBBg3Du3Dm9msLCQkyePBn16tWDra0thg4diqtXr+rVZGdnIygoCGq1Gmq1GkFBQbhz547BG0hERERUVQwOTnl5eWjbti2WLl1aZpkQAoGBgfjzzz+xdetW/P7773B1dUX//v2Rl5cn1U2bNg1btmzB+vXrceDAAeTm5sLf3x8lJSVSzejRo5GSkoLY2FjExsYiJSUFQUFBj7mZRERERE/O4FuuDB48GIMHDy532blz55CcnIzU1FS0bt0aALBs2TI4ODhg3bp1ePvtt6HRaLBixQqsXr0a/fv3BwCsWbMGLi4u+OWXXzBw4ECcOnUKsbGxSE5ORteuXQEA3333Hby9vXHmzBm0bNnycbeXiIiI6LFV6TlOhYWFAAArKytpnrm5OSwtLXHgwAEAwNGjR6HVauHr6yvVODs7w9PTE4mJiQCApKQkqNVqKTQBQLdu3aBWq6UaIiIiometSm/y26pVK7i6uiI8PBwRERGwtbXFkiVLkJmZiYyMDABAZmYmLC0tUadOHb3nOjo6IjMzU6pxcHAos34HBwep5mGFhYVScAOAnJwcAIBWq4VWq62S7SOi6k93k9/i4mJ+NxARABj0XVClwUmpVGLz5s0YN24c7O3tYW5ujv79+1d4aO9BQggoFApp+sGfK6p50Pz58zF37twy8+Pi4mBjY2PAVhCRKbuSCwAWSE5OxrVUY3dDRM+D/Px82bVVGpwAoGPHjkhJSYFGo0FRURHq16+Prl27olOnTgAAJycnFBUVITs7W2+v040bN9C9e3epJisrq8y6b968CUdHx3JfNzw8HCEhIdJ0Tk4OXFxc4Ovri9q1a1flJhJRNXb88m3gxBF069YNbRvbG7sdInoO6I5SyVHlwUlHrVYDuH/C+JEjR/Dxxx8DuB+slEol4uPjMWrUKABARkYGUlNTsXDhQgCAt7c3NBoNDh8+jC5dugAADh06BI1GI4Wrh6lUKqhUqjLzlUollEpllW8fEVVPFhYW0p/8biAiAAZ9FxgcnHJzc3H+/HlpOj09HSkpKbC3t0fjxo2xceNG1K9fH40bN8aJEycwdepUBAYGSieDq9VqjBs3DqGhoahbty7s7e0RFhYGLy8v6So7Dw8PDBo0CMHBwYiIiAAAjB8/Hv7+/ryijoiIiIzG4OB05MgR9OnTR5rWHR4bO3YsIiMjkZGRgZCQEGRlZaFBgwZ48803MXv2bL11fPbZZ7CwsMCoUaNQUFCAfv36ITIyEubm5lJNVFQUpkyZIgWuoUOHljt2FBEREdGzohBCCGM38TTk5ORArVZDo9HwHCcikqRcuoXA5cmI+Uc3tHOta+x2iOg5YEhm4L3qiIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKJwYmIiIhIJgYnIiIiIpkYnIiIiIhkYnAiIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYLYzdARPSsKBQK6ef2C+7/KYQwUjdEVB1xjxMR1QgPhiY584mIysPgREQm71HhiOGJiOTioToieq7l5+fj9OnTj/38jh07yqpTKBQ4evToY71Gq1atYGNj81jPJaLqhcGJiJ5rp0+flh1+ntTjvs7Ro0fRoUOHKu6GiJ5HDE5E9Fxr1arVY+8JAgwLQ0+yx4mIagYGJyJ6rtnY2DyzvTnca0REj8KTw4mIiIhkYnAiIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCaDg9P+/fsREBAAZ2dnKBQKxMTE6C3Pzc3FO++8g0aNGsHa2hoeHh5Yvny5Xo2Pjw8UCoXe47XXXtOryc7ORlBQENRqNdRqNYKCgnDnzh2DN5CIiIioqhgcnPLy8tC2bVssXbq03OXTp09HbGws1qxZg1OnTmH69OmYPHkytm7dqlcXHByMjIwM6REREaG3fPTo0UhJSUFsbCxiY2ORkpKCoKAgQ9slIiIiqjIGjxw+ePBgDB48uMLlSUlJGDt2LHx8fAAA48ePR0REBI4cOYJhw4ZJdTY2NnBycip3HadOnUJsbCySk5PRtWtXAMB3330Hb29vnDlzBi1btjS0bSIiIqInVuXnOPXs2RPbtm3DtWvXIITA3r17cfbsWQwcOFCvLioqCvXq1UPr1q0RFhaGu3fvSsuSkpKgVqul0AQA3bp1g1qtRmJiYlW3TERERCRLld+r7ssvv0RwcDAaNWoECwsLmJmZ4fvvv0fPnj2lmjFjxsDd3R1OTk5ITU1FeHg4jh8/jvj4eABAZmYmHBwcyqzbwcEBmZmZ5b5uYWEhCgsLpemcnBwAgFarhVarrcpNJCITxe8KoprJkM/+UwlOycnJ2LZtG1xdXbF//35MmjQJDRo0QP/+/QHcP79Jx9PTE82bN0enTp1w7Ngx6SabCoWizLqFEOXOB4D58+dj7ty5ZebHxcXBxsamKjaNiEzcrl27jN0CERlBfn6+7NoqDU4FBQWYNWsWtmzZgiFDhgAA2rRpg5SUFCxevFgKTg/r0KEDlEolzp07hw4dOsDJyQlZWVll6m7evAlHR8dy1xEeHo6QkBBpOicnBy4uLvD19UXt2rWrYOuIyNT5+fkZuwUiMgLdUSo5qjQ46Q6LmZnpnzplbm6O0tLSCp938uRJaLVaNGjQAADg7e0NjUaDw4cPo0uXLgCAQ4cOQaPRoHv37uWuQ6VSQaVSlZmvVCqhVCofd5OIqAbhdwVRzWTIZ9/g4JSbm4vz589L0+np6UhJSYG9vT0aN26M3r17491334W1tTVcXV2xb98+/Pjjj1iyZAkA4MKFC4iKioKfnx/q1auHtLQ0hIaGon379ujRowcAwMPDA4MGDUJwcLA0TMH48ePh7+/PK+qIiIjIaBRCCGHIE3799Vf06dOnzPyxY8ciMjISmZmZCA8PR1xcHG7fvg1XV1eMHz8e06dPh0KhwJUrV/DGG28gNTUVubm5cHFxwZAhQzBnzhzY29tL67t9+zamTJmCbdu2AQCGDh2KpUuX4oUXXpDVZ05ODtRqNTQaDQ/VEdVgFZ0XWR4Dvw6JyEQYkhkMDk7VBYMTEQEMTkT0aIZkBt6rjoiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKJwYmIiIhIJgYnIiIiIpkYnIiIiIhkYnAiIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKJwYmIiIhIJgYnIjJpSqWySuuIqGZjcCIik2ZmJu9rTm4dEdVs/KYgIpNWWlpapXVEVLMxOBGRSVOpVFVaR0Q1G4MTEZm0wsLCKq0jopqNwYmITJpWq63SOiKq2RiciIiIiGRicCIik2ZtbV2ldURUsxkcnPbv34+AgAA4OztDoVAgJiZGb3lubi7eeecdNGrUCNbW1vDw8MDy5cv1agoLCzF58mTUq1cPtra2GDp0KK5evapXk52djaCgIKjVaqjVagQFBeHOnTsGbyAR1Wz16tWr0joiqtkMDk55eXlo27Ytli5dWu7y6dOnIzY2FmvWrMGpU6cwffp0TJ48GVu3bpVqpk2bhi1btmD9+vU4cOAAcnNz4e/vj5KSEqlm9OjRSElJQWxsLGJjY5GSkoKgoKDH2EQiqsnq1KlTpXVEVMOJJwBAbNmyRW9e69atxUcffaQ3r0OHDuJf//qXEEKIO3fuCKVSKdavXy8tv3btmjAzMxOxsbFCCCHS0tIEAJGcnCzVJCUlCQDi9OnTsnrTaDQCgNBoNI+zaURkIpydnQWARz6cnZ2N3SoRGYkhmaHKz3Hq2bMntm3bhmvXrkEIgb179+Ls2bMYOHAgAODo0aPQarXw9fWVnuPs7AxPT08kJiYCAJKSkqBWq9G1a1epplu3blCr1VINEZEc169fr9I6IqrZLKp6hV9++SWCg4PRqFEjWFhYwMzMDN9//z169uwJAMjMzISlpWWZ3eKOjo7IzMyUahwcHMqs28HBQap5WGFhod44LDk5OQDuX2LMy4yJSKdu3booLi6GhYUFbt26pbeM3xVENZMhn/2nEpySk5Oxbds2uLq6Yv/+/Zg0aRIaNGiA/v37V/g8IQQUCoU0/eDPFdU8aP78+Zg7d26Z+XFxcbCxsXmMLSEiU/RwWHrQrl27nmEnRPS8yM/Pl11bpcGpoKAAs2bNwpYtWzBkyBAAQJs2bZCSkoLFixejf//+cHJyQlFREbKzs/X2Ot24cQPdu3cHADg5OSErK6vM+m/evAlHR8dyXzs8PBwhISHSdE5ODlxcXODr64vatWtX5WYSkYny8/MzdgtEZAS6o1RyVGlw0h0We/gu4+bm5tINNDt27AilUon4+HiMGjUKAJCRkYHU1FQsXLgQAODt7Q2NRoPDhw+jS5cuAIBDhw5Bo9FI4ephKpWq3HtNKZVKKJXKKttGIjJd/K4gqpkM+ewbHJxyc3Nx/vx5aTo9PR0pKSmwt7dH48aN0bt3b7z77ruwtraGq6sr9u3bhx9//BFLliwBAKjVaowbNw6hoaGoW7cu7O3tERYWBi8vL+lQnoeHBwYNGoTg4GBEREQAAMaPHw9/f3+0bNnS0JaJqAZbvHgxwsLCZNURET2KwcHpyJEj6NOnjzStOzw2duxYREZGYv369QgPD8eYMWNw+/ZtuLq64pNPPsHEiROl53z22WewsLDAqFGjUFBQgH79+iEyMhLm5uZSTVRUFKZMmSJdfTd06NAKx44iIqrIX3/9VaV1RFSzKYQQwthNPA05OTlQq9XQaDQ8x4moBgsKCsKaNWseWffGG29g9erVz6AjInreGJIZeK86IjJpjRo1AgDY2trC1dVVb5mbmxtsbW316oiIKlPlwxEQET1PdPegy8vLQ+/evdG+fXtcuHABTZs2RVFRkTQEAe9VR0RyMDgRkUlzcnKSfn5wnKYTJ05UWEdEVBEeqiMik9awYcMqrSOimo3BiYhMmu6elxYWFuWe42RhYaFXR0RUGQYnIjJpurHgSkpK0Lp1awQGBsLLywuBgYF48cUXUVJSoldHRFQZnuNERCbtwoULAABfX99yz3EaOHAg/vvf/0p1RESVYXAiIpPWtGlTAMB///tfODg4YMyYMcjPz4eNjQ2ioqLw3//+V6+OiKgyPFRHRCbt7bffBgAoFAr8+eef8PPzg62tLfz8/PDnn39CoVDo1RERVYZ7nIjIpH3//fcAACEEateuLd1wfMmSJTAzM4Pu5gnff/89pk2bZqw2iaia4B4nIjJpD5679PAdph6c5jlORCQHgxMRmTR3d3cAQNu2bXH37l1MnDgR7dq1w8SJE3H37l20adNGr46IqDI8VEdEJs3LywsAcP78eXh6euLixYsAgJSUFMTGxuLmzZt6dURElWFwIiKT9tdffwG4f686rVaLd999F+7u7khPT8cXX3yBoqIivToiosowOBGRSXNwcABw/5YqmZmZWLRokbTM3NwcDRs2xLVr16Q6IqLK8BwnIqoR3N3dkZubi8WLF8PPzw+LFy9Gbm4uz20iIoNwjxMRmbQbN24AAA4ePIgRI0bA0tISV65cwYEDB7Bnzx4cPHhQr46IqDIMTkRk0ho0aAAAaNWqVbm3XPHw8MCpU6ekOiKiyijEwwObmIicnByo1WpoNBrUrl3b2O0QkZGUlJTAzs4OBQUFsLS0xPDhw2FtbY2CggJER0ejqKgI1tbWuHv3LszNzY3dLhEZgSGZgXuciMikFRUVoaCgAADQv39/TJo0CdeuXUPDhg2Rk5ODXbt2oaCgQApQRESV4cnhRGTS3n33XQDAyy+/jLS0NPTq1Quvv/46evXqhVOnTiEwMFCvjoioMtzjREQm7dy5cwCARYsWwc3NDXv37sXPP/+MwYMHo0+fPrhw4QJiYmKkOiKiynCPExGZtObNmwP4/5v9PmzFihV6dUREleHJ4URk0goKCmBjYwMLCws4Ozvj8uXL0rLGjRvj+vXrKC4uRn5+Ps9xIqqhDMkM3ONERCbN2toanTt3RnFxMa5cuYLRo0djyZIlGD16NK5cuYLi4mJ07tyZoYmIZOEeJyIyaSUlJWjWrBkKCgqQlZVVZrmjoyNsbGxw7tw5DkdAVENxjxMR0f8kJCTg4sWLiImJQX5+PiZOnIh27dph4sSJyM/Px5YtW5Ceno6EhARjt0pE1QCvqiMik5aRkQEA8PT0hLW1Nb788kvs2rULfn5+UCqV8PT01KsjIqoM9zgRkUnT3UolNTUVJSUl2LdvH/bv3499+/ahpKQEqampenVERJUxODjt378fAQEBcHZ2hkKhQExMjN5yhUJR7mPRokVSjY+PT5nlr732mt56srOzERQUBLVaDbVajaCgINy5c+exNpKIaq6XXnoJbm5umDx5Mpo2bYoBAwZgyZIlGDBgAJo2bYopU6bA3d0dL730krFbJaJqwODglJeXh7Zt22Lp0qXlLs/IyNB7rFy5EgqFAiNGjNCrCw4O1quLiIjQWz569GikpKQgNjYWsbGxSElJQVBQkKHtElENZ25ujldeeQVHjhzBvXv3sHz5cqxcuRLLly/HvXv3cOTIEYwcOZInhhORLE90VZ1CocCWLVukWxaUJzAwEHfv3sXu3buleT4+PmjXrh0+//zzcp9z6tQpvPjii0hOTkbXrl0BAMnJyfD29sbp06fRsmXLR/bGq+qICPj/q+rq1auHv/76CxcvXpSWubu7o27durh16xavqiOqwZ6bm/xmZWVh586d+OGHH8osi4qKwpo1a+Do6IjBgwdjzpw5sLOzAwAkJSVBrVZLoQkAunXrBrVajcTExHKDU2FhIQoLC6XpnJwcAIBWq4VWq63qTSOiamLfvn24ePEixo0bV2b08NLSUgwbNgyzZ8/G3r170bt3byN1SUTGZEhOeKrB6YcffoCdnR2GDx+uN3/MmDFwd3eHk5MTUlNTER4ejuPHjyM+Ph4AkJmZCQcHhzLrc3BwQGZmZrmvNX/+fMydO7fM/Li4ONjY2FTB1hBRdbR//34AwOzZs9G5c2dMmjQJjRs3xuXLl7Fp0ybMnj0bAPDzzz8jLy/PmK0SkZHk5+fLrn2qwWnlypUYM2YMrKys9OYHBwdLP3t6eqJ58+bo1KkTjh07hg4dOgC4fxjwYUKIcucDQHh4OEJCQqTpnJwcuLi4wNfXl4fqiGowa2trLFmyBN27d8eePXtQUlKC+Ph4vPPOO5g6dSr69u2LxMREDBw4EH369DF2u0RkBLqjVHI8teCUkJCAM2fOYMOGDY+s7dChA5RKJc6dO4cOHTrAycmp3BF+b968CUdHx3LXoVKpoFKpysxXKpVQKpWGbwARmQTdeUtmZmZQKpUwM7t/TYxSqYS5ubk0bW5uzu8KohrKkM/+UxvHacWKFejYsSPatm37yNqTJ09Cq9VK46h4e3tDo9Hg8OHDUs2hQ4eg0WjQvXv3p9UyEZmgGzduAAAOHjyIwMBAJCcno6CgAMnJyQgMDMTBgwf16oiIKmPwHqfc3FycP39emk5PT0dKSgrs7e3RuHFjAPd3eW3cuBGffvppmedfuHABUVFR8PPzQ7169ZCWlobQ0FC0b98ePXr0AAB4eHhg0KBBCA4OloYpGD9+PPz9/WVdUUdEpKP7D9m8efMQERGBXr16Scvc3d3xySefYNasWRwAk4hkMXiP05EjR9C+fXu0b98eABASEoL27dvjgw8+kGrWr18PIQRef/31Ms+3tLTE7t27MXDgQLRs2RJTpkyBr68vfvnlF71LgaOiouDl5QVfX1/4+vqiTZs2WL169eNsIxHVYLoBMBMTE3H27FnEx8cjJCQE8fHxOHPmDJKSkjgAJhHJ9kTjOD3POI4TEelER0dj5MiR8Pf3x7vvvotr166hYcOGWLRoEXbs2IFNmzaVufqXiGqO52YcJyKi58Hw4cOxadMmhISE6B2qc3NzY2giIoPwJr9EVGNUNJwJEZFcDE5EZPJ0h+q8vLyQkJCAdevWISEhAV5eXhg5ciSio6ON3SIRVRM8x4mITJruXnVeXl6IiYlBSUkJdu3aBT8/P5ibmyMwMBCpqam8Vx1RDWZIZuAeJyIyaQkJCbh48SJmzZoFIQT27duH/fv3Y9++fRBCIDw8HOnp6UhISDB2q0RUDTA4EZFJy8jIAHB/DLmmTZtiwIABWLJkCQYMGICmTZvizz//1KsjIqoMr6ojIpOmG9jyjTfeKLPs0qVL0nwOgElEcjA4EZFJ6969OxQKBYQQsLCwQEhICNzd3ZGeno4lS5aguLgYCoWCt3MiIll4qI6ITNrevXuhuwbG19cXDRs2xMmTJ9GwYUP4+voCAIQQ2Lt3rzHbJKJqglfVEZFJ8/X1RXx8PDp16oSUlBQUFxdLyywsLNCmTRscO3YMAwYMQFxcnBE7JSJj4cjhRET/k52dDeD+fTb9/f3h6+uLs2fPokWLFoiLi8OOHTv06oiIKsNDdURk0jp27AgAsLGxwaZNm+Dh4QFLS0t4eHhg06ZNsLGx0asjIqoM9zgRkUl7+eWXERERgfz8fNSpUwcFBQUAgCVLlsDa2lqafvnll43ZJhFVE9zjREQm7fbt29LPupBU3vSDdUREFWFwIiKT5uDgUKV1RFSz8VAdEZm00tJS6edBgwbB2toa58+fR7NmzVBQUIDY2NgydUREFeEeJyIyab/++qv0s4WFBaZPn45Zs2Zh+vTpsLCwKLeOiKgi3ONERCbt8uXLAIBx48Zh9+7d6NWrl7TM3d0db731FlauXCnVERFVhnuciMikNW7cGACQlpaGs2fPIj4+HiEhIYiPj8eZM2eQlpamV0dEVBkGJyIyaX379gUAJCUlYfjw4VCpVOjcuTNUKhWGDx+O5ORkvToiosrwlitEZNJKSkrQoEED3Lx5U2/cJuD+oJj5+flwcHDA9evXYW5ubsROichYDMkM3ONERCbN3Nwc33zzDQDg4f8n6qaXL1/O0EREsjA4EZHJGz58ODZv3gxHR0e9+Y6Ojti8eTOGDx9upM6IqLrhoToiqjGKiorw1VdfYc+ePejbty8mT54MS0tLY7dFREbGQ3VERA+Jjo5Gy5YtERYWhl27diEsLAwtW7ZEdHS0sVsjomqEwYmITF50dDRGjhwJLy8vJCQkYN26dUhISICXlxdGjhzJ8EREsvFQHRGZtJKSEjRr1gxeXl6IiYlBSUkJdu3aBT8/P5ibmyMwMBCpqak4d+4cTxAnqqF4qI6I6H8SEhJw8eJFzJo1C2Zm+l95ZmZmCA8PR3p6OhISEozUIRFVJwxORGTSMjIyAACenp7lLtfN19UREVXG4OC0f/9+BAQEwNnZGQqFAjExMXrLFQpFuY9FixZJNYWFhZg8eTLq1asHW1tbDB06FFevXtVbT3Z2NoKCgqBWq6FWqxEUFIQ7d+481kYSUc3VoEEDAEBqamq5y3XzdXVERJUxODjl5eWhbdu2WLp0abnLMzIy9B4rV66EQqHAiBEjpJpp06Zhy5YtWL9+PQ4cOIDc3Fz4+/ujpKREqhk9ejRSUlIQGxuL2NhYpKSkICgo6DE2kYhqspdeeglubm6YN28eSktL9ZaVlpZi/vz5cHd3x0svvWSkDomoWhFPAIDYsmVLpTXDhg0Tffv2labv3LkjlEqlWL9+vTTv2rVrwszMTMTGxgohhEhLSxMARHJyslSTlJQkAIjTp0/L6k2j0QgAQqPRGLBFRGSKNm/eLBQKhQgICBD79+8X69atE/v37xcBAQFCoVCIzZs3G7tFIjIiQzKDxdMMZVlZWdi5cyd++OEHad7Ro0eh1Wrh6+srzXN2doanpycSExMxcOBAJCUlQa1Wo2vXrlJNt27doFarkZiYiJYtW5Z5rcLCQhQWFkrTOTk5AACtVgutVvs0No+IqomAgACsX78eM2fORK9evaT57u7uWL9+PQICAvg9QVSDGfL5f6rB6YcffoCdnZ3e7QwyMzNhaWmJOnXq6NU6OjoiMzNTqnFwcCizPgcHB6nmYfPnz8fcuXPLzI+Li4ONjc2TbAYRmQCVSoWFCxciNjYWmZmZcHJywqBBg2BpaYldu3YZuz0iMqL8/HzZtU81OK1cuRJjxoyBlZXVI2uFEFAoFNL0gz9XVPOg8PBwhISESNM5OTlwcXGBr68vx3EiImzZsgUzZ87ExYsXpXl79uzBggUL8PLLLxuvMSIyOt1RKjmeWnBKSEjAmTNnsGHDBr35Tk5OKCoqQnZ2tt5epxs3bqB79+5STVZWVpl13rx5s8xNOnVUKhVUKlWZ+UqlEkql8kk2hYiquejoaLz22mvw9/fH6tWrcfXqVTRq1AgLFy7Ea6+9hk2bNvFGv0Q1mCE54amN47RixQp07NgRbdu21ZvfsWNHKJVKxMfHS/MyMjKQmpoqBSdvb29oNBocPnxYqjl06BA0Go1UQ0QkR0lJCUJDQ+Hv74/Nmzfj3r17+O2333Dv3j1s3rwZ/v7+CAsL07uql4ioIgbvccrNzcX58+el6fT0dKSkpMDe3h6NGzcGcH+X18aNG/Hpp5+Web5arca4ceMQGhqKunXrwt7eHmFhYfDy8kL//v0BAB4eHhg0aBCCg4MREREBABg/fjz8/f3LPTGciKgiupHDJ0yYgBYtWkiH6pYsWQI3NzeMHz8e27dvR0JCAnx8fIzaKxE9/wze43TkyBG0b98e7du3BwCEhISgffv2+OCDD6Sa9evXQwiB119/vdx1fPbZZwgMDMSoUaPQo0cP2NjYYPv27Xr3iYqKioKXlxd8fX3h6+uLNm3aYPXq1Ya2S0Q1nG5E8PDw8HJv8jtr1iy9OiKiyvAmv0Rk0nbv3o3+/fujZ8+e2LdvX5mb/Pbq1QsHDx7EL7/8gn79+hm7XSIyAt7kl4hIpoqu1CUiKg+DExGZtBs3bgAADh48iMDAQCQnJ6OgoADJyckIDAzEwYMH9eqIiCrD4EREJk1389558+bhxIkT6NWrF15//XX06tULqamp+OSTT/TqiIgqw+BERCZNd5PfxMREnD17FvHx8QgJCUF8fDzOnDmDpKQk3uSXiGRjcCIik2Zubo5PP/0UO3bswPDhw5GWloaioiKkpaVh+PDh2LFjBxYvXqx3VS8RUUV4VR0R1QgzZszAZ599huLiYmmehYUFpk+fjoULFxqxMyIyNkMyw1O9Vx0R0fMgOjoaixcvxpAhQ+Dr64uzZ8+iRYsWiIuLw+LFi9GtWzfecoWIZOEeJyIyaSUlJWjWrBm8vLwQExNTZhynwMBApKam4ty5czxcR1RDcRwnIqL/0d1yZdasWTAz0//KMzMzQ3h4ONLT05GQkGCkDomoOmFwIiKTpruViqenZ7nLdfN5yxUikoPBiYhMmm58ptTU1HKX6+ZzHCcikoPBiYhMmm4cp3nz5qG0tFRvWWlpKebPn89xnIhINgYnIjJpD47jVN4tVziOExEZgsMREJHJGz58ODZt2oTQ0FD06tVLmu/u7o5NmzZxKAIiko3DERBRjVFSUoK9e/fi559/xuDBg9GnTx/uaSIiDoBJRFQec3Nz9O7dG3l5eejduzdDExEZjOc4EREREcnE4EREREQkE4MTERERkUw8x4mIaoyioiJ89dVX2LNnD86fP4/JkyfD0tLS2G0RUTXCPU5EVCPMmDEDtra2CAsLw65duxAWFgZbW1vMmDHD2K0RUTXCPU5EZPJmzJiBRYsWwdHREXPnzoVKpUJhYSHmzJmDRYsWAQAWLlxo5C6JqDrgOE5EZNKKiopga2uLunXr4urVqxBCYNeuXfDz84NCoUCjRo1w69Yt5OXl8bAdUQ1lSGbgoToiMmnLli1DcXEx/v3vf8PCQn8nu4WFBT766CMUFxdj2bJlRuqQiKoTBiciMmkXLlwAAPj7+5e7XDdfV0dEVBkGJyIyaU2bNgUA7Nixo9zluvm6OiKiyvAcJyIyaQ+e43Tp0iUkJCRI96p76aWX4OrqynOciGo43quOiOh/LC0tMX36dCxatAg2NjYoLS0FACxZsgRmZmYoLS3Fu+++y9BERLLwUB0Rmbxu3boBAB7ewa6b1i0nInoUg4PT/v37ERAQAGdnZygUCsTExJSpOXXqFIYOHQq1Wg07Ozt069YNly9flpb7+PhAoVDoPV577TW9dWRnZyMoKAhqtRpqtRpBQUG4c+eOwRtIRDVbSUkJQkNDERAQgPz8fCxevBh+fn5YvHgx8vPzERAQgLCwMJSUlBi7VSKqBgwOTnl5eWjbti2WLl1a7vILFy6gZ8+eaNWqFX799VccP34cs2fPhpWVlV5dcHAwMjIypEdERITe8tGjRyMlJQWxsbGIjY1FSkoKgoKCDG2XiGq4hIQEXLx4EbNmzYKVlRWmTJmC8ePHY8qUKbCyskJ4eDjS09ORkJBg7FaJqBow+BynwYMHY/DgwRUuf//99+Hn56c3Cm+TJk3K1NnY2MDJyancdZw6dQqxsbFITk5G165dAQDfffcdvL29cebMGbRs2dLQtomohsrIyAAAeHp6lrtcN19XR0RUmSo9Oby0tBQ7d+7EjBkzMHDgQPz+++9wd3dHeHg4AgMD9WqjoqKwZs0aODo6YvDgwZgzZw7s7OwAAElJSVCr1VJoAu6fg6BWq5GYmFhucCosLERhYaE0nZOTAwDQarXQarVVuZlEVI3Ur18fAJCSkoKuXbtK3we6P1NSUqQ6flcQ1UyGfParNDjduHEDubm5+M9//oN///vfWLBgAWJjYzF8+HDs3bsXvXv3BgCMGTMG7u7ucHJyQmpqKsLDw3H8+HHEx8cDADIzM+Hg4FBm/Q4ODsjMzCz3tefPn4+5c+eWmR8XFwcbG5sq3Eoiqk5KSkrg4OCAkJAQhIeHw8zs/hkK8fHxKC0txfz58+Ho6IicnBzs2rXLyN0SkTHk5+fLrq3yPU4AMGzYMEyfPh0A0K5dOyQmJuKbb76RglNwcLD0HE9PTzRv3hydOnXCsWPH0KFDBwCAQqEos34hRLnzASA8PBwhISHSdE5ODlxcXODr68txnIhquOLiYrz22mtYsWIFQkNDkZWVBUdHR3z66ac4cuQI1q9fj4CAAGO3SURGojtKJUeVBqd69erBwsICL774ot58Dw8PHDhwoMLndejQAUqlEufOnUOHDh3g5OSErKysMnU3b96Eo6NjuetQqVRQqVRl5iuVSiiVSgO3hIhMyahRo2BhYYHQ0FD07dtXmu/u7o5NmzZh+PDhRuyOiIzNkJxQpeM4WVpaonPnzjhz5oze/LNnz8LV1bXC5508eRJarRYNGjQAAHh7e0Oj0eDw4cNSzaFDh6DRaNC9e/eqbJmIaojhw4fj/PnziI+PR0hICOLj43Hu3DmGJiIyiMF7nHJzc3H+/HlpOj09HSkpKbC3t0fjxo3x7rvv4tVXX0WvXr3Qp08fxMbGYvv27fj1118B3B+uICoqCn5+fqhXrx7S0tIQGhqK9u3bo0ePHgDu76EaNGgQgoODpWEKxo8fD39/f15RR0SPzdzcHL1790ZeXh569+4Nc3NzY7dERNWNMNDevXsFgDKPsWPHSjUrVqwQzZo1E1ZWVqJt27YiJiZGWnb58mXRq1cvYW9vLywtLUXTpk3FlClTxK1bt/Re59atW2LMmDHCzs5O2NnZiTFjxojs7GzZfWo0GgFAaDQaQzeRiExYUVGRiImJEUVFRcZuhYieE4ZkBt7kl4hqFK1Wi127dsHPz4/nPxIRAMMyA+9VR0RERCQTgxMRERGRTAxORERERDIxOBERERHJxOBEREREJFOVjhz+PNFdLGjIMOpEZPq0Wi3y8/ORk5PDq+qICMD/ZwU5Aw2YbHC6e/cuAMDFxcXInRAREVF1cPfuXajV6kprTHYcp9LSUly/fh12dnYV3hiYiGoe3Q3Ar1y5wjHeiAjA/T1Nd+/ehbOzM8zMKj+LyWSDExFReTg4LhE9CZ4cTkRERCQTgxMRERGRTAxORFSjqFQqzJkzByqVytitEFE1xHOciIiIiGTiHiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyKqEpmZmZg8eTKaNGkClUoFFxcXBAQEYPfu3QAANzc3KBQKJCcn6z1v2rRp8PHxkabz8vIwc+ZMNGnSBFZWVqhfvz58fHywY8cOXLx4EQqFotLHhx9+WKZOrVajW7du2L59e5m+N2/eDB8fH6jVatSqVQtt2rTBRx99hNu3b8PHx6fS13Jzc6vw/SgsLETr1q0xfvz4MstmzJgBV1dX5OTkIDIyUm+djo6OCAgIwMmTJx/vF0FETxWDExE9sYsXL6Jjx47Ys2cPFi5ciBMnTiA2NhZ9+vTBP//5T6nOysoKM2fOrHRdEydORExMDJYuXYrTp08jNjYWI0aMwK1bt+Di4oKMjAzpERoaitatW+vNCwsLk9b1yy+/ICMjA4cOHUKXLl0wYsQIpKamSsvff/99vPrqq+jcuTN+/vlnpKam4tNPP8Xx48exevVqREdHS+s9fPiw3jozMjLw22+/VbgdKpUKP/74IyIjIxEbGyvNT05OxmeffYbIyEhp5PLatWsjIyMD169fx86dO5GXl4chQ4agqKjIsF8EET19gojoCQ0ePFg0bNhQ5ObmllmWnZ0thBDC1dVVTJ06VVhaWoqdO3dKy6dOnSp69+4tTavVahEZGSnrdefMmSPatm1bZn56eroAIH7//XdpXk5OjgAgvvzySyGEEIcOHRIAxOeff17uunV9V7ZOOT788EPRsGFDkZ2dLQoKCkSrVq3E1KlTpeWrVq0SarVa7znbtm0TAMQff/xh0GsR0dPHPU5E9ERu376N2NhY/POf/4StrW2Z5S+88IL0s5ubGyZOnIjw8HCUlpaWuz4nJyfs2rULd+/erbIetVotvvvuOwCAUqkEAERFRaFWrVqYNGlSuc95sO8n8f7776NBgwaYMmUK/vWvfwEA5s+fX2H9nTt3sHbtWr1eiej5YWHsBoioejt//jyEEGjVqpWs+n/9619YtWoVoqKiEBQUVGb5t99+izFjxqBu3bpo27YtevbsiZEjR6JHjx4G99a9e3eYmZmhoKAApaWlcHNzw6hRowAA586dQ5MmTZ56OLGwsMCPP/6IDh06oLS0FAcOHIC1tbVejUajQa1atSCEQH5+PgBg6NChst9TInp2uMeJiJ6I+N/NBxQKhaz6+vXrIywsDB988EG55/D06tULf/75J3bv3o0RI0bg5MmTeOmll/Dxxx8b3NuGDRvw+++/Y9u2bWjWrBm+//572NvbS33L7flJeXh4YMSIERgwYAA6d+5cZrmdnR1SUlJw9OhRfPPNN2jatCm++eabZ9IbERmGwYmInkjz5s2hUChw6tQp2c8JCQlBQUEBli1bVu5ypVKJl156Ce+99x7i4uLw0Ucf4eOPPzb4ZGkXFxc0b94cQ4YMwffff49XX30VN27cAAC0aNECFy5cgFarNWidj8vCwgIWFuXv5DczM0OzZs3QqlUrTJgwAUFBQXj11VefSV9EZBgGJyJ6Ivb29hg4cCC+/vpr5OXllVl+586dMvNq1aqF2bNn45NPPkFOTs4jX+PFF19EcXEx7t2799h99u7dG56envjkk08AAKNHj0Zubm6F4a28vp+V6dOn4/jx49iyZYvReiCi8jE4EdETW7ZsGUpKStClSxds3rwZ586dw6lTp/Dll1/C29u73OeMHz8earUa69at05vv4+ODiIgIHD16FBcvXsSuXbswa9Ys9OnTR7p8/3GFhoYiIiIC165dQ9euXTFjxgyEhoZixowZSEpKwqVLl7B792688sor+OGHH57otZ5E7dq18fbbb2POnDnSoVAiej4wOBHRE3N3d8exY8fQp08fhIaGwtPTEwMGDMDu3buxfPnycp+jVCrx8ccfl9mLNHDgQPzwww/w9fWFh4cHJk+ejIEDB+Knn3564j79/f3h5uYm7XVasGAB1q5di0OHDmHgwIFo3bo1QkJC0KZNG4wdO/aJX+9JTJ06FadOncLGjRuN2gcR6VMI/neGiIiISBbucSIiIiKSicGJiOgxJSQkoFatWhU+iMj08FAdEdFjKigowLVr1ypc3qxZs2fYDRE9CwxORERERDLxUB0RERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQk0/8BHQS7KwtNfisAAAAASUVORK5CYII=\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,
   "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>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>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>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",
       "    <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",
       "      <td>08/17/2017 12:00:00 AM</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
      "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5133580</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>3394646</td>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3072927</td>\n",
       "      <td>2003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3390096</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>4555084</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>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>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>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",
       "    <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",
       "      <td>08/17/2017 12:00:00 AM</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": {
      "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>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": {
      "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>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": {
      "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>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 value 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",
       "<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",
       "      <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",
    "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: 1,094,861\n",
      "total nulls after dropping nulls in CNSTRCT_YR: 1,073,432\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: 10,247\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('median value of CNSTRCT_YR: {}'.format(median))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>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": [
    "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",
    "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>955513</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>710320</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>52827</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>521520</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>503732</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",
       "955513  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "710320  MULTIPOLYGON (((-73.8247828135812 40.762948467...   \n",
       "52827   MULTIPOLYGON (((-74.25311647359251 40.50309522...   \n",
       "521520  MULTIPOLYGON (((-74.06293248238293 40.61512359...   \n",
       "503732  MULTIPOLYGON (((-74.01121069434295 40.70334382...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "955513  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "710320                   Bowne House  4113403      1661.0   \n",
       "52827               Conference House  5087656      1680.0   \n",
       "521520     Alice Austen House Museum  5042188      1690.0   \n",
       "503732               Fraunces Tavern  1078965      1719.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "955513  01/28/2020 12:00:00 AM  Constructed    550741       24.54       2100   \n",
       "710320  08/22/2017 12:00:00 AM  Constructed    776399       26.12       2100   \n",
       "52827   04/13/2020 12:00:00 AM  Constructed    702698       38.64       2100   \n",
       "521520  04/13/2020 12:00:00 AM   Alteration    806241       22.26       2100   \n",
       "503732  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",
       "955513        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "710320        64.0           0          0  4050130006  4050130006  Photogramm   \n",
       "52827         38.0           0          0  5078570090  5078570090  Photogramm   \n",
       "521520        21.0           0          0  5028300049  5028300049  Photogramm   \n",
       "503732         7.0           0          0  1000070035  1000070035  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "955513  {5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}  \n",
       "710320  {71A760A6-6D2F-48F8-8C93-C8B66071B868}  \n",
       "52827   {CB13BCBA-9DF3-4455-9206-824FE91F2EDE}  \n",
       "521520  {93B1CB7B-BE72-40DB-9B66-01E92DF4AE6E}  \n",
       "503732  {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": {
      "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>955513</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",
       "955513  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "955513  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "955513  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",
       "955513        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "955513  {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 III, we will focus on data visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}