notebooks/queries/Demographics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example queries for Demographics on COVID-19 Knowledge Graph\n",
    "[Work in progress]\n",
    "\n",
    "This notebook demonstrates how to run Cypher queries to get [Demographics from the American Community Survey 2018 5-year data](https://data.census.gov/cedsci/table?q=DP02&tid=ACSDP5Y2018.DP05) at multiple levels of geographic granularity. \n",
    "\n",
    "Demographics includes total population, and population by gender, race, ethnicity, and age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from py2neo import Graph"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "#### Connect to COVID-19-Net Knowledge Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = Graph(\"bolt://132.249.238.185:7687\", user=\"reader\", password=\"demo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demographics\n",
    "The following characteristics are available for demographics. Characteristics ending with Pct are values in percent, whereas all other values are counts. For details see [Subject Definitions](https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2018_ACSSubjectDefinitions.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "             'DP05_0001E': 'totalPopulation',\n",
    "             \n",
    "             # sex\n",
    "             'DP05_0002E': 'male',\n",
    "             'DP05_0003E': 'female',\n",
    "             \n",
    "             # age\n",
    "             'DP05_0005E': 'age0_4',\n",
    "             'DP05_0006E': 'age5_9',\n",
    "             'DP05_0007E': 'age10_14',\n",
    "             'DP05_0008E': 'age15_19',\n",
    "             'DP05_0009E': 'age20_24',\n",
    "             'DP05_0010E': 'age25_34',\n",
    "             'DP05_0011E': 'age35_44',\n",
    "             'DP05_0012E': 'age45_54',\n",
    "             'DP05_0013E': 'age55_59',\n",
    "             'DP05_0014E': 'age60_64',\n",
    "             'DP05_0015E': 'age65_74',\n",
    "             'DP05_0016E': 'age75_84',\n",
    "             'DP05_0017E': 'age85_',\n",
    "\n",
    "              # race\n",
    "             'DP05_0037E': 'white',\n",
    "             'DP05_0038E': 'blackOrAfricanAmerican',\n",
    "             'DP05_0039E': 'americanIndianAndAlaskaNative',\n",
    "             'DP05_0044E': 'asian',\n",
    "             'DP05_0052E': 'nativeHawaiianAndOtherPacificIslander',\n",
    "             'DP05_0057E': 'otherRace',\n",
    "             'DP05_0058E': 'twoOrMoreRaces',\n",
    "             \n",
    "              # ethnicity\n",
    "             'DP05_0071E': 'hispanicOrLatino',\n",
    "             'DP05_0076E': 'notHispanicOrLatino'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data are available at multiple levels of geographic granularity\n",
    "* US County\n",
    "* US Zip Code\n",
    "* US Census Tract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get demographics by US County"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the state and county fips codes are available, demographcis can be retrieved directly.\n",
    "\n",
    "Note, fips codes are represented as strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_fips = '06'\n",
    "county_fips = '073'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>d.stateFips</th>\n",
       "      <th>d.countyFips</th>\n",
       "      <th>d.totalPopulation</th>\n",
       "      <th>White</th>\n",
       "      <th>Black or African American</th>\n",
       "      <th>American Indian and Alaskan Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Native Hawaiian and other Pacific Islander</th>\n",
       "      <th>other Race</th>\n",
       "      <th>two or more Races</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06</td>\n",
       "      <td>073</td>\n",
       "      <td>3302833</td>\n",
       "      <td>2335447</td>\n",
       "      <td>166412</td>\n",
       "      <td>20980</td>\n",
       "      <td>390418</td>\n",
       "      <td>13903</td>\n",
       "      <td>205307</td>\n",
       "      <td>170366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  d.stateFips d.countyFips  d.totalPopulation    White  \\\n",
       "0          06          073            3302833  2335447   \n",
       "\n",
       "   Black or African American  American Indian and Alaskan Native   Asian  \\\n",
       "0                     166412                               20980  390418   \n",
       "\n",
       "   Native Hawaiian and other Pacific Islander  other Race  two or more Races  \n",
       "0                                       13903      205307             170366  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (d:Demographics{countyFips:$county_fips, stateFips:$state_fips})\n",
    "RETURN d.stateFips, d.countyFips, d.totalPopulation, \n",
    "       d.white AS `White`, \n",
    "       d.blackOrAfricanAmerican AS `Black or African American`, \n",
    "       d.americanIndianAndAlaskaNative AS `American Indian and Alaskan Native`, \n",
    "       d.asian AS `Asian`, \n",
    "       d.nativeHawaiianAndOtherPacificIslander AS `Native Hawaiian and other Pacific Islander`,\n",
    "       d.otherRace AS `other Race`,\n",
    "       d.twoOrMoreRaces AS `two or more Races`\n",
    "\"\"\"\n",
    "df = graph.run(query, county_fips=county_fips, state_fips=state_fips).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: Get the same data by traversing the KG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "county = 'San Diego County'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>County</th>\n",
       "      <th>White</th>\n",
       "      <th>Black or African American</th>\n",
       "      <th>American Indian and Alaskan Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Native Hawaiian and other Pacific Islander</th>\n",
       "      <th>other Race</th>\n",
       "      <th>two or more Races</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>2335447</td>\n",
       "      <td>166412</td>\n",
       "      <td>20980</td>\n",
       "      <td>390418</td>\n",
       "      <td>13903</td>\n",
       "      <td>205307</td>\n",
       "      <td>170366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             County    White  Black or African American  \\\n",
       "0  San Diego County  2335447                     166412   \n",
       "\n",
       "   American Indian and Alaskan Native   Asian  \\\n",
       "0                               20980  390418   \n",
       "\n",
       "   Native Hawaiian and other Pacific Islander  other Race  two or more Races  \n",
       "0                                       13903      205307             170366  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (a:Admin2{name:$admin2})-[:HAS_DEMOGRAPHICS]-(d:Demographics)\n",
    "RETURN a.name as County,\n",
    "       d.white AS `White`, \n",
    "       d.blackOrAfricanAmerican AS `Black or African American`, \n",
    "       d.americanIndianAndAlaskaNative AS `American Indian and Alaskan Native`, \n",
    "       d.asian AS `Asian`, \n",
    "       d.nativeHawaiianAndOtherPacificIslander AS `Native Hawaiian and other Pacific Islander`,\n",
    "       d.otherRace AS `other Race`,\n",
    "       d.twoOrMoreRaces AS `two or more Races`\n",
    "\"\"\"\n",
    "df = graph.run(query, admin2=county).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x='County', y=['White', 'Black or African American', 'American Indian and Alaskan Native', \n",
    "                           'Asian', 'Native Hawaiian and other Pacific Islander', 'other Race', \n",
    "                           'two or more Races'], title='Population by Race', rot=0).legend(bbox_to_anchor=(1.2, 0.5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get demographics by Zip code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_code = '90210'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Total population and gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>d.postalCode</th>\n",
       "      <th>d.totalPopulation</th>\n",
       "      <th>d.male</th>\n",
       "      <th>d.female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90210</td>\n",
       "      <td>19909</td>\n",
       "      <td>9599</td>\n",
       "      <td>10310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  d.postalCode  d.totalPopulation  d.male  d.female\n",
       "0        90210              19909    9599     10310"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (d:Demographics{postalCode: $postal_code})\n",
    "RETURN d.postalCode, d.totalPopulation, d.male, d.female\n",
    "\"\"\"\n",
    "df = graph.run(query, postal_code=zip_code).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Total population and ethnicity\n",
    "Note, Zip code areas may cross city boundaries. Place names are the preferred names used by the US Postal Service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Zip code</th>\n",
       "      <th>p.placeName</th>\n",
       "      <th>d.totalPopulation</th>\n",
       "      <th>Hispanic or Latino</th>\n",
       "      <th>not Hispanic or Latino</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91910</td>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>74297</td>\n",
       "      <td>45307</td>\n",
       "      <td>28990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91911</td>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>85365</td>\n",
       "      <td>61189</td>\n",
       "      <td>24176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91913</td>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>53072</td>\n",
       "      <td>24788</td>\n",
       "      <td>28284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91914</td>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>16357</td>\n",
       "      <td>7490</td>\n",
       "      <td>8867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91915</td>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>31740</td>\n",
       "      <td>15287</td>\n",
       "      <td>16453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Zip code  p.placeName  d.totalPopulation  Hispanic or Latino  \\\n",
       "0    91910  Chula Vista              74297               45307   \n",
       "1    91911  Chula Vista              85365               61189   \n",
       "2    91913  Chula Vista              53072               24788   \n",
       "3    91914  Chula Vista              16357                7490   \n",
       "4    91915  Chula Vista              31740               15287   \n",
       "\n",
       "   not Hispanic or Latino  \n",
       "0                   28990  \n",
       "1                   24176  \n",
       "2                   28284  \n",
       "3                    8867  \n",
       "4                   16453  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place_name = 'Chula Vista'\n",
    "\n",
    "query = \"\"\"\n",
    "MATCH (p:PostalCode{placeName:$place_name})-[:HAS_DEMOGRAPHICS]-(d:Demographics)\n",
    "RETURN p.name as `Zip code`, p.placeName, d.totalPopulation, d.hispanicOrLatino as `Hispanic or Latino`, \n",
    "       d.notHispanicOrLatino as `not Hispanic or Latino`\n",
    "\"\"\"\n",
    "df = graph.run(query, place_name=place_name).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x='Zip code', y=['Hispanic or Latino', 'not Hispanic or Latino'], rot=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Total population and age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>p.name</th>\n",
       "      <th>d.totalPopulation</th>\n",
       "      <th>d.age0_4</th>\n",
       "      <th>d.age5_9</th>\n",
       "      <th>d.age10_14</th>\n",
       "      <th>d.age15_19</th>\n",
       "      <th>d.age20_24</th>\n",
       "      <th>d.age25_34</th>\n",
       "      <th>d.age35_44</th>\n",
       "      <th>d.age45_54</th>\n",
       "      <th>d.age55_59</th>\n",
       "      <th>d.age60_64</th>\n",
       "      <th>d.age65_74</th>\n",
       "      <th>d.age75_84</th>\n",
       "      <th>d.age85_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90210</td>\n",
       "      <td>19909</td>\n",
       "      <td>710</td>\n",
       "      <td>1014</td>\n",
       "      <td>1482</td>\n",
       "      <td>1066</td>\n",
       "      <td>519</td>\n",
       "      <td>1956</td>\n",
       "      <td>2292</td>\n",
       "      <td>3143</td>\n",
       "      <td>1282</td>\n",
       "      <td>1093</td>\n",
       "      <td>2562</td>\n",
       "      <td>1695</td>\n",
       "      <td>1095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  p.name  d.totalPopulation  d.age0_4  d.age5_9  d.age10_14  d.age15_19  \\\n",
       "0  90210              19909       710      1014        1482        1066   \n",
       "\n",
       "   d.age20_24  d.age25_34  d.age35_44  d.age45_54  d.age55_59  d.age60_64  \\\n",
       "0         519        1956        2292        3143        1282        1093   \n",
       "\n",
       "   d.age65_74  d.age75_84  d.age85_  \n",
       "0        2562        1695      1095  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (p:PostalCode{name:$zip_code})-[:HAS_DEMOGRAPHICS]-(d:Demographics)\n",
    "RETURN p.name, d.totalPopulation, d.age0_4, d.age5_9, d.age10_14, d.age15_19, d.age20_24, \n",
    "       d.age25_34, d.age35_44, d.age45_54, d.age55_59, d.age60_64, d.age65_74, d.age75_84, d.age85_\n",
    "\"\"\"\n",
    "df = graph.run(query, zip_code=zip_code).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check: The sum of all age groups should add up to the total population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19909\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['d.age0_4'] + df['d.age5_9'] + df['d.age10_14'] + df['d.age15_19'] + df['d.age20_24'] + df['d.age25_34'] + \\\n",
    "                 df['d.age35_44'] + df['d.age45_54'] + df['d.age55_59'] + df['d.age60_64'] + df['d.age65_74'] + df['d.age75_84'] + \\\n",
    "                 df['d.age85_']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get demographics by US Census tract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Total population and race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "county = 'San Diego County'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>County</th>\n",
       "      <th>Total</th>\n",
       "      <th>White</th>\n",
       "      <th>Black or African American</th>\n",
       "      <th>American Indian and Alaskan Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Native Hawaiian and other Pacific Islander</th>\n",
       "      <th>other Race</th>\n",
       "      <th>two or more Races</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>5963</td>\n",
       "      <td>4605</td>\n",
       "      <td>117</td>\n",
       "      <td>141</td>\n",
       "      <td>700</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>6194</td>\n",
       "      <td>3318</td>\n",
       "      <td>971</td>\n",
       "      <td>106</td>\n",
       "      <td>811</td>\n",
       "      <td>32</td>\n",
       "      <td>866</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>6755</td>\n",
       "      <td>6008</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>538</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>3963</td>\n",
       "      <td>3327</td>\n",
       "      <td>72</td>\n",
       "      <td>19</td>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>3321</td>\n",
       "      <td>2019</td>\n",
       "      <td>426</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>251</td>\n",
       "      <td>332</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>4481</td>\n",
       "      <td>3757</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>338</td>\n",
       "      <td>0</td>\n",
       "      <td>210</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>4621</td>\n",
       "      <td>3569</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>839</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>5231</td>\n",
       "      <td>3031</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>10228</td>\n",
       "      <td>8311</td>\n",
       "      <td>130</td>\n",
       "      <td>34</td>\n",
       "      <td>932</td>\n",
       "      <td>0</td>\n",
       "      <td>472</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>San Diego County</td>\n",
       "      <td>5480</td>\n",
       "      <td>3486</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>1443</td>\n",
       "      <td>23</td>\n",
       "      <td>184</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             County  Total  White  Black or African American  \\\n",
       "0  San Diego County   5963   4605                        117   \n",
       "1  San Diego County   6194   3318                        971   \n",
       "2  San Diego County   6755   6008                         13   \n",
       "3  San Diego County   3963   3327                         72   \n",
       "4  San Diego County   3321   2019                        426   \n",
       "5  San Diego County   4481   3757                         28   \n",
       "6  San Diego County   4621   3569                         26   \n",
       "7  San Diego County   5231   3031                         48   \n",
       "8  San Diego County  10228   8311                        130   \n",
       "9  San Diego County   5480   3486                          0   \n",
       "\n",
       "   American Indian and Alaskan Native  Asian  \\\n",
       "0                                 141    700   \n",
       "1                                 106    811   \n",
       "2                                  17    538   \n",
       "3                                  19    372   \n",
       "4                                   5     32   \n",
       "5                                   0    338   \n",
       "6                                  22     64   \n",
       "7                                   0   2011   \n",
       "8                                  34    932   \n",
       "9                                  24   1443   \n",
       "\n",
       "   Native Hawaiian and other Pacific Islander  other Race  two or more Races  \n",
       "0                                           0          76                324  \n",
       "1                                          32         866                 90  \n",
       "2                                          37           0                142  \n",
       "3                                           0          31                142  \n",
       "4                                         251         332                256  \n",
       "5                                           0         210                148  \n",
       "6                                           0         839                101  \n",
       "7                                          11           0                130  \n",
       "8                                           0         472                349  \n",
       "9                                          23         184                320  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (a:Admin2{name: $admin2})<-[:IN]-(t:Tract)-[:HAS_DEMOGRAPHICS]-(d:Demographics)\n",
    "RETURN a.name AS County, \n",
    "       d.totalPopulation as Total, \n",
    "       d.white AS White, \n",
    "       d.blackOrAfricanAmerican AS `Black or African American`, \n",
    "       d.americanIndianAndAlaskaNative AS `American Indian and Alaskan Native`, \n",
    "       d.asian AS Asian,\n",
    "       d.nativeHawaiianAndOtherPacificIslander AS `Native Hawaiian and other Pacific Islander`, \n",
    "       d.otherRace AS `other Race`,\n",
    "       d.twoOrMoreRaces AS `two or more Races`\n",
    "\"\"\"\n",
    "df = graph.run(query, admin2=county).to_data_frame()\n",
    "df.fillna(0, inplace=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Total population and age group\n",
    "This example shows how to aggregate tract-level data to the country level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Country</th>\n",
       "      <th>Total</th>\n",
       "      <th>age0_4</th>\n",
       "      <th>age5_9</th>\n",
       "      <th>age10_14</th>\n",
       "      <th>age15_19</th>\n",
       "      <th>age20_24</th>\n",
       "      <th>age25_34</th>\n",
       "      <th>age35_44</th>\n",
       "      <th>age45_54</th>\n",
       "      <th>age55_59</th>\n",
       "      <th>age60_64</th>\n",
       "      <th>age65_74</th>\n",
       "      <th>age75_84</th>\n",
       "      <th>age85_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>322903030</td>\n",
       "      <td>19836850</td>\n",
       "      <td>20311494</td>\n",
       "      <td>20817419</td>\n",
       "      <td>21204226</td>\n",
       "      <td>22286970</td>\n",
       "      <td>44567976</td>\n",
       "      <td>40763210</td>\n",
       "      <td>42589573</td>\n",
       "      <td>21611374</td>\n",
       "      <td>19675357</td>\n",
       "      <td>28535419</td>\n",
       "      <td>14497679</td>\n",
       "      <td>6205483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country      Total    age0_4    age5_9  age10_14  age15_19  age20_24  \\\n",
       "0  United States  322903030  19836850  20311494  20817419  21204226  22286970   \n",
       "\n",
       "   age25_34  age35_44  age45_54  age55_59  age60_64  age65_74  age75_84  \\\n",
       "0  44567976  40763210  42589573  21611374  19675357  28535419  14497679   \n",
       "\n",
       "    age85_  \n",
       "0  6205483  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (c:Country{iso:'US'})-[:IN]-(a1:Admin1)-[:IN]-(a2:Admin2)-[:HAS_DEMOGRAPHICS]-(d:Demographics)\n",
    "RETURN c.name AS Country, sum(d.totalPopulation) AS Total, \n",
    "       sum(d.age0_4) AS age0_4, sum(d.age5_9) AS age5_9, \n",
    "       sum(d.age10_14) AS age10_14, sum(d.age15_19) AS age15_19, sum(d.age20_24) AS age20_24, \n",
    "       sum(d.age25_34) AS age25_34, sum(d.age35_44) AS age35_44, sum(d.age45_54) AS age45_54, \n",
    "       sum(d.age55_59) AS age55_59, sum(d.age60_64) AS age60_64, sum(d.age65_74) as age65_74, \n",
    "       sum(d.age75_84) AS age75_84, sum(d.age85_) AS age85_\n",
    "\"\"\"\n",
    "df = graph.run(query).to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[['age0_4', 'age5_9', 'age10_14', 'age15_19','age20_24', 'age25_34', 'age35_44', 'age45_54', 'age55_59', \\\n",
    "         'age60_64', 'age65_74', 'age75_84', 'age85_']].plot(kind='bar', title =\"US Population by Age\", figsize=(15, 10), legend=True, fontsize=12)\n",
    "ax.set_xlabel(\"Age\", fontsize=12)\n",
    "ax.set_ylabel(\"Population\", fontsize=12)\n",
    "plt.show();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}