api-examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d41dc4c2",
   "metadata": {},
   "source": [
    "# API Examples\n",
    "## A Guide to the National Structure Inventory (NSI) API in Python\n",
    "Author: Mark Bauer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1ea295",
   "metadata": {},
   "source": [
    "This notebook is intended to demonstrate how to use the National Structure Inventory (NSI) API in Python. To learn more about the NSI API, please visit the [API Reference Guide](https://www.hec.usace.army.mil/confluence/nsi/technicalreferences/latest/api-reference-guide) and the [NSI Documentation](https://www.hec.usace.army.mil/confluence/nsi)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64328343",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee52a946",
   "metadata": {},
   "source": [
    "For the bounding box argument, we will use the bounding box of Lower Manhattan, New York City.\n",
    "\n",
    "![bbox](images/bbox.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb88436",
   "metadata": {},
   "source": [
    "Source: [bboxfinder.com](http://bboxfinder.com/#0.000000,0.000000,0.000000,0.000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a893a410",
   "metadata": {},
   "source": [
    "For the FIPS code argument, we will use Manhattan borough - **36061**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33144644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>COUNTYNS</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>36</td>\n",
       "      <td>061</td>\n",
       "      <td>00974129</td>\n",
       "      <td>0500000US36061</td>\n",
       "      <td>36061</td>\n",
       "      <td>New York</td>\n",
       "      <td>06</td>\n",
       "      <td>58680498</td>\n",
       "      <td>28551707</td>\n",
       "      <td>MULTIPOLYGON (((-73.99950 40.70033, -73.99750 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STATEFP COUNTYFP  COUNTYNS        AFFGEOID  GEOID      NAME LSAD  \\\n",
       "989      36      061  00974129  0500000US36061  36061  New York   06   \n",
       "\n",
       "        ALAND    AWATER                                           geometry  \n",
       "989  58680498  28551707  MULTIPOLYGON (((-73.99950 40.70033, -73.99750 ...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the FIPS code for Manhattan\n",
    "path = \"https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_500k.zip\"\n",
    "gdf = gpd.read_file(path)\n",
    "\n",
    "# FIPS code\n",
    "manhattan_gdf = gdf.loc[gdf['GEOID'] == '36061']\n",
    "\n",
    "manhattan_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7817133f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual plot\n",
    "manhattan_gdf.plot(facecolor='None')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "620dc0da",
   "metadata": {},
   "source": [
    "# National Structure Inventory (NSI) API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8739018f",
   "metadata": {},
   "source": [
    "# Structures Endpoint\n",
    "> The structures endpoint provides structure data to the user as a geojson feature collection (fc) or a feature stream (fs), the default is feature collection. The structure of a feature returned by the API can be seen in this geojson feature block. The type of return can be declared by the fmt directive (e.g. &fmt=fc)\n",
    "\n",
    "Source: https://www.hec.usace.army.mil/confluence/nsi/technicalreferences/latest/api-reference-guide#id-.APIReferenceGuidev2022-Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf69176",
   "metadata": {},
   "source": [
    "## Structures By Bounding Box\n",
    "Note: There are five points because you have to *close* the box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a0dab6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6301, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                type                                           features\n",
       "0  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "1  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "2  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "3  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "4  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set variables\n",
    "root_url = \"https://nsi.sec.usace.army.mil/nsiapi/\"\n",
    "structures = \"structures?bbox=\"\n",
    "bbox = \"-74.024840,40.699282,-74.024840,40.719809,-73.999305,40.699282,-73.999305,40.719809,-74.024840,40.699282\"\n",
    "\n",
    "# build full url and read as JSON in pandas\n",
    "url = f\"{root_url}{structures}{bbox}\"\n",
    "df = pd.read_json(url)\n",
    "\n",
    "# preview data\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "732f47d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FeatureCollection    6301\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm records are only feature collections\n",
    "df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97d51aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "1    {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "2    {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "3    {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "4    {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "Name: features, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview feature JSON\n",
    "df['features'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6de12683",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6301, 32)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>type</th>\n",
       "      <th>geometry.type</th>\n",
       "      <th>geometry.coordinates</th>\n",
       "      <th>properties.fd_id</th>\n",
       "      <th>properties.bid</th>\n",
       "      <th>properties.occtype</th>\n",
       "      <th>properties.st_damcat</th>\n",
       "      <th>properties.bldgtype</th>\n",
       "      <th>properties.found_type</th>\n",
       "      <th>properties.cbfips</th>\n",
       "      <th>...</th>\n",
       "      <th>properties.val_vehic</th>\n",
       "      <th>properties.source</th>\n",
       "      <th>properties.med_yr_blt</th>\n",
       "      <th>properties.firmzone</th>\n",
       "      <th>properties.o65disable</th>\n",
       "      <th>properties.u65disable</th>\n",
       "      <th>properties.x</th>\n",
       "      <th>properties.y</th>\n",
       "      <th>properties.ground_elv</th>\n",
       "      <th>properties.ground_elv_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-74.008328, 40.706836]</td>\n",
       "      <td>554665148</td>\n",
       "      <td>87G7PX4R+PMM-0-0-0-0</td>\n",
       "      <td>RES1-1SWB</td>\n",
       "      <td>RES</td>\n",
       "      <td>W</td>\n",
       "      <td>B</td>\n",
       "      <td>360610007001003</td>\n",
       "      <td>...</td>\n",
       "      <td>27000</td>\n",
       "      <td>P</td>\n",
       "      <td>1939</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-74.008328</td>\n",
       "      <td>40.706836</td>\n",
       "      <td>21.934981</td>\n",
       "      <td>6.685782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-74.018638, 40.70607]</td>\n",
       "      <td>554076983</td>\n",
       "      <td>87G7PX4J+CGH-14-14-16-12</td>\n",
       "      <td>COM8</td>\n",
       "      <td>COM</td>\n",
       "      <td>W</td>\n",
       "      <td>I</td>\n",
       "      <td>360610317044001</td>\n",
       "      <td>...</td>\n",
       "      <td>45000</td>\n",
       "      <td>P</td>\n",
       "      <td>1997</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-74.018638</td>\n",
       "      <td>40.706070</td>\n",
       "      <td>11.275930</td>\n",
       "      <td>3.436903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-74.018789, 40.705926]</td>\n",
       "      <td>554076985</td>\n",
       "      <td>87G7PX4J+9FG-7-6-7-6</td>\n",
       "      <td>COM8</td>\n",
       "      <td>COM</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "      <td>360610317044001</td>\n",
       "      <td>...</td>\n",
       "      <td>45000</td>\n",
       "      <td>P</td>\n",
       "      <td>1997</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-74.018789</td>\n",
       "      <td>40.705926</td>\n",
       "      <td>11.673391</td>\n",
       "      <td>3.558049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-74.018777, 40.706243]</td>\n",
       "      <td>554076986</td>\n",
       "      <td>87G7PX4J+FFX-5-5-4-5</td>\n",
       "      <td>COM8</td>\n",
       "      <td>COM</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "      <td>360610317044001</td>\n",
       "      <td>...</td>\n",
       "      <td>45000</td>\n",
       "      <td>P</td>\n",
       "      <td>1997</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-74.018777</td>\n",
       "      <td>40.706243</td>\n",
       "      <td>11.516893</td>\n",
       "      <td>3.510349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-74.018781, 40.706251]</td>\n",
       "      <td>554076989</td>\n",
       "      <td>87G7PX4J+GF5-1-1-2-2</td>\n",
       "      <td>COM8</td>\n",
       "      <td>COM</td>\n",
       "      <td>W</td>\n",
       "      <td>I</td>\n",
       "      <td>360610317044001</td>\n",
       "      <td>...</td>\n",
       "      <td>45000</td>\n",
       "      <td>P</td>\n",
       "      <td>1997</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-74.018781</td>\n",
       "      <td>40.706251</td>\n",
       "      <td>11.516893</td>\n",
       "      <td>3.510349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      type geometry.type     geometry.coordinates  properties.fd_id  \\\n",
       "0  Feature         Point  [-74.008328, 40.706836]         554665148   \n",
       "1  Feature         Point   [-74.018638, 40.70607]         554076983   \n",
       "2  Feature         Point  [-74.018789, 40.705926]         554076985   \n",
       "3  Feature         Point  [-74.018777, 40.706243]         554076986   \n",
       "4  Feature         Point  [-74.018781, 40.706251]         554076989   \n",
       "\n",
       "             properties.bid properties.occtype properties.st_damcat  \\\n",
       "0      87G7PX4R+PMM-0-0-0-0          RES1-1SWB                  RES   \n",
       "1  87G7PX4J+CGH-14-14-16-12               COM8                  COM   \n",
       "2      87G7PX4J+9FG-7-6-7-6               COM8                  COM   \n",
       "3      87G7PX4J+FFX-5-5-4-5               COM8                  COM   \n",
       "4      87G7PX4J+GF5-1-1-2-2               COM8                  COM   \n",
       "\n",
       "  properties.bldgtype properties.found_type properties.cbfips  ...  \\\n",
       "0                   W                     B   360610007001003  ...   \n",
       "1                   W                     I   360610317044001  ...   \n",
       "2                   S                     C   360610317044001  ...   \n",
       "3                   S                     C   360610317044001  ...   \n",
       "4                   W                     I   360610317044001  ...   \n",
       "\n",
       "   properties.val_vehic  properties.source  properties.med_yr_blt  \\\n",
       "0                 27000                  P                   1939   \n",
       "1                 45000                  P                   1997   \n",
       "2                 45000                  P                   1997   \n",
       "3                 45000                  P                   1997   \n",
       "4                 45000                  P                   1997   \n",
       "\n",
       "   properties.firmzone  properties.o65disable  properties.u65disable  \\\n",
       "0                 None                   0.25                   0.03   \n",
       "1                 None                   0.25                   0.03   \n",
       "2                 None                   0.25                   0.03   \n",
       "3                 None                   0.25                   0.03   \n",
       "4                 None                   0.25                   0.03   \n",
       "\n",
       "  properties.x properties.y  properties.ground_elv  properties.ground_elv_m  \n",
       "0   -74.008328    40.706836              21.934981                 6.685782  \n",
       "1   -74.018638    40.706070              11.275930                 3.436903  \n",
       "2   -74.018789    40.705926              11.673391                 3.558049  \n",
       "3   -74.018777    40.706243              11.516893                 3.510349  \n",
       "4   -74.018781    40.706251              11.516893                 3.510349  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize JSON to a flat table\n",
    "df = pd.json_normalize(df['features'])\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547859cf",
   "metadata": {},
   "source": [
    "## Structures By FIPS\n",
    "FIPS codes: https://transition.fcc.gov/oet/info/maps/census/fips/fips.txt\n",
    "\n",
    "We will use Manhattan's FIPS code: 36061."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20d4389c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(163033, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FeatureCollection</td>\n",
       "      <td>{'type': 'Feature', 'geometry': {'type': 'Poin...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                type                                           features\n",
       "0  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "1  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "2  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "3  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin...\n",
       "4  FeatureCollection  {'type': 'Feature', 'geometry': {'type': 'Poin..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_url = \"https://nsi.sec.usace.army.mil/nsiapi/\"\n",
    "structures = \"structures?fips=\"\n",
    "fips = \"36061\"\n",
    "\n",
    "url = f\"{root_url}{structures}{fips}\"\n",
    "df = pd.read_json(url)\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb21a837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(163033, 32)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>geometry.type</th>\n",
       "      <th>geometry.coordinates</th>\n",
       "      <th>properties.fd_id</th>\n",
       "      <th>properties.bid</th>\n",
       "      <th>properties.occtype</th>\n",
       "      <th>properties.st_damcat</th>\n",
       "      <th>properties.bldgtype</th>\n",
       "      <th>properties.found_type</th>\n",
       "      <th>properties.cbfips</th>\n",
       "      <th>...</th>\n",
       "      <th>properties.val_vehic</th>\n",
       "      <th>properties.source</th>\n",
       "      <th>properties.med_yr_blt</th>\n",
       "      <th>properties.firmzone</th>\n",
       "      <th>properties.o65disable</th>\n",
       "      <th>properties.u65disable</th>\n",
       "      <th>properties.x</th>\n",
       "      <th>properties.y</th>\n",
       "      <th>properties.ground_elv</th>\n",
       "      <th>properties.ground_elv_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-73.99841, 40.714656]</td>\n",
       "      <td>554079197</td>\n",
       "      <td>87G8P272+VJ8-1-1-2-1</td>\n",
       "      <td>RES3C</td>\n",
       "      <td>RES</td>\n",
       "      <td>W</td>\n",
       "      <td>B</td>\n",
       "      <td>360610029003003</td>\n",
       "      <td>...</td>\n",
       "      <td>135000</td>\n",
       "      <td>X</td>\n",
       "      <td>1939</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-73.998410</td>\n",
       "      <td>40.714656</td>\n",
       "      <td>30.833406</td>\n",
       "      <td>9.398022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-73.997441, 40.714627]</td>\n",
       "      <td>554079269</td>\n",
       "      <td>87G8P273+V23-2-5-3-4</td>\n",
       "      <td>RES3A</td>\n",
       "      <td>RES</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>360610029003004</td>\n",
       "      <td>...</td>\n",
       "      <td>27000</td>\n",
       "      <td>X</td>\n",
       "      <td>1939</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-73.997441</td>\n",
       "      <td>40.714627</td>\n",
       "      <td>36.259209</td>\n",
       "      <td>11.051806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-74.01107, 40.719515]</td>\n",
       "      <td>554096858</td>\n",
       "      <td>87G7PX9Q+RH3-1-1-1-1</td>\n",
       "      <td>IND6</td>\n",
       "      <td>IND</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>360610039002001</td>\n",
       "      <td>...</td>\n",
       "      <td>36000</td>\n",
       "      <td>X</td>\n",
       "      <td>1975</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-74.011070</td>\n",
       "      <td>40.719515</td>\n",
       "      <td>9.820276</td>\n",
       "      <td>2.993220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-73.997247, 40.721022]</td>\n",
       "      <td>554126006</td>\n",
       "      <td>87G8P2C3+C42-8-6-8-7</td>\n",
       "      <td>RES3A</td>\n",
       "      <td>RES</td>\n",
       "      <td>W</td>\n",
       "      <td>S</td>\n",
       "      <td>360610041006002</td>\n",
       "      <td>...</td>\n",
       "      <td>54000</td>\n",
       "      <td>X</td>\n",
       "      <td>1939</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-73.997247</td>\n",
       "      <td>40.721022</td>\n",
       "      <td>32.165671</td>\n",
       "      <td>9.804096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Feature</td>\n",
       "      <td>Point</td>\n",
       "      <td>[-73.984015, 40.718307]</td>\n",
       "      <td>554175328</td>\n",
       "      <td>87G8P298+89H-10-11-11-11</td>\n",
       "      <td>EDU1</td>\n",
       "      <td>PUB</td>\n",
       "      <td>S</td>\n",
       "      <td>S</td>\n",
       "      <td>360610014022000</td>\n",
       "      <td>...</td>\n",
       "      <td>234000</td>\n",
       "      <td>X</td>\n",
       "      <td>1959</td>\n",
       "      <td>None</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-73.984015</td>\n",
       "      <td>40.718307</td>\n",
       "      <td>23.983126</td>\n",
       "      <td>7.310057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      type geometry.type     geometry.coordinates  properties.fd_id  \\\n",
       "0  Feature         Point   [-73.99841, 40.714656]         554079197   \n",
       "1  Feature         Point  [-73.997441, 40.714627]         554079269   \n",
       "2  Feature         Point   [-74.01107, 40.719515]         554096858   \n",
       "3  Feature         Point  [-73.997247, 40.721022]         554126006   \n",
       "4  Feature         Point  [-73.984015, 40.718307]         554175328   \n",
       "\n",
       "             properties.bid properties.occtype properties.st_damcat  \\\n",
       "0      87G8P272+VJ8-1-1-2-1              RES3C                  RES   \n",
       "1      87G8P273+V23-2-5-3-4              RES3A                  RES   \n",
       "2      87G7PX9Q+RH3-1-1-1-1               IND6                  IND   \n",
       "3      87G8P2C3+C42-8-6-8-7              RES3A                  RES   \n",
       "4  87G8P298+89H-10-11-11-11               EDU1                  PUB   \n",
       "\n",
       "  properties.bldgtype properties.found_type properties.cbfips  ...  \\\n",
       "0                   W                     B   360610029003003  ...   \n",
       "1                   M                     S   360610029003004  ...   \n",
       "2                   S                     S   360610039002001  ...   \n",
       "3                   W                     S   360610041006002  ...   \n",
       "4                   S                     S   360610014022000  ...   \n",
       "\n",
       "   properties.val_vehic  properties.source  properties.med_yr_blt  \\\n",
       "0                135000                  X                   1939   \n",
       "1                 27000                  X                   1939   \n",
       "2                 36000                  X                   1975   \n",
       "3                 54000                  X                   1939   \n",
       "4                234000                  X                   1959   \n",
       "\n",
       "   properties.firmzone  properties.o65disable  properties.u65disable  \\\n",
       "0                 None                   0.25                   0.03   \n",
       "1                 None                   0.25                   0.03   \n",
       "2                 None                   0.25                   0.03   \n",
       "3                 None                   0.25                   0.03   \n",
       "4                 None                   0.25                   0.03   \n",
       "\n",
       "  properties.x properties.y  properties.ground_elv  properties.ground_elv_m  \n",
       "0   -73.998410    40.714656              30.833406                 9.398022  \n",
       "1   -73.997441    40.714627              36.259209                11.051806  \n",
       "2   -74.011070    40.719515               9.820276                 2.993220  \n",
       "3   -73.997247    40.721022              32.165671                 9.804096  \n",
       "4   -73.984015    40.718307              23.983126                 7.310057  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.json_normalize(df['features'])\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02b428c",
   "metadata": {},
   "source": [
    "# Stats Endpoint\n",
    "\n",
    "> The stats endpoint provides access to a set of statistics based only on the bbox argument.\n",
    "\n",
    "Source: https://www.hec.usace.army.mil/confluence/nsi/technicalreferences/latest/api-reference-guide#id-.APIReferenceGuidev2022-Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c199d415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "num_structures     6301.000000\n",
       "yrbuilt_min           0.000000\n",
       "yrbuilt_max        2017.000000\n",
       "num_story_mean        1.468497\n",
       "resunits_sum      14366.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_url = \"https://nsi.sec.usace.army.mil/nsiapi/\"\n",
    "stats = \"stats?bbox=\"\n",
    "bbox = \"-74.024840,40.699282,-74.024840,40.719809,-73.999305,40.699282,-73.999305,40.719809,-74.024840,40.699282\"\n",
    "\n",
    "# JSON comes as a Series, so we have to convert back to a DataFrame to access columns\n",
    "url = f\"{root_url}{stats}{bbox}\"\n",
    "df = pd.read_json(url, typ='series')\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37f383fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_structures    6.301000e+03\n",
       "yrbuilt_min       0.000000e+00\n",
       "yrbuilt_max       2.017000e+03\n",
       "num_story_mean    1.468497e+00\n",
       "resunits_sum      1.436600e+04\n",
       "empnum_sum        1.333500e+04\n",
       "students_sum      3.038000e+03\n",
       "sqft_mean         2.926842e+03\n",
       "sqft_sum          1.844203e+07\n",
       "pop2amu65_sum     3.000800e+04\n",
       "pop2amo65_sum     5.476000e+03\n",
       "pop2pmu65_sum     1.405450e+05\n",
       "pop2pmo65_sum     1.227700e+04\n",
       "val_struct_sum    4.282322e+09\n",
       "val_cont_sum      3.107250e+09\n",
       "val_vehic_sum     5.123700e+08\n",
       "med_yr_blt_min    1.939000e+03\n",
       "med_yr_blt_max    2.017000e+03\n",
       "ground_elv_max    4.463640e+01\n",
       "ground_elv_min   -5.314961e+00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview full series\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe6e5964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>num_structures</td>\n",
       "      <td>6.301000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yrbuilt_min</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yrbuilt_max</td>\n",
       "      <td>2.017000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>num_story_mean</td>\n",
       "      <td>1.468497e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>resunits_sum</td>\n",
       "      <td>1.436600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>empnum_sum</td>\n",
       "      <td>1.333500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>students_sum</td>\n",
       "      <td>3.038000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sqft_mean</td>\n",
       "      <td>2.926842e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sqft_sum</td>\n",
       "      <td>1.844203e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pop2amu65_sum</td>\n",
       "      <td>3.000800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pop2amo65_sum</td>\n",
       "      <td>5.476000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pop2pmu65_sum</td>\n",
       "      <td>1.405450e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pop2pmo65_sum</td>\n",
       "      <td>1.227700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>val_struct_sum</td>\n",
       "      <td>4.282322e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>val_cont_sum</td>\n",
       "      <td>3.107250e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>val_vehic_sum</td>\n",
       "      <td>5.123700e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>med_yr_blt_min</td>\n",
       "      <td>1.939000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>med_yr_blt_max</td>\n",
       "      <td>2.017000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ground_elv_max</td>\n",
       "      <td>4.463640e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ground_elv_min</td>\n",
       "      <td>-5.314961e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              stat         value\n",
       "0   num_structures  6.301000e+03\n",
       "1      yrbuilt_min  0.000000e+00\n",
       "2      yrbuilt_max  2.017000e+03\n",
       "3   num_story_mean  1.468497e+00\n",
       "4     resunits_sum  1.436600e+04\n",
       "5       empnum_sum  1.333500e+04\n",
       "6     students_sum  3.038000e+03\n",
       "7        sqft_mean  2.926842e+03\n",
       "8         sqft_sum  1.844203e+07\n",
       "9    pop2amu65_sum  3.000800e+04\n",
       "10   pop2amo65_sum  5.476000e+03\n",
       "11   pop2pmu65_sum  1.405450e+05\n",
       "12   pop2pmo65_sum  1.227700e+04\n",
       "13  val_struct_sum  4.282322e+09\n",
       "14    val_cont_sum  3.107250e+09\n",
       "15   val_vehic_sum  5.123700e+08\n",
       "16  med_yr_blt_min  1.939000e+03\n",
       "17  med_yr_blt_max  2.017000e+03\n",
       "18  ground_elv_max  4.463640e+01\n",
       "19  ground_elv_min -5.314961e+00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pushing into a dataframe and reset index, rename columns\n",
    "df = (\n",
    "    pd.DataFrame(df)\n",
    "    .reset_index()\n",
    "    .rename(columns={\"index\":\"stat\", 0:\"value\"})\n",
    ")\n",
    "\n",
    "print(df.shape)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "693be022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   stat    20 non-null     object \n",
      " 1   value   20 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 448.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# confirm values are numeric\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93888e12",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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