diff --git a/Intern-basics/Customer Service Requests Analysis.docx b/Intern-basics/Customer Service Requests Analysis.docx
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diff --git a/Intern-basics/Customer Service Requests Analysis.md b/Intern-basics/Customer Service Requests Analysis.md
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+DESCRIPTION
+
+Background of Problem Statement :
+
+NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.
+
+Problem Objective :
+
+Perform a service request data analysis of New York City 311 calls. You will focus on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.
+Domain: Customer Service
+
+Analysis Tasks to be performed:
+
+(Perform a service request data analysis of New York City 311 calls) 
+
+Import a 311 NYC service request.
+Read or convert the columns ‘Created Date’ and Closed Date’ to datetime datatype and create a new column ‘Request_Closing_Time’ as the time elapsed between request creation and request closing. (Hint: Explore the package/module datetime)
+Provide major insights/patterns that you can offer in a visual format (graphs or tables); at least 4 major conclusions that you can come up with after generic data mining.
+Order the complaint types based on the average ‘Request_Closing_Time’, grouping them for different locations.
+Perform a statistical test for the following:
+Please note: For the below statements you need to state the Null and Alternate and then provide a statistical test to accept or reject the Null Hypothesis along with the corresponding ‘p-value’.
+
+Whether the average response time across complaint types is similar or not (overall)
+Are the type of complaint or service requested and location related?
diff --git a/Intern-basics/NYC311.ipynb b/Intern-basics/NYC311.ipynb
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+++ b/Intern-basics/NYC311.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "### import libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import style\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (48,49) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
+     ]
+    }
+   ],
+   "source": [
+    "NYC311 = pd.read_csv ('311_Service_Requests_from_2010_to_Present.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unique Key</th>\n",
+       "      <th>Created Date</th>\n",
+       "      <th>Closed Date</th>\n",
+       "      <th>Agency</th>\n",
+       "      <th>Agency Name</th>\n",
+       "      <th>Complaint Type</th>\n",
+       "      <th>Descriptor</th>\n",
+       "      <th>Location Type</th>\n",
+       "      <th>Incident Zip</th>\n",
+       "      <th>Incident Address</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Bridge Highway Name</th>\n",
+       "      <th>Bridge Highway Direction</th>\n",
+       "      <th>Road Ramp</th>\n",
+       "      <th>Bridge Highway Segment</th>\n",
+       "      <th>Garage Lot Name</th>\n",
+       "      <th>Ferry Direction</th>\n",
+       "      <th>Ferry Terminal Name</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>32310363</td>\n",
+       "      <td>12/31/2015 11:59:45 PM</td>\n",
+       "      <td>01-01-16 0:55</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Noise - Street/Sidewalk</td>\n",
+       "      <td>Loud Music/Party</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>10034.0</td>\n",
+       "      <td>71 VERMILYEA AVENUE</td>\n",
+       "      <td>...</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>40.865682</td>\n",
+       "      <td>-73.923501</td>\n",
+       "      <td>(40.86568153633767, -73.92350095571744)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>32309934</td>\n",
+       "      <td>12/31/2015 11:59:44 PM</td>\n",
+       "      <td>01-01-16 1:26</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Blocked Driveway</td>\n",
+       "      <td>No Access</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>11105.0</td>\n",
+       "      <td>27-07 23 AVENUE</td>\n",
+       "      <td>...</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>40.775945</td>\n",
+       "      <td>-73.915094</td>\n",
+       "      <td>(40.775945312321085, -73.91509393898605)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32309159</td>\n",
+       "      <td>12/31/2015 11:59:29 PM</td>\n",
+       "      <td>01-01-16 4:51</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Blocked Driveway</td>\n",
+       "      <td>No Access</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>10458.0</td>\n",
+       "      <td>2897 VALENTINE AVENUE</td>\n",
+       "      <td>...</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>40.870325</td>\n",
+       "      <td>-73.888525</td>\n",
+       "      <td>(40.870324522111424, -73.88852464418646)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>32305098</td>\n",
+       "      <td>12/31/2015 11:57:46 PM</td>\n",
+       "      <td>01-01-16 7:43</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Illegal Parking</td>\n",
+       "      <td>Commercial Overnight Parking</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>10461.0</td>\n",
+       "      <td>2940 BAISLEY AVENUE</td>\n",
+       "      <td>...</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>40.835994</td>\n",
+       "      <td>-73.828379</td>\n",
+       "      <td>(40.83599404683083, -73.82837939584206)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>32306529</td>\n",
+       "      <td>12/31/2015 11:56:58 PM</td>\n",
+       "      <td>01-01-16 3:24</td>\n",
+       "      <td>NYPD</td>\n",
+       "      <td>New York City Police Department</td>\n",
+       "      <td>Illegal Parking</td>\n",
+       "      <td>Blocked Sidewalk</td>\n",
+       "      <td>Street/Sidewalk</td>\n",
+       "      <td>11373.0</td>\n",
+       "      <td>87-14 57 ROAD</td>\n",
+       "      <td>...</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>40.733060</td>\n",
+       "      <td>-73.874170</td>\n",
+       "      <td>(40.733059618956815, -73.87416975810375)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 53 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unique Key            Created Date    Closed Date Agency  \\\n",
+       "0    32310363  12/31/2015 11:59:45 PM  01-01-16 0:55   NYPD   \n",
+       "1    32309934  12/31/2015 11:59:44 PM  01-01-16 1:26   NYPD   \n",
+       "2    32309159  12/31/2015 11:59:29 PM  01-01-16 4:51   NYPD   \n",
+       "3    32305098  12/31/2015 11:57:46 PM  01-01-16 7:43   NYPD   \n",
+       "4    32306529  12/31/2015 11:56:58 PM  01-01-16 3:24   NYPD   \n",
+       "\n",
+       "                       Agency Name           Complaint Type  \\\n",
+       "0  New York City Police Department  Noise - Street/Sidewalk   \n",
+       "1  New York City Police Department         Blocked Driveway   \n",
+       "2  New York City Police Department         Blocked Driveway   \n",
+       "3  New York City Police Department          Illegal Parking   \n",
+       "4  New York City Police Department          Illegal Parking   \n",
+       "\n",
+       "                     Descriptor    Location Type  Incident Zip  \\\n",
+       "0              Loud Music/Party  Street/Sidewalk       10034.0   \n",
+       "1                     No Access  Street/Sidewalk       11105.0   \n",
+       "2                     No Access  Street/Sidewalk       10458.0   \n",
+       "3  Commercial Overnight Parking  Street/Sidewalk       10461.0   \n",
+       "4              Blocked Sidewalk  Street/Sidewalk       11373.0   \n",
+       "\n",
+       "        Incident Address  ... Bridge Highway Name Bridge Highway Direction  \\\n",
+       "0    71 VERMILYEA AVENUE  ...                 NaN                      NaN   \n",
+       "1        27-07 23 AVENUE  ...                 NaN                      NaN   \n",
+       "2  2897 VALENTINE AVENUE  ...                 NaN                      NaN   \n",
+       "3    2940 BAISLEY AVENUE  ...                 NaN                      NaN   \n",
+       "4          87-14 57 ROAD  ...                 NaN                      NaN   \n",
+       "\n",
+       "  Road Ramp Bridge Highway Segment Garage Lot Name Ferry Direction  \\\n",
+       "0       NaN                    NaN             NaN             NaN   \n",
+       "1       NaN                    NaN             NaN             NaN   \n",
+       "2       NaN                    NaN             NaN             NaN   \n",
+       "3       NaN                    NaN             NaN             NaN   \n",
+       "4       NaN                    NaN             NaN             NaN   \n",
+       "\n",
+       "  Ferry Terminal Name   Latitude  Longitude  \\\n",
+       "0                 NaN  40.865682 -73.923501   \n",
+       "1                 NaN  40.775945 -73.915094   \n",
+       "2                 NaN  40.870325 -73.888525   \n",
+       "3                 NaN  40.835994 -73.828379   \n",
+       "4                 NaN  40.733060 -73.874170   \n",
+       "\n",
+       "                                   Location  \n",
+       "0   (40.86568153633767, -73.92350095571744)  \n",
+       "1  (40.775945312321085, -73.91509393898605)  \n",
+       "2  (40.870324522111424, -73.88852464418646)  \n",
+       "3   (40.83599404683083, -73.82837939584206)  \n",
+       "4  (40.733059618956815, -73.87416975810375)  \n",
+       "\n",
+       "[5 rows x 53 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(300698, 53)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unique Key', 'Created Date', 'Closed Date', 'Agency', 'Agency Name',\n",
+       "       'Complaint Type', 'Descriptor', 'Location Type', 'Incident Zip',\n",
+       "       'Incident Address', 'Street Name', 'Cross Street 1', 'Cross Street 2',\n",
+       "       'Intersection Street 1', 'Intersection Street 2', 'Address Type',\n",
+       "       'City', 'Landmark', 'Facility Type', 'Status', 'Due Date',\n",
+       "       'Resolution Description', 'Resolution Action Updated Date',\n",
+       "       'Community Board', 'Borough', 'X Coordinate (State Plane)',\n",
+       "       'Y Coordinate (State Plane)', 'Park Facility Name', 'Park Borough',\n",
+       "       'School Name', 'School Number', 'School Region', 'School Code',\n",
+       "       'School Phone Number', 'School Address', 'School City', 'School State',\n",
+       "       'School Zip', 'School Not Found', 'School or Citywide Complaint',\n",
+       "       'Vehicle Type', 'Taxi Company Borough', 'Taxi Pick Up Location',\n",
+       "       'Bridge Highway Name', 'Bridge Highway Direction', 'Road Ramp',\n",
+       "       'Bridge Highway Segment', 'Garage Lot Name', 'Ferry Direction',\n",
+       "       'Ferry Terminal Name', 'Latitude', 'Longitude', 'Location'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Noise - Street/Sidewalk', 'Blocked Driveway', 'Illegal Parking',\n",
+       "       'Derelict Vehicle', 'Noise - Commercial',\n",
+       "       'Noise - House of Worship', 'Posting Advertisement',\n",
+       "       'Noise - Vehicle', 'Animal Abuse', 'Vending', 'Traffic',\n",
+       "       'Drinking', 'Bike/Roller/Skate Chronic', 'Panhandling',\n",
+       "       'Noise - Park', 'Homeless Encampment', 'Urinating in Public',\n",
+       "       'Graffiti', 'Disorderly Youth', 'Illegal Fireworks',\n",
+       "       'Ferry Complaint', 'Agency Issues', 'Squeegee', 'Animal in a Park'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311['Complaint Type'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Loud Music/Party', 'No Access', 'Commercial Overnight Parking',\n",
+       "       'Blocked Sidewalk', 'Posted Parking Sign Violation',\n",
+       "       'Blocked Hydrant', 'With License Plate', 'Partial Access',\n",
+       "       'Unauthorized Bus Layover', 'Double Parked Blocking Vehicle',\n",
+       "       'Double Parked Blocking Traffic', 'Vehicle', 'Loud Talking',\n",
+       "       'Banging/Pounding', 'Car/Truck Music', 'Tortured',\n",
+       "       'In Prohibited Area', 'Congestion/Gridlock', 'Neglected',\n",
+       "       'Car/Truck Horn', 'In Public', 'Other (complaint details)', nan,\n",
+       "       'No Shelter', 'Truck Route Violation', 'Unlicensed',\n",
+       "       'Overnight Commercial Storage', 'Engine Idling',\n",
+       "       'After Hours - Licensed Est', 'Detached Trailer',\n",
+       "       'Underage - Licensed Est', 'Chronic Stoplight Violation',\n",
+       "       'Loud Television', 'Chained', 'Building', 'In Car',\n",
+       "       'Police Report Requested', 'Chronic Speeding',\n",
+       "       'Playing in Unsuitable Place', 'Drag Racing',\n",
+       "       'Police Report Not Requested', 'Nuisance/Truant', 'Homeless Issue',\n",
+       "       'Language Access Complaint', 'Disruptive Passenger',\n",
+       "       'Animal Waste'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311['Descriptor'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Complaint Type</th>\n",
+       "      <th>City</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>ARVERNE</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>ASTORIA</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>BAYSIDE</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>BELLEROSE</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>BREEZY POINT</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Complaint Type          City  count\n",
+       "0   Animal Abuse       ARVERNE     38\n",
+       "1   Animal Abuse       ASTORIA    125\n",
+       "2   Animal Abuse       BAYSIDE     37\n",
+       "3   Animal Abuse     BELLEROSE      7\n",
+       "4   Animal Abuse  BREEZY POINT      2"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Complaint_type_city = pd.DataFrame({'count':\n",
+    "                                   NYC311.groupby(['Complaint Type','City']).size()}).reset_index()\n",
+    "Complaint_type_city.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Borough      Complaint Type         Descriptor               \n",
+       "BRONX        Animal Abuse           Chained                      132\n",
+       "                                    In Car                        36\n",
+       "                                    Neglected                    673\n",
+       "                                    No Shelter                    71\n",
+       "                                    Other (complaint details)    311\n",
+       "                                                                ... \n",
+       "Unspecified  Noise - Vehicle        Engine Idling                 11\n",
+       "             Posting Advertisement  Vehicle                        1\n",
+       "             Traffic                Truck Route Violation          1\n",
+       "             Vending                In Prohibited Area             2\n",
+       "                                    Unlicensed                     5\n",
+       "Length: 288, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.groupby(['Borough','Complaint Type','Descriptor']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (48,49) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
+     ]
+    }
+   ],
+   "source": [
+    "NYC311_df = pd.read_csv(\"311_Service_Requests_from_2010_to_Present.csv\", parse_dates=[\"Created Date\", \"Closed Date\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NYC311_df['Request_Closing_Time'] = NYC311_df['Closed Date'] - NYC311_df['Created Date']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "NYC311_df['Status'].value_counts().plot(kind='bar',alpha=0.6,figsize=(7,7))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Complaint type Breakdown with bar plot to figure out majority of complaint types and top 10 complaints\n",
+    "NYC311_df['Complaint Type'].value_counts().head(10).plot(kind='barh',figsize=(8,8));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Borough      Complaint Type         Descriptor               \n",
+       "BRONX        Animal Abuse           Chained                      132\n",
+       "                                    In Car                        36\n",
+       "                                    Neglected                    673\n",
+       "                                    No Shelter                    71\n",
+       "                                    Other (complaint details)    311\n",
+       "                                                                ... \n",
+       "Unspecified  Noise - Vehicle        Engine Idling                 11\n",
+       "             Posting Advertisement  Vehicle                        1\n",
+       "             Traffic                Truck Route Violation          1\n",
+       "             Vending                In Prohibited Area             2\n",
+       "                                    Unlicensed                     5\n",
+       "Length: 288, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NYC311.groupby([\"Borough\",\"Complaint Type\",\"Descriptor\"]).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>Complaint Type</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Blocked Driveway</td>\n",
+       "      <td>77044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Illegal Parking</td>\n",
+       "      <td>75361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Noise - Street/Sidewalk</td>\n",
+       "      <td>48612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Noise - Commercial</td>\n",
+       "      <td>35577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Derelict Vehicle</td>\n",
+       "      <td>17718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Noise - Vehicle</td>\n",
+       "      <td>17083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Animal Abuse</td>\n",
+       "      <td>7778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Traffic</td>\n",
+       "      <td>4498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Homeless Encampment</td>\n",
+       "      <td>4416</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Noise - Park</td>\n",
+       "      <td>4042</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Complaint Type  count\n",
+       "0         Blocked Driveway  77044\n",
+       "1          Illegal Parking  75361\n",
+       "2  Noise - Street/Sidewalk  48612\n",
+       "3       Noise - Commercial  35577\n",
+       "4         Derelict Vehicle  17718\n",
+       "5          Noise - Vehicle  17083\n",
+       "6             Animal Abuse   7778\n",
+       "7                  Traffic   4498\n",
+       "8      Homeless Encampment   4416\n",
+       "9             Noise - Park   4042"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "majorcomplints=NYC311.dropna(subset=[\"Complaint Type\"])\n",
+    "majorcomplints=NYC311.groupby(\"Complaint Type\")\n",
+    "\n",
+    "sortedComplaintType = majorcomplints.size().sort_values(ascending = False)\n",
+    "sortedComplaintType = sortedComplaintType.to_frame('count').reset_index()\n",
+    "\n",
+    "sortedComplaintType\n",
+    "sortedComplaintType.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sortedComplaintType = sortedComplaintType.head()\n",
+    "plt.figure(figsize=(5,5))\n",
+    "plt.pie(sortedComplaintType['count'],labels=sortedComplaintType[\"Complaint Type\"], autopct=\"%1.1f%%\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Group dataset by complaint type to display plot against city\n",
+    "groupedby_complainttype = NYC311_df.groupby('Complaint Type')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(77044, 54)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grp_data = groupedby_complainttype.get_group('Blocked Driveway')\n",
+    "grp_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unique Key                             0\n",
+       "Created Date                           0\n",
+       "Closed Date                         2164\n",
+       "Agency                                 0\n",
+       "Agency Name                            0\n",
+       "Complaint Type                         0\n",
+       "Descriptor                          5914\n",
+       "Location Type                        131\n",
+       "Incident Zip                        2615\n",
+       "Incident Address                   44410\n",
+       "Street Name                        44410\n",
+       "Cross Street 1                     49279\n",
+       "Cross Street 2                     49779\n",
+       "Intersection Street 1             256840\n",
+       "Intersection Street 2             257336\n",
+       "Address Type                        2815\n",
+       "City                                2614\n",
+       "Landmark                          300349\n",
+       "Facility Type                       2171\n",
+       "Status                                 0\n",
+       "Due Date                               3\n",
+       "Resolution Description                 0\n",
+       "Resolution Action Updated Date      2187\n",
+       "Community Board                        0\n",
+       "Borough                                0\n",
+       "X Coordinate (State Plane)          3540\n",
+       "Y Coordinate (State Plane)          3540\n",
+       "Park Facility Name                     0\n",
+       "Park Borough                           0\n",
+       "School Name                            0\n",
+       "School Number                          0\n",
+       "School Region                          1\n",
+       "School Code                            1\n",
+       "School Phone Number                    0\n",
+       "School Address                         0\n",
+       "School City                            0\n",
+       "School State                           0\n",
+       "School Zip                             1\n",
+       "School Not Found                       0\n",
+       "School or Citywide Complaint      300698\n",
+       "Vehicle Type                      300698\n",
+       "Taxi Company Borough              300698\n",
+       "Taxi Pick Up Location             300698\n",
+       "Bridge Highway Name               300455\n",
+       "Bridge Highway Direction          300455\n",
+       "Road Ramp                         300485\n",
+       "Bridge Highway Segment            300485\n",
+       "Garage Lot Name                   300698\n",
+       "Ferry Direction                   300697\n",
+       "Ferry Terminal Name               300696\n",
+       "Latitude                            3540\n",
+       "Longitude                           3540\n",
+       "Location                            3540\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#To get nan values in the entire dataset\n",
+    "NYC311.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fix blank values in City column\n",
+    "NYC311['City'].dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(298084,)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Shape after dropping nan values\n",
+    "NYC311['City'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "283"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#count of null values in grouped city column data\n",
+    "grp_data['City'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6245: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._update_inplace(new_data)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#fix those NAN with \"unknown city\" value instead\n",
+    "grp_data['City'].fillna('Unknown City', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Scatter plot displaying all the cities that raised complaint of type 'Blocked Driveway'\n",
+    "plt.figure(figsize=(20, 15))\n",
+    "plt.scatter(grp_data['Complaint Type'],grp_data['City'])\n",
+    "plt.title('Plot showing list of cities that raised complaint of type Blocked Driveway')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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
+}