2-data-inspection-cleaning-wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis Using Python: A Beginner’s Guide Featuring NYC Open Data  \n",
    "## Part 2: Data Inspection, Cleaning and Wrangling in Python\n",
    "\n",
    "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 Making a Box Plot\n",
    "       * 4.4 Calculating Interquartile Range (IQR)\n",
    "       * 4.5 Dropping Null Values\n",
    "       * 4.6 Replacing Null Values   \n",
    "   5. Analysis\n",
    "       * 5.1. Lineplot of Year Built\n",
    "       * 5.2. Attempting to Identify the Oldest Building in NYC   \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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in data as CSV in static form\n",
    "\n",
    "Copy this link address shown below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![building_footprints_csv](images/building-footprints-csv.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Web Page Link: \n",
    "https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh\n",
    "\n",
    "### Documentation/Metadata: \n",
    "https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attribute Information:\n",
    "---------------------------------------------\n",
    "| Attribute | Description | Field Type | Sensitive Field (Y/N) | Notes| \n",
    "|------------ | ------------- | -------- | ----------- | ----------|\n",
    "| BASE_BBL | Borough, block, and lot number for the tax lot that the footprint is physically located within. | text | No\n",
    "| MPLUTO_BBL | Borough, block, and lot number to be used for joining the building footprints data to DCP's MapPLUTO data, which aggregates data for condominium buildings using DOF's billing BBL. For non-condominium buildings the billing BBL is the same as the BASE_BBL. For condominium buildings the billing BBL may be the same for multiple buildings on different physical tax lots if they are part of the same billing unit for DOF purposes. | text | No\n",
    "| BIN | Building Identification Number. A number assigned by City Planning and used by Dept. of Buildings to reference information pertaining to an individual building. The first digit is a borough code (1 = Manhattan, 2 = The Bronx, 3 = Brooklyn, 4 = Queens, 5 = Staten Island). The remaining 6 digits are unique for buildings within that borough. In some cases where these 6 digits are all zeros (e.g. 1000000, 2000000, etc.) the BIN is unassigned or unknown. | double | No\n",
    "| NAME | Building name (limited to commonly known names). This field has not been actively maintained since the original creation of this dataset. | text | No\n",
    "| LSTMODDATE | Feature last modified date | date | No\n",
    "| LSTSTATTYPE | Feature last status type (Demolition, Alteration, Geometry, Initialization, Correction, Marked for Construction, Marked For Demolition, Constructed) | text | No\n",
    "| CNSTRCT_YR | The year construction of the building was completed. <br>Originally this column was populated using the Department of Finance Real Property Assessment Database (RPAD). Beginning in 2017 this will be the first year the completed structure is visible in available orthoimagery. Records where this is zero or NULL mean that this information was not available. | double | No\n",
    "| DOITT_ID | Unique identifier assigned by DOITT.  | double | No\n",
    "| HEIGHTROOF | Building Height is calculated as the difference from the building elevation from the Elevation point feature class and the elevation in the interpolated TIN model. This is the height of the roof above the ground elevation, NOT its height above sea level. Records where this is zero or NULL mean that this information was not available. | double | No | The units of measurement are based on the coordinate reference system used, which for all Planimetrics is New York State Plane Coordinates, Long Island East Zone, NAD83, US foot. All measurements in the attribute tables are in US foot. The building height information is updated through our planimetric updates every 4 years. In between those 4-year update cycles, DoITT manually updates building height information from a variety of sources -- (1) Final as-built heights as shown in plan drawings posted on Department of Buildings BIS website (2) EagleView Oblique imagery, direct measurements taken on photogrammetrically controlled aerial imagery (3) Cyclomedia imagery, direct measurements were taken on photogrammetrically controlled terrestrial imagery (for buildings less than 60’ tall, only)\n",
    "| FEAT_CODE | Type of Building. List of values:<br>2100 = Building<br>5100 = Building Under Construction<br>5110 = Garage<br>2110 = Skybridge<br>1001 = Gas Station Canopy<br>1002 = Storage Tank<br>1003 = Placeholder (triangle for permitted bldg)<br>1004 = Auxiliary Structure (non-addressable, not garage)<br>1005 = Temporary Structure (e.g. construction trailer) | long | No\n",
    "| GROUNDELEV | Lowest Elevation at the building ground level. Calculated from LiDAR or photogrammetrically. | double | No | The units of measurement are based on the coordinate reference system used, which for all Planimetrics is New York State Plane Coordinates, Long Island East Zone, NAD83, US foot. All measurements in the attribute tables are in US foot.\n",
    "| GEOM_SOURCE | Indicates the reference source used to add or update the feature. Photogrammetric means the feature was added or updated using photogrammetric stereo-compilation methodology. This is the most accurate update method and should conform to the ASPRS accuracy standards. Other (Manual) means the feature was added or updated by heads-up digitizing from orthophotos or approximated from a plan drawing. These features will be generally be less accurate and may not conform to the ASPRS accuracy standards. | text | No"
   ]
  },
  {
   "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",
    "from matplotlib.ticker import FuncFormatter\n",
    "import seaborn as sns\n",
    "\n",
    "# to plot matplotlib figures inline on the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.13\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "numpy     : 1.23.1\n",
      "pandas    : 1.4.3\n",
      "matplotlib: 3.5.2\n",
      "seaborn   : 0.11.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# printing verions of Python modules and packages with **watermark** \n",
    "# documention for installing watermark: https://github.com/rasbt/watermark\n",
    "\n",
    "%load_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": "markdown",
   "metadata": {},
   "source": [
    "If you receive `HTTPError: HTTP Error 404: Not Found`, you need to replace the CSV link. The path might have been updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>Constructed</td>\n",
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       "                                            the_geom NAME      BIN  \\\n",
       "0  MULTIPOLYGON (((-73.96664570466969 40.62599676...  NaN  3170958   \n",
       "1  MULTIPOLYGON (((-74.16790202462265 40.63936048...  NaN  5028452   \n",
       "2  MULTIPOLYGON (((-74.19510813278613 40.55610681...  NaN  5078368   \n",
       "3  MULTIPOLYGON (((-73.96113466505085 40.57743931...  NaN  3245111   \n",
       "4  MULTIPOLYGON (((-73.75421559146166 40.75591276...  NaN  4161096   \n",
       "\n",
       "   CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  \\\n",
       "0      1925.0  08/22/2017 12:00:00 AM  Constructed     96807   29.749853   \n",
       "1      1965.0  08/22/2017 12:00:00 AM  Constructed    326368   22.630000   \n",
       "2      1970.0  08/22/2017 12:00:00 AM  Constructed    746627   35.760000   \n",
       "3      1928.0  08/22/2017 12:00:00 AM  Constructed    786626   37.500000   \n",
       "4      1950.0  08/22/2017 12:00:00 AM  Constructed    746409   18.015113   \n",
       "\n",
       "   FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  MPLUTO_BBL  \\\n",
       "0       2100        40.0           0          0  3065220021  3065220021   \n",
       "1       2100        39.0           0          0  5012640036  5012640036   \n",
       "2       2100        51.0           0          0  5060190091  5060190091   \n",
       "3       2100         6.0           0          0  3086910048  3086910048   \n",
       "4       2100        93.0           0          0  4075020005  4075020005   \n",
       "\n",
       "   GEOMSOURCE                                GLOBALID  \n",
       "0  Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
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       "4  Photogramm  {525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}  "
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    }
   ],
   "source": [
    "# reading in our data as a pandas dataframe called 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 the data\n",
    "building_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Inspection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Previewing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <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>1083342</th>\n",
       "      <td>MULTIPOLYGON (((-73.75612604606732 40.74547031...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4451730</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>296444</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4076210001</td>\n",
       "      <td>4076210001</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{D12F068F-17A2-4C50-AA45-FCF3822F3E3C}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083343</th>\n",
       "      <td>MULTIPOLYGON (((-73.83440749536311 40.75742362...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4540380</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>04/11/2019 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1114668</td>\n",
       "      <td>185.779728</td>\n",
       "      <td>2100</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4050660001</td>\n",
       "      <td>4050660001</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{E549C8DC-0457-43CB-88F6-136472CE6946}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083344</th>\n",
       "      <td>MULTIPOLYGON (((-74.0000149828318 40.688200830...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3394115</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1114346</td>\n",
       "      <td>81.127098</td>\n",
       "      <td>2100</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3003040018</td>\n",
       "      <td>3003040018</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{A200FA56-7C7C-42F7-AA99-2C9DD5BF9EF6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083345</th>\n",
       "      <td>MULTIPOLYGON (((-73.75575981238948 40.74634860...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4446731</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>471119</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4076210001</td>\n",
       "      <td>4076210001</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{943AE915-DA7C-4B6D-A082-32BB35961261}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083346</th>\n",
       "      <td>MULTIPOLYGON (((-73.7563415969857 40.746859582...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4451963</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>19653</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2100</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4076210001</td>\n",
       "      <td>4076210001</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{BF9FB2DD-DFF3-4172-B3B2-7B5852B306AF}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom NAME      BIN  \\\n",
       "1083342  MULTIPOLYGON (((-73.75612604606732 40.74547031...  NaN  4451730   \n",
       "1083343  MULTIPOLYGON (((-73.83440749536311 40.75742362...  NaN  4540380   \n",
       "1083344  MULTIPOLYGON (((-74.0000149828318 40.688200830...  NaN  3394115   \n",
       "1083345  MULTIPOLYGON (((-73.75575981238948 40.74634860...  NaN  4446731   \n",
       "1083346  MULTIPOLYGON (((-73.7563415969857 40.746859582...  NaN  4451963   \n",
       "\n",
       "         CNSTRCT_YR              LSTMODDATE   LSTSTATYPE  DOITT_ID  \\\n",
       "1083342      1980.0  08/22/2017 12:00:00 AM  Constructed    296444   \n",
       "1083343      2006.0  04/11/2019 12:00:00 AM  Constructed   1114668   \n",
       "1083344      2009.0  08/22/2017 12:00:00 AM  Constructed   1114346   \n",
       "1083345      1980.0  08/22/2017 12:00:00 AM  Constructed    471119   \n",
       "1083346      1980.0  08/22/2017 12:00:00 AM  Constructed     19653   \n",
       "\n",
       "         HEIGHTROOF  FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN    BASE_BBL  \\\n",
       "1083342   28.000000       2100       103.0           0          0  4076210001   \n",
       "1083343  185.779728       2100        22.0           0          0  4050660001   \n",
       "1083344   81.127098       2100        17.0           0          0  3003040018   \n",
       "1083345   28.000000       2100       101.0           0          0  4076210001   \n",
       "1083346   28.000000       2100        96.0           0          0  4076210001   \n",
       "\n",
       "         MPLUTO_BBL  GEOMSOURCE                                GLOBALID  \n",
       "1083342  4076210001  Other (Man  {D12F068F-17A2-4C50-AA45-FCF3822F3E3C}  \n",
       "1083343  4050660001  Photogramm  {E549C8DC-0457-43CB-88F6-136472CE6946}  \n",
       "1083344  3003040018  Photogramm  {A200FA56-7C7C-42F7-AA99-2C9DD5BF9EF6}  \n",
       "1083345  4076210001  Other (Man  {943AE915-DA7C-4B6D-A082-32BB35961261}  \n",
       "1083346  4076210001  Other (Man  {BF9FB2DD-DFF3-4172-B3B2-7B5852B306AF}  "
      ]
     },
     "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": [
       "(1083347, 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 1,083,347 rows and 16 columns.\n"
     ]
    }
   ],
   "source": [
    "# more readable\n",
    "rows, columns = building_df.shape\n",
    "print('This dataset has {:,} rows and {:,} columns.'.format(rows, 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": [
       "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing the columns\n",
    "building_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing the data types of our columns\n",
    "building_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1083347 entries, 0 to 1083346\n",
      "Data columns (total 16 columns):\n",
      " #   Column      Non-Null Count    Dtype  \n",
      "---  ------      --------------    -----  \n",
      " 0   the_geom    1083347 non-null  object \n",
      " 1   NAME        2254 non-null     object \n",
      " 2   BIN         1083347 non-null  int64  \n",
      " 3   CNSTRCT_YR  1072987 non-null  float64\n",
      " 4   LSTMODDATE  1083347 non-null  object \n",
      " 5   LSTSTATYPE  1083025 non-null  object \n",
      " 6   DOITT_ID    1083347 non-null  int64  \n",
      " 7   HEIGHTROOF  1080700 non-null  float64\n",
      " 8   FEAT_CODE   1083347 non-null  int64  \n",
      " 9   GROUNDELEV  1082783 non-null  float64\n",
      " 10  SHAPE_AREA  1083347 non-null  int64  \n",
      " 11  SHAPE_LEN   1083347 non-null  int64  \n",
      " 12  BASE_BBL    1083347 non-null  int64  \n",
      " 13  MPLUTO_BBL  1083347 non-null  int64  \n",
      " 14  GEOMSOURCE  1083029 non-null  object \n",
      " 15  GLOBALID    1083347 non-null  object \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 132.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# printing the column names, non-null counts, and datatypes of our columns\n",
    "building_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1083347, step=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing index type\n",
    "building_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our unique id is truly unique: True\n"
     ]
    }
   ],
   "source": [
    "# printing True/False if column is unique on our unique key (DOITT_ID)\n",
    "unique = building_df['DOITT_ID'].is_unique\n",
    "print('our unique id is truly unique: {}'.format(unique))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Calculating Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>1.083347e+06</td>\n",
       "      <td>1.072987e+06</td>\n",
       "      <td>1.083347e+06</td>\n",
       "      <td>1.080700e+06</td>\n",
       "      <td>1.083347e+06</td>\n",
       "      <td>1.082783e+06</td>\n",
       "      <td>1083347.0</td>\n",
       "      <td>1083347.0</td>\n",
       "      <td>1.083347e+06</td>\n",
       "      <td>1.083347e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.712971e+06</td>\n",
       "      <td>1.939623e+03</td>\n",
       "      <td>5.948542e+05</td>\n",
       "      <td>2.769527e+01</td>\n",
       "      <td>2.693444e+03</td>\n",
       "      <td>5.512157e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.564182e+09</td>\n",
       "      <td>3.564182e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.026635e+06</td>\n",
       "      <td>2.946186e+01</td>\n",
       "      <td>3.621961e+05</td>\n",
       "      <td>2.176582e+01</td>\n",
       "      <td>1.198560e+03</td>\n",
       "      <td>4.154859e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.858398e+08</td>\n",
       "      <td>9.858398e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000002e+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>3.129698e+06</td>\n",
       "      <td>1.920000e+03</td>\n",
       "      <td>2.880715e+05</td>\n",
       "      <td>1.852000e+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>4.067896e+06</td>\n",
       "      <td>1.930000e+03</td>\n",
       "      <td>5.763400e+05</td>\n",
       "      <td>2.612286e+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>4.019560e+09</td>\n",
       "      <td>4.019560e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.483418e+06</td>\n",
       "      <td>1.955000e+03</td>\n",
       "      <td>8.664315e+05</td>\n",
       "      <td>3.113075e+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>1.301889e+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  1.083347e+06  1.072987e+06  1.083347e+06  1.080700e+06  1.083347e+06   \n",
       "mean   3.712971e+06  1.939623e+03  5.948542e+05  2.769527e+01  2.693444e+03   \n",
       "std    1.026635e+06  2.946186e+01  3.621961e+05  2.176582e+01  1.198560e+03   \n",
       "min    1.000002e+06  1.652000e+03  1.000000e+00  0.000000e+00  1.000000e+03   \n",
       "25%    3.129698e+06  1.920000e+03  2.880715e+05  1.852000e+01  2.100000e+03   \n",
       "50%    4.067896e+06  1.930000e+03  5.763400e+05  2.612286e+01  2.100000e+03   \n",
       "75%    4.483418e+06  1.955000e+03  8.664315e+05  3.113075e+01  2.100000e+03   \n",
       "max    5.799523e+06  2.024000e+03  1.301889e+06  1.550000e+03  5.110000e+03   \n",
       "\n",
       "         GROUNDELEV  SHAPE_AREA  SHAPE_LEN      BASE_BBL    MPLUTO_BBL  \n",
       "count  1.082783e+06   1083347.0  1083347.0  1.083347e+06  1.083347e+06  \n",
       "mean   5.512157e+01         0.0        0.0  3.564182e+09  3.564182e+09  \n",
       "std    4.154859e+01         0.0        0.0  9.858398e+08  9.858398e+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  4.019560e+09  4.019560e+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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing descriptive statistics of our numeric columns\n",
    "building_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>1083347</td>\n",
       "      <td>2254</td>\n",
       "      <td>1083347</td>\n",
       "      <td>1083025</td>\n",
       "      <td>1083029</td>\n",
       "      <td>1083347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1083347</td>\n",
       "      <td>1896</td>\n",
       "      <td>1781</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1083347</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>800551</td>\n",
       "      <td>1080561</td>\n",
       "      <td>1012022</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 the_geom  \\\n",
       "count                                             1083347   \n",
       "unique                                            1083347   \n",
       "top     MULTIPOLYGON (((-73.96664570466969 40.62599676...   \n",
       "freq                                                    1   \n",
       "\n",
       "                           NAME              LSTMODDATE   LSTSTATYPE  \\\n",
       "count                      2254                 1083347      1083025   \n",
       "unique                     1896                    1781           13   \n",
       "top     Adler Place Town Houses  08/22/2017 12:00:00 AM  Constructed   \n",
       "freq                         52                  800551      1080561   \n",
       "\n",
       "        GEOMSOURCE                                GLOBALID  \n",
       "count      1083029                                 1083347  \n",
       "unique           3                                 1083347  \n",
       "top     Photogramm  {31298F86-3088-4F53-B3DB-71A9EFA6FA1F}  \n",
       "freq       1012022                                       1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing descriptive statistics of our non-numeric columns\n",
    "building_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>mode</th>\n",
       "      <td>1920.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CNSTRCT_YR\n",
       "mode      1920.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning the mode of CNSTRCT_YR\n",
    "(building_df\n",
    " .loc[:, ['CNSTRCT_YR']]\n",
    " .mode(numeric_only=True)\n",
    " .rename(index={0: \"mode\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "10                                        5\n",
       "20                                        5\n",
       "Educational Park                          5\n",
       "Park Maintenance                          4\n",
       "21                                        4\n",
       "22                                        4\n",
       "Thomas Greene Playground-Building         4\n",
       "5                                         4\n",
       "4                                         4\n",
       "Lawrence C Thompson Mem Park-Building     4\n",
       "Name: NAME, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the NAME column - top 20\n",
    "(building_df['NAME']\n",
    " .value_counts()\n",
    " .head(20)\n",
    ")"
   ]
  },
  {
   "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>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>0.290921</td>\n",
       "      <td>0.156298</td>\n",
       "      <td>-0.357479</td>\n",
       "      <td>0.190892</td>\n",
       "      <td>0.086993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988683</td>\n",
       "      <td>0.988683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <td>0.290921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203557</td>\n",
       "      <td>0.025437</td>\n",
       "      <td>-0.132578</td>\n",
       "      <td>-0.002913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.284805</td>\n",
       "      <td>0.284806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOITT_ID</th>\n",
       "      <td>0.156298</td>\n",
       "      <td>0.203557</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.160192</td>\n",
       "      <td>0.539663</td>\n",
       "      <td>-0.016972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.075930</td>\n",
       "      <td>0.075930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <td>-0.357479</td>\n",
       "      <td>0.025437</td>\n",
       "      <td>-0.160192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.363283</td>\n",
       "      <td>-0.002517</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.328044</td>\n",
       "      <td>-0.328044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <td>0.190892</td>\n",
       "      <td>-0.132578</td>\n",
       "      <td>0.539663</td>\n",
       "      <td>-0.363283</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.090359</td>\n",
       "      <td>0.090359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <td>0.086993</td>\n",
       "      <td>-0.002913</td>\n",
       "      <td>-0.016972</td>\n",
       "      <td>-0.002517</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.106117</td>\n",
       "      <td>0.106117</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>0.988683</td>\n",
       "      <td>0.284805</td>\n",
       "      <td>0.075930</td>\n",
       "      <td>-0.328044</td>\n",
       "      <td>0.090359</td>\n",
       "      <td>0.106117</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>0.988683</td>\n",
       "      <td>0.284806</td>\n",
       "      <td>0.075930</td>\n",
       "      <td>-0.328044</td>\n",
       "      <td>0.090359</td>\n",
       "      <td>0.106117</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    0.290921  0.156298   -0.357479   0.190892    0.086993   \n",
       "CNSTRCT_YR  0.290921    1.000000  0.203557    0.025437  -0.132578   -0.002913   \n",
       "DOITT_ID    0.156298    0.203557  1.000000   -0.160192   0.539663   -0.016972   \n",
       "HEIGHTROOF -0.357479    0.025437 -0.160192    1.000000  -0.363283   -0.002517   \n",
       "FEAT_CODE   0.190892   -0.132578  0.539663   -0.363283   1.000000    0.001660   \n",
       "GROUNDELEV  0.086993   -0.002913 -0.016972   -0.002517   0.001660    1.000000   \n",
       "SHAPE_AREA       NaN         NaN       NaN         NaN        NaN         NaN   \n",
       "SHAPE_LEN        NaN         NaN       NaN         NaN        NaN         NaN   \n",
       "BASE_BBL    0.988683    0.284805  0.075930   -0.328044   0.090359    0.106117   \n",
       "MPLUTO_BBL  0.988683    0.284806  0.075930   -0.328044   0.090359    0.106117   \n",
       "\n",
       "            SHAPE_AREA  SHAPE_LEN  BASE_BBL  MPLUTO_BBL  \n",
       "BIN                NaN        NaN  0.988683    0.988683  \n",
       "CNSTRCT_YR         NaN        NaN  0.284805    0.284806  \n",
       "DOITT_ID           NaN        NaN  0.075930    0.075930  \n",
       "HEIGHTROOF         NaN        NaN -0.328044   -0.328044  \n",
       "FEAT_CODE          NaN        NaN  0.090359    0.090359  \n",
       "GROUNDELEV         NaN        NaN  0.106117    0.106117  \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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute pairwise correlation of columns, excluding NA/null values\n",
    "building_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation in the form of heat map\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "sns.heatmap(\n",
    "    building_df.corr(), \n",
    "    annot=True, \n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation in the form of heat map\n",
    "fig, ax = plt.subplots(figsize=(10,6))\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(), \n",
    "    annot=True, \n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Identifying Null/NA Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "the_geom            0\n",
       "NAME          1081093\n",
       "BIN                 0\n",
       "CNSTRCT_YR      10360\n",
       "LSTMODDATE          0\n",
       "LSTSTATYPE        322\n",
       "DOITT_ID            0\n",
       "HEIGHTROOF       2647\n",
       "FEAT_CODE           0\n",
       "GROUNDELEV        564\n",
       "SHAPE_AREA          0\n",
       "SHAPE_LEN           0\n",
       "BASE_BBL            0\n",
       "MPLUTO_BBL          0\n",
       "GEOMSOURCE        318\n",
       "GLOBALID            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nulls per column\n",
    "(building_df\n",
    " .isnull()\n",
    " .sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total nulls in dataframe: 1,095,304\n"
     ]
    }
   ],
   "source": [
    "# total nulls in dataframe\n",
    "null_count = (\n",
    "    building_df\n",
    "    .isnull()\n",
    "    .sum()\n",
    "    .sum()\n",
    ")\n",
    "\n",
    "print('total nulls in dataframe: {:,}'.format(null_count))"
   ]
  },
  {
   "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>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>1083342</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>1083343</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>1083344</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>1083345</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>1083346</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>1083347 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",
       "1083342     False  True  False       False       False       False     False   \n",
       "1083343     False  True  False       False       False       False     False   \n",
       "1083344     False  True  False       False       False       False     False   \n",
       "1083345     False  True  False       False       False       False     False   \n",
       "1083346     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",
       "1083342       False      False       False       False      False     False   \n",
       "1083343       False      False       False       False      False     False   \n",
       "1083344       False      False       False       False      False     False   \n",
       "1083345       False      False       False       False      False     False   \n",
       "1083346       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",
       "1083342       False       False     False  \n",
       "1083343       False       False     False  \n",
       "1083344       False       False     False  \n",
       "1083345       False       False     False  \n",
       "1083346       False       False     False  \n",
       "\n",
       "[1083347 rows x 16 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return a boolean same-sized object of nulls\n",
    "building_df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>99.791941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <td>0.956296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <td>0.244335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <td>0.052061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <td>0.029723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <td>0.029353</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           99.791941\n",
       "CNSTRCT_YR      0.956296\n",
       "HEIGHTROOF      0.244335\n",
       "GROUNDELEV      0.052061\n",
       "LSTSTATYPE      0.029723\n",
       "GEOMSOURCE      0.029353\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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating a percentage of the number of nulls to total number of records of each column\n",
    "missing_data = (building_df.isnull().sum() / len(building_df)) * 100\n",
    "\n",
    "# creating a dataframe\n",
    "missing_data = pd.DataFrame(missing_data, columns=['perc_missing']) \n",
    "missing_data = missing_data.sort_values(by='perc_missing', ascending=False)\n",
    "\n",
    "missing_data"
   ]
  },
  {
   "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": [
    "## Attribute Information for CNSTRCT_YR:\n",
    "---------------------------------------------\n",
    "| Attribute | Description | Field Type | Sensitive Field (Y/N) | Notes| \n",
    "|------------ | ------------- | -------- | ----------- | ----------|\n",
    "| CNSTRCT_YR | The year construction of the building was completed. <br>Originally this column was populated using the Department of Finance Real Property Assessment Database (RPAD). Beginning in 2017 this will be the first year the completed structure is visible in available orthoimagery. Records where this is zero or NULL mean that this information was not available. | double | No"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Previewing Column Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "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 dataframe to series\n",
    "print(type(building_df[\"CNSTRCT_YR\"]))\n",
    "print(type(building_df[[\"CNSTRCT_YR\"]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "1083342    1980.0\n",
       "1083343    2006.0\n",
       "1083344    2009.0\n",
       "1083345    1980.0\n",
       "1083346    1980.0\n",
       "Name: CNSTRCT_YR, Length: 1083347, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning a series of the 'CNSTRCT_YR' column\n",
    "building_df[\"CNSTRCT_YR\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1083342</th>\n",
       "      <td>1980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083343</th>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083344</th>\n",
       "      <td>2009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083345</th>\n",
       "      <td>1980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083346</th>\n",
       "      <td>1980.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1083347 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",
       "1083342      1980.0\n",
       "1083343      2006.0\n",
       "1083344      2009.0\n",
       "1083345      1980.0\n",
       "1083346      1980.0\n",
       "\n",
       "[1083347 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning a dataframe of the 'CNSTRCT_YR' column double brackets\n",
    "building_df[[\"CNSTRCT_YR\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <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>956312</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>710956</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>431254</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>52887</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>522025</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",
       "956312  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "710956  MULTIPOLYGON (((-73.8247828135812 40.762948467...   \n",
       "431254  MULTIPOLYGON (((-73.82449114078894 40.76279389...   \n",
       "52887   MULTIPOLYGON (((-74.25311647359251 40.50309522...   \n",
       "522025  MULTIPOLYGON (((-74.06293248238293 40.61512359...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "956312  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "710956                   Bowne House  4113403      1661.0   \n",
       "431254                           NaN  4610542      1661.0   \n",
       "52887               Conference House  5087656      1680.0   \n",
       "522025     Alice Austen House Museum  5042188      1690.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "956312  01/28/2020 12:00:00 AM  Constructed    550741   24.540000       2100   \n",
       "710956  08/22/2017 12:00:00 AM  Constructed    776399   26.120000       2100   \n",
       "431254  08/17/2017 12:00:00 AM  Constructed   1190137   14.846959       5110   \n",
       "52887   04/13/2020 12:00:00 AM  Constructed    702698   38.640000       2100   \n",
       "522025  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",
       "956312        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "710956        64.0           0          0  4050130006  4050130006  Photogramm   \n",
       "431254        63.0           0          0  4050130006  4050130006  Photogramm   \n",
       "52887         38.0           0          0  5078570090  5078570090  Photogramm   \n",
       "522025        21.0           0          0  5028300049  5028300049  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "956312  {5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}  \n",
       "710956  {71A760A6-6D2F-48F8-8C93-C8B66071B868}  \n",
       "431254  {36678A28-E8EE-45E8-BAA2-9A3F5118E176}  \n",
       "52887   {CB13BCBA-9DF3-4455-9206-824FE91F2EDE}  \n",
       "522025  {93B1CB7B-BE72-40DB-9B66-01E92DF4AE6E}  "
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>1082320</th>\n",
       "      <td>MULTIPOLYGON (((-73.9842217154888 40.641315363...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3418384</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>04/01/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1301776</td>\n",
       "      <td>135.0</td>\n",
       "      <td>2100</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3053010020</td>\n",
       "      <td>3053010020</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{CA9B48A7-3478-42C4-A3AA-206B9F7A4D07}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079124</th>\n",
       "      <td>MULTIPOLYGON (((-73.81079758791336 40.67271521...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4620907</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>02/14/2024 12:00:00 AM</td>\n",
       "      <td>Investigate Construction</td>\n",
       "      <td>1301390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5100</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4117490003</td>\n",
       "      <td>4117490003</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{BA629582-2A56-4119-915F-56D009E51CFC}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081997</th>\n",
       "      <td>MULTIPOLYGON (((-73.98447675226453 40.58450295...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3428762</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>03/12/2024 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1301636</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2100</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3069530047</td>\n",
       "      <td>3069530047</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{51B3191D-13B3-44DC-98E7-EEA1BFBC9B6C}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081695</th>\n",
       "      <td>MULTIPOLYGON (((-74.18798789974564 40.59222443...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5175635</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>03/13/2024 12:00:00 AM</td>\n",
       "      <td>Marked for Construction</td>\n",
       "      <td>1301645</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5100</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5026290001</td>\n",
       "      <td>5026290001</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{13368DC0-1DBA-4049-B233-D8B68B54E8CE}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082243</th>\n",
       "      <td>MULTIPOLYGON (((-73.80418130510829 40.78316567...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4623685</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>03/26/2024 12:00:00 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1301747</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2100</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4047460026</td>\n",
       "      <td>4047460026</td>\n",
       "      <td>Other (Man</td>\n",
       "      <td>{F338DB3E-DE6C-4488-86E1-CBEA1C9E86DB}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  the_geom NAME      BIN  \\\n",
       "1082320  MULTIPOLYGON (((-73.9842217154888 40.641315363...  NaN  3418384   \n",
       "1079124  MULTIPOLYGON (((-73.81079758791336 40.67271521...  NaN  4620907   \n",
       "1081997  MULTIPOLYGON (((-73.98447675226453 40.58450295...  NaN  3428762   \n",
       "1081695  MULTIPOLYGON (((-74.18798789974564 40.59222443...  NaN  5175635   \n",
       "1082243  MULTIPOLYGON (((-73.80418130510829 40.78316567...  NaN  4623685   \n",
       "\n",
       "         CNSTRCT_YR              LSTMODDATE                LSTSTATYPE  \\\n",
       "1082320      2024.0  04/01/2024 12:00:00 AM               Constructed   \n",
       "1079124      2024.0  02/14/2024 12:00:00 AM  Investigate Construction   \n",
       "1081997      2024.0  03/12/2024 12:00:00 AM               Constructed   \n",
       "1081695      2024.0  03/13/2024 12:00:00 AM   Marked for Construction   \n",
       "1082243      2024.0  03/26/2024 12:00:00 AM                       NaN   \n",
       "\n",
       "         DOITT_ID  HEIGHTROOF  FEAT_CODE  GROUNDELEV  SHAPE_AREA  SHAPE_LEN  \\\n",
       "1082320   1301776       135.0       2100        58.0           0          0   \n",
       "1079124   1301390         NaN       5100        29.0           0          0   \n",
       "1081997   1301636        40.0       2100         8.0           0          0   \n",
       "1081695   1301645        45.0       5100        29.0           0          0   \n",
       "1082243   1301747        30.0       2100        61.0           0          0   \n",
       "\n",
       "           BASE_BBL  MPLUTO_BBL  GEOMSOURCE  \\\n",
       "1082320  3053010020  3053010020  Other (Man   \n",
       "1079124  4117490003  4117490003  Other (Man   \n",
       "1081997  3069530047  3069530047  Other (Man   \n",
       "1081695  5026290001  5026290001  Other (Man   \n",
       "1082243  4047460026  4047460026  Other (Man   \n",
       "\n",
       "                                       GLOBALID  \n",
       "1082320  {CA9B48A7-3478-42C4-A3AA-206B9F7A4D07}  \n",
       "1079124  {BA629582-2A56-4119-915F-56D009E51CFC}  \n",
       "1081997  {51B3191D-13B3-44DC-98E7-EEA1BFBC9B6C}  \n",
       "1081695  {13368DC0-1DBA-4049-B233-D8B68B54E8CE}  \n",
       "1082243  {F338DB3E-DE6C-4488-86E1-CBEA1C9E86DB}  "
      ]
     },
     "execution_count": 31,
     "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": 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>statistic</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>1.072987e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>1.939623e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>std</td>\n",
       "      <td>2.946186e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>1.652000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25%</td>\n",
       "      <td>1.920000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50%</td>\n",
       "      <td>1.930000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75%</td>\n",
       "      <td>1.955000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max</td>\n",
       "      <td>2.024000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  statistic    CNSTRCT_YR\n",
       "0     count  1.072987e+06\n",
       "1      mean  1.939623e+03\n",
       "2       std  2.946186e+01\n",
       "3       min  1.652000e+03\n",
       "4       25%  1.920000e+03\n",
       "5       50%  1.930000e+03\n",
       "6       75%  1.955000e+03\n",
       "7       max  2.024000e+03"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Descriptive statistics include those that summarize the central tendency, \n",
    "dispersion and shape of a dataset's distribution, excluding ``NaN`` values.\n",
    "\n",
    "Reset index for readability.\n",
    "\"\"\"\n",
    "\n",
    "(building_df['CNSTRCT_YR']\n",
    " .describe()\n",
    " .reset_index()\n",
    " .rename(columns={'index': \"statistic\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# detecting any nulls in our 'CNSTRCT_YR' column\n",
    "(building_df['CNSTRCT_YR']\n",
    " .isnull()\n",
    " .values\n",
    " .any()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10360"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing all the null values in our 'CNSTRCT_YR' column\n",
    "(building_df['CNSTRCT_YR']\n",
    " .isnull()\n",
    " .sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1072987"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing all the not null values in our 'CNSTRCT_YR' column\n",
    "(building_df['CNSTRCT_YR']\n",
    " .notnull()\n",
    " .sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percent nulls: 0.9563%\n"
     ]
    }
   ],
   "source": [
    "# printing the percent total of all the null values in our 'CNSTRCT_YR' column.\n",
    "nulls_sum = building_df['CNSTRCT_YR'].isnull().sum() \n",
    "nulls_perc = round(nulls_sum / len(building_df) * 100, 4)\n",
    "\n",
    "print('percent nulls: {}%'.format(nulls_perc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percent not nulls: 99.0437%\n"
     ]
    }
   ],
   "source": [
    "# printing the percent total of all the not null values in our 'CNSTRCT_YR' column.\n",
    "nonnulls_sum = building_df['CNSTRCT_YR'].notnull().sum() \n",
    "nonnulls_perc = round(nonnulls_sum / len(building_df) * 100, 4)\n",
    "\n",
    "print('percent not nulls: {}%'.format(nonnulls_perc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Exploring Distribution of Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a histogram of our 'CNSTRCT_YR' column with 25 bins\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "building_df['CNSTRCT_YR'].hist(ax=ax, bins=25)\n",
    "\n",
    "# adding labels\n",
    "plt.xlabel('Construction Year', fontsize=15)\n",
    "plt.ylabel('Counts', fontsize=15) \n",
    "plt.title('Histogram of Construction Year Values', fontsize=15) \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 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a histogram of our 'CNSTRCT_YR' column with 50 bins\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "building_df['CNSTRCT_YR'].hist(ax=ax, bins=50)\n",
    "\n",
    "plt.xlabel('Construction Year', fontsize=15)\n",
    "plt.ylabel('Counts', fontsize=15)\n",
    "plt.title('Histogram of Construction Year Values', fontsize=15)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1920.0    130103\n",
       "1930.0    112506\n",
       "1925.0    109007\n",
       "1950.0     62639\n",
       "1910.0     52233\n",
       "           ...  \n",
       "1747.0         1\n",
       "1725.0         1\n",
       "1797.0         1\n",
       "1789.0         1\n",
       "1735.0         1\n",
       "Name: CNSTRCT_YR, Length: 251, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prints the counts of values in our 'CNSTRCT_YR' column in descending order\n",
    "building_df['CNSTRCT_YR'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1652.0       1\n",
       "1661.0       2\n",
       "1680.0       1\n",
       "1690.0       1\n",
       "1694.0       1\n",
       "          ... \n",
       "2020.0    1594\n",
       "2021.0    1455\n",
       "2022.0    1536\n",
       "2023.0     854\n",
       "2024.0      45\n",
       "Name: CNSTRCT_YR, Length: 251, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counts of values sorted by the index (in this case, year) ascending order\n",
    "building_df['CNSTRCT_YR'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1652.0    1\n",
       "1661.0    2\n",
       "1680.0    1\n",
       "1690.0    1\n",
       "1694.0    1\n",
       "1706.0    1\n",
       "1719.0    1\n",
       "1720.0    1\n",
       "1722.0    1\n",
       "1725.0    1\n",
       "Name: CNSTRCT_YR, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the first ten rows sorted by the index ascending order\n",
    "(building_df['CNSTRCT_YR']\n",
    " .value_counts()\n",
    " .sort_index(ascending=True)\n",
    " .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2024.0      45\n",
       "2023.0     854\n",
       "2022.0    1536\n",
       "2021.0    1455\n",
       "2020.0    1594\n",
       "2019.0    1331\n",
       "2018.0    2350\n",
       "2017.0    1962\n",
       "2016.0    1866\n",
       "2015.0    1090\n",
       "Name: CNSTRCT_YR, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the first ten rows sorted by the index descending order\n",
    "(building_df['CNSTRCT_YR']\n",
    " .value_counts()\n",
    " .sort_index(ascending=False)\n",
    " .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Making a Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a box plot from our 'CNSTRCT_YR' column\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "building_df.boxplot(column=['CNSTRCT_YR'], ax=ax)\n",
    "\n",
    "plt.title('Box Plot of Construction Year Value', fontsize=13)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 Calculating Interquartile Range (IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1920.0\n",
      "1955.0\n"
     ]
    }
   ],
   "source": [
    "# printing the 25% and 75% quantiles of our 'CNSTRCT_YR' column\n",
    "print(building_df['CNSTRCT_YR'].quantile(0.25))\n",
    "print(building_df['CNSTRCT_YR'].quantile(0.75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# saving the 25% (i.e. Q1) and 75% (i.e. Q3) quantiles of our 'CNSTRCT_YR' column\n",
    "Q1 = building_df['CNSTRCT_YR'].quantile(0.25)\n",
    "Q3 = building_df['CNSTRCT_YR'].quantile(0.75)\n",
    "\n",
    "# Computing the interquartile range (IQR)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# printing the IQR of our 'CNSTRCT_YR' column\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1867.5\n",
      "2007.5\n"
     ]
    }
   ],
   "source": [
    "# printing the lower 1.5*IQR whisker = Q1 - 1.5 * IQR\n",
    "print((Q1 - 1.5 * IQR))\n",
    "\n",
    "# printing the upper 1.5*IQR whisker = Q1 - 1.5 * IQR\n",
    "print((Q3 + 1.5 * IQR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 Dropping Null Values for CNSTRCT_YR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1072987\n"
     ]
    }
   ],
   "source": [
    "# dropping all records where 'CNSTRCT_YR' as nan\n",
    "building_df_dropna = building_df.dropna(subset=['CNSTRCT_YR'])\n",
    "\n",
    "# printing number of records\n",
    "print(building_df_dropna.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a histogram of our 'CNSTRCT_YR' column with 10 bins\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "building_df_dropna['CNSTRCT_YR'].hist(bins=10, ax=ax)\n",
    "\n",
    "plt.xlabel('Construction Year', fontsize=12)\n",
    "plt.ylabel('Counts', fontsize=12)\n",
    "plt.title('Histogram of Construction Year Values Excluding Nulls', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 Filling In Null/NA Values With Median for CNSTRCT_YR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of nulls for CNSTRCT_YR: 10,360\n"
     ]
    }
   ],
   "source": [
    "# saving a new dataframe to fill nans with the median value of 'CNSTRCT_YR'\n",
    "building_df_fillna = building_df.copy()\n",
    "\n",
    "counts = building_df_fillna['CNSTRCT_YR'].isnull().sum()\n",
    "print('number of nulls for CNSTRCT_YR: {:,}'.format(counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
       "      <th>HEIGHTROOF</th>\n",
       "      <th>FEAT_CODE</th>\n",
       "      <th>GROUNDELEV</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>BASE_BBL</th>\n",
       "      <th>MPLUTO_BBL</th>\n",
       "      <th>GEOMSOURCE</th>\n",
       "      <th>GLOBALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MULTIPOLYGON (((-73.96664570466969 40.62599676...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3170958</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>96807</td>\n",
       "      <td>29.749853</td>\n",
       "      <td>2100</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>3065220021</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{31298F86-3088-4F53-B3DB-71A9EFA6FA1F}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MULTIPOLYGON (((-74.16790202462265 40.63936048...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5028452</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>326368</td>\n",
       "      <td>22.630000</td>\n",
       "      <td>2100</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>5012640036</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F5F8CDA5-69E2-46F8-8F69-BA95C025B520}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MULTIPOLYGON (((-74.19510813278613 40.55610681...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5078368</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746627</td>\n",
       "      <td>35.760000</td>\n",
       "      <td>2100</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>5060190091</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{9F644794-F72C-4582-9E5E-B337E2B97068}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MULTIPOLYGON (((-73.96113466505085 40.57743931...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3245111</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>786626</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>3086910048</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{F916B22D-E25B-44AE-9FA9-2A51191B9CDF}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MULTIPOLYGON (((-73.75421559146166 40.75591276...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4161096</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>08/22/2017 12:00:00 AM</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>746409</td>\n",
       "      <td>18.015113</td>\n",
       "      <td>2100</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>4075020005</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>{525F2C24-616B-4F29-98A3-8FEA5D4B1A7D}</td>\n",
       "    </tr>\n",
       "  </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": 63,
     "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",
    "building_df_fillna.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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",
    "print('number of nulls for CNSTRCT_YR: {:,}'.format(counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Trends of Year Built"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>1652.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661.0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.0</th>\n",
       "      <td>1594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021.0</th>\n",
       "      <td>1455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022.0</th>\n",
       "      <td>1536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023.0</th>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024.0</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            count\n",
       "CNSTRCT_YR       \n",
       "1652.0          1\n",
       "1661.0          2\n",
       "1680.0          1\n",
       "1690.0          1\n",
       "1694.0          1\n",
       "...           ...\n",
       "2020.0       1594\n",
       "2021.0       1455\n",
       "2022.0       1536\n",
       "2023.0        854\n",
       "2024.0         45\n",
       "\n",
       "[251 rows x 1 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returning number of buildings by year as 'g'\n",
    "g = (\n",
    "    building_df_dropna\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",
    "g"
   ]
  },
  {
   "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",
       "    </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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set index to int\n",
    "g.index = g.index.astype('int64')\n",
    "\n",
    "g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# plotting line chart of number of buildings by 'CNSTRCT_YR'\n",
    "ax.plot(g['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": 68,
   "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>1900</th>\n",
       "      <td>7580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>26368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count\n",
       "CNSTRCT_YR       \n",
       "1900         7580\n",
       "1901        26368\n",
       "1902          502\n",
       "1903          526\n",
       "1904          575"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previewing number of buildings constructed after 1900\n",
    "g.loc[g.index >= 1900].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FwBFuf/+jgd/vNQTCaA1BNO4HBNGsuLzAreN3BNG3Afc8Qz7q9ruOII3zeu85PkRgDP8pwf/fLILPhpBPEaQ+nyKoAvxPVf2/BGtLhfvfuJTRn+HVbn8lQQXiuwg+B54BPgf8ZrzW2EqEpZqGYbQgLsW3AzjYO/E3cv9PAO9R1T81et+GYRjNxCI4htFiiMhrRKTHhca/TBCBeHIcHucNBCmh6xq9b8MwjGbT8p1ADWMacgZBGFoIvD1naYNDrRKM6jgCeJtXdWEYhjFlsBSVYRiGYRhTDktRGYZhGIYx5bAUVQQLFy7UFStWNHsZhmEYhmGMwV133bVVVUf1LDOBE8GKFSu4885GjCcyDMMwDGM8EZHIzvGWojIMwzAMY8phAscwDMMwjCmHCRzDMAzDMKYcJnAMwzAMw5hymMAxDMMwDGPKYQLHMAzDMIwphwkcwzAMwzCmHCZwDMMwDMOYcpjAMQzDMAxjymECxzAMwzCMKYcJHMMwDMMwphwmcAzDMAzDmHKYwDEMwzAMY8phAsdoGXYOjLBrcKTZyzAMwzCmACZwjJagUFBe8bUbee1/3dTspRiGYRhTgLZmL8AwAEYKBTbuHESk2SsxDMMwpgIWwTFagkIh+K0KqtrcxRiGYRiTHhM4RkuQ90RNvmACxzAMw6gPEzhGS1DwBY5FcAzDMIw6MYFjtAQFL2pj+sYwDMOoFxM4Rkvgp6UsRWUYhmHUiwkcoyXIW4rKMAzDaCAmcIyWIKyiCi6bwDEMwzDqwwSO0RJYFZVhGIbRSEzgGC2BH7WxFJVhGIZRLyZwjJbALxP301WGYRiGkQYTOEZL4KelChbBMQzDMOrEBI7REhTMg2MYhmE0EBM4RkuQ96uoLIJjGIZh1IkJHKMlsEZ/hmEYRiMxgWO0BGUmY4vgGIZhGHViAsdoCco9OE1ciGEYhjElMIFjtASWojIMwzAaiQkcoyWwFJVhGIbRSEzgGC2BVVEZhmEYjcQEjtESWIrKMAzDaCQmcIyWQC1FZRiGYTSQCRE4InKxiGwWkQe9bf8pIo+IyP0i8msRmetdd76IrBKRR0XkNG/7cSLygLvuGyIibnuniFzutt8mIiu8+5wjIo+7n3Mm4vkayclbFZVhGIbRQCYqgvMj4PSKbdcAR6nqMcBjwPkAInIEcBZwpLvPt0Uk6+7zHeA84GD3E+7zXGC7qh4EfBX4ktvXfOBTwHOBE4FPici8cXh+Rp1YisowDMNoJBMicFT1RqC3YtsfVTXn/rwVWO4unwFcpqpDqroGWAWcKCJLgNmqeosG+YwfA6/z7nOJu3wlcKqL7pwGXKOqvaq6nUBUVQotowWwKirDMAyjkbSKB+ddwO/d5WXAOu+69W7bMne5cnvZfZxo2gksqLGvUYjIeSJyp4jcuWXLlrqejJEcq6IyDMMwGknTBY6IfALIAT8NN0XcTGtsT3uf8o2q31PV41X1+EWLFtVetNFwbJq4YRiG0UiaKnCc6ffVwNlaKqNZD+zj3Ww5sNFtXx6xvew+ItIGzCFIiVXbl9FiFAqWojIMwzAaR9MEjoicDnwMeK2q9ntXXQWc5Sqj9icwE9+uqpuA3SJykvPXvB34jXefsELqTOA6J5j+ALxcROY5c/HL3TajxbAqKsMwDKORtE3Eg4jIz4EXAQtFZD1BZdP5QCdwjav2vlVV36uqD4nIFcDDBKmr96tq3u3qfQQVWd0Enp3Qt3MRcKmIrCKI3JwFoKq9IvJZ4A53u8+oapnZ2WgNrIrKMAzDaCQTInBU9c0Rmy+qcfsLgQsjtt8JHBWxfRB4Y5V9XQxcHHuxRlOwKirDMAyjkTTdZGwYUJ6WsgiOYRiGUS8mcIyWwCI4hmEYRiMxgWO0BFZFZRiGYTQSEzhGS2BVVIZhGEYjMYFjtARlERzz4BiGYRh1YgLHaAnKysQtRWUYhmHUiQkcoyXIe5rGqqgMwzCMejGBY7QEalVUhmEYRgMxgWO0BHnz4BiGYRgNxASO0RKUVVGZvjEMwzDqxASO0RJYFZVhGIbRSEzgGC1B2agG8+AYhmEYdWICx2gJyhv9tYbAufB/H+YDP7u7zABtGIZhTA5M4BgtQVkVVYsInMtuX8fV929i58BIs5diGIZhJMQEjtEStGKjv5xb04i5ng3DMCYdJnCMliBf1geniQvxCNeUK9hwLMMwjMmGCRyjJWjFKqpwHTmL4BiGYUw6TOAYLUErVlGFKapciwguwzAMIz4mcIyWoNBiJmN/Dbm8pagMwzAmGyZwjJag0GJl4n4UyUzGhmEYkw8TOEZL0MgqqkJB6xZJ/v3NZGwYhjH5MIFjtASNTFGd9f1bOeNbN9XVoM8XOBbBMQzDmHy0NXsBhgEV08Tr1BN3PtlLQWEoV6CrPZtuPS2WMjMMwzCSYREcoyVoVBWVqhYFUqGeCE7eTMaGYRiTGRM4RkvQqFEN5d6ZOvbjm4wtgmMYhjHpMIFjtASNSgnlGySUrEzcMAxjcmMCx2gJGlVF1agITs5MxoZhGJMaEzhGS9CoKqp8g0Y+WJm4YRjG5MYEjtESlEdw0u/H1yL1RHBarfGgYRiGkQwTOEZL4Ntc6ql+8qMt9QgTS1EZhmFMbkzgGC1Bw6qoGhR5MZOxYRjG5MYEjtESNKyKqlFmZSsTNwzDmNSYwDFagvJOxg0SOPWkqKzRn2EYxqTGBI7REjTK1NsogeOvJ2ceHMMwjEmHCRyjJWhUFVWjBE6j+ukYhmEYzcEEjtES+OXdjepf0zCBYykqwzCMSYcJHKMlKGv01yBzcF2zqApmMjYMw5jMmMAxWoLxqKJqmFCyCI5hGMakwwSO0RIUWqyKyjw4hmEYkxsTOEZLMC59cBqVorIIjmEYxqTDBI7REvgaohWqqGwWlWEYxuRmQgSOiFwsIptF5EFv23wRuUZEHne/53nXnS8iq0TkURE5zdt+nIg84K77hoiI294pIpe77beJyArvPue4x3hcRM6ZiOdrJKcsRdUCERy/943NojIMw5h8TFQE50fA6RXbPg5cq6oHA9e6vxGRI4CzgCPdfb4tIll3n+8A5wEHu59wn+cC21X1IOCrwJfcvuYDnwKeC5wIfMoXUkbr0NqN/ixFZRiGMdmYEIGjqjcCvRWbzwAucZcvAV7nbb9MVYdUdQ2wCjhRRJYAs1X1Fg0mM/644j7hvq4ETnXRndOAa1S1V1W3A9cwWmgZLUC+5crES5fNZGwYhjH5aKYHZy9V3QTgfi9225cB67zbrXfblrnLldvL7qOqOWAnsKDGvowWo1FVVLmG7aekcMxkbBiGMfloRZOxRGzTGtvT3qf8QUXOE5E7ReTOLVu2xFqo0TgaVUVVaFB5t82iMgzDmNw0U+A849JOuN+b3fb1wD7e7ZYDG9325RHby+4jIm3AHIKUWLV9jUJVv6eqx6vq8YsWLarjaRlpKBvV0KAqqvrMyqXLfjTHMAzDmBw0U+BcBYRVTecAv/G2n+Uqo/YnMBPf7tJYu0XkJOeveXvFfcJ9nQlc53w6fwBeLiLznLn45W6b0WK0WoO+vCdqzINjGIYx+WibiAcRkZ8DLwIWish6gsqmLwJXiMi5wFrgjQCq+pCIXAE8DOSA96tq3u3qfQQVWd3A790PwEXApSKyiiByc5bbV6+IfBa4w93uM6paaXY2WoCGVVHpOERwLEVlGIYx6ZgQgaOqb65y1alVbn8hcGHE9juBoyK2D+IEUsR1FwMXx16s0RQaNmyzUREcbw1mMjYMw5h8tKLJ2JiGNGxIpp/qqmc/eUtRGYZhTGZM4BgtQbkHJ/1+fDGSr2NHflbKGv0ZhmFMPkzgGC2BHySpJ4JTKIvgpF9PvqwPjkVwDMMwJhsmcIyWoFEm47IITh3l3WXDPy1FZRiGMekwgWO0BI3qX1MulNKvx9/PiPXBMQzDmHSYwDFagjJh0iiTcR3CxC8NtzJxwzCMyYcJHKMlGI9Gf/VEcPI2TdwwDGNSYwLHaDqqWmYyriOA07AIjp8mGzEPjmEYxqTDBI7RdCr1Qz0pqlyD+uD4+7EIjmEYxuTDBI7RdEL/TTYTDH9vuWniFsExDMOYdJjAMZpOKGhCgQPpK6lyDarGKhv5YCZjwzCMSYcJHKPpFCM4IqUoTsr0UqPKxMtnWlmKyjAMY7JhAsdoOqGYyEggcvxtSWlcoz9/2Kai9TifDcMwjAnHBI7RdEIdkskITt+kHtdQaJDJuPK+1s3YMAxjcmECx2g6ec9kHKao0uqJhvXTqfDdmNHYMAxjcmECx2g6ZR6chqaoGhfBMYFjGIYxuTCBYzSdMK2UyQiZMIKTUlA0qry78vGtF45hGMbkwgSO0XTyDayialSZeKU4GrFSccMwjEmFCRyj6fhVVBmpM4LToEZ/o1NUFsExDMOYTJjAMZqOX0WVde/IhkRw6qiiGp2isgiOYRjGZMIEjtF0/CqqTJ0m40KDOhCPTlFZBMcwDGMyYQLHaDp+FVUocNIGX8YrgmN9cAzDMCYXJnCMpuNXUdU7cDPfoCqqyhSZmYwNwzAmF6kEjoh0i0hHoxdjTE8aWUVVaFQfnEoPjpmMDcMwJhWxBI6IfFlETnSXXwX0AjtE5DXjuThjehCKCZGgkgoaM028kQLHIjiGYRiTi7gRnLOBB93lfwfeCrwW+Px4LMqYXoTBEX9UQ8tFcMxkbBiGMaloi3m7HlXtF5EFwAGq+ksAEdlv/JZmTBcKXhVVvtAioxrcfduzwkhebVSDYRjGJCOuwHlMRM4GDgKuARCRhcDAeC3MmD6E0ZqMV0WV1vLiV041Ypp4Z1uWkXzOBI5hGMYkI67A+X/A14Fh4Fy37TTgj+OxKGN6EaaVyqeJp4zg5BsTwQnX1NmWYc+QpagMwzAmG7EEjqreATyvYttPgZ+Ox6KM6UUoRLJSGraZNvri368egZPzBA6YydgwDGOyEUvgiMhLqlw1BKxX1acatyRjuhGKEhHI1llFlW+wB6erPQtYmbhhGMZkI26K6iJgqbu8DVjgLm8G9haR+4GzVPXxBq/PmAb4VVQFrbPRX4METpgi63ARHJtFZRiGMbmIWyZ+EfANYK6qLgXmEnhyvusu3wF8exzWZ0wDClGzqNKWiTc6ReUiODaLyjAMY3IRN4LzIWCJquYAVHVARD4BbFTVC0Xkw8D68VqkMbXxq6iKJuOUeqLMZNyAWVShB8dmURmGYUwu4kZw+oATKrYdB/S7y/b11kiNX0XVKhGcUpm4MxmbwDEMw5hUxI3g/DvwRxG5ClgHLAdeA3zQXX8qcGXjl2dMB0IhkhGKVVSpy8QbZTLOl/rggJWJG4ZhTDbilon/WETuBN5AYDZ+DDhZVR92118NXD1uqzSmNAU/RSXB5bRVVA0b1aBhFZWZjA3DMCYjcSM4ODHz8DiuxZim5BtYRdW4UQ3B7zCCM2Jl4oZhGJOKuH1w5gP/AjwbmOlfp6ovaPyyjOlEMYKTETKF+lJUZWXi9YxqcIKm0yI4hmEYk5K4EZyfAZ3AFZSMxYbREIpl4iIUwk7GKQMmZQKnDlGSr6iiMg+OYRjG5CKuwHkesEhVh8ZzMcb0JO9VUeW1gaMa6ikTd3ctmoytisowDGNSEbdM/H6CyinDaDihwBHBmyZev8m4HlESjmYoRnBM4BiGYUwq4gqc64D/E5F/FZF3+T/1LkBE/klEHhKRB0Xk5yLSJSLzReQaEXnc/Z7n3f58EVklIo+KyGne9uNE5AF33TdEgjOliHSKyOVu+20isqLeNRuNxU9RFWdRNaBMPK1ICu4b/O6yTsaGYRiTkrgC528IOhW/DHib9/PWeh5cRJYB/wAcr6pHAVngLODjwLWqejBwrfsbETnCXX8kcDrwbRHJut19BzgPONj9nO62nwtsV9WDgK8CX6pnzUbj8auoitPEWy2CYyZjwzCMSUXcPjgvHuc1dIvICNADbATOB17krr8EuAH4GHAGcJnzAq0RkVXAiSLyJDBbVW8BEJEfA68Dfu/u82m3ryuBb4qIqNZh0DAail9Fla2ziqpS1BQKWhRNcVHVkgcnrKKyMnHDMIxJRdUITpjicZcz1X7qeXBV3QB8GVgLbAJ2quofgb1UdZO7zSZgsbvLMoJOyiHr3bZllM/CCreX3cfN0tpJaRq6/3zPE5E7ReTOLVu21PO0jISUpajqraKqEEZpojjhXTIC7Rk3qsEiOIZhGJOKWgJlp3c5B4xU/ITbUuO8NWcA+xN0SJ4hIrXSXlFfxbXG9lr3Kd+g+j1VPV5Vj1+0aFHthRsNxa+iKqao6uyDk61j5EMYrclmfMFlAscwDGMyUStFdaR3ef9xevyXAmtUdQuAiPyKoCT9GRFZoqqbRGQJsNndfj2wj3f/5QQprfWUV3mF2/37rBeRNmAO0DtOz8dIgV9Fla2jikpVi/tqzwr5gqaL4HieoDbnejaTsWEYxuSiagRHVdd5l5+q9lPn468FThKRHpcSOxVYCVwFnONucw7wG3f5KuAsVxm1P4GZ+HaXxtotIie5/by94j7hvs4ErjP/TWvhp6hCu0yaiEl4F/FSS2n2k/fW0541k7FhGMZkpGoER0QuJSKVU4mqvj3tg6vqbSJyJXA3QcrrHuB7BOMgrhCRcwlE0Bvd7R8SkSsIZmLlgPerat7t7n3Aj4BuAnPx7932i4BLnSG5l6AKy2gh/CqqsNFfmtRSKGbaMkI2mz61FHZAzmSENqe4zGRsGIYxuaiVolrlXV5IEAX5LfAUsC/wGoIKp7pQ1U8Bn6rYPEQQzYm6/YXAhRHb7wSOitg+iBNIRmvSqCqq8qnkdQgcLQmlMIJjJmPDMIzJRVWBo6oXhJdF5A/Aq1T1L962U4B/G9/lGdOB0G9T7yyqnB/BqcMc7BuVQw+ORXAMwzAmF3FnUZ0E3Fqx7Tbg5MYux5iO5P1p4g1IUWV8gVPPfryydfPgGIZhTC7i9rG5B/i8iHQDuN8XAveO07qMaUShKCioL7UUFcFJIUyiUlQ2i8owDGNyEVfgvAN4PrBTRJ4h6JFzCkG1kmHUhV+1VM+oBj+1VE8Ep1CIMBlbmbhhGMakIu6ohieB54nIvsASYJOqrh3PhRnTh2Ln4IyQcYKkrhRVWUfk5MLE9/KYydgwDGNyUqtMPCq6s979FK9XVftqa9RFwYu8FOqoovJTS6VUV/L1+F4eMxkbhmFMTmpFcHLU7oMj7vpsjdsYxpjkvSqqfB1VVH7/mmwd/Wv8xoNtGWv0ZxiGMRmpJXDGazyDYZQRRl5E6pshVRbBCfeTptw875WJF4WSCRzDMIzJRK0+OPWOYTCMWPgpqlDX1GMy9s3BqUzG6gmcrJmMDcMwJiO1PDjfU9Xz3OWqYxvqGdVgGOBVUWWkaDiut0w8U4fJ2K/GKpqMLYJjGIYxqaiVolrjXV5V9VaGUSfFKioRstKYKqq2Orw8uUJEisoiOIZhGJOKWimqL3iXL6h2O8Ool7IqKm1Ao7+skJEGmYxtmrhhGMakJFYfHBF5SbXrVPW6xi3HmI6UVVFJaDJOsZ8yYZLeZOx7edrdfkasTNwwDGNSEXcW1UUVfy8COgh64hzQ0BUZ047IKqpUEZxAhGQy9UVwIkc+mAfHMAxjUhG3k3FZybiIZIFPArvHY1HG9CKyiiqVByf43eZ5Z+rx8mQzQnum1MlYVREnnAzDMIzWJu4sqjJUNU8wbPOjjV2OMR0J7S1Zr/opTQQnjNbUOwW8ON3czcZyu7IojmEYxiQilcBxvAwwY4JRNwVPUDhPb8ohmcHvtqzU1zAwX0pRBfuzieKGYRiTjbgm43WU98HpAbqA/zceizKmFw2roioTSuk7EBf34/bRnhGGgZF8ga52m0xiGIYxGYhrMn5rxd99wGOquqvB6zGmIX7/mozU450JQjiBOThTtu8kFLyqLggjOHkrFTcMw5hExDUZ/3m8F2JMX0opKuqaIRWajLMZIVuHb6bY6M/txOZRGYZhTD7G9OCIyLNE5CwROdD9faGI3C8iPxORheO/RGOq41cthRGcdFVUheJ+6orgaGUEJ33JuWEYhtEcagocEXkv8Bfgn4A7ROTrwEnAdwl64XxtvBdoTH3CzE+mbAp4+jLxQOCE2+orEwdoy1g3Y8MwjMnGWCmqfwFeqKr3iMgJwK3A3qq6RUQuBx4a9xUaUx71IiaaCS6nieBElYmn20+5wCl2M7Z5VIZhGJOGsVJUi1X1HgBVvQPoV9Ut7u9tBNVUhlEXkSmqOlJL9XYgjjYZmwfHMAxjMpG0D87IuKzCmNb4VVT19K8JU0iZjBTFSV3l5plyk7FFcAzDMCYPY6WoekTkRu/vWd7fAnSPz7KM6YRfRdW4CE56k7E/iwqgPZt+X4ZhGEZzGEvgnFvxd+XQzR80cC3GNCVf1ugv2JZqmvg4mYyzxQiOCRzDMIzJQk2Bo6qXTNRCjOlLWRVVKHDqmCbul4mn6mTspcygZDLOWYrKMAxj0hC3k7FhjBuNqqLKe+bgMIKTTii5FFW2okzcUlSGYRiThnqGbRpGQ4iqoko3TTzcT6a+CI6WR3DarEzcMAxj0mECx2g6UVVUqaaJh5GgTKnEu55p4mEUKDQZW6M/wzCMyUNVgSMit3qXPzUxyzGmI8UqqoxfRZV8P2G0JpORYtSlnjLxMApUmkVlERzDMIzJQq0IziEi0uUuf3giFmNMT3zvTD0pqoJX3l1XufmoRn82bNMwDGOyUctk/BvgMRF5Euiu6IdTRFVfMB4LM6YPYRaprIqqnhELIsWoS13TxJ38t1lUhmEYk4+qAkdV3ykipwArgBMY3QPHMBpCvkFVVAXPZJzJpI+6jEpRmcnYMAxj0jFWH5ybgJtEpMN64hjjRXmjvzqmiXsm47Y6Rj4UKiI47VYmbhiGMemIVUWlqheLyItF5GIR+YP7/ZLxXpwxPQgFhQgNmgJeXwQnV9Hor80a/UUyMJznqW19zV6GYRhGJLEEjoj8PXA58DTwK2AT8DMRefc4rs2YJpQiL77JOPl+/MhLMYJTp1kZSmXiNqqhnA//4l5e9OUbWNfb3+ylGIZhjCJuJ+OPAi9T1fvCDSJyOfBL4PvjsTBj+lCcISWC1jFDqqzRnzTCg1M+i8qGbZazfvsAqrBxxwD7zO9p9nIMwzDKiCtwFgAPV2x7FJjf2OUY0xEt9sERnMe4PpOxl+qqZ1RDmOYqmoytD04Zw7ngeAxb6s4wjBYkbifjm4CviEgPgIjMAP4TuHm8FmZMH/wqKufnLYqeJBQjONlMUeDUM2yzmKKyMvFIQmFj1WWGYbQicQXOe4FjgJ0i8gywA3gW8J56FyAic0XkShF5RERWisjJIjJfRK4Rkcfd73ne7c8XkVUi8qiInOZtP05EHnDXfUMkyFGISKeIXO623yYiK+pds9FY/IhJtp4GfZ5QqsesHJ6vzWRcm1DYDOdM+BmG0XrEraLapKovBPYHXgPsr6ovVNWNDVjD14H/U9XDCETTSuDjwLWqejBwrfsbETkCOAs4Ejgd+LaIZN1+vgOcBxzsfk53288FtqvqQcBXgS81YM1GAykUq5bq87vkvBlSxf2kiLrkXSoqW2kyNg9OGSNO2FgExzCMViTRsE1VXa+qt6vq+kY8uIjMBl6AayKoqsOqugM4Awj77lwCvM5dPgO4TFWHVHUNsAo4UUSWALNV9RYNchs/rrhPuK8rgVPD6I7RGpRVURX719Szn0x9ERx3l3AfxVlUdiIvY8RSVIZhtDDNniZ+ALAF+KGI3CMiP3D+nr1UdRME0SNgsbv9MmCdd//1btsyd7lye9l9VDUH7CQwTZchIueJyJ0icueWLVsa9fyMGBS8lFBdKSqvTLwx+ymvorJGf+WYB8cwjFam2QKnDTgW+I6qPgfow6WjqhAVedEa22vdp3yD6vdU9XhVPX7RokW1V200lIIXwWlUo79sHdPEw6nhoUgKU1RmMi6n6MGx42IYRgsypsARkYyIvEREOsbh8dcD61X1Nvf3lQSC5xmXdsL93uzdfh/v/suBjW778ojtZfcRkTZgDtDb8GdipMavogqTh6ka9Pkm4zoiOMW+PBVl4jkrEy+jWCaes+NiGEbrMabAUdUC8BtVHW70g6vq08A6ETnUbTqVoN/OVcA5bts5BJPNcdvPcpVR+xOYiW93aazdInKS89e8veI+4b7OBK7TNDXIxrigquXTxMNOxmkiOPlSJKieaeKFikZ/YZm4dTIukS9o0SdlKSrDMFqRuI3+bhSRk1T11nFYwweBn7oI0WrgnQTC6woRORdYC7wRQFUfEpErCERQDni/qubdft4H/AjoBn7vfiAwMF8qIqsIIjdnjcNzMFKS9yqowGvQp4H4SeIHL0SYlevpiFzZ6M9MxiV8UTNiERzDMFqQuALnKeD3IvIbAsNu8ayhqv9ezwJU9V7g+IirTq1y+wuBCyO23wkcFbF9ECeQjNajciyCuDSVaiBysgnq3XJeg756vDyVs6jarEx8FH73YovgGIbRisQVON3A/7jLy2vczjASUahoqgeBhyanSr6gRaESh7KGgQ3oZBymy9rq6KkzVfGjNmYyNgyjFYklcFT1neO9EGN6Uul3AZcaKmhiH44vTIpenkbMosqYybgS349kERzDMFqRuBEcRORwApPuXqr6AWcM7lTV+8dtdcaUx6+gCklbAZX3+tfUFcHRillUWTMZV+JXTlkVlWEYrUisPjgi8kbgRoKmeW93m2cBXxmndRnThEJFtARKhuOk/pmofjqNnCZuEZwS5sExDKPVidvo7zPAy1T1vUBYtXQfwewow0hNZRUVlISFJjxv5rwITlsdJuPRHhyL4FTii5phEziGYbQgcQXOYgJBA6UKKiWiI7BhJKGyisq/nDiC4wmcesrE/VQXQLuViY+irEzchJ9hGC1IXIFzF/C2im1nAbc3djnGdKNaFRUkFyd+mXgjG/3VM+F8qmJ9cAzDaHXimoz/Afija7w3Q0T+ABwCvHzcVmZMC6pWUZG8m3Ep3SVFwVRPo7+smYyrMpyzKirDMFqbuGXij4jIYcCrgasJmv1drap7xnNxxtTHFyUh9VZRtWWlaAxuxDRxMxmPxjw4hmG0OrHLxFW1X0T+CqwBNpq4MRpBVAQnbUoo9OxkvD449ZSJV5qMbZp4CSsTNwyj1YlbJr6viPwFeBL4X+BJEblJRPYbz8UZU5+oKqriRPG0jf78MvE6h3ZCyWQ8YhGcIiNWJm4YRosT12R8CYHReK6qLgbmAXe47YaRmlCAZCIiOEmDL3nPZFxs9Jfi5FsZVQpnUVkEp8SwVVEZhtHixE1RHQe8XFVHAFR1j4h8DNg2biszxp1CQREh0cTuhq/BnRsb0cnYbxqYViT5j1sUOHV0RZ6q2KgGwzBanbgRnFuBEyu2HQ/c0tjlGBPFSL7AS7/6Zz7ws3uauo5KMQHpq6iipomnMQZXGp/b6ogGTVXMZGwYRqtTNYIjIp/x/nwC+J2I/C9BBdU+wCuBn43v8ozxYvPuIVZv6WNH/0hT19HIKqqCbzIujmpIsaaKWVSWohqNeXAMw2h1aqWo9qn4+1fu92JgCPg10DUeizLGn7DypdkVMCUPTmlb2i7EZREcaUAEp8VMxis37WJdbz8vP3Lvpq4DrIrKMIzWp6rAUdV3TuRCjIllKBeMFGv2yaly7hOAC5ikb/RX4cFR1UQ+o9EenNaI4Hz4ivt4eNMubvvXU9lrdnO/W5jJ2DCMVid2HxwR6QEOAmb621X15kYvyhh/ihGcfIFCQcuqmCaSqCqqtF2I/SoqESEjgcDJF7TYrC/pfvzfuYImFkuNZHv/MAA7+keaLnBG/E7GFsExDKMFiSVwROTtwDeBYWDAu0qBfcdhXcY4U5ZiyBfoymSbso6oKqpQ4KQtE/cjL8P5AnnV+Eqe0b6gTCa9WGokQy2SVgQzGRuG0frE/dz/D+ANqnrNeC7GmDhGCZz25gicSr8LkLpJ32hhAuTTd0T2K7vashmGcwVyBaWtOYeq+JqF6cVmYiZjwzBanbhl4sPADeO4DmOCGcq3hkm0EOXBSZuiqqx+ct6ZJPspFJRQV/lZu3b3RzNP5q1iDIfyqE0Y2TIMw2gl4gqcfwO+IiILx3MxxsQxNNIaAicfWUUV/C6kFSZOjIQCJcnJ14/e+F6bZpeKFwpaFBVDLRAxqRR6FsUxDKPViCtwHgNeCzwjInn3UxCR5sfKjVQMt0gEJ7IPTlgmniBFVRm9gZIoSSRwIhoPQvNLxf3XyxenzaLyPTPUAlElwzAMn7genEuBHwOXU24yNiYp/gmqmSenqGniaaqoorw8afZT0NEpM2h+qfhQrrVMvZWl4RbBMQyj1YgrcBYA/66aYjSz0ZK0SqO2MCASXUWVXOCURXBSRIJyVSI4peGdzfkXaJXXq7gGS1EZhtHixE1R/RB423guxJhY/Eqc4XzzMo35WtPEE5wz8xGRlzSipDBGiipNZ+RGUJaiaoUqqgqR5ffFMQzDaAXiRnBOBD4gIp8AnvGvUNUXNHxVxrjTMimqiCqqYmopSQTHiZhsNkIopYgEVQqcosm4SdVCQyOeIG2BCE5lxKYV0maGYRg+cQXO992PMUVolZRHVBVVNkUVVc0IThovzygPTnPLxFvFFB5iHhzDMFqdWAJHVS8Z74UYE0urnDAbVkUVaTIOfqcRSm2jUlTNNRm3SsQtpPI90wqiyzAMwyfuqIZ3VbtOVS9u3HKMiWKoRU6Yja6iKjcZuzLxhqSomuvBGWqRiFtxDU4gi4CqRXAMw2g94qaoKg3GewMHAn8FTOBMQlolRRVVRVWPd8aPBGVSmIxLkaDy7aUUVfMjOK3gdwkFzYyONvYM5VpiTYZhGD5xU1QvrtzmojqHN3xFxoTQKn1VoqqoShGcBPsJIzhZP4JTT7l5ucJJM/ahkfiVU77huFkUBU5nlj1DuaYJP8MwjGrELROP4kfAuQ1ahzHBtE4Ep3oVVSJhEmEyzqQwGYePWZGhKgqnppmMW0SQhoSCZkZH8B2psmzcMAyj2cT14FQKoR7grcCORi/ImBjK+uBMhSqqCO9MMYKTYD/VGv0122TcKp6pkPA909MZjFY3D45hGK1GXA9ODqj8ZN8AvLuxyzEmivKqnOalPAoNrqLyhUkYzUlTJp4dlaIyk7FPKGh6XASnFaJKhmEYPnEFzv4Vf/ep6tZGL8aYOFqtTDyqiqreCE42RQSnaHquiFmGEZxWMBm3RASnaDIOIjitILoMwzB84pqMnxrvhRgTS9kJs4nfvkPtERnBaZDASRLBCSM0lcM2sxbBKSP03MzodB4cMxkbhtFi1BQ4InI9o1NTPqqqpzZ2ScZE0ConzJp9cBKcM3MNSnVFrQe8PjgtEMFpCYHjjsPMosBp/poMwzB8xorg/KTK9mXAPxCYjY1JSKucMBuVoipEdCAuCpxEfXDK7xvSnmnuLKpW8UwBqGoxRdXdYSZjwzBak5oCR1Uv8v8WkQXA+QTm4suBz4zf0ozxpGUEjhMmfkaoWEVV56iGNBGcMAU1ahZVMYLTrBSVP/29uWIi53WN7mxzHhwTOIZhtBix+uCIyGwR+SywCtgLOFZVz1PV9eO6OmPc8E9ILTdNvI4qqraIKqokXp7QYuM3DITWMhk3O0UVRmvasxk6wv5AOfPgGIbRWtQUOCLSLSLnA6sJuhafoqpvU9UnJmR1xrjhd8Ntrgcn+B1V3l13FVU2hVlZR3t5oLXKxJtdRRWKmfaseMKvNSI4zRZ/hmG0DmNFcNYA/wz8B/BtYC8ReYn/04hFiEhWRO4Rkavd3/NF5BoRedz9nufd9nwRWSUij4rIad7240TkAXfdN0SCM5SIdIrI5W77bSKyohFrnuyUlYk3c1RDLXNwilENUUIpWTVWYdR+oCSWLIIDQ/lAHHe0ZWhvCz5CWiFF9fU/Pc6zLvgjq7fsafZSDMNoAcYyGQ8SVFG9r8r1ChzQgHV8CFgJzHZ/fxy4VlW/KCIfd39/TESOAM4CjgSWAn8SkUNUNQ98BzgPuBX4HXA68HuCcRLbVfUgETkL+BLwdw1Y86RmclRRpYjgeEKpLVW5efl9Q9qbPIuqVfoWQUnkdWQzxQhOs9cEcNfa7QyM5Fm5aTcHLJrZ7OUYhtFkakZwVHWFqu5f46ducSMiy4FXAT/wNp8BXOIuXwK8ztt+maoOqeoaAk/QiSKyBJitqreoqgI/rrhPuK8rgVPD6M50plUiAqUITmlbmgZ9USMWMnX002llk3HzU1TOg9OWoaOtdVJUgy7tOtgCw0gNw2g+9QzbbBRfAz4K+J+Qe6nqJgD3e7HbvgxY591uvdu2zF2u3F52H1XNATuBBZWLEJHzROROEblzy5YtdT6l1sYv84XmNvqLniZefl0coiJBbXWYlavNohppgTLxZkdLIk3GLSBwQl/ZYJPL6A3DaA2aKnBE5NXAZlW9K+5dIrZpje217lO+QfV7qnq8qh6/aNGimMuZnIzkFf+cP9TEb7y1qqiSlInnIsrE00wTz1dr9JdpdgSn3DOlCY5Noxn2BE6zq8t8BkcKZb8Nw5jeNDuC83zgtSLyJHAZ8BIR+QnwjEs74X5vdrdfD+zj3X85sNFtXx6xvew+ItIGzAF6x+PJTBYqDaHNNIg2qoqqEFEmnmaaeKFKBKetjhP5nU/2cvX9G8e+YQ0q01LNTFOVPDilKqpWMBkPWIrKMAyPpgocVT1fVZer6goC8/B1qvpW4CrgHHezc4DfuMtXAWe5yqj9gYOB210aa7eInOT8NW+vuE+4rzPdYzT/62YTqUxxtIYHp0FVVH4kKMU08VzEfqC+MvEP/+I+Pvjze+jtG05835BRr1kTBUW4lvYWMxmbB8cwDJ+408Qnmi8CV4jIucBa4I0AqvqQiFwBPAzkgPe7CioIKr1+BHQTVE/93m2/CLhURFYRRG7Omqgn0aq0ksCpVUWVppNxlAenMRGc5IblkN6+YVRh58AI82d0JL4/jI7YNPM1K/PgtLWOB8cEjmEYPi0jcFT1BuAGd3kbEDnEU1UvBC6M2H4ncFTE9kGcQDICwoqcmZ1t7BnKtUgfnNK2VNPEI4RSmmni1Tw4YZl4mhRVeMIdGE5/4h2uMM42U+CE75eOtgwd2daZRWUeHMMwfJrtwTGaQHhynNUV6NuhJp4QClFVVKlmSFUXOPWalSF9mXguXyiKonqqe0JREWbOmurBKUtRtcaohnyhVBk4YBEcwzAwgTMtGaoQOK0QwSn3zgS/600tpYkERZmVwTMZJ0xRDXpCZLCOCE4oQmd2utesFUzGbdIynYz9PkGWojIMA0zgTEvCk1F3RxsigQBoVofe8GEzUVVUDYrgJEpRVWn015HSTOunpeqJLISv2eyu9lTraCTlfXBao9Gfn5ayFJVhGGACZ1oSRgM62zKpT9yNouY08QRLitpPo4Z2AnR3BF6TpNEB//b1CJzwNSumFZvYzC66D05zRYV/bJt5bAzDaB1M4ExDwhNUp9dqv1kCJ9IcXE8EJzt6mngak3FliqrHCZz+hGkmX+DUE1kIX7NWSFENR3hwml0mXn6cTeAYhmECZ1oSnow6shk624ITdzgheqIJIyZ+RiiVd0ZrRHBSlJtXmoy724PjlLQSyhc1aSM4uXyBfEERgR4ncJo5XmPEE8it0sm4UZEywzCmDiZwpiFFgdOWodNFcJpVSRXZB6dBM6SKHpwEJ98o0zOUUlRJT57+7dOajP2IW7NfL/A9OFJcT7NNxubBMQyjEhM405DQo1CWomrSCSpKUKTxzjSqTLyqBydlBGegAZEFP+LWiNdrJF+oq6tyGK1pJQ+OpagMw6jEBM40xI/gNN1kHFFFVZwmnqZMPGLkQ5LxCtUETsmDk4u9L2jMibf0emVLEZM6Xq8P/uweTv7CtWzdM1TXetqzmWKZ+EhLeXAsgmMYhgmcaUlZJ9omm4xrTxOPv59Ik3GamVZVOhl3tYdVVMmOUyO8IWHforIUVR2VQo89s5uhXIG1vf2p7j/ivX+Kjf6a7sHxU1QWwTEMwwTOtKSU8sg2P0XVoCqqWibjfIIITrVZVJ1tGTISHKck3Yz9lFbaE68vcBoRcQsrwdKOjihFcKQ4wmI4X6CZM2wtRWUYRiUmcKYhxRNme6YhKY96aFQVVWgkju5kHH891aaJi0jJh5PgBFoWwUkpKMJoTUdbhk63hvoETs79Trcev9FfJiPFkvpmRnH81yRX0MQjNQzDmHqYwJmGDEWYVpvVHK1WFVWqCE6kwEnuwaksEwevkiqBMBhoQHXPcIMjOOE6kvqJiuspjmoI1tIKRuPKqM1gkz1BhmE0HxM405CWMhm7h41OLSWpogp21BYlcBIEFgpVGv1BulLxhlZRtfmCNN3rlcsXSkMpGxDBCdflb0/KD/+6hpd+5c91VXZVHo96JrcbhjE1MIEzDSmLCNR5wqyXfOQ0cXddohEL4X3ri+BUmyYOpVLxJKmdoYaajLN1953p99ZQb4oqFMeh0Em7pt8/8DSrNu/hvnU7Ut0fIiI45sMxjGmPCZxpyHA+og9Os6uoysrEU6SoIrwzbSm8PFH7CenuCLoIp43g1F8mXv/r1Yjhn6MiOHVWUvXV6QmC0REbm0dlGIYJnGlI2AU36GTsTKtNrqLKRJiMU5WJRwilZJGgGimq9uDfJZEHZ7gRJuPGeab6G7Ce4VzY6C84RvX2wgnX1JfSEwQwmKuM4JgHxzCmOyZwpiF+H5xmV1GFEZOM1CdMokzGbdn0AicqRdUTRnASCAPf7Fp5Eo5LMeLWXvJMpU0p+sbitBGT4jTxtsakqIpVXUN1CJwKQWMpKsMwTOBMQ6L64DTbg1PviIVaEZw008SzEf8ZRQ9OkhRVWcSkviqqjmz9ZeLlKap0giKM1HRWenDSiq6hMIKTXpRUChobuGkYhgmcaUgrVlFl6qyiivLytDm3crpZVKP/NcIqqiRDMxvRgM7vW1RvBGeggSbjMIJTTxWVqnoenHoiOJaiMgyjHBM405ChiCqq5s2iiuqDE/yut0w83E8jpomDX0UV/0TciFENfsSt3pSiL2oa0egvWFd6k/FQrlD0WvUN1RPBCdY0q6vN/W0RHMOY7pjAmYZEVuU0e5p43VPAg98ZaVQEZ/R1PcU+OAlGNXgn2nxBU0U5hrzXq16B0wjT83C+wmRcR6O/Ps93U0/vmlDQzOvpKPvbMIzpiwmcachQC5mMo6qo0lU/uQhO2bDN4HcSD04pojT6XyMcuDmQMoID6aI4URG3RlRRpU0JNbIPjr+eeqqoBooCpx0wgWMYhgmcaclwA8uO66VWFVWS2Y1hdqTMyxNGcBKluuJEcJJ4cCqqe1JEKRoZcWtIFVWuPEVVj8m4rwHrgZKgmVOM4JgHxzCmOyZwpiGhmOlqgGm1XmpVUeXTNPqLmEqeqIoqQnCFhCbjJCfiUAylEUch4evV6fctaoFGfx1Fk7GUbU+C77vpa0CZ+Nxui+AYhhFgAmcaElUmPtmrqEKTcfSwzeQpqraoKqo6ponPqyOy0MjRGo0c1VAZwUkjcBoRUYKSCCymqKyTsWFMe0zgTEOGG2harZdaVVRJUkuRQzvT9NPJh43+Rl9XLBOPKXBUtSiG5roTb5qoybiNaqgzRdVRrKIKOxknr6Iqi+DU48EZDo9zh/vbUlSGMd0xgdMkbnp8K6/5r5t47JndE/7YfifjVq6iSpKiykWajJOnqIqCKyJF1ZMwRTWUK6AaCIAZncm7IPv7gYphmw0QOP3DOTSJ0ckRloO3t5WPakhnMm5QFZU7HnMtgmMYhsMETpP47X0beWDDTm54dPOEP3Y4i6oRno56CQWFrydKKar4+4k2GbsITppZVNnRAqdURRVT4Ljj3NWeKd43jTekkREcP0VV0HSiZHQfnDrKxIfr9+CM5AvkC0o2I0UhaR4cwzBM4DSJ7f3DAOypo7lZWqIiOE0zGUc01pMU08SLZeJebqktRQSnlsm4J+E08fB23R3Z4qDONCfe0F/SkW1AJ+OKNFDSqEmhoMXjGR7f9mx6k7E/f6p/OJ8qohQe0+72bFFIDlkVlWFMe0zgNImiwBlM7ztIQ76g5AuKSHCCavaohoY3+vPe0Zk0EZwIT1BId8IITihwutqzqQzKIaEg9Uc1DOcLqcRAZXotqbF32OuBEwrR+srES4+fK2iqiFLpOGeKx9kiOIZhmMBpEtv7RwDYMzQyoY/rG0RFpOlVVOE5OhNR3p2q0V/dERy3hshp4sk8OH5kobuOMnH/Ncv4orTOxnpRf49FZYk4+I3+UgiuirRUf4qIZinlmqXLRcps2KZhGCZwmsT2viCCs3uCIzh+TxWg+SbjCFNvmiqqqBELmTpSXVECJ6mPxo/gJPXv+JSGbQb7qCetGD5++PySrmekYkyDv556PTjB38n/Hwa9VGA9XifDMKYWJnCaQKGg7BgIIzgTK3BKhtXgRNDsMvFGVVEVapiM03hwGlFFFXYt9k3GaUTJqLLsOl6zUHTNnxGUUycd11BpMPbXNZJiPZWPn6YXzqBv5m7Llm0zDGP6YgKnCewezBVPpBMfwSlVUEF90YBGEFVFVYy8JFhSLiJFla7RX/l9fbo8H00c/0tYqtzte3DqiOCEr1U9ojQUEAtCgZMw0lE5piG43JhOxv76klCMlLVl6e5wZm4rEzeMaY8JnCYQGoyhCRGcCg9FZzaMLDTnhBAVMUk3qiH47ZuM0wicqI7I/v5CcREnQhA2m6vbZNxAURpWUS2YGTbEa4AHp60OD45bT/jyV3py4jDopQLDtgdpZn4ZhjG1MIHTBHp9gTPBEZxqJ8tmpKhUNTJiksY7ExXBaUsTwYkYHeGTxCxcVr5c1yyqitcsZdWSqhYjNvNndAJpTMajPTj1jGoIPTjzXQfiSk9OHAYjvE6DTYpIGobROpjAaQKhwRhg9+DEVlFVpjt8k3GasuN6CB9OpNT7BiDUOqrEXlNkmXiqaqzqjf4AetpDH87YwrSYOunI0lWM/NQzbNP5ptrTCRy/s/KsrrCzcjKBPZwbHcGpp9VAGLFZNCsUXCkiODm/oWL642wYxtTCBE4TCEvEIfjGmuQEXC+VhtVsRshmBNVkZtxGEFVBBYHYCUVO3GNTq0w82ciH6iZjoBiJiXMCHSzzhjSmkzHgNftLtq8wWtPdkfWEWro+OOUenHqGbQaPHwqcSk9OHEpm7vIqqokW7IZhtBYmcJqAH8GB+oYMJiXqG3izKqmKXYOr+F0gvjiJ8vKE+1WNX3IeNfzTJ0klVal8OZPaZKyq1aNuCV+vMDrS05FNXBEWEllFVVeZuIvgzKwnglOqVmvPZmjLSOoxFIZhTB1M4DQB32QME+vDGc6X98GB+n04ab8ph2IiSkskraQqenkqUktJozhRZes+SYTKgN/oL6XJOPS8tLlIG5RSVUMJT+B+v5jujnQzm0by5RFAKPlx0jX6K4/gpCsTLx1n8PsVpRc4aediGYbROjRV4IjIPiJyvYisFJGHRORDbvt8EblGRB53v+d59zlfRFaJyKMicpq3/TgRecBd9w1xpg4R6RSRy93220RkxYQ/0QpGCZwJ/DANu75GeSjSVOUUCsobvnMz/++ndyW+b62eM0kjOMXqp4p9ZRIajWtFlYCiMIhTXu1XUXWmPOlWVr35l5NHcEpCoP4IjtfoL2UfnOFcgeF8gbaMMMdNAU/X6K90nIPf7v2c0ofzX9c+zjEX/JEH1u9MdX/DMFqDZkdwcsCHVfVw4CTg/SJyBPBx4FpVPRi41v2Nu+4s4EjgdODbIpJ1+/oOcB5wsPs53W0/F9iuqgcBXwW+NBFPrBbb+8qNxRPZC6d0wswWt9UTwdm6Z4i71+7gjw89kziSU2zOF5WiSmgQjioTh+SVVEWTcdUIjjOxxklR5UrekLQzksKTdFTELakgDcVMT0fJE5TYg5MLq6hGl4knTVENeJ6gmW4KeJpRDX7HaP932gjOPet2kC8oD2wwgWMYk5mmChxV3aSqd7vLu4GVwDLgDOASd7NLgNe5y2cAl6nqkKquAVYBJ4rIEmC2qt6iwVn2xxX3Cfd1JXCqSBUH6QQRlomH33wnspJqKDc6xVCqpEp+cgk7MucKmrjEt1AjHRS+QnFFU1SZOJSEUlwDdb6YNqudoorlwfEiJmlnUUVFcDpTVi2VBEVbMYIzMJKwiqrmLKpk6wmjNTM62orHtZ5RDaEILJWKp4vg7HD/nzsGhse4pWEYrUyzIzhFXOroOcBtwF6qugkCEQQsdjdbBqzz7rbebVvmLlduL7uPquaAncCCiMc/T0TuFJE7t2zZ0qBnFU34Abp8XjcwsSmqYh+cdt9knH6MgG+YrjRPj0W1KipI1qTP76dTqZVCT05ck/GYHhyXooojVCKniSeOmEQInJRl4sUITj0pqgiBXPTgpDU9d2aZUUcEJ4zUhCKyOHAzZbO/Ha7KcWf/xLZwMAyjsbSEwBGRmcAvgX9U1V21bhqxTWtsr3Wf8g2q31PV41X1+EWLFo215LrodSmq5fN7gIk1GdeM4KQQOGEEB2DnQLITQig6ogJqSTw4viip3FeSCI6q1vQFQTKTsV9FlbY/S6nJn5dSTF0mXqqi6m53gqIBVVSdKVNUYUn4DC+iVE8EJ5xDVZpHlU7ghB65HSZwDGNS03SBIyLtBOLmp6r6K7f5GZd2wv3e7LavB/bx7r4c2Oi2L4/YXnYfEWkD5gC9jX8m8VDVYgRnn2ZGcCJSHmkiOP633KQnhGIEJ+JdmKSKKk4kKE5X5FADiVQ3GfckSDVFTRNPbDJuoCD1q6iKzyOtwGmL6mSczIPV5wmuMIKTJuoyWM2Dk9I0Hwr1ymIAwzAmF82uohLgImClqn7Fu+oq4Bx3+RzgN972s1xl1P4EZuLbXRprt4ic5Pb59or7hPs6E7hOm9gBbPdQjlxB6enIssD1/phQk3FUJ9q6IjjDkZfj0KgqqlpppSQTxceK3gCJzLkDXnVPZ1sGkcCnkksQ6QijNOV9i4I1pE5RlfXBSerBiTAZp2z0F6ajZnT6EZw6ysQ7Kjw4KSI4uwdzRaG7I2FE0jCM1qLZEZznA28DXiIi97qfVwJfBF4mIo8DL3N/o6oPAVcADwP/B7xfVcNPsfcBPyAwHj8B/N5tvwhYICKrgH/GVWQ1ix0uPTWvp4PZrl3+hEZwGtwHx4/abE8YwQm1S1S0pBTBaYzAibOfsZr8AYmqoYa8/iwiUrpvguNcOYcK6q+i6vYiSqmHbTZA4JRFcMLy+3rKxNvKPThpBI4v0uv14Kzd1s83r3vceuoYRpNoa+aDq+pNRHtkAE6tcp8LgQsjtt8JHBWxfRB4Yx3LbChhBdW8Ge3F0tgJraKq0QcnTedXX9TsTBjSjyNM4piMwzRWvfvJjWEwBj+CE38Wld+Arn84z+BIvvjaj0XNiFvSsuxipKMUMYnTz8cnNBmXdTJOWdUVCq4ZHW30dLoITh1l4p2jysST72t7mWCvL0X1nT8/wc9vX8vi2V286fh9xr6DYRgNpdkRnGlH+KE5r6eDmU2J4Iz+Bp62Kgdgp5+iSuvBiUgJhRojjnem2ORvAlJUJQ/O2MdqYLjcG5KmkirKZJx2tEb5qIZ0JuPIMvE2KbsuLmFko6fTX08dJuP2TNnvNH1wdvT7KdeRuuZZPb1zAIBndg6m3odhGOkxgTPBhKXU83o6vAhOMzw4o6ty6k1RJfUslKqoRl+XSWAOrtW7JhQrsUzGocCpMkkc8FI7Y79mo0cIJE+dRJnCSymq9MM2u9qdJyhXSDTstfawzWRiwI/g+P2F4pb0h4QisLtCSKZKUXnv5+Fcoa5xD9vc//q2hO0TDMNoDCZwJpgwBD5/RgezmhHBaeAJEyoETuoqqlqdjGPsp0b34WIEJ8bJd6xJ4pCsiqroDXHm1zTN/ioHbUL60RoDnsnY9wQlWc9IsZNx6Ri1ZQSR4HVIIpb6vD442Uy69UD1Kqqk+4HRaal60lTb9pjAMYxmYgJnggkjOHN72pnVFczfaUofnAaYVqG8982OlB6cyMhLAu9MLJNxrDLx2nOoIH4n43xBGc4XECkJku4UpeKRZf3t6aqowhN+KNLSVFKNRKSoRCSV0bjf64MDMKMzXfPBRo5qqBTpaXvhqCrb+oYA6HW/DcOYWEzgTDDhN8L5M0opqgkdttlA0yqUf8NNmqIKNUeUMMkkSC3VEjhtKTw41eZQgReFGeMkPFhRQQXpIgvDEWXiaSM4pSqq4H0X97n4RDX689eUROD4VVTB73Q+nEoPTigG06WoykV62nENgZk8OBZhJMcwjInFBM4EE2Uybs6wzfr7qgzl8mXfthOnqBpURVVrP0mmideKKIXETaNURhX8y+lMxqM7B6efReUERYpuxlEenODv4Jgl8eH4fXCgJHSSVFKp6qgy8fD5pUq5Voj0tKXivqixFJVhNAcTOBPMdq8PThia3zOUS2ysTEsYEWjECTNMT81yJ6idA8OJqk5qmYOLwqRBEZx6hVJIGGVIEsEJSWN+jZxF1YAqKkjWtLDWesAbuJlgTaMjOMlTZn5EMnzPlEY1pG97sHhWZ9nfSdnqpaV6+4Yn7P/bMIwSJnAmmO1eH5xsRphRxwyeNEQ2jktZRRV+u100u5Pu9iwj+WQTxQvFiMno68JtcQRT7XLzBAJHY6SoYkZhKtMm/n0TCZxiWb9X9Za2D85wuehKM66h1Oiv/Bil8uAMl0dwwt9J3kOlOVSl45y2iSGUejmtWDgDSJ+i6vUiOPmCsmsCe10ZhhFgAmeC8VNUQMloPEE+nNJso9EnzKSejjCcP7e7nbk9wfNIYjSumaJKUUUVGcHJpkhRxfHgjJWiGi6NaQgpTrlOUUXlT39PXSbeEJPx6FEN/pqSiK5iH5zK9ST4XxgcqX6cB1OkqMKIzf4LAoGTOkVVYSzeaj4cw5hwTOBMIKpalqICSs3+JsiH08hZVH5Pn7nu+STx4cRKUdXrwZEUqa4aHpz2rJDNCLmC1jxe4cnVT1F1pSgTb+SwzdIsqtBk3JZ4PY00Gft9cPzfSVJm/gDRkHr64GyviOCkLROvFDS95sMxjAnHBM4E0j+cZzhfoKs9U/xADiupdk2UwMnXSFElTHmEEZw5Pe3M7Q4iODsTVFLVqqJK0qAvngdn7OcWx4MjIvTEMBpXGnrBO/EmMhmHYwjqM4XnnSATKUU4emKWvPuEjznKZOy6GYd9cuLQ7/XB8X8niSiFQrLL7/Scskw8ly+wezCHCOw7vwdIXyZeWTm1bY+VihvGRGMCZwIJv8XNd9EOYMKb/UWaVlOOagjD93O7O4opqiTfeCeiiqq0n/rW49MVw7tSnI/UFiFwUgzbjIrgJEkpDkSUracxGZf64ER7cJKlqKIjOEk8OKVxGL4HJ12ZeCjOZ3e1M3+Gi0imnCge9r4J12KVVIYx8ZjAmUDCb4NzPYFT7IUzQRGcRnbGDQ2Yc3t8D06DU1R1ppZKJuMYEZwYjf4gXjfjqNRJGvNrZEoxhSk8jIqUVXUVhVpyD47v4YLkJuN8QUcNIy32wUnhwemMKMdPKnBCMTOvJ52nzCcUNActnhn8bR4cw5hwTOBMIL1ek7+QUrO/iamyaOR06pJgay+KtiQpqlpVVGGRTpzy2ngm4/jrqVVFBfEqqUpl4qOrqFKZjMtSMMkFTlTKrK4UVUUEJ6no8rsqh4Ky2AcniQcnwuuUpmM0lMTM3J6OokcubYoq9OAcsngWYN2MDaMZtDV7AdOJ0gdoe3FbWEU1Uc3+as6iSvqN14tIhSfQRlVRlToZx9hPjZlW4X5yMSI4cWZRgV9JVf01q5wkDvWZjKM8U0kibiWDcUQEpwEm41Kjv3hr6i9WUJU+gtJ4cIYiyvGLEZyEVVTlgt1FcNxEcRnjPVFJ6Lk5ZO9A4Gy1FJVhTDgWwZlAih4cP4IzgR4cVfX6qkQ0jktsMnaCrTtdiqrQoCqqXAyTcZJp4pkx/itKEZxaVVTlE679y0mE5FCNUQ3D+ULsxoqlSeKeoIjZtNAn6v3jry+uwOkr9sApHZ80VVTRHaNdOX7CPjhhifi8ng662rN0tmVSTRRX1eL/+sEuRdVrKSrDmHBM4Ewg2yM8OGEX4ImI4PiGVf8baeinSGoy9r/xzukOnlOSzq+FBlVRFWpFghJMEy81+qv9bxGnf0xkBCdFH5yoCE4mI4kr34qTxL319NRhMh4dwQnXE09w9UVFcFKMagjFR1k5fls4qiG+AIRS9HGOqwhMY5wH2DWQI1dQZnW2sWRONzC6L45hGOOPCZwJZHuxiqqUoprIPjhRc6ggfRXVDq+Kal5PWCaePEUVZeptVBVVkghOLkajP4g3NHMwIrIQtwuyT7XXLGklVeUkcUhbRRU2+ov24IwkXM8Mbz1hJ+NEZeIRx9kXgEnSeDv6y3tUpfXhhGMaFszsYMHMYB/19MHJF5QNOwZS398wpismcCaQ0piGKJPxBAicKnOE0vbB2en3wUlxMijUGrGQQJjUqqIKRU+caeLFSNAYdos4Iw6iTMYlYZSgTDysEvJMxpC82V+xiqpjdASnlpeokpGiyTg6ghM7RRVGcDojIjiJGv2N7vQM6UrF/TEqUIrkJB3XEFZMLZjZWRRJ9cyj+tHNT/L8L17HtSufSXV/w5iumMCZQCrHNECpD87uCRQ4ndWiAQlOvMO5AnuGcmQzwuyutjJTZlzyMaqoksyQykYok1DgJKvGqv1vEacaaiCqw25Hcg9O1ahbQoFTOYcK0qWohqp4cIqN/uKajIdrRHAS/C8UPTgVArArRSVVsXFlRYoqaQQnrJhaMKODjrYMs7vaKGj6njp/XbUVgJuf2Jbq/oYxXTGBM4FUjmkAr9HfBAzji+qB4/+dJIKz0zsZiEjp225//InitVJUqUY1REVwJH4EpyRwat+uO4YZNnpGUooy8ZExXrPYEZyIFFV7MpOxqo7pwYmbEory4HSnKFsfihCSkO5Y76j4ApI6RVWM4AT3XzgzmEyetpvxE1v2ALDa/TYMIx4mcCaQyhA4wMzOiRu2GTXXCNLNNtrpVVBBcEIJJ4rHPUEVRzXUaNAXRyvV7mScKbtNzf3UKDf3iTPrKKq6J00fnKjRGpC8VLwUURqdEor7euULimpwfCqPUWkWVbKqrrIqquI08RQenLb6U1S+aR6C1CvUkaKaEQibsGoyTTfjwZE863r7AVi9tS/x/Q1jOmMCZwKJSlGFJuMkVVQ3P7GVVZuTf5ur5sFJmu6A0slgjmeYTpqmqiUosgmGZNYqEw+1XLKZVmOkqDqC62tHcKo3oKu3k7H/d1IPTk+EByeuwKlmMPbXE79MvHoVVb1l4pBu4GalyXhud7oIzjbPZOz/TtPN+MltfcVqw3W9/YknyBvGdMYEzgQxMJxncKRAR1um7CSTdFTDut5+3vqD23jPpXcmXsNwPpyPVN1kHDe9VHkyAMrSVHFoVIqqUNNkHDy3RCmqMUzGcaZwR1X3dHqVT3ENp8Vhm9VEaT7eCa9mo7+YEZPhKukpf1v8Rn+jPTidbRmyGWE4V4i9n6hUIKQbuBl+AQlFe9pxDWGkZsHMMIIT/E7TzfiJzaWoTUFh7bb+xPswjOmKCZwJohS9aS/rQVMUOMO5WCe9+9fvpKDwxJa+Ytl5XKr5OUSS91UJn0+YooLkpsxSo7/R1yWJvBQjOJEmY7efBAJnrDLxOJGYyjlL4X47E5R3FwrqzX6qzxgeNRur6HkZyccStiNVDMaQfNhmX0RVl4gkjuJECUn/77gRnKFcnv7hPG0ZKfammpfSZBx6bRbMCD04we+tKSI4lZHaJ7ZYmsow4mICZ4II+2D4EQ8I0iozOrKoBieasXh4087i5Qc37qxxy9EUK2AqKk6Cbck8HX6JeEhSU2Ycc3CsCE6NcvMkEZxCsdFfzGGbtQROxJRrSDYewe8aXDkqIHwNhxJWLfkRnLZsho5sBtV4r/tIlYou8EY15GJ6cMIITmf5tJhSN+N4UaWwY3TlcQ49OXEFzk7PfxMe67B5ZdLqp20VJuPQg5OmF05oMA7F1uqtZjRuRQoFTdxHzBh/bBbVBLHX7C4+e8aRZZ6DkJldbfQN59kzmCtGdKqxctPu4uUHNuzkbw5eFHsN1UzG4E5aQ/E9HX6Tv5C5CU2ZDa+iqtURuc6RDz5+5KMa1VIn3e1ZdjASS+AMVSnr97clraLqrlxPR5bhgQIDw/lRa62kOGiz2vuH+CmzvghPkP933G7GgxEdo6EkJOPOo6osEYcGpKhcaipMVaXpZhxGcE49fC+uvGs9qy2C03IM5fK88bu38Mim3Txn37k878CFPO+gBRy377wxo8HG+GIRnAli0axO3nbyCt5w3PJR180sjmsY+5viyk27ipcf3JAsglPsg9MecYJKOA26OIfKi+DMSWjKrFVFlWRUQ5xp4kka/UXNxvIpnjzjmIw70ptfqxmM/W2xq6giZlGBZ+yNsZ5Sifjo41P04MSN4BT74FSsJ+HAzVDAjEpRtSXz4GyPiLCmKRPPF5Tt/cOIlKIuYaoqqcm4UNBixOZlR+wF1FcqvnNghB/+dU2iTtHG2Hz3htXcv34nw/kCt63p5at/eow3fvcW3vKDWxPbCIzGYgKnBZgZThQfo1R8e98wm3YOFv9+cMOuGrceTTEiEPENPOm4hsqSWv/yzgZUUZU6GcfYT6F6aimToBqr1n58ShGcGrOoxvCGxKmkqmYwhtJrWE8VFSQzGg/nwiqq6gI5eSfjyghOsoGbUdVqkHzgZhjBiXo/hxPF47C9fxjVwJvW5o5JsYoq4cluw44BBkcKLJrVyXP2mQsEHpwk87V8vvanx7jgtw/znRueSHV/YzSrNu/mW9evAuB7bzuO7771OM45eT8WzOjg1tW9vP7bfy2mGY2JxwROCzArZiVVGL05ZvkcOtoyrO3tL3oH4lAzIpDQJLojYnBoaDiO+62lZooqgQen1gypUKzkY/RniTuLaiwPjqo2pD9LrdcrqSCtlqJKYuqt6cFJ2CyyWgRnRkfCCM5IFQ9Oe8IUVWia997P/kTxuL2L/DENIWk9OOGJ8cBFM1g0q5OZnW3sHBhJPdfqz49tAeBPKzenur9RTqGgnP+rBxjOF/i74/fh5UfuzelH7c0FZxzF1f9wCkcsmc2T2/p5/bf+yk2Pb232cqclJnBagGI34zEiOA87gXPUsjkcvmQ2kMxoPOw+7GulPBKnqMo8C8lMmaWU0OjrklQ/1SoTL3p54kwlr2FW9hkrCjOcL1DQQDS2VUQ7UpmMawjSuH1RooZtAvS0x4+YVOtiHKwn2aiG0IMzozKCEzb7i+nBGStSFjtFVWx70F62PWmaqrKCCmC+28f2/uFYgj0krJg6cNFMRIQDFs0A0jX827hjoOjfWblpF5t22vDOevn5HWu548ntLJzZyb++8vCy65bM6eYX7z2Zlx+xF7sGc5zzw9v59988aCmrCcYETgsQtxdOKHCOWDKbo5cFAueBBD6cal1xwfd0xP3GWyNFFbeKqlb1U4Ma/bXVMCvf8OhmXvn1v/DJ/3kAVSU8N0eVm/v0jCFSBoere526E5x4qw3ahOSCdKBYRVUeMemOUREWMhzHgxO3k/FQ9HqSR3Bqz6KKO/crKiIZ/J2sVHxrX3kFFQTVanN72lEttVeIQ2gwPmjxTAAOWOgEToqURzjPKuT6R7Yk3oePqvLYM7v5/o2reesPbuNN/30LP73tqVg+wsnAjv5hbnxsC7+5dwMrN+0qE+67Bkf466qtfPF3jwDw6dceUVZNGjKjs43vvvU43v/iA1FVfnzLU7zoyzfww7+uif1FwKgPq6JqAWbGHLgZVlAdvmR28SSTSODESFHFLhNvQBVVIUYVVawITgwvjy9wevuG+ezVD/PrezYAgXA8+YCF5AvBcx8rgtM9RlonTItUpoMg2YykWhGcUPQkHrZZpWopXgTH9eSJEFyJp4mHEZxRpudkEZxQBHZ11DeqoZSiKj9RJZ0o3luM4HSWbV8wo4Md/SNs2zNcnE01FqUUlRM47neaSqqbnMA5fMlsVm7axXWPbOYtz9038X4A7l+/g/f/7G7W9ZZHgW5f08vnrl7Jq49ZwmFLZtPmRnrM6Mxy9LK5HLhoxqh2B63CcK7ADY9u5ncPbOKedTt4qqKhYkdbhkP2msmewRxPetedethiXnX0kqr7zWSEj5x2GK991jI+e/XD3LRqKxf89mF+dfcGLn7HCSyaFe+9YKTDBE4LMCtGFdVwrsCqzbsRgcP2nlX8AH8ogcApDtvMjj5BhZ1f45wwR/IFdg/lyEgpvQYlsbM9dqO/4PdERHByeWXlpl1c/+hmLvrLGrb1DdPZluHFhy7m/x56mk9d9SCvPmZp1f34hNGCsCNxpUCrJibAS53EMRmHjRlrlGXHEaSqWqySiioTh3gRk1KbgeoRnNjrGVNwjb2efEEZzhcQGX2Mkg7bjGp7AMkjONsiIjgQCJ4ntvS5UvFZsfb1RGUEx6Wokjb7U9ViBOcTrzyct150G39dtZXBkbFbA1RSKCgf/+UDrOsdYOHMDl5wyCJeeMgi8gXlsjvWcfuaXn5x1/rI+87taefYfeexcGYHuwZyxWKED556EM87cGGidTSKR57exc9vW8tV920s+9zqbMtw5NLZLJrVyaNP7+bJbf3Foo6ObIZD957FsfvO5UMvPSSWaDt071lceu6JXLtyM5/+7UM8sGEnb/zuzVx67nPZZ37PuD2/6Y4JnBZgZtfYKapVm/cwkldWLOhhRmcbh+w1i45shie39bNrcITZXaNDpJXEMhnHOEH5k8T9k7ufolLVMf/xQ/FSuw/OmMspeXBq9MG54q51XH7nuuL2kw9YwBf+9mj2nd/Dm79/K7et6eVnt62tup/KtXW1ZxgcCcynlc3qBqqkTSDZwM3iaI2osv4Ept7hfIF8QWnLyKjXfqx0m09ND05bfA/OUC5YT0c2M2o9pYGbY6/HT09VvtdKEZxknbnr9eBsjTAZB38nKxXf3jfMtr5hejqyLJnTBcABC10EJ2Gzv0ee3s3WPcPsNbuT5x+0gCOWzObhTbu4bU0vLzwkfh8tgKvu28jDm3axZE4X1//Li8oE0t8eu5zVW/bw2/s2sb1/mIIquYLSu2eYu9duZ/PuIa57ZLTB+b71O7jiPSdz1LI5xW2BKNvGPvO72W/BjNjrU1Wuum8j96zdwQGLZnDoXrM4bO/Zo1JIgyN5vn7t43zvxtXF6O4he83k9c9ZzgsOWcghe80qe5/vGhzh0ad309ORHXVdXESElx6xF8/aZy7v+OHtPLRxF3/7nZu55J0ncsTS2Yn3Z4yNCZwWIM5E8bCCKjQXt2czHLZkFvev38mDG3bG+gYUq3FcjBNUNb9CV3u27MQf1dTQp5YwKU0Tr6+8e/HsTrefoBfRCw9ZxMuO2IuXH7FX8aT4xTccw+lfu7F4fMZKUUGQShkcGY4UOMUTb0QEp9hDJ0JQ7BnKceZ3bmZeTwc/fOcJtRszJhCktSJKScqya5uMS16eXYMjtGcC8RL12ha7KndGrSe+J6haryFIPmwzqjO3/3fcFFWUyRiSV1L56anwfbq/8+Cs3dbPSL4Q+yQbRm9OOWgRIsJLDlvMw5t2cf0jmxMJnKFcni//8VEA/ullh0RGfw5YNJMPvfTgUdtVlQ07Brh77Q76hnLM6W5ndlc7v7hrHb+5dyPv/NEd/Pr/PY/l83rYOTDCx395P79/8GkyAq8+Zinvf/FBHLp37cjX6i17+MSvH+SW1dtGXXfgohm85LDFvPiwxXS2Zfj4Lx/g8c17EIGzn7svbz5xX45cOrvql7LZXe2csGJ+nMM0JotmdXLZeSfxnkvv4uYntvF3/30LF73jBE7cvzH7N0qYwGkBZsXw4Kz0DMYhRy2bk0jg1KzKSWBa3ek+7P2uryFzuzt4emSQHf0jYwqcfIwqqkRl4hEfTi84eBE/eucJLJ7VxeFLZkV+gO2/cAb//LJD+MLvA9NgnO6jteZRleZQjT7OtVIn37xuFY88HfisPvHrB3nBIcFr2hlxIgmjOnFSQlFjGirXM5bA2bhjgCtd6iFy2KaL4Dzy9G6O+fQfgSD1+qUzj+GVFR6FsAdOpf/G39Y3hh8NvDENkWX02bLbjEUpglORokrYvLK32MW4IkUVdjPeE6+bsV8iHtLdkWXZ3G427BhgXW9/0ZMzFqH/5pSDFwDw4sMW883rV3HdI5v51GuOiO2L+emta1m/fYBD9prJG44d3bC0FiLC8nk9LJ9Xno45cf/5bN41xC2rt/GOH97Bp19zJOf/+n7W9Q7Q05FlOFfgqvs2ctV9G3nJYYt5zj5z2W/hDPZfMIPujiw7B0bYOTDMvWt38N0bVzOcKzCvp523nbQfm3YO8tgzu3n0md08saWPJ7as4ft/WVN87AMWzeA/zzyG4/abeGExq6udH77zBP7p8nv53QNP87aLbuO7bzuOFx+6eMLXMpUxgdMCxElRPVwRwQE42oV04zb8qzZsE5KZjKMqqELm9rTz9K5A4Cyd211zP8UUVVR5dwwPjqpy25pebnXf2KqZjF8U40Pj3FP25+r7N/HAhp1lE66rUavce6jKmIZgWybyfmu29nHRTauBIJr2y7vXF09ytSI4care+qtUUAXbajf6G8kXuPimNXz92sfpH87T05Hl1ceMNlUesHAmJ+4/n8ef2c1IPvDG7B7K8U+X38vSud082zWqK19PRASnM57gguqDNsHrZBxjP6rqlYmXC5N5Ccc1VE4SDyl2M44ZwamsoAo5YNEMNriS7zgCZyiX57bVvQA8/6BAMD97n7nMn9HB2t5+ntjSN+oxotg1OMJ/Xfc4AB897bAx07hx6WjL8N23HceZ37mZxzfv4a0X3QbAUctm8803H0t7W4bv/fkJLrtjHdc9sjkyxeXzhmOX84lXHV6MmEHwHr7rqe1c/8hmrn90M+t6B3j7yftVjUJNFJ1tWf7rzccyu+sBLrtjHe++5E6+dtazi17AZvDUtj4ef2YPJ6yYH1kZNtkwgdMCFBv9VfnWqqqlFNXSKIETz2gcp0w8TgRnR5WTASSb31PTO1OjimpwJM91j2zmezeu5t51O4DgZHnsvvPGfMxqtGWDD9or7ljHGc9eNubtu2tEPqImiVfer/LE+9mrH2Ykr7zp+OW84JBFfOBn9xSfW70Rt2rdfqF2FdXWPUO844e3FwX0K47am3979RGRwrWjLcMV7zm5+Leq8q+/fpCf376Wd//4Tq76wPNZMie4X3EOVcTctWIEJ4bJOIyeRUW4ih6cGAJwcKTAcK5AR1tmVMPAxGXiLkKzsNJknNCD4/fA8Tlw0Uz+8vhW58PZa8z93LN2BwMjeQ7daxaLZwVenmxGeOEhi/j1PRu4/pHNkQJn865Bblm9ja72LLO62vjDg0+zvX+EE1fM59TDGxtlmNPdzo/edSKv/9Zf2bx7iHc8bwXnv/KwYqXgBWccxftfchDXrdzMmq19PLmtjye39jOcLzC7u5053e0smNHBG49fHhnJbs9mOOmABZx0wALOf+XhsfyBE0U2I3zhb49mVlcb3//LGj7483vYtmeYs5+776geWo0iX1Ae37yb3r5hdg2MsKN/hPs37OSmx7eytjeoENtrdidfedOzi6J4smICpwUololXqaJ6ZtcQ2/tHmN3VxlJnOASKRuPVW/vYPTjCrDGMxuG39HqHN4bh/GopKojX7K9YRVVD4Fx9/yb6h/OcfOACejqy/P7Bp7l25eaiGJzb0845J6/gnOetKPvWloZlc7v5p5cdEuu2YQTnF3euY89gjmP2mVM0eg9U6Rrsb/PNr9e7b6azOtv4yGmHsWhWJw9u2MV3/xy01I9+veJXvdWKmFQzPT+za5C3fP9WntjSxz7zu/nc645O5NcQET5zxpGs2bqHW1f38u4f38kV7zmZno620iTxSE+QE1wxysSHctVTgbW8TpX4BuPKE1+S+WpDuTy7B3NkMzLK9J/WgxMVwYH4peJhB91TDi4/Ub34sMX8+p4NXPfIZt79ggOK21WVK+5cx+euXhmZMv/YKw4bF3GwbG43v//Q3/DMrqFIw+3iWV2cdWK6svZKWkXchIgI//rKw5nT3c6X//gYn7rqIb5zwxOcdeI+nHXCviye1cmOgRG27Rli91CO9kyGtqzQns2wdG7XmFaAEFXl2pWb+dL/PcLjm6ON6nO621kws4PVW/p460W3cd7fHMA/v/wQdg/mWLlpF489s4eswOLZXSye1cncng4GhvPsHhxh91CO5fO6OXLpnMh9NwMTOC3AWI3+Ht4URGiOqDDBdbQF5YoPbNjJb+7dyNHL5tDZnmFOdzuLZ3UVRcJdT/XytT89zl/ch11oavYJIwJrtvXx5NY+9p7TVTV8uzNibk9Ikm+8tVJUJ6yYz4IZHWzrG+YXd60fVXp65NLZnHnccv7uhH1i/4M3kuXzurl9Dfz0trX89La1iMApBy3kE686vBg1iIosVKa2hnMFPnP1wwB86KUHF/tifOS0Q3l40y5ufGzLKD8HlERPPA9OruyxfcJj53uJ1m/v5+wf3MZT2/o5bO9ZXHruc1P162jPZvjO2cfxum//lQc37OL137qZeTPa2bxrqOyxo9azefcgP/jLav782BbuXbeDjASVa13tWWZ3tbNkThdhbK9WiqpvKM/KTbt4eucgG3cOsK438K+s297P0EiB5fO6iybxyhJxgHkz4puMt/cF7/n5MzpG+bjC3jdba0wUHxjOs2nnABt3DLKut59sRkZVEBUrqZzAGcrleXJrP2t7+1nX28/67QP0D+c4cNFMDtl7Fjc8FqR0Tqn4Jv7CgxeRzQi3rN7G6771V159zBJO3H8+X/7jY9zoRjqcuP98Zne1s3twhD1DOV506CKO2y99lHQsFszsHJXamy6ICB94ycEsndvNf123ijVb+/janx7nG9cGacFqVsSejiyvOGoJZx63nOfuPz/SP9g/nOO+dTv56jWPcfuTQbpy8axOViycwRwXAVuxoIdTDl7E0cvmoKp8+4Yn+Pq1j/PfN67mx7c8FbvdAsDzDlzA+198EM87cEHTxeS0ETgicjrwdSAL/EBVv9jkJRWZNcawTb/BXyVHLZvDAxt28sn/ebBse3tWWDIn+PAO01szO9t41/NXcNIBo0114Yf8z25bWyyXXjSrk33mdbPP/B72mdfDgpkdzOluL6bE5kZEcMK87YMbd/LYM7vZa3YXs7vaIt/otVJUhy+Zze2feCkPb9zFrau3ccvqbeweHOHFhy3mlUctYcXC+KWj48GFrzuaFx26mHvWbueetTt4aONO/vL4Vl759b8U0wpREZww8nLdI5t56Vf+zMBwng07Bjhw0QzefvKK4u2yGeE7Zx/L/z34NC89fHQqIhQ4967bwfdvXM1ZJ+5TNYJXK6Lkp6h6+4a5fc02Pnv1SjbsGOCoZbO59F3PZV4dkbF5Mzq46Jzjef23b+bRZ3aXXXf4ktFVMaEH58lt/Xzuf1eWXedPF/AbXEYJ7VD0bNgxwCu+/peq6/PXFFbc+YSi56lt/bzjh7czki+QyyvzZ3Sw1+wu9p7TRUGVBzfs5L51wZqiBGkYwdm0Y5Av/G4l3R1ZOtoyrN8+wBOb97B6ax9bdpeLnwMX9oxKT4YRnPvW7+DFX76Bp7b1jTmQtj0royp05vS08/4XH8T3XZo3TIdC8C3+gtceyRnPXtr0E9R042+PXc7rn7OMW57Yxk9vW8sfHnqaXEGLabhZXW3kVRnJKYO5PE9t6+eXd6/nl3evZ9ncbpbN66azLUNnW5b+4RxrtvaVDWie19POB19yMGeftG9kh/QA4R9OPZjnH7SQf7r8Xtb29jOjI8thS2ZzmKtk27x7iM27h9g1MEJPR5DG7Olo4441vdz8xDZufmIbz9pnLh9+2SG8IGErgkYyLQSOiGSBbwEvA9YDd4jIVar6cHNXFjCz2Ogvxxu/ezO5gpLLK0O5PIMjhWJeP0rgvPP5K9i8a5BdgyMM5QoMjuTp7Rth656hYj51Vmcb73j+Cs49Zf9Rpd0hZx63nLXb+lmzrY+NOwZ4eucgW3YPsWX3EHev3RF5n6gT3yL3DcwXSrO72jhq2RyOXjaHI5bOpj2bYVvfcNE4Xc2vmM0IRy+fw9HL55SF0VuB7o4sr33WUl77rMAQuKN/mK9c8xg/ufWpYvi3u2N06uSgxTPJSOC3Co2kGYELXntUZE+YNxwXXa1y5LI5xX4mF/5uJd+49nHOPH45+7tvZfN6OoofOuu3B8ogMkXltv111VaO/ew1xe3P2XcuP3rniZFpyKQctHgW1//Li7hv3Q6627N0d2SZ1dVeViEUsnxeNwctnklv3zAvOHghLzp0Mc87cAHt2QyD7v9he/8wm3YMsmnnANv7h3ldhGdq0axOjt13Lo8+vZslc7tZMqeLJXO6WD6vh33md7PPvB4627Ks3x5EPTbvHuS1zxq9n3kz2pnV1cbuwRw3PDr2eIOu9gxnRrxm4euxezDHf9+4OvK+HdkMe88JRNOSOV383fH7jLrN3rO7WDizg617hlmztY+MwIoFPey3YAb7zO9m+bweutuzrNq8h0ef2c3qLX286ui9R7UyAPjnlx3Ce194ANc9spmr79vErWu2cfIBC7jgjCOLfh1j4hERnnfQQp530EIGR/JkM1K1JcCarX388q5A4GzYMcCGHaNnjLVng0jg6UfuzXkvPCBWzzSA4/abx3UffiFb9gyx16yuWNWlOwdG+MmtT3HRTWu4b90O3n7x7bzo0EV88lWHc9DieA0uG4nE6TMy2RGRk4FPq+pp7u/zAVT1C1G3P/744/XOO++cwBXCC//z+lHtwX1mdrbxf//4N6PKLKsxOJIvfnAfuWROYkd8vqA8vSsIlQch/QF29AemtF2DObrbs3z+b48edQLcsnuIr1zzGE9u7eOZXYM8vWtwzIqY7771WE4/qnq788nEyk27uOC3D3Hr6l6+ftazIw3Lm3cPsr1vBJFA3Mzuamfx7OQnlEJBueGxzfz3n1dz25reMW//5hP35Qt/e3TZtnW9/bzgP69HNYgKHbvvPJ5/0ALe8fz9i8K7GbSSEXT1lj08vnkPHdkM7dkMGQlmTj2zM3h/5wvKkUtnc/TyORy0aGZVc+iDG3Zy11Pb6R/O0z+cYyhXYMmcLg5YNJMDF81g6ZzuWCeRR5/ezeObd3PQ4pmsWDCjqZVARmuQLwSFKHuGgvfV0Eie9rYMByycwbK53eNmWK7GwHCeH938JN+6fhV7hgJf2tnP3Zd/fOkhdXsloxCRu1T1+FHbp4nAORM4XVX/3v39NuC5qvqBqNs3Q+D09g3zyKZdZDNCWzZokNbZlik2z5vX0zEpP8hUlWd2DfHAhqBfz8ObdpEVYd6MDubPaGe/+TN43XOWRVYKTVZUld6+4Qn1E9y7bgfXrXyG3v5htvePsKN/mD2DOXcyzaOqXPi3R0f22bh77XbyBeWY5XNqhK0NwzCSsXXPEF+95jF+fvtaCgrfesuxvCqizUS9THeB80bgtAqBc6KqftC7zXnAeQD77rvvcU899VRT1moYhmEYU4lHn97NL+9ez/njVIVXTeBMna/NtVkP+Ant5cBG/waq+j1VPV5Vj1+0qHmmKMMwDMOYShy69yz+9ZWHT3jaeboInDuAg0VkfxHpAM4CrmrymgzDMAzDGCemRRWVquZE5APAHwjKxC9W1YeavCzDMAzDMMaJaSFwAFT1d8Dvmr0OwzAMwzDGn+mSojIMwzAMYxphAscwDMMwjCmHCRzDMAzDMKYcJnAMwzAMw5hymMAxDMMwDGPKYQLHMAzDMIwphwkcwzAMwzCmHCZwDMMwDMOYcpjAMQzDMAxjymECxzAMwzCMKYcJHMMwDMMwphyiqs1eQ8shIluAp8Zp9wuBreO076mAHZ/a2PGpjR2f2tjxGRs7RrVpxeOzn6ouqtxoAmeCEZE7VfX4Zq+jVbHjUxs7PrWx41MbOz5jY8eoNpPp+FiKyjAMwzCMKYcJHMMwDMMwphwmcCae7zV7AS2OHZ/a2PGpjR2f2tjxGRs7RrWZNMfHPDiGYRiGYUw5LIJjGIZhGMaUwwSOYRiGYRhTDhM4dSIiF4vIZhF50Nv2LBG5RUQeEJHfishs77rzRWSViDwqIqd5249zt18lIt8QEZno5zIeJDk+IvIyEbnLbb9LRF7i3WdKHh9I/h5y1+8rIntE5F+8bVPyGKX4HzvGXfeQu77LbZ/2x0dE2kXkErd9pYic791nqh6ffUTkevd8HxKRD7nt80XkGhF53P2e591n2nxOJz0+k+pzWlXtp44f4AXAscCD3rY7gBe6y+8CPusuHwHcB3QC+wNPAFl33e3AyYAAvwde0ezn1oTj8xxgqbt8FLDBu8+UPD5Jj5F3/S+BXwD/MtWPUcL3UBtwP/As9/cC+x8rOz5vAS5zl3uAJ4EVU/z4LAGOdZdnAY+5z+L/AD7utn8c+JK7PK0+p1Mcn0nzOW0RnDpR1RuB3orNhwI3usvXAG9wl88g+HAZUtU1wCrgRBFZAsxW1Vs0eJf8GHjduC9+AkhyfFT1HlXd6LY/BHSJSOdUPj6Q+D2EiLwOWE1wjMJtU/YYJTw+LwfuV9X73H23qWrejk/x+CgwQ0TagG5gGNg1xY/PJlW9213eDawElhF8Hl/ibnYJpec7rT6nkx6fyfQ5bQJnfHgQeK27/EZgH3d5GbDOu916t22Zu1y5fapS7fj4vAG4R1WHmH7HB6ocIxGZAXwMuKDi9tPtGFV7Dx0CqIj8QUTuFpGPuu12fAKuBPqATcBa4Muq2ss0OT4isoIgAnEbsJeqboLgJA8sdjebtp/TMY+PT0t/TpvAGR/eBbxfRO4iCPkNu+1R+UitsX2qUu34ACAiRwJfAt4TborYx1Q+PlD9GF0AfFVV91Tcfrodo2rHpw04BTjb/X69iJyKHZ/w+JwI5IGlBOmXD4vIAUyD4yMiMwlSu/+oqrtq3TRi25T/nE5wfMLbt/zndFszH3yqoqqPEITKEZFDgFe5q9ZTHq1YDmx025dHbJ+S1Dg+iMhy4NfA21X1Cbd5Wh0fqHmMngucKSL/AcwFCiIySPDBNG2O0Rj/Y39W1a3uut8R+FN+gh0fCDw4/6eqI8BmEfkrcDzwF6bw8RGRdoL/kZ+q6q/c5mdEZImqbnLplc1u+7T7nE54fCbN57RFcMYBEVnsfmeATwLfdVddBZzl8pX7AwcDt7vw324ROcm5zt8O/KYJS58Qqh0fEZkL/C9wvqr+Nbz9dDs+UP0YqerfqOoKVV0BfA34vKp+c7odoxr/Y38AjhGRHuczeSHwsB2f4vFZC7xEAmYAJwGPTOXj457PRcBKVf2Kd9VVwDnu8jmUnu+0+pxOenwm1ed0Mx3OU+EH+DlBPnuEQMGeC3yIwIn+GPBFXMdod/tPELjyH8VzmBN8i3rQXfdN/z6T+SfJ8SH4IO4D7vV+Fk/l45PmPeTd79OUV1FNyWOU4n/srQTmxweB/7DjU/Y/NpOg+u4h4GHgI9Pg+JxCkCq53/tceSVBhd21wOPu93zvPtPmczrp8ZlMn9M2qsEwDMMwjCmHpagMwzAMw5hymMAxDMMwDGPKYQLHMAzDMIwphwkcwzAMwzCmHCZwDMMwDMOYcpjAMQxj2iMi3xWRf2v2OgzDaBwmcAzDqImIvEVE7hSRPSKySUR+LyKnjOPjvUhE1o99y9T7f4eI3ORvU9X3qupnG/w4LxORZ0RkobetU0RWish7at3XMIz6MYFjGEZVROSfcR2Tgb2AfYFvE0wabhquS3FLo6rXAFcDX/c2f5KgKd/3GvEYk+E4GEazMIFjGEYkIjIH+AzwflX9lar2qeqIqv5WVT/ibtMpIl8TkY3u52si0umue5GIrBeRD4vIZhf9eae3/1eKyMMisltENojIv7jxAb8HlrqI0R4RWSoinxaRK0XkJyKyC3iHiPxIRD7n7a8s8iMi+4jIr0Rki4hsE5FvisjhBGMLTnb73uFuW7mvd4vIKhHpFZGrRGSpd52KyHtF5HER2S4i33Kt6aP4Z+CFIvIqETkK+ADwbmC2iFzkjskGEfmciGTd/g8UkevcmreKyE9de/zw8Z8UkY+JyP1An4kcw4jGBI5hGNU4GegiGKpXjU8QzDN6NvAsgmnVn/Su3xuYAywjGCHwLRGZ5667CHiPqs4CjgKuU9U+4BXARlWd6X7CgX1nAFcSDBn9aa2FO7FwNfAUsMI9/mWquhJ4L3CL2/fciPu+BPgC8CZgidvHZRU3ezVwgnvObwJOi1qHqu4E3kcgqi4GLtBgOOElQA44CHgOwWDMvw+X4B5/KXA4weDHT1fs+s0EAzTnqmqu1rEwjOmKCRzDMKqxANg6xgn0bOAzqrpZVbcAFwBv864fcdePqOrvgD3Aod51R4jIbFXdrqp3j7GeW1T1f1S1oKoDY9z2RAKB8BEXeRpU1ZvGuI//nC5W1btVdQg4nyDis8K7zRdVdYeqrgWuJxB4kajqb4FbCT5vvyEiexGIuH90a9sMfBU4y91+lapeo6pD7ph+hWBoqM83VHVdjONgGNMWEziGYVRjG7BwjBTIUoIIR8hTbltxHxUCqZ9g4CPAGwiG+j0lIn8WkZPHWM+6eMsGgqjHUymjG2XPSVX3EByLZd5tnvYu+8+pGg8RTO0uAPsB7cAmEdnh0mT/DYQTwBeLyGUudbUL+AmwsGJ/SY6FYUxLTOAYhlGNW4BB4HU1brOR4IQdsq/bNiaqeoeqnkFwYv8f4Irwqmp3qfi7D+jx/t7bu7wO2LeKOBtrwnDZc3K+oAXAhjHuF5d1wBCwUFXnup/Zqnqku/4Lbo3HqOpsgunolR4fm5JsGGNgAscwjEicf+TfCXwzrxORHhFpF5FXiMh/uJv9HPikiCxy5dD/ThBxqImIdIjI2SIyR1VHgF1A3l39DLDAmZxrcS/wShGZLyJ7A//oXXc7QbXSF0Vkhoh0icjzvf0vF5GOKvv9GfBOEXm2M0x/HrhNVZ8c63nFQVU3AX8E/j8RmS0iGWcsDtNQswhSeTtEZBnwkUY8rmFMN0zgGIZRFVX9CkEl0CeBLQTRhw8QRFwAPgfcCdwPPADc7bbF4W3Aky4N816CSAWq+giBcFrtUjhLq9z/UuA+4EkCwXC5t+488BoCE+9aYD3wd+7q6whSRk+LyNaI53wt8G/ALwlE0oE4f0wDeTvQATwMbCcwTy9x110AHAvsBP4X+FWDH9swpgWiapFOwzAMwzCmFhbBMQzDMAxjymECxzAMwzCMKYcJHMMwDMMwphwmcAzDMAzDmHKYwDEMwzAMY8phAscwDMMwjCmHCRzDMAzDMKYcJnAMwzAMw5hy/P82eMXhZ3WH4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# plotting line chart of number of buildings by 'CNSTRCT_YR' built after 1900\n",
    "ax.plot(g.loc[g.index >= 1900], 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 Built After 1900 in NYC', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the cumulative sum of buildings by 'CNSTRCT_YR'\n",
    "g['cumsum'] = g['count'].cumsum()\n",
    "\n",
    "# previewing first five rows\n",
    "g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "# plotting line chart of cumulative number of buildings by 'CNSTRCT_YR'\n",
    "ax.plot(g['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": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "# plotting line chart of cumulative number of buildings by 'CNSTRCT_YR' built after 1900\n",
    "ax.plot(g.loc[g.index >= 1900, ['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 Built After 1900 in NYC', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Attempting to identify the oldest building in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>956312</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>710956</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>52887</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>522025</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>504224</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",
       "956312  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "710956  MULTIPOLYGON (((-73.8247828135812 40.762948467...   \n",
       "52887   MULTIPOLYGON (((-74.25311647359251 40.50309522...   \n",
       "522025  MULTIPOLYGON (((-74.06293248238293 40.61512359...   \n",
       "504224  MULTIPOLYGON (((-74.01121069434295 40.70334382...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "956312  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "710956                   Bowne House  4113403      1661.0   \n",
       "52887               Conference House  5087656      1680.0   \n",
       "522025     Alice Austen House Museum  5042188      1690.0   \n",
       "504224               Fraunces Tavern  1078965      1719.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "956312  01/28/2020 12:00:00 AM  Constructed    550741       24.54       2100   \n",
       "710956  08/22/2017 12:00:00 AM  Constructed    776399       26.12       2100   \n",
       "52887   04/13/2020 12:00:00 AM  Constructed    702698       38.64       2100   \n",
       "522025  04/13/2020 12:00:00 AM   Alteration    806241       22.26       2100   \n",
       "504224  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",
       "956312        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "710956        64.0           0          0  4050130006  4050130006  Photogramm   \n",
       "52887         38.0           0          0  5078570090  5078570090  Photogramm   \n",
       "522025        21.0           0          0  5028300049  5028300049  Photogramm   \n",
       "504224         7.0           0          0  1000070035  1000070035  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "956312  {5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}  \n",
       "710956  {71A760A6-6D2F-48F8-8C93-C8B66071B868}  \n",
       "52887   {CB13BCBA-9DF3-4455-9206-824FE91F2EDE}  \n",
       "522025  {93B1CB7B-BE72-40DB-9B66-01E92DF4AE6E}  \n",
       "504224  {4B8DF813-9EAD-402A-81D9-58ECCB4ED502}  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting by the 'CNSTRCT_YR' in ascending order and \n",
    "# dropping records that don't have a building name (i.e. NAME)\n",
    "\n",
    "(building_df\n",
    " .sort_values(by='CNSTRCT_YR')\n",
    " .dropna(subset=['NAME'])\n",
    " .head()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>the_geom</th>\n",
       "      <th>NAME</th>\n",
       "      <th>BIN</th>\n",
       "      <th>CNSTRCT_YR</th>\n",
       "      <th>LSTMODDATE</th>\n",
       "      <th>LSTSTATYPE</th>\n",
       "      <th>DOITT_ID</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>956312</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",
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       "      <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",
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      ],
      "text/plain": [
       "                                                 the_geom  \\\n",
       "956312  MULTIPOLYGON (((-73.92083460429942 40.64439765...   \n",
       "\n",
       "                                NAME      BIN  CNSTRCT_YR  \\\n",
       "956312  Pieter Claesen Wyckoff House  3221967      1652.0   \n",
       "\n",
       "                    LSTMODDATE   LSTSTATYPE  DOITT_ID  HEIGHTROOF  FEAT_CODE  \\\n",
       "956312  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",
       "956312        14.0           0          0  3079170009  3079170009  Photogramm   \n",
       "\n",
       "                                      GLOBALID  \n",
       "956312  {5CAA55A5-5C4F-4F2B-B738-EB17A488BD84}  "
      ]
     },
     "execution_count": 75,
     "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 incorrect values with the median, and detect outliers with box plots. This step is crucial if you are interested in modeling or predicting future values. In Part III, we will focus on data visualization."
   ]
  }
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