sample-analysis-policies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5eb2b68b",
   "metadata": {},
   "source": [
    "# National Flood Insurance Program Data for NYC\n",
    "## Policies Sample Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbc9148c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['savefig.facecolor'] = 'white'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f54d766c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "printing packages and versions:\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.13\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "numpy     : 1.23.1\n",
      "pandas    : 1.4.3\n",
      "geopandas : 0.11.1\n",
      "matplotlib: 3.5.2\n",
      "seaborn   : 0.11.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('printing packages and versions:\\n')\n",
    "\n",
    "%reload_ext watermark\n",
    "%watermark -v -p numpy,pandas,geopandas,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9729c9c",
   "metadata": {},
   "source": [
    "# FEMA NFIP Policies Data Dictionary\n",
    "https://www.fema.gov/openfema-data-page/fima-nfip-redacted-policies-v2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6d0a7b",
   "metadata": {},
   "source": [
    "# Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b4a2da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (506278, 81)\n",
      "CPU times: user 29.6 s, sys: 4.62 s, total: 34.2 s\n",
      "Wall time: 34.3 s\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>censusBlockGroupFips</th>\n",
       "      <th>censusTract</th>\n",
       "      <th>countyCode</th>\n",
       "      <th>reportedZipCode</th>\n",
       "      <th>reportedCity</th>\n",
       "      <th>propertyState</th>\n",
       "      <th>femaRegion</th>\n",
       "      <th>...</th>\n",
       "      <th>elevatedBuildingIndicator</th>\n",
       "      <th>contentsDeductibleCode</th>\n",
       "      <th>buildingDeductibleCode</th>\n",
       "      <th>crsClassCode</th>\n",
       "      <th>construction</th>\n",
       "      <th>condominiumCoverageTypeCode</th>\n",
       "      <th>cancellationDateOfFloodPolicy</th>\n",
       "      <th>basementEnclosureCrawlspaceType</th>\n",
       "      <th>baseFloodElevation</th>\n",
       "      <th>agricultureStructureIndicator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6cdba47d-1e40-4680-8ca5-f2f4a61cb174</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>3.606101e+11</td>\n",
       "      <td>3.606101e+10</td>\n",
       "      <td>36061</td>\n",
       "      <td>10011</td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>NY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f07086ce-f925-431e-ab70-fe1d9ab4e653</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>3.606101e+11</td>\n",
       "      <td>3.606101e+10</td>\n",
       "      <td>36061</td>\n",
       "      <td>10011</td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>NY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1ded539c-1bdd-4f20-bb3d-314a92a9f9f1</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>3.606101e+11</td>\n",
       "      <td>3.606101e+10</td>\n",
       "      <td>36061</td>\n",
       "      <td>10012</td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>NY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b3eb3d0a-446d-4493-bcba-b48dabc701aa</td>\n",
       "      <td>-73.9</td>\n",
       "      <td>40.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36061</td>\n",
       "      <td></td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>MS</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0b1069b8-6b2b-4cb1-9ecd-af9aa8bd2170</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>3.606101e+11</td>\n",
       "      <td>3.606101e+10</td>\n",
       "      <td>36061</td>\n",
       "      <td>10001</td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>NY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     id  longitude  latitude  \\\n",
       "0  6cdba47d-1e40-4680-8ca5-f2f4a61cb174      -74.0      40.7   \n",
       "1  f07086ce-f925-431e-ab70-fe1d9ab4e653      -74.0      40.7   \n",
       "2  1ded539c-1bdd-4f20-bb3d-314a92a9f9f1      -74.0      40.7   \n",
       "3  b3eb3d0a-446d-4493-bcba-b48dabc701aa      -73.9      40.8   \n",
       "4  0b1069b8-6b2b-4cb1-9ecd-af9aa8bd2170      -74.0      40.7   \n",
       "\n",
       "   censusBlockGroupFips   censusTract  countyCode reportedZipCode  \\\n",
       "0          3.606101e+11  3.606101e+10       36061           10011   \n",
       "1          3.606101e+11  3.606101e+10       36061           10011   \n",
       "2          3.606101e+11  3.606101e+10       36061           10012   \n",
       "3                   NaN           NaN       36061                   \n",
       "4          3.606101e+11  3.606101e+10       36061           10001   \n",
       "\n",
       "            reportedCity propertyState  femaRegion  ...  \\\n",
       "0  Currently Unavailable            NY         2.0  ...   \n",
       "1  Currently Unavailable            NY         2.0  ...   \n",
       "2  Currently Unavailable            NY         2.0  ...   \n",
       "3  Currently Unavailable            MS         4.0  ...   \n",
       "4  Currently Unavailable            NY         2.0  ...   \n",
       "\n",
       "  elevatedBuildingIndicator contentsDeductibleCode  buildingDeductibleCode  \\\n",
       "0                     False                      0                       0   \n",
       "1                     False                      0                       0   \n",
       "2                     False                      0                       0   \n",
       "3                     False                      1                       1   \n",
       "4                     False                      1                       0   \n",
       "\n",
       "   crsClassCode construction  condominiumCoverageTypeCode  \\\n",
       "0           NaN        False                            N   \n",
       "1           NaN        False                            N   \n",
       "2           NaN        False                            N   \n",
       "3           NaN        False                            N   \n",
       "4           NaN        False                            N   \n",
       "\n",
       "   cancellationDateOfFloodPolicy  basementEnclosureCrawlspaceType  \\\n",
       "0                           None                              NaN   \n",
       "1                           None                              NaN   \n",
       "2                           None                              2.0   \n",
       "3                           None                              NaN   \n",
       "4                           None                              2.0   \n",
       "\n",
       "   baseFloodElevation  agricultureStructureIndicator  \n",
       "0                 NaN                          False  \n",
       "1                 NaN                          False  \n",
       "2                 NaN                          False  \n",
       "3                 NaN                          False  \n",
       "4                 NaN                          False  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "path = 'data/'\n",
    "all_files = glob.glob(os.path.join(path, \"policies-*.json\"))\n",
    "\n",
    "# concat all counties into one dataframe\n",
    "df = pd.concat((pd.read_json(file) for file in all_files), ignore_index=True)\n",
    "\n",
    "# reverse column order\n",
    "df = df.loc[:, df.columns[::-1]]\n",
    "\n",
    "# examine data\n",
    "print('shape of data: {}'.format(df.shape))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97611139",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506278 entries, 0 to 506277\n",
      "Data columns (total 81 columns):\n",
      " #   Column                                  Non-Null Count   Dtype  \n",
      "---  ------                                  --------------   -----  \n",
      " 0   id                                      506278 non-null  object \n",
      " 1   longitude                               506278 non-null  float64\n",
      " 2   latitude                                506278 non-null  float64\n",
      " 3   censusBlockGroupFips                    503883 non-null  float64\n",
      " 4   censusTract                             503883 non-null  float64\n",
      " 5   countyCode                              506278 non-null  int64  \n",
      " 6   reportedZipCode                         506278 non-null  object \n",
      " 7   reportedCity                            506278 non-null  object \n",
      " 8   propertyState                           506278 non-null  object \n",
      " 9   femaRegion                              506277 non-null  float64\n",
      " 10  floodZoneCurrent                        375333 non-null  object \n",
      " 11  mapPanelSuffix                          489431 non-null  object \n",
      " 12  mapPanelNumber                          487939 non-null  float64\n",
      " 13  programTypeIndicator                    506278 non-null  bool   \n",
      " 14  nfipCommunityName                       389977 non-null  object \n",
      " 15  nfipCommunityNumberCurrent              375359 non-null  float64\n",
      " 16  nfipRatedCommunityNumber                506278 non-null  int64  \n",
      " 17  grandfatheringTypeCode                  482689 non-null  float64\n",
      " 18  mandatoryPurchaseFlag                   506278 non-null  bool   \n",
      " 19  disasterAssistanceCoverageRequiredCode  385156 non-null  float64\n",
      " 20  stateOwnedIndicator                     506278 non-null  bool   \n",
      " 21  tenantIndicator                         506278 non-null  bool   \n",
      " 22  rentalPropertyIndicator                 506278 non-null  bool   \n",
      " 23  propertyPurchaseDate                    163471 non-null  object \n",
      " 24  endorsementEffectiveDate                415741 non-null  object \n",
      " 25  rolloverTransferCode                    504785 non-null  object \n",
      " 26  waitingPeriodType                       340782 non-null  object \n",
      " 27  floodproofedIndicator                   506278 non-null  bool   \n",
      " 28  postFirmVzoneIndicator                  506278 non-null  bool   \n",
      " 29  insuranceToValueCode                    254331 non-null  float64\n",
      " 30  buildingDescriptionCode                 406212 non-null  float64\n",
      " 31  enclosureTypeCode                       185863 non-null  object \n",
      " 32  AdditionalContentsRate                  432441 non-null  float64\n",
      " 33  basicContentsRate                       432441 non-null  float64\n",
      " 34  additionalBuildingRate                  432536 non-null  float64\n",
      " 35  basicBuildingRate                       432536 non-null  float64\n",
      " 36  buildingReplacementCost                 491627 non-null  float64\n",
      " 37  premiumPaymentIndicator                 238135 non-null  float64\n",
      " 38  communityProbationSurcharge             444970 non-null  float64\n",
      " 39  reserveFundAssessment                   506278 non-null  int64  \n",
      " 40  iccPremium                              501816 non-null  float64\n",
      " 41  subsidizedRateType                      281831 non-null  object \n",
      " 42  cancellationVoidanceReasonCode          13657 non-null   object \n",
      " 43  totalInsurancePremiumOfThePolicy        506278 non-null  int64  \n",
      " 44  totalContentsInsuranceCoverage          506263 non-null  float64\n",
      " 45  totalBuildingInsuranceCoverage          506278 non-null  int64  \n",
      " 46  smallBusinessIndicatorBuilding          506278 non-null  bool   \n",
      " 47  regularEmergencyProgramIndicator        506278 non-null  object \n",
      " 48  rateMethod                              506276 non-null  object \n",
      " 49  primaryResidenceIndicator               506278 non-null  bool   \n",
      " 50  postFIRMConstructionIndicator           506278 non-null  bool   \n",
      " 51  policyTermIndicator                     506278 non-null  int64  \n",
      " 52  policyTerminationDate                   506278 non-null  object \n",
      " 53  policyEffectiveDate                     506278 non-null  object \n",
      " 54  policyCount                             506278 non-null  int64  \n",
      " 55  policyCost                              506278 non-null  int64  \n",
      " 56  originalNBDate                          506146 non-null  object \n",
      " 57  originalConstructionDate                506114 non-null  object \n",
      " 58  occupancyType                           506278 non-null  int64  \n",
      " 59  obstructionType                         62818 non-null   float64\n",
      " 60  numberOfFloorsInInsuredBuilding         503938 non-null  float64\n",
      " 61  nonProfitIndicator                      506278 non-null  bool   \n",
      " 62  lowestFloorElevation                    111387 non-null  float64\n",
      " 63  lowestAdjacentGrade                     110567 non-null  float64\n",
      " 64  locationOfContents                      339937 non-null  float64\n",
      " 65  houseOfWorshipIndicator                 506278 non-null  bool   \n",
      " 66  hfiaaSurcharge                          506278 non-null  int64  \n",
      " 67  ratedFloodZone                          504080 non-null  object \n",
      " 68  federalPolicyFee                        506278 non-null  int64  \n",
      " 69  elevationDifference                     111439 non-null  float64\n",
      " 70  elevationCertificateIndicator           185924 non-null  object \n",
      " 71  elevatedBuildingIndicator               506278 non-null  bool   \n",
      " 72  contentsDeductibleCode                  417425 non-null  object \n",
      " 73  buildingDeductibleCode                  491189 non-null  object \n",
      " 74  crsClassCode                            19 non-null      float64\n",
      " 75  construction                            506278 non-null  bool   \n",
      " 76  condominiumCoverageTypeCode             506137 non-null  object \n",
      " 77  cancellationDateOfFloodPolicy           13657 non-null   object \n",
      " 78  basementEnclosureCrawlspaceType         400634 non-null  float64\n",
      " 79  baseFloodElevation                      111115 non-null  float64\n",
      " 80  agricultureStructureIndicator           506278 non-null  bool   \n",
      "dtypes: bool(15), float64(29), int64(11), object(26)\n",
      "memory usage: 262.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de4d227e",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79a07f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>policyTerminationDate</th>\n",
       "      <th>policyEffectiveDate</th>\n",
       "      <th>originalNBDate</th>\n",
       "      <th>policyTerminationYear</th>\n",
       "      <th>policyEffectiveYear</th>\n",
       "      <th>originalNBYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01 00:00:00+00:00</td>\n",
       "      <td>2009-01-01 00:00:00+00:00</td>\n",
       "      <td>2005-01-01 00:00:00+00:00</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2005.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01 00:00:00+00:00</td>\n",
       "      <td>2009-01-01 00:00:00+00:00</td>\n",
       "      <td>2005-01-01 00:00:00+00:00</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01 00:00:00+00:00</td>\n",
       "      <td>2009-01-01 00:00:00+00:00</td>\n",
       "      <td>2006-01-01 00:00:00+00:00</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-02 00:00:00+00:00</td>\n",
       "      <td>2009-01-02 00:00:00+00:00</td>\n",
       "      <td>1991-11-27 00:00:00+00:00</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1991.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-02 00:00:00+00:00</td>\n",
       "      <td>2009-01-02 00:00:00+00:00</td>\n",
       "      <td>2002-01-02 00:00:00+00:00</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2002.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      policyTerminationDate       policyEffectiveDate  \\\n",
       "0 2010-01-01 00:00:00+00:00 2009-01-01 00:00:00+00:00   \n",
       "1 2010-01-01 00:00:00+00:00 2009-01-01 00:00:00+00:00   \n",
       "2 2010-01-01 00:00:00+00:00 2009-01-01 00:00:00+00:00   \n",
       "3 2010-01-02 00:00:00+00:00 2009-01-02 00:00:00+00:00   \n",
       "4 2010-01-02 00:00:00+00:00 2009-01-02 00:00:00+00:00   \n",
       "\n",
       "             originalNBDate  policyTerminationYear  policyEffectiveYear  \\\n",
       "0 2005-01-01 00:00:00+00:00                 2010.0               2009.0   \n",
       "1 2005-01-01 00:00:00+00:00                 2010.0               2009.0   \n",
       "2 2006-01-01 00:00:00+00:00                 2010.0               2009.0   \n",
       "3 1991-11-27 00:00:00+00:00                 2010.0               2009.0   \n",
       "4 2002-01-02 00:00:00+00:00                 2010.0               2009.0   \n",
       "\n",
       "   originalNBYear  \n",
       "0          2005.0  \n",
       "1          2005.0  \n",
       "2          2006.0  \n",
       "3          1991.0  \n",
       "4          2002.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert date fields to datetime\n",
    "df['policyTerminationDate'] = pd.to_datetime(df['policyTerminationDate'], errors='coerce')\n",
    "df['policyEffectiveDate'] = pd.to_datetime(df['policyEffectiveDate'], errors='coerce')\n",
    "df['originalNBDate'] = pd.to_datetime(df['originalNBDate'], errors='coerce')\n",
    "\n",
    "# extract year from datatime \n",
    "df['policyTerminationYear'] = df['policyTerminationDate'].dt.year\n",
    "df['policyEffectiveYear'] = df['policyEffectiveDate'].dt.year\n",
    "df['originalNBYear'] = df['originalNBDate'].dt.year\n",
    "\n",
    "# preview columns\n",
    "cols = [\n",
    "    'policyTerminationDate', 'policyEffectiveDate', 'originalNBDate',\n",
    "    'policyTerminationYear', 'policyEffectiveYear', 'originalNBYear'\n",
    "]\n",
    "\n",
    "df.loc[:, cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fc406cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506278 entries, 0 to 506277\n",
      "Data columns (total 3 columns):\n",
      " #   Column                 Non-Null Count   Dtype              \n",
      "---  ------                 --------------   -----              \n",
      " 0   policyTerminationDate  506277 non-null  datetime64[ns, UTC]\n",
      " 1   policyEffectiveDate    506277 non-null  datetime64[ns, UTC]\n",
      " 2   originalNBDate         506145 non-null  datetime64[ns, UTC]\n",
      "dtypes: datetime64[ns, UTC](3)\n",
      "memory usage: 11.6 MB\n"
     ]
    }
   ],
   "source": [
    "# summarize column information\n",
    "cols = [\n",
    "    'policyTerminationDate',\n",
    "    'policyEffectiveDate',\n",
    "    'originalNBDate'\n",
    "]\n",
    "\n",
    "df.loc[:, cols].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "487d0880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "policyTerminationDate      1\n",
       "policyEffectiveDate        1\n",
       "originalNBDate           133\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count nulls \n",
    "cols = [\n",
    "    'policyTerminationDate',\n",
    "    'policyEffectiveDate',\n",
    "    'originalNBDate'\n",
    "]\n",
    "\n",
    "df.loc[:, cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d57b9a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>censusBlockGroupFips</th>\n",
       "      <th>censusTract</th>\n",
       "      <th>countyCode</th>\n",
       "      <th>reportedZipCode</th>\n",
       "      <th>reportedCity</th>\n",
       "      <th>propertyState</th>\n",
       "      <th>femaRegion</th>\n",
       "      <th>...</th>\n",
       "      <th>crsClassCode</th>\n",
       "      <th>construction</th>\n",
       "      <th>condominiumCoverageTypeCode</th>\n",
       "      <th>cancellationDateOfFloodPolicy</th>\n",
       "      <th>basementEnclosureCrawlspaceType</th>\n",
       "      <th>baseFloodElevation</th>\n",
       "      <th>agricultureStructureIndicator</th>\n",
       "      <th>policyTerminationYear</th>\n",
       "      <th>policyEffectiveYear</th>\n",
       "      <th>originalNBYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6cdba47d-1e40-4680-8ca5-f2f4a61cb174</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>3.606101e+11</td>\n",
       "      <td>3.606101e+10</td>\n",
       "      <td>36061</td>\n",
       "      <td>10011</td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>NY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f07086ce-f925-431e-ab70-fe1d9ab4e653</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>3.606101e+11</td>\n",
       "      <td>3.606101e+10</td>\n",
       "      <td>36061</td>\n",
       "      <td>10011</td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>NY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1ded539c-1bdd-4f20-bb3d-314a92a9f9f1</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>3.606101e+11</td>\n",
       "      <td>3.606101e+10</td>\n",
       "      <td>36061</td>\n",
       "      <td>10012</td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>NY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b3eb3d0a-446d-4493-bcba-b48dabc701aa</td>\n",
       "      <td>-73.9</td>\n",
       "      <td>40.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36061</td>\n",
       "      <td></td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>MS</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>1991.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0b1069b8-6b2b-4cb1-9ecd-af9aa8bd2170</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>3.606101e+11</td>\n",
       "      <td>3.606101e+10</td>\n",
       "      <td>36061</td>\n",
       "      <td>10001</td>\n",
       "      <td>Currently Unavailable</td>\n",
       "      <td>NY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2002.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     id  longitude  latitude  \\\n",
       "0  6cdba47d-1e40-4680-8ca5-f2f4a61cb174      -74.0      40.7   \n",
       "1  f07086ce-f925-431e-ab70-fe1d9ab4e653      -74.0      40.7   \n",
       "2  1ded539c-1bdd-4f20-bb3d-314a92a9f9f1      -74.0      40.7   \n",
       "3  b3eb3d0a-446d-4493-bcba-b48dabc701aa      -73.9      40.8   \n",
       "4  0b1069b8-6b2b-4cb1-9ecd-af9aa8bd2170      -74.0      40.7   \n",
       "\n",
       "   censusBlockGroupFips   censusTract  countyCode reportedZipCode  \\\n",
       "0          3.606101e+11  3.606101e+10       36061           10011   \n",
       "1          3.606101e+11  3.606101e+10       36061           10011   \n",
       "2          3.606101e+11  3.606101e+10       36061           10012   \n",
       "3                   NaN           NaN       36061                   \n",
       "4          3.606101e+11  3.606101e+10       36061           10001   \n",
       "\n",
       "            reportedCity propertyState  femaRegion  ... crsClassCode  \\\n",
       "0  Currently Unavailable            NY         2.0  ...          NaN   \n",
       "1  Currently Unavailable            NY         2.0  ...          NaN   \n",
       "2  Currently Unavailable            NY         2.0  ...          NaN   \n",
       "3  Currently Unavailable            MS         4.0  ...          NaN   \n",
       "4  Currently Unavailable            NY         2.0  ...          NaN   \n",
       "\n",
       "  construction  condominiumCoverageTypeCode  cancellationDateOfFloodPolicy  \\\n",
       "0        False                            N                           None   \n",
       "1        False                            N                           None   \n",
       "2        False                            N                           None   \n",
       "3        False                            N                           None   \n",
       "4        False                            N                           None   \n",
       "\n",
       "  basementEnclosureCrawlspaceType  baseFloodElevation  \\\n",
       "0                             NaN                 NaN   \n",
       "1                             NaN                 NaN   \n",
       "2                             2.0                 NaN   \n",
       "3                             NaN                 NaN   \n",
       "4                             2.0                 NaN   \n",
       "\n",
       "   agricultureStructureIndicator  policyTerminationYear  policyEffectiveYear  \\\n",
       "0                          False                 2010.0               2009.0   \n",
       "1                          False                 2010.0               2009.0   \n",
       "2                          False                 2010.0               2009.0   \n",
       "3                          False                 2010.0               2009.0   \n",
       "4                          False                 2010.0               2009.0   \n",
       "\n",
       "   originalNBYear  \n",
       "0          2005.0  \n",
       "1          2005.0  \n",
       "2          2006.0  \n",
       "3          1991.0  \n",
       "4          2002.0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return dataframe where specified columns are not null\n",
    "condition = (\n",
    "    (df['policyTerminationDate'].notnull())\n",
    "     & (df['policyEffectiveDate'].notnull())\n",
    ")\n",
    "\n",
    "df = df.loc[condition].reset_index(drop=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "baaa66de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "policyTerminationDate    0\n",
       "policyEffectiveDate      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    'policyTerminationDate',\n",
    "    'policyEffectiveDate'\n",
    "]\n",
    "\n",
    "df.loc[:, cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8bb1821e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>policyTerminationDate</th>\n",
       "      <th>policyEffectiveDate</th>\n",
       "      <th>originalNBDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506277</td>\n",
       "      <td>506277</td>\n",
       "      <td>506145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017-10-14 06:30:48.520079360+00:00</td>\n",
       "      <td>2016-10-11 17:31:36.368194304+00:00</td>\n",
       "      <td>2011-03-11 16:38:56.228551424+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010-01-01 00:00:00+00:00</td>\n",
       "      <td>2009-01-01 00:00:00+00:00</td>\n",
       "      <td>1950-10-10 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014-07-05 00:00:00+00:00</td>\n",
       "      <td>2013-06-29 00:00:00+00:00</td>\n",
       "      <td>2007-07-14 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017-10-06 00:00:00+00:00</td>\n",
       "      <td>2016-10-06 00:00:00+00:00</td>\n",
       "      <td>2012-07-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2021-04-10 00:00:00+00:00</td>\n",
       "      <td>2020-04-10 00:00:00+00:00</td>\n",
       "      <td>2014-11-16 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2025-02-16 00:00:00+00:00</td>\n",
       "      <td>2024-02-16 00:00:00+00:00</td>\n",
       "      <td>2024-01-13 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     policyTerminationDate  \\\n",
       "count                               506277   \n",
       "mean   2017-10-14 06:30:48.520079360+00:00   \n",
       "min              2010-01-01 00:00:00+00:00   \n",
       "25%              2014-07-05 00:00:00+00:00   \n",
       "50%              2017-10-06 00:00:00+00:00   \n",
       "75%              2021-04-10 00:00:00+00:00   \n",
       "max              2025-02-16 00:00:00+00:00   \n",
       "\n",
       "                       policyEffectiveDate  \\\n",
       "count                               506277   \n",
       "mean   2016-10-11 17:31:36.368194304+00:00   \n",
       "min              2009-01-01 00:00:00+00:00   \n",
       "25%              2013-06-29 00:00:00+00:00   \n",
       "50%              2016-10-06 00:00:00+00:00   \n",
       "75%              2020-04-10 00:00:00+00:00   \n",
       "max              2024-02-16 00:00:00+00:00   \n",
       "\n",
       "                            originalNBDate  \n",
       "count                               506145  \n",
       "mean   2011-03-11 16:38:56.228551424+00:00  \n",
       "min              1950-10-10 00:00:00+00:00  \n",
       "25%              2007-07-14 00:00:00+00:00  \n",
       "50%              2012-07-07 00:00:00+00:00  \n",
       "75%              2014-11-16 00:00:00+00:00  \n",
       "max              2024-01-13 00:00:00+00:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics\n",
    "cols = [\n",
    "    'policyTerminationDate',\n",
    "    'policyEffectiveDate',\n",
    "    'originalNBDate'\n",
    "]\n",
    "\n",
    "df.loc[:, cols].describe(datetime_is_numeric=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a9d3691",
   "metadata": {},
   "source": [
    "# Policies in Force"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "561ffa93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As of 2023-11-30:\n",
      "------\n",
      "Policies in Force: 59,370\n",
      "Total Insurance Coverage: $16,420,691,000\n",
      "Premium of Policy and Federal Policy Fee: $45,933,558\n"
     ]
    }
   ],
   "source": [
    "condition = (df['policyTerminationDate'] > '2023-11-30') & (df['policyEffectiveDate'] <= '2023-11-30')\n",
    "pif_df = df.loc[condition].reset_index(drop=True)\n",
    "\n",
    "as_of_date = pif_df['policyEffectiveDate'].max().strftime('%Y-%m-%d')\n",
    "count = pif_df['policyCount'].sum()\n",
    "coverage = (pif_df['totalBuildingInsuranceCoverage'] + pif_df['totalContentsInsuranceCoverage']).sum()\n",
    "policy_fee = (pif_df['totalInsurancePremiumOfThePolicy'] + pif_df['federalPolicyFee']).sum()\n",
    "\n",
    "# policy statistics\n",
    "print('As of {}:\\n------'.format(as_of_date))\n",
    "print('Policies in Force: {:,}'.format(count))\n",
    "print('Total Insurance Coverage: ${:,.0f}'.format(coverage))\n",
    "print('Premium of Policy and Federal Policy Fee: ${:,.0f}'.format(policy_fee))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3c39c24",
   "metadata": {},
   "source": [
    "# Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cac5f55",
   "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>originalNBYear</th>\n",
       "      <th>policyCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>33906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>37367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>37474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>32206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>135479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>59042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>41678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>28400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>20103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>16672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>9141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>9344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021</td>\n",
       "      <td>9775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022</td>\n",
       "      <td>38251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023</td>\n",
       "      <td>12774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    originalNBYear  policyCount\n",
       "0             2009        33906\n",
       "1             2010        37367\n",
       "2             2011        37474\n",
       "3             2012        32206\n",
       "4             2013       135479\n",
       "5             2014        59042\n",
       "6             2015        41678\n",
       "7             2016        28400\n",
       "8             2017        20103\n",
       "9             2018        16672\n",
       "10            2019         9141\n",
       "11            2020         9344\n",
       "12            2021         9775\n",
       "13            2022        38251\n",
       "14            2023        12774"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# starting year of 2009\n",
    "original_date = (\n",
    "    df.loc[df['originalNBYear'].between(2009, 2023)]\n",
    "    .groupby(by='originalNBYear')['policyCount']\n",
    "    .sum()\n",
    "    .reset_index()\n",
    "    .astype({'originalNBYear':'int'})\n",
    "    .sort_values(by='originalNBYear')\n",
    ")\n",
    "\n",
    "# preview data\n",
    "original_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e15cdf9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.lineplot(\n",
    "    x=original_date['originalNBYear'],\n",
    "    y=original_date['policyCount'],\n",
    "    color='#1f77b4'\n",
    ")\n",
    "\n",
    "label = 'Count of NFIP Policies by Original Flood Policy Year in New York City'\n",
    "plt.title(label, fontsize=12, pad=10)\n",
    "plt.xlabel('Original Flood Policy Year', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "\n",
    "xticks = np.arange(2009, 2024, 1)\n",
    "ax.set_xticks(xticks)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/original-policies.png', bbox_inches='tight', dpi=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "152b6704",
   "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>policyEffectiveYear</th>\n",
       "      <th>policyCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>37539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>38142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>39855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>41366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>56768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>56943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>57267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>57232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>55883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>54871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>53935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>53792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021</td>\n",
       "      <td>55257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022</td>\n",
       "      <td>59012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023</td>\n",
       "      <td>57469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    policyEffectiveYear  policyCount\n",
       "0                  2009        37539\n",
       "1                  2010        38142\n",
       "2                  2011        39855\n",
       "3                  2012        41366\n",
       "4                  2013        56768\n",
       "5                  2014        56943\n",
       "6                  2015        57267\n",
       "7                  2016        57232\n",
       "8                  2017        55883\n",
       "9                  2018        54871\n",
       "10                 2019        53935\n",
       "11                 2020        53792\n",
       "12                 2021        55257\n",
       "13                 2022        59012\n",
       "14                 2023        57469"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "effective_date = (\n",
    "    df.loc[df['policyEffectiveYear'] < 2024]\n",
    "    .groupby(by='policyEffectiveYear')['policyCount']\n",
    "    .sum()\n",
    "    .reset_index()\n",
    "    .astype({'policyEffectiveYear':'int'})\n",
    "    .sort_values(by='policyEffectiveYear')\n",
    ")\n",
    "\n",
    "# preview data\n",
    "effective_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3930ff2d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.lineplot(\n",
    "    x=effective_date['policyEffectiveYear'],\n",
    "    y=effective_date['policyCount'],\n",
    "    color='#1f77b4'\n",
    ")\n",
    "\n",
    "label = 'Count of NFIP Policies by Effective Flood Policy Year in New York City'\n",
    "plt.title(label, fontsize=12, pad=10)\n",
    "plt.xlabel('Effective Flood Policy Year', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "\n",
    "xticks = np.arange(2009, 2024, 1)\n",
    "ax.set_xticks(xticks)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/effective-policies.png', bbox_inches='tight', dpi=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e441605",
   "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>policyEffectiveYear</th>\n",
       "      <th>totalInsuranceCoverageB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>8.193513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>8.665323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>9.152814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>9.800809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>14.163300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>14.976931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>15.460696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>15.675451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>15.484719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>15.263786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>15.056406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>14.889852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021</td>\n",
       "      <td>15.452752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022</td>\n",
       "      <td>16.333926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023</td>\n",
       "      <td>15.833636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    policyEffectiveYear  totalInsuranceCoverageB\n",
       "0                  2009                 8.193513\n",
       "1                  2010                 8.665323\n",
       "2                  2011                 9.152814\n",
       "3                  2012                 9.800809\n",
       "4                  2013                14.163300\n",
       "5                  2014                14.976931\n",
       "6                  2015                15.460696\n",
       "7                  2016                15.675451\n",
       "8                  2017                15.484719\n",
       "9                  2018                15.263786\n",
       "10                 2019                15.056406\n",
       "11                 2020                14.889852\n",
       "12                 2021                15.452752\n",
       "13                 2022                16.333926\n",
       "14                 2023                15.833636"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total insurance coverage in billions\n",
    "df['totalInsuranceCoverageB'] = (\n",
    "    (df['totalBuildingInsuranceCoverage'] + df['totalContentsInsuranceCoverage']) / 1_000_000_000\n",
    ")\n",
    "\n",
    "total_coverage = (\n",
    "    df.loc[df['policyEffectiveYear'] < 2024]\n",
    "    .groupby(by='policyEffectiveYear')['totalInsuranceCoverageB']\n",
    "    .sum()\n",
    "    .reset_index()\n",
    "    .astype({'policyEffectiveYear':'int'})\n",
    "    .sort_values(by='policyEffectiveYear')\n",
    ")\n",
    "\n",
    "# preview data\n",
    "total_coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "09654045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.lineplot(\n",
    "    x=total_coverage['policyEffectiveYear'],\n",
    "    y=total_coverage['totalInsuranceCoverageB'],\n",
    "    color='#1f77b4'\n",
    ")\n",
    "\n",
    "label = 'Total NFIP Insurance Coverage by Effective Flood Policy Year in New York City'\n",
    "plt.title(label, fontsize=12, pad=10)\n",
    "plt.xlabel('Effective Flood Policy Year', fontsize=12)\n",
    "plt.ylabel('Total Insurance Coverage ($Billions)', fontsize=12)\n",
    "\n",
    "xticks = np.arange(2009, 2024, 1)\n",
    "ax.set_xticks(xticks)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/total-coverage.png', bbox_inches='tight', dpi=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "524fc1c4",
   "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>policyEffectiveYear</th>\n",
       "      <th>totalPremiumPolicyFeeM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>28.378794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>30.970685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>33.251425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>37.585601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>53.444978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>55.038439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>55.823127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>57.681163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>58.193313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>58.505415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>59.811382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>60.122882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021</td>\n",
       "      <td>63.560793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022</td>\n",
       "      <td>51.232601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023</td>\n",
       "      <td>44.285636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    policyEffectiveYear  totalPremiumPolicyFeeM\n",
       "0                  2009               28.378794\n",
       "1                  2010               30.970685\n",
       "2                  2011               33.251425\n",
       "3                  2012               37.585601\n",
       "4                  2013               53.444978\n",
       "5                  2014               55.038439\n",
       "6                  2015               55.823127\n",
       "7                  2016               57.681163\n",
       "8                  2017               58.193313\n",
       "9                  2018               58.505415\n",
       "10                 2019               59.811382\n",
       "11                 2020               60.122882\n",
       "12                 2021               63.560793\n",
       "13                 2022               51.232601\n",
       "14                 2023               44.285636"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# premium and policy fees in millions\n",
    "df['totalPremiumPolicyFeeM'] = (\n",
    "    (df['totalInsurancePremiumOfThePolicy'] + df['federalPolicyFee']) / 1_000_000\n",
    ")\n",
    "\n",
    "total_policy_fee = (\n",
    "    df.loc[df['policyEffectiveYear'] < 2024]\n",
    "    .groupby(by='policyEffectiveYear')['totalPremiumPolicyFeeM']\n",
    "    .sum()\n",
    "    .reset_index()\n",
    "    .astype({'policyEffectiveYear':'int'})\n",
    "    .sort_values(by='policyEffectiveYear')\n",
    ")\n",
    "\n",
    "# preview data\n",
    "total_policy_fee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b05b3bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "sns.lineplot(\n",
    "    x=total_policy_fee['policyEffectiveYear'],\n",
    "    y=total_policy_fee['totalPremiumPolicyFeeM'],\n",
    "    color='#1f77b4'\n",
    ")\n",
    "\n",
    "label = 'Total NFIP Written Premium + Federal Policy Fee by\\n\\\n",
    "Effective Flood Policy Year in New York City'\n",
    "plt.title(label, fontsize=12, pad=10)\n",
    "plt.xlabel('Effective Flood Policy Year', fontsize=12)\n",
    "plt.ylabel('Total Written Premium + Federal Policy Fee ($Millions)', fontsize=12)\n",
    "\n",
    "xticks = np.arange(2009, 2024, 1)\n",
    "ax.set_xticks(xticks)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/total-policy-fee.png', bbox_inches='tight', dpi=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f608031",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}