notebooks/dataprep/03a-USCensusDP03Poverty.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selected Economic Characteristics: Poverty from the American Community Survey\n",
    "\n",
    "**[Work in progress]**\n",
    "\n",
    "This notebook downloads [selected economic characteristics (DP03)](https://data.census.gov/cedsci/table?tid=ACSDP5Y2018.DP03) from the American Community Survey 2018 5-Year Data.\n",
    "\n",
    "Data source: [American Community Survey 5-Year Data 2018](https://www.census.gov/data/developers/data-sets/acs-5year.html)\n",
    "\n",
    "Authors: Peter Rose (pwrose@ucsd.edu), Ilya Zaslavsky (zaslavsk@sdsc.edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = None  # display all rows\n",
    "pd.options.display.max_columns = None  # display all columsns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/peter/Library/Application Support/com.Neo4j.Relate/data/dbmss/dbms-8bf637fc-0d20-4d9f-9c6f-f7e72e92a4da/import\n"
     ]
    }
   ],
   "source": [
    "NEO4J_IMPORT = Path(os.getenv('NEO4J_IMPORT'))\n",
    "print(NEO4J_IMPORT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download selected variables\n",
    "\n",
    "* [Selected economic characteristics for US](https://data.census.gov/cedsci/table?tid=ACSDP5Y2018.DP03)\n",
    "\n",
    "* [List of variables as HTML](https://api.census.gov/data/2018/acs/acs5/profile/groups/DP03.html) or [JSON](https://api.census.gov/data/2018/acs/acs5/profile/groups/DP03/)\n",
    "\n",
    "* [Description of variables](https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2018_ACSSubjectDefinitions.pdf)\n",
    "\n",
    "* [Example URLs for API](https://api.census.gov/data/2018/acs/acs5/profile/examples.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify variables from DP03 group and assign property names\n",
    "\n",
    "Names must follow the [Neo4j property naming conventions](https://neo4j.com/docs/getting-started/current/graphdb-concepts/#graphdb-naming-rules-and-recommendations)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = {# PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL\n",
    "             'DP03_0119PE': 'povertyAllFamiliesPct',\n",
    "             'DP03_0128PE': 'povertyAllPeoplePct'\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fields = \",\".join(variables.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p.povertyAllFamiliesPct = toFloat(row.povertyAllFamiliesPct),\n",
      "p.povertyAllPeoplePct = toFloat(row.povertyAllPeoplePct),\n"
     ]
    }
   ],
   "source": [
    "for v in variables.values():\n",
    "    if 'Pct' in v:\n",
    "        print('p.' + v + ' = toFloat(row.' + v + '),')\n",
    "    else:\n",
    "        print('p.' + v + ' = toInteger(row.' + v + '),')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "print(len(variables.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download county-level data using US Census API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_county = f'https://api.census.gov/data/2018/acs/acs5/profile?get={fields}&for=county:*'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DP03_0119PE</td>\n",
       "      <td>DP03_0128PE</td>\n",
       "      <td>state</td>\n",
       "      <td>county</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>02</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>02</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.6</td>\n",
       "      <td>14.7</td>\n",
       "      <td>04</td>\n",
       "      <td>013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.3</td>\n",
       "      <td>21.3</td>\n",
       "      <td>04</td>\n",
       "      <td>009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0            1      2       3\n",
       "0  DP03_0119PE  DP03_0128PE  state  county\n",
       "1          6.9          8.2     02     195\n",
       "2         39.0         40.5     02     158\n",
       "3         10.6         14.7     04     013\n",
       "4         16.3         21.3     04     009"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json(url_county, dtype='str')\n",
    "df.fillna('', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Add column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[1:].copy() # skip first row of labels\n",
    "columns = list(variables.values())\n",
    "columns.append('stateFips')\n",
    "columns.append('countyFips')\n",
    "df.columns = columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove Puerto Rico (stateFips = 72) to limit data to US States\n",
    "\n",
    "TODO handle data for Puerto Rico (GeoNames represents Puerto Rico as a country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query(\"stateFips != '72'\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save list of state fips (required later to get tract data by state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['01', '02', '04', '05', '06', '08', '09', '10', '11', '12', '13', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '44', '45', '46', '47', '48', '49', '50', '51', '53', '54', '55', '56']\n"
     ]
    }
   ],
   "source": [
    "stateFips = list(df['stateFips'].unique())\n",
    "stateFips.sort()\n",
    "print(stateFips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>povertyAllFamiliesPct</th>\n",
       "      <th>povertyAllPeoplePct</th>\n",
       "      <th>stateFips</th>\n",
       "      <th>countyFips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>02</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>02</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.6</td>\n",
       "      <td>14.7</td>\n",
       "      <td>04</td>\n",
       "      <td>013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.3</td>\n",
       "      <td>21.3</td>\n",
       "      <td>04</td>\n",
       "      <td>009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>04</td>\n",
       "      <td>023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  povertyAllFamiliesPct povertyAllPeoplePct stateFips countyFips\n",
       "1                   6.9                 8.2        02        195\n",
       "2                  39.0                40.5        02        158\n",
       "3                  10.6                14.7        04        013\n",
       "4                  16.3                21.3        04        009\n",
       "5                  19.9                24.0        04        023"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>povertyAllFamiliesPct</th>\n",
       "      <th>povertyAllPeoplePct</th>\n",
       "      <th>stateFips</th>\n",
       "      <th>countyFips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>8.7</td>\n",
       "      <td>12.5</td>\n",
       "      <td>06</td>\n",
       "      <td>073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    povertyAllFamiliesPct povertyAllPeoplePct stateFips countyFips\n",
       "897                   8.7                12.5        06        073"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example data\n",
    "df[(df['stateFips'] == '06') & (df['countyFips'] == '073')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['source'] = 'American Community Survey 5 year'\n",
    "df['aggregationLevel'] = 'Admin2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(NEO4J_IMPORT / \"03a-USCensusDP03PovertyAdmin2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download zip-level data using US Census API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_zip = f'https://api.census.gov/data/2018/acs/acs5/profile?get={fields}&for=zip%20code%20tabulation%20area:*'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DP03_0119PE</td>\n",
       "      <td>DP03_0128PE</td>\n",
       "      <td>state</td>\n",
       "      <td>zip code tabulation area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>51</td>\n",
       "      <td>23833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>51</td>\n",
       "      <td>23850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.6</td>\n",
       "      <td>12.6</td>\n",
       "      <td>51</td>\n",
       "      <td>23851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>51</td>\n",
       "      <td>23890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0            1      2                         3\n",
       "0  DP03_0119PE  DP03_0128PE  state  zip code tabulation area\n",
       "1          6.0         14.6     51                     23833\n",
       "2         21.2         19.6     51                     23850\n",
       "3          7.6         12.6     51                     23851\n",
       "4          9.1         11.8     51                     23890"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json(url_zip, dtype='str')\n",
    "df.fillna('', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Add column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[1:].copy() # skip first row\n",
    "columns = list(variables.values())\n",
    "columns.append('stateFips')\n",
    "columns.append('postalCode')\n",
    "df.columns = columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>povertyAllFamiliesPct</th>\n",
       "      <th>povertyAllPeoplePct</th>\n",
       "      <th>stateFips</th>\n",
       "      <th>postalCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>51</td>\n",
       "      <td>23833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>51</td>\n",
       "      <td>23850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.6</td>\n",
       "      <td>12.6</td>\n",
       "      <td>51</td>\n",
       "      <td>23851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>51</td>\n",
       "      <td>23890</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30.1</td>\n",
       "      <td>61.2</td>\n",
       "      <td>51</td>\n",
       "      <td>23302</td>\n",
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      "text/plain": [
       "  povertyAllFamiliesPct povertyAllPeoplePct stateFips postalCode\n",
       "1                   6.0                14.6        51      23833\n",
       "2                  21.2                19.6        51      23850\n",
       "3                   7.6                12.6        51      23851\n",
       "4                   9.1                11.8        51      23890\n",
       "5                  30.1                61.2        51      23302"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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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>povertyAllFamiliesPct</th>\n",
       "      <th>povertyAllPeoplePct</th>\n",
       "      <th>stateFips</th>\n",
       "      <th>postalCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24526</th>\n",
       "      <td>5.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>06</td>\n",
       "      <td>90210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      povertyAllFamiliesPct povertyAllPeoplePct stateFips postalCode\n",
       "24526                   5.8                 7.7        06      90210"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example data\n",
    "df.query(\"postalCode == '90210'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['source'] = 'American Community Survey 5 year'\n",
    "df['aggregationLevel'] = 'PostalCode'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(NEO4J_IMPORT / \"03a-USCensusDP03PovertyZip.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download tract-level data using US Census API\n",
    "Tract-level data are only available by state, so we need to loop over all states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tract_data(state):\n",
    "    url_tract = f'https://api.census.gov/data/2018/acs/acs5/profile?get={fields}&for=tract:*&in=state:{state}'\n",
    "    df = pd.read_json(url_tract, dtype='str')\n",
    "    time.sleep(1)\n",
    "    # skip first row of labels\n",
    "    df = df[1:].copy()\n",
    "    # Add column names\n",
    "    columns = list(variables.values())\n",
    "    columns.append('stateFips')\n",
    "    columns.append('countyFips')\n",
    "    columns.append('tract')\n",
    "    df.columns = columns\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat((get_tract_data(state) for state in stateFips))\n",
    "df.fillna('', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tract'] = df['stateFips'] + df['countyFips'] + df['tract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['source'] = 'American Community Survey 5 year'\n",
    "df['aggregationLevel'] = 'Tract'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>povertyAllFamiliesPct</th>\n",
       "      <th>povertyAllPeoplePct</th>\n",
       "      <th>stateFips</th>\n",
       "      <th>countyFips</th>\n",
       "      <th>tract</th>\n",
       "      <th>source</th>\n",
       "      <th>aggregationLevel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>06</td>\n",
       "      <td>073</td>\n",
       "      <td>06073008339</td>\n",
       "      <td>American Community Survey 5 year</td>\n",
       "      <td>Tract</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>06</td>\n",
       "      <td>073</td>\n",
       "      <td>06073008347</td>\n",
       "      <td>American Community Survey 5 year</td>\n",
       "      <td>Tract</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>06</td>\n",
       "      <td>073</td>\n",
       "      <td>06073008354</td>\n",
       "      <td>American Community Survey 5 year</td>\n",
       "      <td>Tract</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.1</td>\n",
       "      <td>15.9</td>\n",
       "      <td>06</td>\n",
       "      <td>073</td>\n",
       "      <td>06073008505</td>\n",
       "      <td>American Community Survey 5 year</td>\n",
       "      <td>Tract</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.8</td>\n",
       "      <td>10.5</td>\n",
       "      <td>06</td>\n",
       "      <td>073</td>\n",
       "      <td>06073017604</td>\n",
       "      <td>American Community Survey 5 year</td>\n",
       "      <td>Tract</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   povertyAllFamiliesPct povertyAllPeoplePct stateFips countyFips  \\\n",
       "6                    2.5                41.3        06        073   \n",
       "7                    1.7                 3.0        06        073   \n",
       "8                    1.3                 3.3        06        073   \n",
       "9                    9.1                15.9        06        073   \n",
       "10                   6.8                10.5        06        073   \n",
       "\n",
       "          tract                            source aggregationLevel  \n",
       "6   06073008339  American Community Survey 5 year            Tract  \n",
       "7   06073008347  American Community Survey 5 year            Tract  \n",
       "8   06073008354  American Community Survey 5 year            Tract  \n",
       "9   06073008505  American Community Survey 5 year            Tract  \n",
       "10  06073017604  American Community Survey 5 year            Tract  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example data for San Diego County\n",
    "df[(df['stateFips'] == '06') & (df['countyFips'] == '073')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(NEO4J_IMPORT / \"03a-USCensusDP03PovertyTract.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73056, 7)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  }
 ],
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