building age and size analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Ages and Size Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will take a look at information on building ages and sizes. We will compare and look for any paterns, while looking at other information avaliable in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analysis in the next two sections is based on the PLUTO dataset, which consists of information on buildings in New York City\n",
    "(https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). A disclaimer on that page lets us know that dataset may not be 100% accurate and may have missing data, although it is as accurate as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NYC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A short analysis of New York City buildings, looking at average age, size, number of floors and number of units. Buildings in NYC are not the central focus of this notebook, but it would be good to have an idea of this information to compare with buildings in the Brownsville area that are of interest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading up necesary libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miss_\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3343: DtypeWarning: Columns (19,20,22,24,26,63,64) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>borough</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>cd</th>\n",
       "      <th>ct2010</th>\n",
       "      <th>cb2010</th>\n",
       "      <th>schooldist</th>\n",
       "      <th>council</th>\n",
       "      <th>zipcode</th>\n",
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       "      <th>...</th>\n",
       "      <th>appbbl</th>\n",
       "      <th>appdate</th>\n",
       "      <th>plutomapid</th>\n",
       "      <th>firm07_flag</th>\n",
       "      <th>pfirm15_flag</th>\n",
       "      <th>version</th>\n",
       "      <th>dcpedited</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>BX</td>\n",
       "      <td>4660</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.877873</td>\n",
       "      <td>-73.863604</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MN</td>\n",
       "      <td>445</td>\n",
       "      <td>54</td>\n",
       "      <td>103.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10003.0</td>\n",
       "      <td>E028</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.725333</td>\n",
       "      <td>-73.988711</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MN</td>\n",
       "      <td>467</td>\n",
       "      <td>55</td>\n",
       "      <td>103.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10003.0</td>\n",
       "      <td>L003</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.731190</td>\n",
       "      <td>-73.987722</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BK</td>\n",
       "      <td>2621</td>\n",
       "      <td>7501</td>\n",
       "      <td>301.0</td>\n",
       "      <td>573.00</td>\n",
       "      <td>3003.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11222.0</td>\n",
       "      <td>E238</td>\n",
       "      <td>...</td>\n",
       "      <td>3.026210e+09</td>\n",
       "      <td>1/23/2008</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.726484</td>\n",
       "      <td>-73.951001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MN</td>\n",
       "      <td>1970</td>\n",
       "      <td>58</td>\n",
       "      <td>109.0</td>\n",
       "      <td>213.03</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10027.0</td>\n",
       "      <td>E080</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.817053</td>\n",
       "      <td>-73.952741</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>858208</th>\n",
       "      <td>SI</td>\n",
       "      <td>5016</td>\n",
       "      <td>55</td>\n",
       "      <td>503.0</td>\n",
       "      <td>132.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>10306.0</td>\n",
       "      <td>E162</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.557440</td>\n",
       "      <td>-74.137239</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858209</th>\n",
       "      <td>SI</td>\n",
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       "      <td>63</td>\n",
       "      <td>502.0</td>\n",
       "      <td>114.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10306.0</td>\n",
       "      <td>E159</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.581080</td>\n",
       "      <td>-74.103509</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858210</th>\n",
       "      <td>BK</td>\n",
       "      <td>7690</td>\n",
       "      <td>206</td>\n",
       "      <td>315.0</td>\n",
       "      <td>642.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>11229.0</td>\n",
       "      <td>E276</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.612185</td>\n",
       "      <td>-73.944649</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858211</th>\n",
       "      <td>MN</td>\n",
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       "      <td>450</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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.828169</td>\n",
       "      <td>-73.933870</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858212</th>\n",
       "      <td>MN</td>\n",
       "      <td>2156</td>\n",
       "      <td>44</td>\n",
       "      <td>112.0</td>\n",
       "      <td>269.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10033.0</td>\n",
       "      <td>L045</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.851227</td>\n",
       "      <td>-73.929597</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>858213 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       borough  block   lot     cd  ct2010  cb2010  schooldist  council  \\\n",
       "0           BX   4660    34  212.0  380.00     NaN        11.0     12.0   \n",
       "1           MN    445    54  103.0   38.00  4000.0         1.0      2.0   \n",
       "2           MN    467    55  103.0   40.00  2000.0         1.0      2.0   \n",
       "3           BK   2621  7501  301.0  573.00  3003.0        14.0     33.0   \n",
       "4           MN   1970    58  109.0  213.03  1001.0         5.0      9.0   \n",
       "...        ...    ...   ...    ...     ...     ...         ...      ...   \n",
       "858208      SI   5016    55  503.0  132.04     NaN        31.0     51.0   \n",
       "858209      SI   3571    63  502.0  114.02     NaN        31.0     50.0   \n",
       "858210      BK   7690   206  315.0  642.00     NaN        22.0     45.0   \n",
       "858211      MN   2105   450    NaN     NaN     NaN         NaN      NaN   \n",
       "858212      MN   2156    44  112.0  269.00     NaN         6.0     10.0   \n",
       "\n",
       "        zipcode firecomp  ...        appbbl    appdate  plutomapid  \\\n",
       "0       10467.0     E062  ...           NaN        NaN           1   \n",
       "1       10003.0     E028  ...           NaN        NaN           1   \n",
       "2       10003.0     L003  ...           NaN        NaN           1   \n",
       "3       11222.0     E238  ...  3.026210e+09  1/23/2008           1   \n",
       "4       10027.0     E080  ...           NaN        NaN           1   \n",
       "...         ...      ...  ...           ...        ...         ...   \n",
       "858208  10306.0     E162  ...           NaN        NaN           3   \n",
       "858209  10306.0     E159  ...           NaN        NaN           3   \n",
       "858210  11229.0     E276  ...           NaN        NaN           3   \n",
       "858211      NaN      NaN  ...           NaN        NaN           3   \n",
       "858212  10033.0     L045  ...           NaN        NaN           3   \n",
       "\n",
       "        firm07_flag  pfirm15_flag version dcpedited   latitude  longitude  \\\n",
       "0               NaN           NaN    21v2       NaN  40.877873 -73.863604   \n",
       "1               NaN           NaN    21v2       NaN  40.725333 -73.988711   \n",
       "2               NaN           NaN    21v2       NaN  40.731190 -73.987722   \n",
       "3               NaN           NaN    21v2       NaN  40.726484 -73.951001   \n",
       "4               NaN           NaN    21v2       NaN  40.817053 -73.952741   \n",
       "...             ...           ...     ...       ...        ...        ...   \n",
       "858208          NaN           NaN    21v2       NaN  40.557440 -74.137239   \n",
       "858209          NaN           1.0    21v2       NaN  40.581080 -74.103509   \n",
       "858210          NaN           NaN    21v2       NaN  40.612185 -73.944649   \n",
       "858211          1.0           1.0    21v2       NaN  40.828169 -73.933870   \n",
       "858212          NaN           NaN    21v2       NaN  40.851227 -73.929597   \n",
       "\n",
       "       notes  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "...      ...  \n",
       "858208   NaN  \n",
       "858209   NaN  \n",
       "858210   NaN  \n",
       "858211   NaN  \n",
       "858212   NaN  \n",
       "\n",
       "[858213 rows x 90 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the dataset\n",
    "pluto = pd.read_csv(\"pluto_21v2.csv\")\n",
    "pluto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.isna of        borough  block   lot     cd  ct2010  cb2010  schooldist  council  \\\n",
       "0           BX   4660    34  212.0  380.00     NaN        11.0     12.0   \n",
       "1           MN    445    54  103.0   38.00  4000.0         1.0      2.0   \n",
       "2           MN    467    55  103.0   40.00  2000.0         1.0      2.0   \n",
       "3           BK   2621  7501  301.0  573.00  3003.0        14.0     33.0   \n",
       "4           MN   1970    58  109.0  213.03  1001.0         5.0      9.0   \n",
       "...        ...    ...   ...    ...     ...     ...         ...      ...   \n",
       "858208      SI   5016    55  503.0  132.04     NaN        31.0     51.0   \n",
       "858209      SI   3571    63  502.0  114.02     NaN        31.0     50.0   \n",
       "858210      BK   7690   206  315.0  642.00     NaN        22.0     45.0   \n",
       "858211      MN   2105   450    NaN     NaN     NaN         NaN      NaN   \n",
       "858212      MN   2156    44  112.0  269.00     NaN         6.0     10.0   \n",
       "\n",
       "        zipcode firecomp  ...        appbbl    appdate  plutomapid  \\\n",
       "0       10467.0     E062  ...           NaN        NaN           1   \n",
       "1       10003.0     E028  ...           NaN        NaN           1   \n",
       "2       10003.0     L003  ...           NaN        NaN           1   \n",
       "3       11222.0     E238  ...  3.026210e+09  1/23/2008           1   \n",
       "4       10027.0     E080  ...           NaN        NaN           1   \n",
       "...         ...      ...  ...           ...        ...         ...   \n",
       "858208  10306.0     E162  ...           NaN        NaN           3   \n",
       "858209  10306.0     E159  ...           NaN        NaN           3   \n",
       "858210  11229.0     E276  ...           NaN        NaN           3   \n",
       "858211      NaN      NaN  ...           NaN        NaN           3   \n",
       "858212  10033.0     L045  ...           NaN        NaN           3   \n",
       "\n",
       "        firm07_flag  pfirm15_flag version dcpedited   latitude  longitude  \\\n",
       "0               NaN           NaN    21v2       NaN  40.877873 -73.863604   \n",
       "1               NaN           NaN    21v2       NaN  40.725333 -73.988711   \n",
       "2               NaN           NaN    21v2       NaN  40.731190 -73.987722   \n",
       "3               NaN           NaN    21v2       NaN  40.726484 -73.951001   \n",
       "4               NaN           NaN    21v2       NaN  40.817053 -73.952741   \n",
       "...             ...           ...     ...       ...        ...        ...   \n",
       "858208          NaN           NaN    21v2       NaN  40.557440 -74.137239   \n",
       "858209          NaN           1.0    21v2       NaN  40.581080 -74.103509   \n",
       "858210          NaN           NaN    21v2       NaN  40.612185 -73.944649   \n",
       "858211          1.0           1.0    21v2       NaN  40.828169 -73.933870   \n",
       "858212          NaN           NaN    21v2       NaN  40.851227 -73.929597   \n",
       "\n",
       "       notes  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "...      ...  \n",
       "858208   NaN  \n",
       "858209   NaN  \n",
       "858210   NaN  \n",
       "858211   NaN  \n",
       "858212   NaN  \n",
       "\n",
       "[858213 rows x 90 columns]>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#detecting N/A values\n",
    "pluto.isna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filled N/A values with 0, as removing them entirely affected the datase in a way that left it unusable.\n",
    "pluto.fillna(value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I filled the NA values (with 0), because dropping them affected the data in a way that made analyzing dificult"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the building square feet (\"bldgarea\" column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6504.289891903292"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"bldgarea\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"bldgarea\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"bldgarea\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Building Square Feet')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto['bldgarea'].plot(kind='hist')\n",
    "plt.title(\"Building Square Feet in NYC\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Building Square Feet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the year the buildings were built (\"yearbuilt\" column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1846.0182646965263"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"yearbuilt\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1930.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"yearbuilt\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1920.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"yearbuilt\"].mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This mean, median and mode of the years of buildings being built is interesting, as a search on Statista reveals that the average building age in NYC is 53 years old (as of 2014). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Years Built')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto['yearbuilt'].plot(kind='hist',range=(1800,2021), bins=10)\n",
    "plt.title(\"Frequency of Years Buildings were Built in NYC\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Years Built\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at numbers of floors in buildings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3384276164540303"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"numfloors\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"numfloors\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"numfloors\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Floors in Building')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto['numfloors'].plot(kind='hist', bins = 20)\n",
    "plt.title(\"Number of Floors in Buildings in NYC\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Number of Floors in Building\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of residental units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.188058209325657"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"unitsres\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"unitsres\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto[\"unitsres\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Residential Units')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto['unitsres'].plot(kind='hist')\n",
    "plt.title(\"Number of Residential Units in Buildings in NYC\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Number of Residential Units\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our information for NYC buildings is: <br>\n",
    "\n",
    "Average building area: 2001 sqr feet<br>\n",
    "\n",
    "Average year buildings were built: 1930<br>\n",
    "\n",
    "Average number of floors: 2.33 floors<br>\n",
    "\n",
    "Average number of residential units per building: 4.1 units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brownsville"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we'll be taking a look at building ages and sizes in the Brownsville area, using the PLUTO dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\miss_\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3343: DtypeWarning: Columns (19,22) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>borough</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>cd</th>\n",
       "      <th>ct2010</th>\n",
       "      <th>cb2010</th>\n",
       "      <th>schooldist</th>\n",
       "      <th>council</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>firecomp</th>\n",
       "      <th>...</th>\n",
       "      <th>appbbl</th>\n",
       "      <th>appdate</th>\n",
       "      <th>plutomapid</th>\n",
       "      <th>firm07_flag</th>\n",
       "      <th>pfirm15_flag</th>\n",
       "      <th>version</th>\n",
       "      <th>dcpedited</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BK</td>\n",
       "      <td>1509</td>\n",
       "      <td>7501</td>\n",
       "      <td>303</td>\n",
       "      <td>379.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>16</td>\n",
       "      <td>41</td>\n",
       "      <td>11233</td>\n",
       "      <td>E222</td>\n",
       "      <td>...</td>\n",
       "      <td>3.015090e+09</td>\n",
       "      <td>6/12/1993</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.682184</td>\n",
       "      <td>-73.917910</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BK</td>\n",
       "      <td>1510</td>\n",
       "      <td>7501</td>\n",
       "      <td>316</td>\n",
       "      <td>373.0</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "      <td>11233</td>\n",
       "      <td>E233</td>\n",
       "      <td>...</td>\n",
       "      <td>3.015100e+09</td>\n",
       "      <td>6/12/1993</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>t</td>\n",
       "      <td>40.682596</td>\n",
       "      <td>-73.914863</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BK</td>\n",
       "      <td>3622</td>\n",
       "      <td>19</td>\n",
       "      <td>316</td>\n",
       "      <td>896.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>23</td>\n",
       "      <td>42</td>\n",
       "      <td>11212</td>\n",
       "      <td>E257</td>\n",
       "      <td>...</td>\n",
       "      <td>3.036220e+09</td>\n",
       "      <td>9/20/2004</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.656548</td>\n",
       "      <td>-73.911000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BK</td>\n",
       "      <td>3621</td>\n",
       "      <td>49</td>\n",
       "      <td>316</td>\n",
       "      <td>896.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>23</td>\n",
       "      <td>42</td>\n",
       "      <td>11212</td>\n",
       "      <td>E257</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.656466</td>\n",
       "      <td>-73.911537</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BK</td>\n",
       "      <td>3557</td>\n",
       "      <td>8</td>\n",
       "      <td>316</td>\n",
       "      <td>898.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "      <td>11212</td>\n",
       "      <td>L120</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.664122</td>\n",
       "      <td>-73.912947</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14578</th>\n",
       "      <td>BK</td>\n",
       "      <td>4717</td>\n",
       "      <td>2</td>\n",
       "      <td>317</td>\n",
       "      <td>890.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "      <td>11212</td>\n",
       "      <td>E257</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.654439</td>\n",
       "      <td>-73.913111</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14579</th>\n",
       "      <td>BK</td>\n",
       "      <td>4717</td>\n",
       "      <td>17</td>\n",
       "      <td>317</td>\n",
       "      <td>890.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "      <td>11212</td>\n",
       "      <td>E257</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.654763</td>\n",
       "      <td>-73.914034</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14580</th>\n",
       "      <td>BK</td>\n",
       "      <td>3565</td>\n",
       "      <td>1</td>\n",
       "      <td>316</td>\n",
       "      <td>892.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "      <td>11212</td>\n",
       "      <td>E283</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>t</td>\n",
       "      <td>40.661316</td>\n",
       "      <td>-73.918585</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14581</th>\n",
       "      <td>BK</td>\n",
       "      <td>3520</td>\n",
       "      <td>82</td>\n",
       "      <td>316</td>\n",
       "      <td>924.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "      <td>11212</td>\n",
       "      <td>L120</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.667114</td>\n",
       "      <td>-73.911906</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14582</th>\n",
       "      <td>BK</td>\n",
       "      <td>3862</td>\n",
       "      <td>1</td>\n",
       "      <td>316</td>\n",
       "      <td>920.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>42</td>\n",
       "      <td>11212</td>\n",
       "      <td>E257</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21v2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.657641</td>\n",
       "      <td>-73.901324</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14583 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      borough  block   lot   cd  ct2010  cb2010  schooldist  council  zipcode  \\\n",
       "0          BK   1509  7501  303   379.0  1000.0          16       41    11233   \n",
       "1          BK   1510  7501  316   373.0  3001.0          23       41    11233   \n",
       "2          BK   3622    19  316   896.0  2003.0          23       42    11212   \n",
       "3          BK   3621    49  316   896.0  2002.0          23       42    11212   \n",
       "4          BK   3557     8  316   898.0  1000.0          23       41    11212   \n",
       "...       ...    ...   ...  ...     ...     ...         ...      ...      ...   \n",
       "14578      BK   4717     2  317   890.0     NaN          18       42    11212   \n",
       "14579      BK   4717    17  317   890.0  5000.0          18       42    11212   \n",
       "14580      BK   3565     1  316   892.0     NaN          23       41    11212   \n",
       "14581      BK   3520    82  316   924.0     NaN          23       41    11212   \n",
       "14582      BK   3862     1  316   920.0     NaN          23       42    11212   \n",
       "\n",
       "      firecomp  ...        appbbl    appdate  plutomapid  firm07_flag  \\\n",
       "0         E222  ...  3.015090e+09  6/12/1993           1          NaN   \n",
       "1         E233  ...  3.015100e+09  6/12/1993           1          NaN   \n",
       "2         E257  ...  3.036220e+09  9/20/2004           1          NaN   \n",
       "3         E257  ...           NaN        NaN           1          NaN   \n",
       "4         L120  ...           NaN        NaN           1          NaN   \n",
       "...        ...  ...           ...        ...         ...          ...   \n",
       "14578     E257  ...           NaN        NaN           1          NaN   \n",
       "14579     E257  ...           NaN        NaN           1          NaN   \n",
       "14580     E283  ...           NaN        NaN           1          NaN   \n",
       "14581     L120  ...           NaN        NaN           3          NaN   \n",
       "14582     E257  ...           NaN        NaN           3          NaN   \n",
       "\n",
       "       pfirm15_flag version dcpedited   latitude  longitude notes  \n",
       "0               NaN    21v2       NaN  40.682184 -73.917910   NaN  \n",
       "1               NaN    21v2         t  40.682596 -73.914863   NaN  \n",
       "2               NaN    21v2       NaN  40.656548 -73.911000   NaN  \n",
       "3               NaN    21v2       NaN  40.656466 -73.911537   NaN  \n",
       "4               NaN    21v2       NaN  40.664122 -73.912947   NaN  \n",
       "...             ...     ...       ...        ...        ...   ...  \n",
       "14578           NaN    21v2       NaN  40.654439 -73.913111   NaN  \n",
       "14579           NaN    21v2       NaN  40.654763 -73.914034   NaN  \n",
       "14580           NaN    21v2         t  40.661316 -73.918585   NaN  \n",
       "14581           NaN    21v2       NaN  40.667114 -73.911906   NaN  \n",
       "14582           NaN    21v2       NaN  40.657641 -73.901324   NaN  \n",
       "\n",
       "[14583 rows x 90 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16 = pd.read_csv(\"pluto_bk16.csv\")\n",
    "pluto_bk16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pluto_bk16.fillna(value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5181.590070630185"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"bldgarea\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2564.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"bldgarea\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"bldgarea\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Building Square Feet')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto_bk16['bldgarea'].plot(kind='hist')\n",
    "plt.title(\"Building Square Feet in General Brownsville Area\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Building Square Feet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Year built"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1809.399711993417"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"yearbuilt\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1925.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"yearbuilt\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1930.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"yearbuilt\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Years Built')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto_bk16['yearbuilt'].plot(kind='hist',range=(1800,2021), bins=10)\n",
    "plt.title(\"Frequency of Years Buildings were Built in Brownsville\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Years Built\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of floors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.278555166975245"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"numfloors\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"numfloors\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"numfloors\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Floors in Building')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto_bk16['numfloors'].plot(kind='hist', bins = 20)\n",
    "plt.title(\"Number of Floors in Buildings in General Brownsville Area\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Number of Floors in Building\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of residential units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.374957141877529"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"unitsres\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"unitsres\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluto_bk16[\"unitsres\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Residential Units')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pluto_bk16['unitsres'].plot(kind='hist', bins=20)\n",
    "plt.title(\"Number of Residential Units in Buildings in General Brownsville Area\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Number of Residential Units\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our information for Brownsville buildings is: <br>\n",
    "\n",
    "Average building area: 2,564 sqr feet<br>\n",
    "\n",
    "Average year buildings were built: 1925<br>\n",
    "\n",
    "Average number of floors: 2 floors<br>\n",
    "\n",
    "Average number of residential units per building:  2 units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brownsville Complaints Buildings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will be using the Brownsville API dataset, because it has both building size and age information, and also complaints information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Unnamed: 0</th>\n",
       "      <th>complaintid</th>\n",
       "      <th>buildingid</th>\n",
       "      <th>boroughid</th>\n",
       "      <th>borough</th>\n",
       "      <th>housenumber</th>\n",
       "      <th>streetname</th>\n",
       "      <th>zip</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>apartment</th>\n",
       "      <th>communityboard</th>\n",
       "      <th>receiveddate</th>\n",
       "      <th>status</th>\n",
       "      <th>unittypeid</th>\n",
       "      <th>spacetypeid</th>\n",
       "      <th>typeid</th>\n",
       "      <th>majorcategoryid</th>\n",
       "      <th>minorcategoryid</th>\n",
       "      <th>codeid</th>\n",
       "      <th>statusdate</th>\n",
       "      <th>statusdescription</th>\n",
       "      <th>address</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>bbl</th>\n",
       "      <th>bldgclass</th>\n",
       "      <th>bldgarea</th>\n",
       "      <th>numbldgs</th>\n",
       "      <th>numfloors</th>\n",
       "      <th>unitsres</th>\n",
       "      <th>unitstotal</th>\n",
       "      <th>landuse</th>\n",
       "      <th>ownername</th>\n",
       "      <th>yearbuilt</th>\n",
       "      <th>yearalter1</th>\n",
       "      <th>yearalter2</th>\n",
       "      <th>ownertype</th>\n",
       "      <th>ownertypelong</th>\n",
       "      <th>unittype</th>\n",
       "      <th>spacetype</th>\n",
       "      <th>type</th>\n",
       "      <th>code</th>\n",
       "      <th>majorcategory</th>\n",
       "      <th>minorcategory</th>\n",
       "      <th>statusdescriptionshort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7419258</td>\n",
       "      <td>314794</td>\n",
       "      <td>3</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>214</td>\n",
       "      <td>HULL STREET</td>\n",
       "      <td>11233</td>\n",
       "      <td>1540</td>\n",
       "      <td>23</td>\n",
       "      <td>3L</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>CLOSE</td>\n",
       "      <td>92</td>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>349</td>\n",
       "      <td>2715</td>\n",
       "      <td>2015-04-06</td>\n",
       "      <td>The Department of Housing Preservation and Dev...</td>\n",
       "      <td>214 HULL STREET</td>\n",
       "      <td>40.679984</td>\n",
       "      <td>-73.907031</td>\n",
       "      <td>3015400023</td>\n",
       "      <td>C1</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HULL STREET ASSOCIATES , LLC</td>\n",
       "      <td>1910</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNKNOWN (USUALLY PRIVATE OWNERSHIP)</td>\n",
       "      <td>BUILDING-WIDE</td>\n",
       "      <td>BUILDING-WIDE</td>\n",
       "      <td>EMERGENCY</td>\n",
       "      <td>NO HEAT</td>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>ENTIRE BUILDING</td>\n",
       "      <td>Single complaint; tenant confirmed resolved</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7419264</td>\n",
       "      <td>311915</td>\n",
       "      <td>3</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>530</td>\n",
       "      <td>HERZL STREET</td>\n",
       "      <td>11212</td>\n",
       "      <td>3620</td>\n",
       "      <td>38</td>\n",
       "      <td>B6</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>CLOSE</td>\n",
       "      <td>91</td>\n",
       "      <td>543</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>348</td>\n",
       "      <td>2713</td>\n",
       "      <td>2015-04-07</td>\n",
       "      <td>The Department of Housing Preservation and Dev...</td>\n",
       "      <td>530 HERZL STREET</td>\n",
       "      <td>40.656544</td>\n",
       "      <td>-73.912252</td>\n",
       "      <td>3036200038</td>\n",
       "      <td>C1</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>2.0</td>\n",
       "      <td>530 HERZL</td>\n",
       "      <td>1930</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNKNOWN (USUALLY PRIVATE OWNERSHIP)</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>ENTIRE APARTMENT</td>\n",
       "      <td>EMERGENCY</td>\n",
       "      <td>NO HEAT</td>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>APARTMENT ONLY</td>\n",
       "      <td>Inspected; no violations issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7419438</td>\n",
       "      <td>873260</td>\n",
       "      <td>3</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>2313</td>\n",
       "      <td>PACIFIC STREET</td>\n",
       "      <td>11233</td>\n",
       "      <td>1435</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>CLOSE</td>\n",
       "      <td>91</td>\n",
       "      <td>543</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>337</td>\n",
       "      <td>2836</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>The Department of Housing Preservation and Dev...</td>\n",
       "      <td>2313 PACIFIC STREET</td>\n",
       "      <td>40.675423</td>\n",
       "      <td>-73.908974</td>\n",
       "      <td>3014350054</td>\n",
       "      <td>B1</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OSOFISAN, OLATUNDE</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNKNOWN (USUALLY PRIVATE OWNERSHIP)</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>ENTIRE APARTMENT</td>\n",
       "      <td>EMERGENCY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DOOR/WINDOW</td>\n",
       "      <td>WINDOW FRAME</td>\n",
       "      <td>Inspected; violations issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7419438</td>\n",
       "      <td>873260</td>\n",
       "      <td>3</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>2313</td>\n",
       "      <td>PACIFIC STREET</td>\n",
       "      <td>11233</td>\n",
       "      <td>1435</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>CLOSE</td>\n",
       "      <td>91</td>\n",
       "      <td>548</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>198</td>\n",
       "      <td>1364</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>The Department of Housing Preservation and Dev...</td>\n",
       "      <td>2313 PACIFIC STREET</td>\n",
       "      <td>40.675423</td>\n",
       "      <td>-73.908974</td>\n",
       "      <td>3014350054</td>\n",
       "      <td>B1</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OSOFISAN, OLATUNDE</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNKNOWN (USUALLY PRIVATE OWNERSHIP)</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>OTHER ROOM/AREA</td>\n",
       "      <td>NON EMERGENCY</td>\n",
       "      <td>BULGING/HOLE/CRACKED</td>\n",
       "      <td>PLASTER PAINT/PLASTER</td>\n",
       "      <td>WALLS WALL</td>\n",
       "      <td>Inspected; no violations issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7419438</td>\n",
       "      <td>873260</td>\n",
       "      <td>3</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>2313</td>\n",
       "      <td>PACIFIC STREET</td>\n",
       "      <td>11233</td>\n",
       "      <td>1435</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>CLOSE</td>\n",
       "      <td>91</td>\n",
       "      <td>547</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>198</td>\n",
       "      <td>1364</td>\n",
       "      <td>2015-04-14</td>\n",
       "      <td>The Department of Housing Preservation and Dev...</td>\n",
       "      <td>2313 PACIFIC STREET</td>\n",
       "      <td>40.675423</td>\n",
       "      <td>-73.908974</td>\n",
       "      <td>3014350054</td>\n",
       "      <td>B1</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>OSOFISAN, OLATUNDE</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNKNOWN (USUALLY PRIVATE OWNERSHIP)</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>LIVING ROOM</td>\n",
       "      <td>NON EMERGENCY</td>\n",
       "      <td>BULGING/HOLE/CRACKED</td>\n",
       "      <td>PLASTER PAINT/PLASTER</td>\n",
       "      <td>WALLS WALL</td>\n",
       "      <td>Inspected; violations issued</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  complaintid  buildingid  boroughid   borough housenumber  \\\n",
       "0           0      7419258      314794          3  BROOKLYN         214   \n",
       "1           1      7419264      311915          3  BROOKLYN         530   \n",
       "2           2      7419438      873260          3  BROOKLYN        2313   \n",
       "3           3      7419438      873260          3  BROOKLYN        2313   \n",
       "4           4      7419438      873260          3  BROOKLYN        2313   \n",
       "\n",
       "       streetname    zip  block  lot apartment  communityboard receiveddate  \\\n",
       "0     HULL STREET  11233   1540   23        3L              16   2015-04-01   \n",
       "1    HERZL STREET  11212   3620   38        B6              16   2015-04-01   \n",
       "2  PACIFIC STREET  11233   1435   54         1              16   2015-04-01   \n",
       "3  PACIFIC STREET  11233   1435   54         1              16   2015-04-01   \n",
       "4  PACIFIC STREET  11233   1435   54         1              16   2015-04-01   \n",
       "\n",
       "  status  unittypeid  spacetypeid  typeid  majorcategoryid  minorcategoryid  \\\n",
       "0  CLOSE          92          550       1               59              349   \n",
       "1  CLOSE          91          543       1               59              348   \n",
       "2  CLOSE          91          543       1               56              337   \n",
       "3  CLOSE          91          548       3               28              198   \n",
       "4  CLOSE          91          547       3               28              198   \n",
       "\n",
       "   codeid statusdate                                  statusdescription  \\\n",
       "0    2715 2015-04-06  The Department of Housing Preservation and Dev...   \n",
       "1    2713 2015-04-07  The Department of Housing Preservation and Dev...   \n",
       "2    2836 2015-04-14  The Department of Housing Preservation and Dev...   \n",
       "3    1364 2015-04-14  The Department of Housing Preservation and Dev...   \n",
       "4    1364 2015-04-14  The Department of Housing Preservation and Dev...   \n",
       "\n",
       "               address   latitude  longitude         bbl bldgclass  bldgarea  \\\n",
       "0      214 HULL STREET  40.679984 -73.907031  3015400023        C1    7600.0   \n",
       "1     530 HERZL STREET  40.656544 -73.912252  3036200038        C1   40000.0   \n",
       "2  2313 PACIFIC STREET  40.675423 -73.908974  3014350054        B1    2130.0   \n",
       "3  2313 PACIFIC STREET  40.675423 -73.908974  3014350054        B1    2130.0   \n",
       "4  2313 PACIFIC STREET  40.675423 -73.908974  3014350054        B1    2130.0   \n",
       "\n",
       "   numbldgs  numfloors  unitsres  unitstotal  landuse  \\\n",
       "0         1        4.0         8           8      2.0   \n",
       "1         1        4.0        46          46      2.0   \n",
       "2         1        3.0         2           2      1.0   \n",
       "3         1        3.0         2           2      1.0   \n",
       "4         1        3.0         2           2      1.0   \n",
       "\n",
       "                      ownername  yearbuilt  yearalter1  yearalter2 ownertype  \\\n",
       "0  HULL STREET ASSOCIATES , LLC       1910           0           0       NaN   \n",
       "1                     530 HERZL       1930           0           0       NaN   \n",
       "2            OSOFISAN, OLATUNDE       2005           0           0       NaN   \n",
       "3            OSOFISAN, OLATUNDE       2005           0           0       NaN   \n",
       "4            OSOFISAN, OLATUNDE       2005           0           0       NaN   \n",
       "\n",
       "                         ownertypelong       unittype         spacetype  \\\n",
       "0  UNKNOWN (USUALLY PRIVATE OWNERSHIP)  BUILDING-WIDE     BUILDING-WIDE   \n",
       "1  UNKNOWN (USUALLY PRIVATE OWNERSHIP)      APARTMENT  ENTIRE APARTMENT   \n",
       "2  UNKNOWN (USUALLY PRIVATE OWNERSHIP)      APARTMENT  ENTIRE APARTMENT   \n",
       "3  UNKNOWN (USUALLY PRIVATE OWNERSHIP)      APARTMENT   OTHER ROOM/AREA   \n",
       "4  UNKNOWN (USUALLY PRIVATE OWNERSHIP)      APARTMENT       LIVING ROOM   \n",
       "\n",
       "            type                  code          majorcategory  \\\n",
       "0      EMERGENCY               NO HEAT         HEAT/HOT WATER   \n",
       "1      EMERGENCY               NO HEAT         HEAT/HOT WATER   \n",
       "2      EMERGENCY                   NaN            DOOR/WINDOW   \n",
       "3  NON EMERGENCY  BULGING/HOLE/CRACKED  PLASTER PAINT/PLASTER   \n",
       "4  NON EMERGENCY  BULGING/HOLE/CRACKED  PLASTER PAINT/PLASTER   \n",
       "\n",
       "     minorcategory                       statusdescriptionshort  \n",
       "0  ENTIRE BUILDING  Single complaint; tenant confirmed resolved  \n",
       "1   APARTMENT ONLY              Inspected; no violations issued  \n",
       "2     WINDOW FRAME                 Inspected; violations issued  \n",
       "3       WALLS WALL              Inspected; no violations issued  \n",
       "4       WALLS WALL                 Inspected; violations issued  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#leading the API dataset\n",
    "brownsville = pd.read_csv(\"brownsville.txt\", parse_dates=[\"statusdate\"])\n",
    "\n",
    "#setting an option to show all columns, since this dataset has many of them and python truncated it originally.\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "\n",
    "brownsville.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34937.97297456343"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"bldgarea\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5850.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"bldgarea\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    245538.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"bldgarea\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Building Square Feet')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brownsville['bldgarea'].plot(kind='hist')\n",
    "plt.title(\"Building Square Feet in Brownsville\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Building Square Feet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Year built"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1924.421941052137"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"yearbuilt\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1929.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"yearbuilt\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1930\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"yearbuilt\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Years Built')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brownsville['yearbuilt'].plot(kind='hist',range=(1800,2021))\n",
    "plt.title(\"Frequency of Years Buildings were Built in Brownsville\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Years Built\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of floors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.9534846171911697"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"numfloors\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"numfloors\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"numfloors\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Floors in Building')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brownsville['numfloors'].plot(kind='hist')\n",
    "plt.title(\"Number of Floors in Buildings in Brownsville\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Number of Floors in Building\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of Residential Units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.50610615312353"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"unitsres\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"unitsres\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"unitsres\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Residential Units')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brownsville['unitsres'].plot(kind='hist')\n",
    "plt.title(\"Number of Residential Units in Buildings in Brownsville\")\n",
    "plt.ylabel(\"Number of Buildings\")\n",
    "plt.xlabel(\"Number of Residential Units\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information for the top buildings in Brownsville with complaints/violations is: <br>\n",
    "\n",
    "Average building area:  34,937 sqr feet<br>\n",
    "\n",
    "Average year buildings were built: 1929<br>\n",
    "\n",
    "Average number of floors:  3 floors<br>\n",
    "\n",
    "Average number of residential units per building:  6 units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will check the names of owning companies of buildings that come up most frequently  in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PLAZA RESIDENCES, LP                                   2292\n",
       "HP MARCUS GARVEY PRESERVATION HOUSING CO MPANY, INC    1514\n",
       "505 ROCKAWAY LLC                                       1472\n",
       "HOWARD AVENUE ASSOCIATES                                988\n",
       "LIVONIA HOUSING DEVELOPMENT FUND COMPANY , INC          965\n",
       "351 RESIDENCE LLC                                       794\n",
       "MHANY MOTHER GASTON HOUSING DEVELOPMENT  FUND CORPO     686\n",
       "RIVERDALE RESIDENCE LLC                                 685\n",
       "PARK MONROE II REHAB HOUSING DEVELOPMENT  FUND CORP     680\n",
       "PITKIN APARTMENTS LLC                                   637\n",
       "Name: ownername, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brownsville[\"ownername\"].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Reports')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "owner_values = brownsville[\"ownername\"].value_counts().head(10)\n",
    "owner_values.plot(kind = \"barh\")\n",
    "plt.title(\"Top Building Owners\")\n",
    "plt.ylabel(\"Owner Names\")\n",
    "plt.xlabel(\"Number of Reports\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The owners that come up most frequently are:<br>\n",
    "1. Plaza Residences, LP<br>\n",
    "2. Hp Marcus Garvey Preservation Housing Company, Inc<br>\n",
    "3. 505 Rockaway LLC<br>\n",
    "4. Howard Avenue Associates<br>\n",
    "5. Livonia Housing Development Fund Company, Inc<br>\n",
    "6. 35 Residence LLC<br>\n",
    "7. Mhany Mother Gaston Housing Development Fund Corporation<br>\n",
    "8. Riverdale Residence LLC<br>\n",
    "9. Park Monroe II Rehad Housing Development Fund Corp.<br>\n",
    "10. Pitkin Apartments LLC<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we'll take a look at complaints and their relationship with the building's age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "brownsville[\"complaints\"] = brownsville[\"code\"] + \"-\" + brownsville[\"majorcategory\"] + \"-\" + brownsville[\"minorcategory\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>yearbuilt</th>\n",
       "      <th>complaints</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1910</td>\n",
       "      <td>NO HEAT-HEAT/HOT WATER-ENTIRE BUILDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1930</td>\n",
       "      <td>NO HEAT-HEAT/HOT WATER-APARTMENT ONLY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>BULGING/HOLE/CRACKED-PLASTER PAINT/PLASTER-WAL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>BULGING/HOLE/CRACKED-PLASTER PAINT/PLASTER-WAL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68123</th>\n",
       "      <td>1968</td>\n",
       "      <td>DIRTY OR UNSANITARY-PLASTER PAINT/PLASTER-WALL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68124</th>\n",
       "      <td>1968</td>\n",
       "      <td>BROKEN OR DEFECTIVE-FLOORING/STAIRS-FLOOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68125</th>\n",
       "      <td>1969</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68126</th>\n",
       "      <td>1969</td>\n",
       "      <td>MICE-UNSANITARY CONDITION-PESTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68127</th>\n",
       "      <td>1969</td>\n",
       "      <td>ROACHES-UNSANITARY CONDITION-PESTS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68128 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       yearbuilt                                         complaints\n",
       "0           1910             NO HEAT-HEAT/HOT WATER-ENTIRE BUILDING\n",
       "1           1930              NO HEAT-HEAT/HOT WATER-APARTMENT ONLY\n",
       "2           2005                                                NaN\n",
       "3           2005  BULGING/HOLE/CRACKED-PLASTER PAINT/PLASTER-WAL...\n",
       "4           2005  BULGING/HOLE/CRACKED-PLASTER PAINT/PLASTER-WAL...\n",
       "...          ...                                                ...\n",
       "68123       1968  DIRTY OR UNSANITARY-PLASTER PAINT/PLASTER-WALL...\n",
       "68124       1968          BROKEN OR DEFECTIVE-FLOORING/STAIRS-FLOOR\n",
       "68125       1969                                                NaN\n",
       "68126       1969                    MICE-UNSANITARY CONDITION-PESTS\n",
       "68127       1969                 ROACHES-UNSANITARY CONDITION-PESTS\n",
       "\n",
       "[68128 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bv_com = brownsville[[\"yearbuilt\", \"complaints\"]]\n",
    "bv_com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yearbuilt  complaints                                             \n",
       "1930       NO HEAT-HEAT/HOT WATER-APARTMENT ONLY                      1600\n",
       "           NO HEAT-HEAT/HOT WATER-ENTIRE BUILDING                     1388\n",
       "1910       NO HEAT-HEAT/HOT WATER-ENTIRE BUILDING                     1264\n",
       "           NO HEAT-HEAT/HOT WATER-APARTMENT ONLY                      1230\n",
       "1930       MICE-UNSANITARY CONDITION-PESTS                             798\n",
       "1910       NO HEAT AND NO HOT WATER-HEAT/HOT WATER-ENTIRE BUILDING     703\n",
       "1930       AT WALL OR CEILING-WATER LEAK-HEAVY FLOW                    568\n",
       "           NO HEAT AND NO HOT WATER-HEAT/HOT WATER-ENTIRE BUILDING     559\n",
       "1910       MICE-UNSANITARY CONDITION-PESTS                             554\n",
       "           AT WALL OR CEILING-WATER LEAK-HEAVY FLOW                    541\n",
       "1930       ROACHES-UNSANITARY CONDITION-PESTS                          483\n",
       "           AT WALL OR CEILING-WATER LEAK-SLOW LEAK                     467\n",
       "           NO HEAT AND NO HOT WATER-HEAT/HOT WATER-APARTMENT ONLY      441\n",
       "           MISSING BROKEN OR MISSING-DOOR/WINDOW-DOOR                  440\n",
       "1910       NO HEAT AND NO HOT WATER-HEAT/HOT WATER-APARTMENT ONLY      418\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bv_com.value_counts().head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Reports')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bv_com_values = bv_com.value_counts().head(10)\n",
    "bv_com_values.plot(kind = \"barh\")\n",
    "plt.title(\"Top Complaints in Relation to Building Age\")\n",
    "plt.ylabel(\"Complaints and Year Building was Built\")\n",
    "plt.xlabel(\"Number of Reports\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}