CIS 9440 Final Project Rodent Complaints VS Weather Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cfafb70c",
   "metadata": {},
   "source": [
    "Sally Wu\n",
    "\n",
    "Course: CIS 9440 Data Warehousing and Analytics - Fall 2023\n",
    "\n",
    "Final Project: Analyzing Relationship Between 311 Rodent Complaints and the NYC Weather from 2016-2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9008544",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries for analysis\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4baf8a7",
   "metadata": {},
   "source": [
    "The purpose of this project is to see if there's a relationship between the number of rodent complaints (and location they're made in) with the NYC weather and percipitation. The 311 Rodent Complaints data was pulled from NYC Open data while the NYC Weather report data was pulled from Kaggle."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9615177",
   "metadata": {},
   "source": [
    "### Part 1: Extract & Initial Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be9b7e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading information from both datasets and holding data in the respective dataframes\n",
    "df_311 = pd.read_csv(\"Cleaned_311_Service_Requests.csv\")\n",
    "df_weather = pd.read_csv(\"NYC_Weather_2016_2022.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f318372",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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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>Full Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Complaint Type</th>\n",
       "      <th>Descriptor</th>\n",
       "      <th>Location Type</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Incident Zip</th>\n",
       "      <th>Incident Address</th>\n",
       "      <th>Street Name</th>\n",
       "      <th>Status</th>\n",
       "      <th>Created Date</th>\n",
       "      <th>Closed Date</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32310161</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rodent</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>Vacant Lot</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>10473</td>\n",
       "      <td>808 HARDING PARK</td>\n",
       "      <td>HARDING PARK</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/8/2016</td>\n",
       "      <td>Outside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32312138</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rodent</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>Other (Explain Below)</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>10468</td>\n",
       "      <td>2327 GRAND CONCOURSE</td>\n",
       "      <td>GRAND CONCOURSE</td>\n",
       "      <td>Assigned</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>2/10/2016</td>\n",
       "      <td>Outside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32313302</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rodent</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>Public Garden</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>11217</td>\n",
       "      <td>143 SOUTH PORTLAND AVENUE</td>\n",
       "      <td>SOUTH PORTLAND AVENUE</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/16/2016</td>\n",
       "      <td>Outside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32313351</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rodent</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>1-2 Family Dwelling</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>11213</td>\n",
       "      <td>1526 PRESIDENT STREET</td>\n",
       "      <td>PRESIDENT STREET</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>Inside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32311356</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rodent</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>1-2 Family Dwelling</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>10461</td>\n",
       "      <td>1343 EDWARDS AVENUE</td>\n",
       "      <td>EDWARDS AVENUE</td>\n",
       "      <td>Assigned</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/20/2016</td>\n",
       "      <td>Inside</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unique Key Full Date  Year  Month  Day Complaint Type    Descriptor  \\\n",
       "0    32310161  1/1/2016  2016      1    1         Rodent  Rat Sighting   \n",
       "1    32312138  1/1/2016  2016      1    1         Rodent  Rat Sighting   \n",
       "2    32313302  1/1/2016  2016      1    1         Rodent  Rat Sighting   \n",
       "3    32313351  1/1/2016  2016      1    1         Rodent  Rat Sighting   \n",
       "4    32311356  1/1/2016  2016      1    1         Rodent  Rat Sighting   \n",
       "\n",
       "           Location Type   Borough  Incident Zip           Incident Address  \\\n",
       "0             Vacant Lot     BRONX         10473           808 HARDING PARK   \n",
       "1  Other (Explain Below)     BRONX         10468       2327 GRAND CONCOURSE   \n",
       "2          Public Garden  BROOKLYN         11217  143 SOUTH PORTLAND AVENUE   \n",
       "3    1-2 Family Dwelling  BROOKLYN         11213      1526 PRESIDENT STREET   \n",
       "4    1-2 Family Dwelling     BRONX         10461        1343 EDWARDS AVENUE   \n",
       "\n",
       "             Street Name    Status Created Date Closed Date Location  \n",
       "0           HARDING PARK    Closed     1/1/2016    1/8/2016  Outside  \n",
       "1        GRAND CONCOURSE  Assigned     1/1/2016   2/10/2016  Outside  \n",
       "2  SOUTH PORTLAND AVENUE    Closed     1/1/2016   1/16/2016  Outside  \n",
       "3       PRESIDENT STREET    Closed     1/1/2016   1/13/2016   Inside  \n",
       "4         EDWARDS AVENUE  Assigned     1/1/2016   1/20/2016   Inside  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming columns in df_311\n",
    "df_311 = df_311.rename(columns={\"YEAR\" : \"Year\",\n",
    "                               \"MONTH\" : \"Month\",\n",
    "                               \"DAY\" : \"Day\"})\n",
    "\n",
    "# Creating new columns in df_311 dataset to deicpher whether the location type is an indoor or outdoor location\n",
    "resident_values= ['1-2 Family Dwelling', '1-2 Family Mixed Use Building', '3+ Family Apt. Building', '3+ Family Mixed Use Building', 'Single Room Occupancy (SRO)',\n",
    "'3+ Family Apartment Building', '1-3 Family Mixed Use Building', '1-3 Family Dwelling', 'Residential Building', 'Residence', 'Apartment', 'Residence',\n",
    "'Residential Property', 'Private House', '3+ Family Apt.', '1-2 FamilyDwelling', '3+ Family', '3+Family Apt.', '3+ Family Apt', 'Private Residence',\n",
    "'Commercial Building', 'Hospital', 'Office Building', 'Government Building', 'Day Care/Nursery', 'School/Pre-School', 'Summer Camp', 'Restaurant/Bar/Deli/Bakery',\n",
    "'Catering Service', 'Building (Non-Residential)', 'Store', 'Commercial Property', 'Restaurant', 'School', 'Retail Store', 'Cafeteria - College/University', 'Cafeteria - Public School']\n",
    "\n",
    "df_311[\"Location\"] = np.where(df_311[\"Location Type\"].isin(resident_values), \"Inside\", \"Outside\")\n",
    "\n",
    "df_311.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a74ce472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113656"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding total unique number of incidents\n",
    "len(df_311[\"Incident Address\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75123efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Vacant Lot', 'Other (Explain Below)', 'Public Garden',\n",
       "       '1-2 Family Dwelling', '1-2 Family Mixed Use Building',\n",
       "       'Vacant Building', '3+ Family Apt. Building',\n",
       "       '3+ Family Mixed Use Building', 'Commercial Building',\n",
       "       'Construction Site', 'Catch Basin/Sewer', 'Parking Lot/Garage',\n",
       "       'Hospital', 'Office Building', 'Government Building',\n",
       "       'Day Care/Nursery', 'School/Pre-School',\n",
       "       'Single Room Occupancy (SRO)', 'Public Stairs', 'Summer Camp',\n",
       "       '3+ Family Apartment Building', 'Street Area',\n",
       "       '1-3 Family Mixed Use Building', 'Other',\n",
       "       'Restaurant/Bar/Deli/Bakery', '1-3 Family Dwelling',\n",
       "       'Residential Building', 'Catering Service', 'Public/Unfenced Area',\n",
       "       'Abandoned Building', 'Grocery Store',\n",
       "       'Building (Non-Residential)', 'Private Residence', 'Ground',\n",
       "       'Apartment', 'Store', 'Commercial Property', 'Restaurant',\n",
       "       'Residence', 'Beach', 'Residential Property', 'School',\n",
       "       'Retail Store', 'Private House', 'Street Vendor',\n",
       "       'Street Fair Vendor', 'Vacant Lot/Property',\n",
       "       'Cafeteria - College/University', 'Cafeteria - Public School',\n",
       "       '3+ Family Apt.', '1-2 FamilyDwelling', '3+ Family',\n",
       "       '3+Family Apt.', '3+ Family Apt'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding all types of unique location types\n",
    "df_311[\"Location Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f700e693",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Full Time</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>rain (mm)</th>\n",
       "      <th>cloudcover (%)</th>\n",
       "      <th>cloudcover_low (%)</th>\n",
       "      <th>cloudcover_mid (%)</th>\n",
       "      <th>cloudcover_high (%)</th>\n",
       "      <th>windspeed_10m (km/h)</th>\n",
       "      <th>winddirection_10m (°)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01T00:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01T01:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01T02:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01T03:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01T04:00</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>279.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Full Time  Year  Month  Day  Temperature  Precipitation  rain (mm)  \\\n",
       "0  2016-01-01T00:00  2016      1    1          7.6            0.0        0.0   \n",
       "1  2016-01-01T01:00  2016      1    1          7.5            0.0        0.0   \n",
       "2  2016-01-01T02:00  2016      1    1          7.1            0.0        0.0   \n",
       "3  2016-01-01T03:00  2016      1    1          6.6            0.0        0.0   \n",
       "4  2016-01-01T04:00  2016      1    1          6.3            0.0        0.0   \n",
       "\n",
       "   cloudcover (%)  cloudcover_low (%)  cloudcover_mid (%)  \\\n",
       "0            69.0                53.0                 0.0   \n",
       "1            20.0                 4.0                 0.0   \n",
       "2            32.0                 3.0                 0.0   \n",
       "3            35.0                 5.0                 0.0   \n",
       "4            34.0                 4.0                 0.0   \n",
       "\n",
       "   cloudcover_high (%)  windspeed_10m (km/h)  winddirection_10m (°)  \n",
       "0                 72.0                  10.0                  296.0  \n",
       "1                 56.0                   9.8                  287.0  \n",
       "2                 99.0                   9.7                  285.0  \n",
       "3                100.0                   9.2                  281.0  \n",
       "4                100.0                   9.1                  279.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming columns in df_weather\n",
    "df_weather = df_weather.rename(columns={\"full_time\" : \"Full Time\",\n",
    "                               \"year\" : \"Year\",\n",
    "                               \"month\" : \"Month\",\n",
    "                                \"day\" : \"Day\",\n",
    "                                \"temperature_2m (°C)\" : \"Temperature\",\n",
    "                                \"precipitation (mm)\" : \"Precipitation\"})\n",
    "\n",
    "df_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d0193cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Descriptor</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32310161</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32312138</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>Assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32313302</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32313351</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32311356</td>\n",
       "      <td>Rat Sighting</td>\n",
       "      <td>Assigned</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unique Key    Descriptor    Status\n",
       "0    32310161  Rat Sighting    Closed\n",
       "1    32312138  Rat Sighting  Assigned\n",
       "2    32313302  Rat Sighting    Closed\n",
       "3    32313351  Rat Sighting    Closed\n",
       "4    32311356  Rat Sighting  Assigned"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying information regarding complaint type\n",
    "df_dim_complaint_type = df_311[[\"Unique Key\", \"Descriptor\", \"Status\"]]\n",
    "df_dim_complaint_type.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "825ba9e4",
   "metadata": {},
   "outputs": [
    {
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       "    }\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>Created Date</th>\n",
       "      <th>Closed Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/8/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>2/10/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/16/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/20/2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Created Date Closed Date  Year  Month  Day\n",
       "0     1/1/2016    1/8/2016  2016      1    1\n",
       "1     1/1/2016   2/10/2016  2016      1    1\n",
       "2     1/1/2016   1/16/2016  2016      1    1\n",
       "3     1/1/2016   1/13/2016  2016      1    1\n",
       "4     1/1/2016   1/20/2016  2016      1    1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying information regarding date\n",
    "df_dim_date = df_311[[\"Created Date\", \"Closed Date\", \"Year\", \"Month\", \"Day\"]]\n",
    "df_dim_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13f5e7f2",
   "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>Borough</th>\n",
       "      <th>Incident Address</th>\n",
       "      <th>Street Name</th>\n",
       "      <th>Incident Zip</th>\n",
       "      <th>Location Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRONX</td>\n",
       "      <td>808 HARDING PARK</td>\n",
       "      <td>HARDING PARK</td>\n",
       "      <td>10473</td>\n",
       "      <td>Vacant Lot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRONX</td>\n",
       "      <td>2327 GRAND CONCOURSE</td>\n",
       "      <td>GRAND CONCOURSE</td>\n",
       "      <td>10468</td>\n",
       "      <td>Other (Explain Below)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>143 SOUTH PORTLAND AVENUE</td>\n",
       "      <td>SOUTH PORTLAND AVENUE</td>\n",
       "      <td>11217</td>\n",
       "      <td>Public Garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>1526 PRESIDENT STREET</td>\n",
       "      <td>PRESIDENT STREET</td>\n",
       "      <td>11213</td>\n",
       "      <td>1-2 Family Dwelling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRONX</td>\n",
       "      <td>1343 EDWARDS AVENUE</td>\n",
       "      <td>EDWARDS AVENUE</td>\n",
       "      <td>10461</td>\n",
       "      <td>1-2 Family Dwelling</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Borough           Incident Address            Street Name  Incident Zip  \\\n",
       "0     BRONX           808 HARDING PARK           HARDING PARK         10473   \n",
       "1     BRONX       2327 GRAND CONCOURSE        GRAND CONCOURSE         10468   \n",
       "2  BROOKLYN  143 SOUTH PORTLAND AVENUE  SOUTH PORTLAND AVENUE         11217   \n",
       "3  BROOKLYN      1526 PRESIDENT STREET       PRESIDENT STREET         11213   \n",
       "4     BRONX        1343 EDWARDS AVENUE         EDWARDS AVENUE         10461   \n",
       "\n",
       "           Location Type  \n",
       "0             Vacant Lot  \n",
       "1  Other (Explain Below)  \n",
       "2          Public Garden  \n",
       "3    1-2 Family Dwelling  \n",
       "4    1-2 Family Dwelling  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying information regarding location of complaints\n",
    "df_dim_location = df_311[[\"Borough\", \"Incident Address\", \"Street Name\", \"Incident Zip\", \"Location Type\"]]\n",
    "df_dim_location.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d0eccc",
   "metadata": {},
   "source": [
    "### Part 2: Transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b694f2e4",
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.408333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.387500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.012500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-6.775000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day  Temperature  Precipitation\n",
       "0  2016      1    1     5.408333            0.0\n",
       "1  2016      1    2     2.387500            0.0\n",
       "2  2016      1    3     3.012500            0.0\n",
       "3  2016      1    4     0.100000            0.0\n",
       "4  2016      1    5    -6.775000            0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new dataframe df_weather_avg that only reports the average temperature and average precipitation for each day for purposes of this analysis\n",
    "df_weather_avg = df_weather.groupby(['Year', 'Month', 'Day']).agg({'Temperature': 'mean', 'Precipitation': 'mean'}).reset_index()\n",
    "df_weather_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8aa580c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Location</th>\n",
       "      <th>Total Complaints</th>\n",
       "      <th>Full Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.408333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>Inside</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.408333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>Outside</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.408333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>Inside</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.408333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>Outside</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.408333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MANHATTAN</td>\n",
       "      <td>Inside</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day  Temperature  Precipitation    Borough Location  \\\n",
       "0  2016      1    1     5.408333            0.0      BRONX   Inside   \n",
       "1  2016      1    1     5.408333            0.0      BRONX  Outside   \n",
       "2  2016      1    1     5.408333            0.0   BROOKLYN   Inside   \n",
       "3  2016      1    1     5.408333            0.0   BROOKLYN  Outside   \n",
       "4  2016      1    1     5.408333            0.0  MANHATTAN   Inside   \n",
       "\n",
       "   Total Complaints  Full Date  \n",
       "0                 7 2016-01-01  \n",
       "1                 3 2016-01-01  \n",
       "2                10 2016-01-01  \n",
       "3                 5 2016-01-01  \n",
       "4                10 2016-01-01  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the 2 datasets on the Year, Month & Day Columns\n",
    "df = pd.merge(df_311, df_weather_avg[[\"Year\", \"Month\", \"Day\", \"Temperature\", \"Precipitation\"]], on=[\"Year\", \"Month\", \"Day\"])\n",
    "\n",
    "# Calculating Total Complaints each day for the descriptor type by borough\n",
    "df = df.groupby([\"Year\", \"Month\", \"Day\", \"Temperature\", \"Precipitation\", \"Borough\", \"Location\"]).size().reset_index(name='Total Complaints')\n",
    "df[\"Full Date\"] = pd.to_datetime(df[[\"Year\", \"Month\", \"Day\"]])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9604d0f2",
   "metadata": {},
   "source": [
    "### Part 2.5: Load\n",
    "\n",
    "The above merged dataframe is downloaded as a CSV and uploaded to BigQuery as the finalized dataset used for querying and creating visualizations on Looker. Some of the visualizations created through BigQuery/Looker are replicated below in Part 3: Analyze using Python's visualizations libraries. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "431afbdb",
   "metadata": {},
   "source": [
    "### Part 3: Analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f3adc1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data visualization Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "98f195ec",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting line graph showing temperature trends and total rodent complaints over time during the 2016-2022 time period\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='Full Date', y='Temperature', data=df, ci=None)\n",
    "sns.lineplot(x='Full Date', y='Total Complaints', data=df, ci=None, label='Total Complaints')\n",
    "plt.title('Temperature and Rodent Complaint Variation Over Time (2016-2022)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Temperature')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7fb382",
   "metadata": {},
   "source": [
    "The above line graph shows seasonality trends from 2016-2022 for both average temperatures (blue line) and total rodent complaints (red line). It's evident that as temperate rises in the warmer months such as spring and summer, so do rodent complaints. However, there are 2 points worth noting; the first is a spike in complaints in 2017 and the second being the drop in 2022. I'm unable to find further news or research explaining why this is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6cb75cf0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting bar graph showing total complaints by borough\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Borough', y='Total Complaints', data=df)\n",
    "plt.title('Total Complaints by Borough')\n",
    "plt.xlabel('Borough')\n",
    "plt.ylabel('Total Complaints')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11e34ff",
   "metadata": {},
   "source": [
    "It appears that the borough with the most rodent complaints is Brooklyn then followed by Manhattan, Bronx, Queens, and finally Staten Island. This is supported by research done by MMPC in which their article \"There Are Now 3 Million Rats in NYC, a 50% Increase Since 2010\" shows that Brooklyn has the most number of rats at ~1.16M; as of 2022, Brooklyn has seen a 56% increase in rate population from 2010. The article further shows that Queens has the second greatest rat population at ~752K and is followed by Manhattan at ~512K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ccff6c8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting bar graph showing total complaints by location type\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Location', y='Total Complaints', data=df)\n",
    "plt.title('Total Complaints by Location')\n",
    "plt.xlabel('Location')\n",
    "plt.ylabel('Total Complaints')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34aa0d2",
   "metadata": {},
   "source": [
    "For the purposes of this analysis, I only considered 2 locations: indoors and outdoors. I grouped all property types that were public into outdoors and the others into indoors. Looking at the bargraph, it appears that majority of the 311 complaints made were for locations indoors. However, this doesn't necessarily mean there are more rodents indoors than outdoors since the liklihood of someone making a rodent complaint is much higher indoors than if they were outdoors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8621b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting bar graph showing complaints by property type\n",
    "top_10_locations = df_311['Location Type'].value_counts().nlargest(10).index\n",
    "df_top_10 = df_311[df_311['Location Type'].isin(top_10_locations)]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='Location Type', data=df_top_10)\n",
    "plt.title('Total Complaints by Location Type')\n",
    "plt.xlabel('Location Type')\n",
    "plt.ylabel('Total Complaints')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e8061c",
   "metadata": {},
   "source": [
    "Taking a closer look at the different location types that rodent complaints were made in, it appears that 3+ Family Apartment Buildings was the location with the most rodent complaints followed by 1-2 family dwellings. This could be because in apartment buildings, rodents have all the necessities to survive: shelter between walls and floorboards, warmth from heaters, and water/food from trash. This is a concerning find but it further proves that NYC is in need of help to eradict the rising rodent population not only outside, but also inside apartment buildings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8f0c59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting heatmap showing correlation between temperature, precipitation and number of complaints\n",
    "correlation_matrix = df[['Temperature', 'Precipitation', 'Total Complaints']].corr()\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5131f4be",
   "metadata": {},
   "source": [
    "Prior to plotting this heatmap, my hypothesis is that 1) there would be a significant positive correlation between the temperature and total complaints and 2) a significant negative correlation between the precipitation and total complaints. The reasoning behind this is for the former 1), I thought as temperatures rose so would the amount of rodents; for the latter 2), I thought that as there is rain or snow, there would not be any rodents running around. However, it appears that only my initial guess for 1) temperature and total complaints was proven correct with a positive correlation value of 0.2. However, my guess for 2) percipitation and total complaints was off as there is only a slight negative correlation between the 2 variables."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}