Relationship between complaint types and property characteristics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 : Does the Complaint Type that you identified in response to Question 1 have an obvious relationship with any particular characteristic or characteristic of the Houses?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of content\n",
    "    1.Data Processing \n",
    "    2.Finding the relationship of Top_Complaint type with any particular characteristic of the Houses\n",
    "        a.Visual analysis\n",
    "        b.Statistical analysis\n",
    "    3.Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib as mlt\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats as stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5892615, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gathering needed data\n",
    "columns_selected  = ['borough','city','complaint_type','incident_address','location_type','street_name','unique_key']\n",
    "df_hpd = pd.read_csv('311.csv',usecols=columns_selected)\n",
    "df_hpd.head(5)\n",
    "df_hpd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building features must be obtained by combining the HPD data set \n",
    "#with the PLUTO building features. From PLUTO, we will use the BRONX data set\n",
    "#building features, test this against the Top_Complaint in the HPD dataset, \n",
    "#and determine whether there is a relationship between them\n",
    "df_hpd_bronx = df_hpd[df_hpd['borough']=='BRONX'].reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lot</th>\n",
       "      <th>Address</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>BldgArea</th>\n",
       "      <th>ResArea</th>\n",
       "      <th>OfficeArea</th>\n",
       "      <th>RetailArea</th>\n",
       "      <th>NumBldgs</th>\n",
       "      <th>NumFloors</th>\n",
       "      <th>LotDepth</th>\n",
       "      <th>BldgDepth</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearAlter1</th>\n",
       "      <th>BuiltFAR</th>\n",
       "      <th>ResidFAR</th>\n",
       "      <th>CommFAR</th>\n",
       "      <th>FacilFAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>122 BRUCKNER BOULEVARD</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>126 BRUCKNER BOULEVARD</td>\n",
       "      <td>13770</td>\n",
       "      <td>752</td>\n",
       "      <td>0</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1931</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.05</td>\n",
       "      <td>6.02</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lot                 Address  LotArea  BldgArea  ResArea  OfficeArea  \\\n",
       "0    1  122 BRUCKNER BOULEVARD    15000         0        0           0   \n",
       "1    4  126 BRUCKNER BOULEVARD    13770       752        0         272   \n",
       "\n",
       "   RetailArea  NumBldgs  NumFloors  LotDepth  BldgDepth  YearBuilt  \\\n",
       "0           0         1        0.0     200.0        0.0          0   \n",
       "1           0         2        1.0     100.0       16.0       1931   \n",
       "\n",
       "   YearAlter1  BuiltFAR  ResidFAR  CommFAR  FacilFAR  \n",
       "0           0      0.00      6.02      5.0       6.5  \n",
       "1        1994      0.05      6.02      5.0       6.5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Processing PLUTO BRONX data set\n",
    "# Selecting needed columns\n",
    "columns_selected1 = ['Address','BldgArea','BldgDepth','BuiltFAR','CommFAR','FacilFAR','Lot','LotArea',\n",
    "                     'LotDepth','NumBldgs','NumFloors','OfficeArea','ResArea','ResidFAR','RetailArea',\n",
    "                     'YearBuilt','YearAlter1']\n",
    "df_pluto_bronx=pd.read_csv('/Users/vba/Documents/BX_18v1.csv',usecols=columns_selected1)\n",
    "df_pluto_bronx.head(2)\n",
    "#df_pluto_bronx.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data before Normalizing\n",
      "   Lot                 Address  LotArea  BldgArea  ResArea  OfficeArea  \\\n",
      "0    1  122 BRUCKNER BOULEVARD    15000         0        0           0   \n",
      "\n",
      "   RetailArea  NumBldgs  NumFloors  LotDepth  BldgDepth  YearBuilt  \\\n",
      "0           0         1        0.0     200.0        0.0          0   \n",
      "\n",
      "   YearAlter1  BuiltFAR  ResidFAR  CommFAR  FacilFAR  \n",
      "0           0       0.0      6.02      5.0       6.5  \n",
      "Data after Normalizing\n",
      "   Lot                 Address   LotArea  BldgArea  ResArea  OfficeArea  \\\n",
      "0  0.0  122 BRUCKNER BOULEVARD  0.000202       0.0      0.0         0.0   \n",
      "\n",
      "   RetailArea  NumBldgs  NumFloors  LotDepth  BldgDepth  YearBuilt  \\\n",
      "0         0.0  0.003984        0.0     0.025        0.0        0.0   \n",
      "\n",
      "   YearAlter1  BuiltFAR  ResidFAR   CommFAR  FacilFAR  \n",
      "0         0.0       0.0     0.602  0.555556      0.65  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 89854 entries, 0 to 89853\n",
      "Data columns (total 17 columns):\n",
      "Lot           89854 non-null float64\n",
      "Address       89785 non-null object\n",
      "LotArea       89854 non-null float64\n",
      "BldgArea      89854 non-null float64\n",
      "ResArea       89854 non-null float64\n",
      "OfficeArea    89854 non-null float64\n",
      "RetailArea    89854 non-null float64\n",
      "NumBldgs      89854 non-null float64\n",
      "NumFloors     89854 non-null float64\n",
      "LotDepth      89854 non-null float64\n",
      "BldgDepth     89854 non-null float64\n",
      "YearBuilt     89854 non-null float64\n",
      "YearAlter1    89854 non-null float64\n",
      "BuiltFAR      89854 non-null float64\n",
      "ResidFAR      89854 non-null float64\n",
      "CommFAR       89854 non-null float64\n",
      "FacilFAR      89854 non-null float64\n",
      "dtypes: float64(16), object(1)\n",
      "memory usage: 11.7+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# The PLUTO data set contained a great deal of variance in data across different columns. \n",
    "# Before merging with HPD_BRONX, let's normalize the future PLUTO_BRONX set.\n",
    "# There are other ways to normalize data, but as PLUTO had continuous variable \n",
    "#characteristics, we'll use the min/max approach in this case.\n",
    "#Standardizing the PLUTO dataset\n",
    "print('Data before Normalizing')\n",
    "print(df_pluto_bronx.head(1))\n",
    "# Skipping address column which is str type we normalization rest of the columns in PLUTO data set.\n",
    "columns_selected2 = ['Address','BldgArea','BldgDepth','BuiltFAR','CommFAR','FacilFAR','Lot','LotArea',\n",
    "                     'LotDepth','NumBldgs','NumFloors','OfficeArea','ResArea','ResidFAR','RetailArea',\n",
    "                     'YearBuilt','YearAlter1']\n",
    "df_pluto_bronx1= pd.read_csv('/Users/vba/Documents/BX_18v1.csv',usecols=columns_selected2)\n",
    "# Replacing each column value in PLUTO_BRONX data set with normalized data using my defined formula\n",
    "for i in df_pluto_bronx1[['BldgArea','BldgDepth','BuiltFAR','CommFAR','FacilFAR','Lot','LotArea',\n",
    "                     'LotDepth','NumBldgs','NumFloors','OfficeArea','ResArea','ResidFAR','RetailArea',\n",
    "                     'YearBuilt','YearAlter1']]:\n",
    "    df_pluto_bronx1[i] = (df_pluto_bronx1[i]-df_pluto_bronx1[i].min())/(df_pluto_bronx1[i].max()-df_pluto_bronx1[i].min())\n",
    "#Priting Normalized data set PLUTO_BRONX\n",
    "print('Data after Normalizing')\n",
    "print(df_pluto_bronx1.head(1))\n",
    "print(df_pluto_bronx1.info())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_key</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>Address</th>\n",
       "      <th>street_name</th>\n",
       "      <th>city</th>\n",
       "      <th>borough</th>\n",
       "      <th>location_type</th>\n",
       "      <th>Lot</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>BldgArea</th>\n",
       "      <th>...</th>\n",
       "      <th>NumBldgs</th>\n",
       "      <th>NumFloors</th>\n",
       "      <th>LotDepth</th>\n",
       "      <th>BldgDepth</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearAlter1</th>\n",
       "      <th>BuiltFAR</th>\n",
       "      <th>ResidFAR</th>\n",
       "      <th>CommFAR</th>\n",
       "      <th>FacilFAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44206980</td>\n",
       "      <td>ELECTRIC</td>\n",
       "      <td>3324 PERRY AVENUE</td>\n",
       "      <td>PERRY AVENUE</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.951909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43821646</td>\n",
       "      <td>ELECTRIC</td>\n",
       "      <td>3324 PERRY AVENUE</td>\n",
       "      <td>PERRY AVENUE</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.951909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21374231</td>\n",
       "      <td>PAINT - PLASTER</td>\n",
       "      <td>3324 PERRY AVENUE</td>\n",
       "      <td>PERRY AVENUE</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.951909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21374232</td>\n",
       "      <td>PAINT - PLASTER</td>\n",
       "      <td>3324 PERRY AVENUE</td>\n",
       "      <td>PERRY AVENUE</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.951909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21374253</td>\n",
       "      <td>PLUMBING</td>\n",
       "      <td>3324 PERRY AVENUE</td>\n",
       "      <td>PERRY AVENUE</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>RESIDENTIAL BUILDING</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>0.951909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key   complaint_type            Address   street_name   city  \\\n",
       "0    44206980         ELECTRIC  3324 PERRY AVENUE  PERRY AVENUE  BRONX   \n",
       "1    43821646         ELECTRIC  3324 PERRY AVENUE  PERRY AVENUE  BRONX   \n",
       "2    21374231  PAINT - PLASTER  3324 PERRY AVENUE  PERRY AVENUE  BRONX   \n",
       "3    21374232  PAINT - PLASTER  3324 PERRY AVENUE  PERRY AVENUE  BRONX   \n",
       "4    21374253         PLUMBING  3324 PERRY AVENUE  PERRY AVENUE  BRONX   \n",
       "\n",
       "  borough         location_type       Lot   LotArea  BldgArea  ...  NumBldgs  \\\n",
       "0   BRONX  RESIDENTIAL BUILDING  0.001203  0.000042  0.000204  ...  0.007968   \n",
       "1   BRONX  RESIDENTIAL BUILDING  0.001203  0.000042  0.000204  ...  0.007968   \n",
       "2   BRONX  RESIDENTIAL BUILDING  0.001203  0.000042  0.000204  ...  0.007968   \n",
       "3   BRONX  RESIDENTIAL BUILDING  0.001203  0.000042  0.000204  ...  0.007968   \n",
       "4   BRONX  RESIDENTIAL BUILDING  0.001203  0.000042  0.000204  ...  0.007968   \n",
       "\n",
       "   NumFloors  LotDepth  BldgDepth  YearBuilt  YearAlter1  BuiltFAR  ResidFAR  \\\n",
       "0   0.045455  0.012594   0.046154   0.951909         0.0  0.003426     0.135   \n",
       "1   0.045455  0.012594   0.046154   0.951909         0.0  0.003426     0.135   \n",
       "2   0.045455  0.012594   0.046154   0.951909         0.0  0.003426     0.135   \n",
       "3   0.045455  0.012594   0.046154   0.951909         0.0  0.003426     0.135   \n",
       "4   0.045455  0.012594   0.046154   0.951909         0.0  0.003426     0.135   \n",
       "\n",
       "   CommFAR  FacilFAR  \n",
       "0      0.0       0.2  \n",
       "1      0.0       0.2  \n",
       "2      0.0       0.2  \n",
       "3      0.0       0.2  \n",
       "4      0.0       0.2  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging both HPD and PLUTO data sets\n",
    "#For this we need to change the column name incident_address in HPD as per column name Address in Bronx\n",
    "df_hpd_bronx.rename(columns={'incident_address':'Address'},inplace= True)\n",
    "#df_hpd_bronx.head(5)\n",
    "df_hpd_bronx.shape\n",
    "#Mergre both data sets\n",
    "df_hpd_pluto_bronx = df_hpd_bronx.merge(df_pluto_bronx1,on='Address',how = 'inner')\n",
    "df_hpd_pluto_bronx.head(5)\n",
    "#df_hpd_pluto_bronx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   unique_key  complaint_type  Address  street_name   city  borough  \\\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",
      "   location_type    Lot  LotArea  BldgArea  ...  NumBldgs  NumFloors  \\\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",
      "   LotDepth  BldgDepth  YearBuilt  YearAlter1  BuiltFAR  ResidFAR  CommFAR  \\\n",
      "0     False      False      False       False     False     False    False   \n",
      "1     False      False      False       False     False     False    False   \n",
      "2     False      False      False       False     False     False    False   \n",
      "3     False      False      False       False     False     False    False   \n",
      "4     False      False      False       False     False     False    False   \n",
      "\n",
      "   FacilFAR  \n",
      "0     False  \n",
      "1     False  \n",
      "2     False  \n",
      "3     False  \n",
      "4     False  \n",
      "\n",
      "[5 rows x 23 columns]\n",
      "unique_key\n",
      "False    1266046\n",
      "Name: unique_key, dtype: int64\n",
      "\n",
      "complaint_type\n",
      "False    1266046\n",
      "Name: complaint_type, dtype: int64\n",
      "\n",
      "Address\n",
      "False    1265977\n",
      "True          69\n",
      "Name: Address, dtype: int64\n",
      "\n",
      "street_name\n",
      "False    1265977\n",
      "True          69\n",
      "Name: street_name, dtype: int64\n",
      "\n",
      "city\n",
      "False    1259066\n",
      "True        6980\n",
      "Name: city, dtype: int64\n",
      "\n",
      "borough\n",
      "False    1266046\n",
      "Name: borough, dtype: int64\n",
      "\n",
      "location_type\n",
      "False    1266046\n",
      "Name: location_type, dtype: int64\n",
      "\n",
      "Lot\n",
      "False    1266046\n",
      "Name: Lot, dtype: int64\n",
      "\n",
      "LotArea\n",
      "False    1266046\n",
      "Name: LotArea, dtype: int64\n",
      "\n",
      "BldgArea\n",
      "False    1266046\n",
      "Name: BldgArea, dtype: int64\n",
      "\n",
      "ResArea\n",
      "False    1266046\n",
      "Name: ResArea, dtype: int64\n",
      "\n",
      "OfficeArea\n",
      "False    1266046\n",
      "Name: OfficeArea, dtype: int64\n",
      "\n",
      "RetailArea\n",
      "False    1266046\n",
      "Name: RetailArea, dtype: int64\n",
      "\n",
      "NumBldgs\n",
      "False    1266046\n",
      "Name: NumBldgs, dtype: int64\n",
      "\n",
      "NumFloors\n",
      "False    1266046\n",
      "Name: NumFloors, dtype: int64\n",
      "\n",
      "LotDepth\n",
      "False    1266046\n",
      "Name: LotDepth, dtype: int64\n",
      "\n",
      "BldgDepth\n",
      "False    1266046\n",
      "Name: BldgDepth, dtype: int64\n",
      "\n",
      "YearBuilt\n",
      "False    1266046\n",
      "Name: YearBuilt, dtype: int64\n",
      "\n",
      "YearAlter1\n",
      "False    1266046\n",
      "Name: YearAlter1, dtype: int64\n",
      "\n",
      "BuiltFAR\n",
      "False    1266046\n",
      "Name: BuiltFAR, dtype: int64\n",
      "\n",
      "ResidFAR\n",
      "False    1266046\n",
      "Name: ResidFAR, dtype: int64\n",
      "\n",
      "CommFAR\n",
      "False    1266046\n",
      "Name: CommFAR, dtype: int64\n",
      "\n",
      "FacilFAR\n",
      "False    1266046\n",
      "Name: FacilFAR, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Check for missing values in newly created df_hpd_pluto_bronx data set\n",
    "df_hpd_pluto_bronx_missing = df_hpd_pluto_bronx.isnull()\n",
    "print(df_hpd_pluto_bronx_missing.head())\n",
    "#Look for value_counts of missing variables in each column\n",
    "columns = list(df_hpd_pluto_bronx_missing)\n",
    "for column in columns:\n",
    "    print(column)\n",
    "    print(df_hpd_pluto_bronx_missing[column].value_counts())\n",
    "    print('')\n",
    "#If there aren't many missing values in any of the columns, the df_hpd_pluto_bronx data frame can be used for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Address</th>\n",
       "      <th>street_name</th>\n",
       "      <th>city</th>\n",
       "      <th>borough</th>\n",
       "      <th>location_type</th>\n",
       "      <th>Lot</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>BldgArea</th>\n",
       "      <th>ResArea</th>\n",
       "      <th>OfficeArea</th>\n",
       "      <th>...</th>\n",
       "      <th>NumBldgs</th>\n",
       "      <th>NumFloors</th>\n",
       "      <th>LotDepth</th>\n",
       "      <th>BldgDepth</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearAlter1</th>\n",
       "      <th>BuiltFAR</th>\n",
       "      <th>ResidFAR</th>\n",
       "      <th>CommFAR</th>\n",
       "      <th>FacilFAR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>complaint_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AGENCY</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLIANCE</th>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25201</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>...</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "      <td>25220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSTRUCTION</th>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>758</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>...</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOOR/WINDOW</th>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49539</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>...</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "      <td>49561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELECTRIC</th>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60465</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>...</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "      <td>60647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATOR</th>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>...</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "      <td>1353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORING/STAIRS</th>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33808</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>...</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "      <td>33819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENERAL</th>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30474</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>...</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "      <td>30499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENERAL CONSTRUCTION</th>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>95617</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>...</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "      <td>96110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>General</th>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>...</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEAT/HOT WATER</th>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>286666</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>...</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "      <td>288239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEATING</th>\n",
       "      <td>171525</td>\n",
       "      <td>171525</td>\n",
       "      <td>168026</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>...</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "      <td>171594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONCONST</th>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45352</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>...</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "      <td>45518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUTSIDE BUILDING</th>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1192</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>...</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAINT - PLASTER</th>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>69950</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>...</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "      <td>70330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAINT/PLASTER</th>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86756</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>...</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "      <td>86801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLUMBING</th>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145147</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>...</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "      <td>145578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Plumbing</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAFETY</th>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10347</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>...</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "      <td>10348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRUCTURAL</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Safety</th>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNSANITARY CONDITION</th>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101074</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>...</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "      <td>101109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unsanitary Condition</th>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>...</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WATER LEAK</th>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45762</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>...</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "      <td>45781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Address  street_name    city  borough  location_type  \\\n",
       "complaint_type                                                               \n",
       "AGENCY                      3            3       3        3              3   \n",
       "APPLIANCE               25220        25220   25201    25220          25220   \n",
       "CONSTRUCTION              766          766     758      766            766   \n",
       "DOOR/WINDOW             49561        49561   49539    49561          49561   \n",
       "ELECTRIC                60647        60647   60465    60647          60647   \n",
       "ELEVATOR                 1353         1353    1353     1353           1353   \n",
       "FLOORING/STAIRS         33819        33819   33808    33819          33819   \n",
       "GENERAL                 30499        30499   30474    30499          30499   \n",
       "GENERAL CONSTRUCTION    96110        96110   95617    96110          96110   \n",
       "General                   222          222     222      222            222   \n",
       "HEAT/HOT WATER         288239       288239  286666   288239         288239   \n",
       "HEATING                171525       171525  168026   171594         171594   \n",
       "NONCONST                45518        45518   45352    45518          45518   \n",
       "OUTSIDE BUILDING         1194         1194    1192     1194           1194   \n",
       "PAINT - PLASTER         70330        70330   69950    70330          70330   \n",
       "PAINT/PLASTER           86801        86801   86756    86801          86801   \n",
       "PLUMBING               145578       145578  145147   145578         145578   \n",
       "Plumbing                    1            1       1        1              1   \n",
       "SAFETY                  10348        10348   10347    10348          10348   \n",
       "STRUCTURAL                  4            4       4        4              4   \n",
       "Safety                     96           96      96       96             96   \n",
       "UNSANITARY CONDITION   101109       101109  101074   101109         101109   \n",
       "Unsanitary Condition     1253         1253    1253     1253           1253   \n",
       "WATER LEAK              45781        45781   45762    45781          45781   \n",
       "\n",
       "                         Lot  LotArea  BldgArea  ResArea  OfficeArea  ...  \\\n",
       "complaint_type                                                        ...   \n",
       "AGENCY                     3        3         3        3           3  ...   \n",
       "APPLIANCE              25220    25220     25220    25220       25220  ...   \n",
       "CONSTRUCTION             766      766       766      766         766  ...   \n",
       "DOOR/WINDOW            49561    49561     49561    49561       49561  ...   \n",
       "ELECTRIC               60647    60647     60647    60647       60647  ...   \n",
       "ELEVATOR                1353     1353      1353     1353        1353  ...   \n",
       "FLOORING/STAIRS        33819    33819     33819    33819       33819  ...   \n",
       "GENERAL                30499    30499     30499    30499       30499  ...   \n",
       "GENERAL CONSTRUCTION   96110    96110     96110    96110       96110  ...   \n",
       "General                  222      222       222      222         222  ...   \n",
       "HEAT/HOT WATER        288239   288239    288239   288239      288239  ...   \n",
       "HEATING               171594   171594    171594   171594      171594  ...   \n",
       "NONCONST               45518    45518     45518    45518       45518  ...   \n",
       "OUTSIDE BUILDING        1194     1194      1194     1194        1194  ...   \n",
       "PAINT - PLASTER        70330    70330     70330    70330       70330  ...   \n",
       "PAINT/PLASTER          86801    86801     86801    86801       86801  ...   \n",
       "PLUMBING              145578   145578    145578   145578      145578  ...   \n",
       "Plumbing                   1        1         1        1           1  ...   \n",
       "SAFETY                 10348    10348     10348    10348       10348  ...   \n",
       "STRUCTURAL                 4        4         4        4           4  ...   \n",
       "Safety                    96       96        96       96          96  ...   \n",
       "UNSANITARY CONDITION  101109   101109    101109   101109      101109  ...   \n",
       "Unsanitary Condition    1253     1253      1253     1253        1253  ...   \n",
       "WATER LEAK             45781    45781     45781    45781       45781  ...   \n",
       "\n",
       "                      NumBldgs  NumFloors  LotDepth  BldgDepth  YearBuilt  \\\n",
       "complaint_type                                                              \n",
       "AGENCY                       3          3         3          3          3   \n",
       "APPLIANCE                25220      25220     25220      25220      25220   \n",
       "CONSTRUCTION               766        766       766        766        766   \n",
       "DOOR/WINDOW              49561      49561     49561      49561      49561   \n",
       "ELECTRIC                 60647      60647     60647      60647      60647   \n",
       "ELEVATOR                  1353       1353      1353       1353       1353   \n",
       "FLOORING/STAIRS          33819      33819     33819      33819      33819   \n",
       "GENERAL                  30499      30499     30499      30499      30499   \n",
       "GENERAL CONSTRUCTION     96110      96110     96110      96110      96110   \n",
       "General                    222        222       222        222        222   \n",
       "HEAT/HOT WATER          288239     288239    288239     288239     288239   \n",
       "HEATING                 171594     171594    171594     171594     171594   \n",
       "NONCONST                 45518      45518     45518      45518      45518   \n",
       "OUTSIDE BUILDING          1194       1194      1194       1194       1194   \n",
       "PAINT - PLASTER          70330      70330     70330      70330      70330   \n",
       "PAINT/PLASTER            86801      86801     86801      86801      86801   \n",
       "PLUMBING                145578     145578    145578     145578     145578   \n",
       "Plumbing                     1          1         1          1          1   \n",
       "SAFETY                   10348      10348     10348      10348      10348   \n",
       "STRUCTURAL                   4          4         4          4          4   \n",
       "Safety                      96         96        96         96         96   \n",
       "UNSANITARY CONDITION    101109     101109    101109     101109     101109   \n",
       "Unsanitary Condition      1253       1253      1253       1253       1253   \n",
       "WATER LEAK               45781      45781     45781      45781      45781   \n",
       "\n",
       "                      YearAlter1  BuiltFAR  ResidFAR  CommFAR  FacilFAR  \n",
       "complaint_type                                                           \n",
       "AGENCY                         3         3         3        3         3  \n",
       "APPLIANCE                  25220     25220     25220    25220     25220  \n",
       "CONSTRUCTION                 766       766       766      766       766  \n",
       "DOOR/WINDOW                49561     49561     49561    49561     49561  \n",
       "ELECTRIC                   60647     60647     60647    60647     60647  \n",
       "ELEVATOR                    1353      1353      1353     1353      1353  \n",
       "FLOORING/STAIRS            33819     33819     33819    33819     33819  \n",
       "GENERAL                    30499     30499     30499    30499     30499  \n",
       "GENERAL CONSTRUCTION       96110     96110     96110    96110     96110  \n",
       "General                      222       222       222      222       222  \n",
       "HEAT/HOT WATER            288239    288239    288239   288239    288239  \n",
       "HEATING                   171594    171594    171594   171594    171594  \n",
       "NONCONST                   45518     45518     45518    45518     45518  \n",
       "OUTSIDE BUILDING            1194      1194      1194     1194      1194  \n",
       "PAINT - PLASTER            70330     70330     70330    70330     70330  \n",
       "PAINT/PLASTER              86801     86801     86801    86801     86801  \n",
       "PLUMBING                  145578    145578    145578   145578    145578  \n",
       "Plumbing                       1         1         1        1         1  \n",
       "SAFETY                     10348     10348     10348    10348     10348  \n",
       "STRUCTURAL                     4         4         4        4         4  \n",
       "Safety                        96        96        96       96        96  \n",
       "UNSANITARY CONDITION      101109    101109    101109   101109    101109  \n",
       "Unsanitary Condition        1253      1253      1253     1253      1253  \n",
       "WATER LEAK                 45781     45781     45781    45781     45781  \n",
       "\n",
       "[24 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We must group data based on complaint type for further analysis\n",
    "df_hpd_pluto_bronx_groupby_complaint_type = df_hpd_pluto_bronx.groupby(by='complaint_type')\n",
    "#Count complaint_type grouped data set\n",
    "df_hpd_pluto_bronx_groupby_complaint_type1=df_hpd_pluto_bronx_groupby_complaint_type.count()\n",
    "#Take mean of the grouped data for futher analysis\n",
    "df_hpd_pluto_bronx_groupby_complaint_type2 = df_hpd_pluto_bronx_groupby_complaint_type.mean()\n",
    "#Droping Unique_key column for visuvalizing future set of data in df_hpd_pluto_bronx_groupby_complaint_type.\n",
    "df_hpd_pluto_bronx_groupby_complaint_type2.drop(['unique_key'],axis=1,inplace=True)\n",
    "df_hpd_pluto_bronx_groupby_complaint_type1.drop(['unique_key'],axis=1,inplace=True)\n",
    "#df_hpd_pluto_bronx_groupby_complaint_type.drop(['unique_key'],axis=1,inplace=True)\n",
    "df_hpd_pluto_bronx_groupby_complaint_type1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.a.Visual analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#df_hpd_pluto_bronx_groupby_complaint_type.drop(['unique_key'],axis=1,inplace =True)\n",
    "import seaborn as sn\n",
    "#Function defination to plot grouped_by data set\n",
    "def plot_grouby_df(data_frame):\n",
    "    #plt.pcolor(data_frame,cmap='RdBu')\n",
    "    #plt.colorbar()\n",
    "    #plt.show()\n",
    "    #labels\n",
    "    fig,ax = plt.subplots()\n",
    "    im = ax.pcolor(data_frame,cmap='RdBu')\n",
    "    row_labels = data_frame.columns\n",
    "    col_labels = data_frame.index\n",
    "    ax.set_xticks(np.arange(data_frame.shape[1])+0.5,minor=False)\n",
    "    ax.set_yticks(np.arange(data_frame.shape[1])+0.5,minor=False)\n",
    "    ax.set_xticklabels(row_labels,minor=False)\n",
    "    ax.set_yticklabels(col_labels,minor=False)\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.xticks(rotation=90)\n",
    "    fig.colorbar(im)\n",
    "    plt.show()\n",
    "#Calling plot_grouby_df method to visualize \n",
    "plot_grouby_df(df_hpd_pluto_bronx_groupby_complaint_type1)\n",
    "#We can observe from the plot1 that HEAT/HOT WATER problem top the complaint list with dark blue(250000)\n",
    "plot_grouby_df(df_hpd_pluto_bronx_groupby_complaint_type2)\n",
    "#We can observe from the plot2 that year built,bldgDepth,FacilFAR,LotDepth,NumbBldgs\n",
    "#had same mean (same vertical color bar )across all complaint_types\n",
    "#Lets do further analysis using statitics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.b.Analytical statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Do the Statistical analysis by using correalation among the futuers of PLUTO\n",
    "df_hpd_pluto_bronx_groupby_complaint_type3=df_hpd_pluto_bronx_groupby_complaint_type2.corr()\n",
    "df_hpd_pluto_bronx_groupby_complaint_type3\n",
    "#Calling plot_grouby_df method to visualize correlation among features of pluto\n",
    "plot_grouby_df(df_hpd_pluto_bronx_groupby_complaint_type3)\n",
    "#It is clear that year built,bldgDepth,FacilFAR,LotDepth,NumbFloors Higly correalated with othervariables\n",
    "#so their group mean is low,it confirms our first visuval analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BldgArea : F-val: 230.14793376600852 P-val: 0.0\n",
      "BldgDepth : F-val: 243.39625105785385 P-val: 0.0\n",
      "BuiltFAR : F-val: 124.82954239742003 P-val: 0.0\n",
      "CommFAR : F-val: 27.231294029636647 P-val: 1.3099634834181875e-97\n",
      "FacilFAR : F-val: 101.84522440172937 P-val: 0.0\n",
      "Lot : F-val: 17.038053824402105 P-val: 2.7346287519058536e-57\n",
      "LotArea : F-val: 113.17197358579828 P-val: 0.0\n",
      "LotDepth : F-val: 266.4562855261722 P-val: 0.0\n",
      "NumBldgs : F-val: 6.615884053134202 P-val: 9.465861729366521e-18\n",
      "NumFloors : F-val: 410.5275317668504 P-val: 0.0\n",
      "OfficeArea : F-val: 16.10129107491888 P-val: 1.2402550731815518e-53\n",
      "ResArea : F-val: 220.0981741292381 P-val: 0.0\n",
      "ResidFAR : F-val: 121.14939660062596 P-val: 0.0\n",
      "RetailArea : F-val: 13.178874762259436 P-val: 2.5976531797340448e-42\n",
      "YearBuilt : F-val: 13.812221144164981 P-val: 9.416037070386035e-45\n",
      "YearAlter1 : F-val: 62.648796059912996 P-val: 1.4319274210383694e-240\n"
     ]
    }
   ],
   "source": [
    "#Test diffrences btw the means of groups(buildings,complaint_types) with metrics f,p values\n",
    "from scipy import stats as stat\n",
    "columns_selected3 = ['BldgArea','BldgDepth','BuiltFAR','CommFAR','FacilFAR','Lot','LotArea',\n",
    "                     'LotDepth','NumBldgs','NumFloors','OfficeArea','ResArea','ResidFAR','RetailArea',\n",
    "                     'YearBuilt','YearAlter1']\n",
    "def anova_oneway(column):\n",
    "    f_val,p_val= stat.f_oneway(df_hpd_pluto_bronx_groupby_complaint_type.get_group('AGENCY')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('APPLIANCE')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('CONSTRUCTION')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('DOOR/WINDOW')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('ELECTRIC')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('ELEVATOR')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('FLOORING/STAIRS')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('GENERAL')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('GENERAL CONSTRUCTION')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('HEAT/HOT WATER')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('HEATING')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('NONCONST')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('OUTSIDE BUILDING')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('PAINT - PLASTER')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('PAINT/PLASTER')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('PLUMBING')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('SAFETY')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('STRUCTURAL')[column],\n",
    "                               df_hpd_pluto_bronx_groupby_complaint_type.get_group('UNSANITARY CONDITION')[column],df_hpd_pluto_bronx_groupby_complaint_type.get_group('WATER LEAK')[column])\n",
    "    print(column,': F-val:',f_val,'P-val:',p_val)\n",
    "#Calling anova method\n",
    "for column in columns_selected3:\n",
    "    anova_oneway(column)\n",
    "#It is clear from the anova oneway test findings that every feature had extremely low p-val and high f-val.\n",
    "#Building features and complaint kinds are highly correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We may conclude that the following building features have an association with complaint_types based on both visual and statistical research.\n",
    "    1.BldgArea \n",
    "    2.BldgDepth \n",
    "    3.BuiltFAR \n",
    "    4.CommFAR \n",
    "    5.FacilFAR \n",
    "    6.Lot \n",
    "    7.LotArea \n",
    "    8.LotDepth \n",
    "    9.YearAlter1 \n",
    "    10.NumFloors \n",
    "    11.OfficeArea \n",
    "    12.ResArea \n",
    "    13.ResidFAR \n",
    "    14.RetailArea \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scripted By: Venkat Alapati"
   ]
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