notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SWANA Census analysis\n",
    "\n",
    "By [Ellis Simani](https://twitter.com/emsimani) and [Ben Welsh](https://palewi.re/who-is-ben-welsh/)\n",
    "\n",
    "This analysis was conducted for the March 28, 2019, Los Angeles Times story [\"Are Arabs and Iranians white? Census says yes, but many disagree\"](https://www.latimes.com/projects/la-me-census-middle-east-north-africa-race/).\n",
    "\n",
    "It found roughly 3 million people of Southwest Asian, Middle Eastern or North African descent live in the United States. No county is home to more of these communities than Los Angeles, where more than 350,000 people can trace their roots to a region that stretches from Mauritania to the mountains of Afghanistan. In past census surveys, more than 80% in this group have called themselves white.\n",
    "\n",
    "Here's how we did it:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Python tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import altair_latimes as lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ThemeRegistry.enable('latimes')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.themes.register('latimes', lat.theme)\n",
    "alt.themes.enable('latimes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many people of these ancestries live in the United States?\n",
    "\n",
    "Doing this requires analysis of the ancestry question asked by the American Community Survey. To come to our answer, we will consult several different sources."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we look to the Census itself. As part of its [2015 National Content Test Race and Ethnicity Analysis Report](https://www2.census.gov/programs-surveys/decennial/2020/program-management/final-analysis-reports/2015nct-race-ethnicity-analysis.pdf) it estimated the population of people from the Middle East and North Africa (MENA) at 3.1 million."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"input/census-mena-estimate.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we consult the table B04006 from the American Community Survey, which Census officials recommended for this analysis. The agency has also recent used this table itself to report on [the number of Irish Americans](https://www.census.gov/newsroom/facts-for-features/2017/cb17-ff05.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "acs_B04006_data = pd.read_csv(\"./input/ACS_17_1YR_B04006_with_ann.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ACS ancestry totals records are filtered down to the subset of ancestries that correspond to the description of the MENA group developed in the Census report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "acs_ancestry_codes = pd.read_csv(\"./input/acs_ancestry_codes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "acs_mena_codes = acs_ancestry_codes[acs_ancestry_codes.mena]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,722,452\n"
     ]
    }
   ],
   "source": [
    "print(\"{:,d}\".format(acs_B04006_data[list(acs_mena_codes.field)].sum(axis=1)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the story we also refer to a slightly wider group of ancestries that including people from countries that neighbor the Middle East, like Afghanistan, Armenia and Turkey. This group is sometimes described as SWANA, referring to Southwest Asia and North Africa. What are the ACS totals if we cast the net wider to include those ancestries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "acs_swana_codes = acs_ancestry_codes[acs_ancestry_codes.swana]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3,793,489\n"
     ]
    }
   ],
   "source": [
    "print(\"{:,d}\".format(acs_B04006_data[list(acs_swana_codes.field)].sum(axis=1)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will look to the IPUMS microdata, which provides a wider set of ancestries provided by 5% of respondants who have their complete questionarre released. This provides the possibility of more precision, but is limited to the answers entered on the first of two boxes that follow the question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipums_ancestry_data = pd.read_csv(\"input/ipums.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same manner as the ACS data, the ancestry codes in the IPUMS data must be restricted to the countries of origin that we are studying. Again, we're collected them all from the source and coded them based on whether they qualify for our work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipums_ancestry_codes = pd.read_csv(\"./input/ipums_ancestry_codes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipums_mena_codes = ipums_ancestry_codes[ipums_ancestry_codes.mena]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipums_mena_data = ipums_ancestry_data[ipums_ancestry_data.ancestry_code.isin(ipums_mena_codes.ancestry_code)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,290,654\n"
     ]
    }
   ],
   "source": [
    "print(\"{:,d}\".format(ipums_mena_data.total.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets do the same for the wider SWANA group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipums_swana_codes = ipums_ancestry_codes[ipums_ancestry_codes.swana]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipums_swana_data = ipums_ancestry_data[ipums_ancestry_data.ancestry_code.isin(ipums_swana_codes.ancestry_code)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3,241,727\n"
     ]
    }
   ],
   "source": [
    "print(\"{:,d}\".format(ipums_swana_data.total.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these slight variations between the totals, depending on the source, we decided to describe this group by saying \"roughly 3 million people of Southwest Asian, Middle Eastern or North African descent live in the United States.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which race do people in this group claim?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can investigate this question by returning the IPUMS microdata, which includes a crosstab of each ancestry group with the question each respondant gave to the question asking their race. To simplify the analysis, we will look at what percentage of people in this group, when confronted with the Census Bureau's limited menu of races, have called themselves white."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First for the narrow MENA group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.89\n"
     ]
    }
   ],
   "source": [
    "print(\"{:.2f}\".format(ipums_mena_data.white_count.sum() / float(ipums_mena_data.total.sum())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then for the broader SWANA group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.82\n"
     ]
    }
   ],
   "source": [
    "print(\"{:.2f}\".format(ipums_swana_data.white_count.sum() / float(ipums_swana_data.total.sum())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's chart that to see the variation between ancestries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
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          "ancestry_code": 416,
          "ancestry_name": "Iranian",
          "black_count": 51,
          "black_percent": 0,
          "chinese_count": 54,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 169,
          "native_percent": 0,
          "other_asian_count": 6647,
          "other_asian_percent": 1.6,
          "other_count": 1177,
          "other_percent": 0.3,
          "three_count": 1037,
          "three_percent": 0.2,
          "total": 423671,
          "two_count": 55763,
          "two_percent": 13.2,
          "white_count": 358773,
          "white_percent": 84.7
         },
         {
          "ancestry_code": 431,
          "ancestry_name": "Armenian",
          "black_count": 366,
          "black_percent": 0.1,
          "chinese_count": 174,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 128,
          "native_percent": 0,
          "other_asian_count": 190,
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          "other_percent": 0.1,
          "three_count": 100,
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          "total": 411205,
          "two_count": 9363,
          "two_percent": 2.3,
          "white_count": 400533,
          "white_percent": 97.4
         },
         {
          "ancestry_code": 425,
          "ancestry_name": "Lebanese",
          "black_count": 608,
          "black_percent": 0.2,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 728,
          "native_percent": 0.2,
          "other_asian_count": 788,
          "other_asian_percent": 0.2,
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          "other_percent": 1,
          "three_count": 1498,
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          "total": 345524,
          "two_count": 12198,
          "two_percent": 3.5,
          "white_count": 326208,
          "white_percent": 94.4
         },
         {
          "ancestry_code": 402,
          "ancestry_name": "Egyptian",
          "black_count": 18215,
          "black_percent": 7.6,
          "chinese_count": 48,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 0,
          "other_asian_percent": 0,
          "other_count": 1145,
          "other_percent": 0.5,
          "three_count": 413,
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          "total": 238999,
          "two_count": 6948,
          "two_percent": 2.9,
          "white_count": 212230,
          "white_percent": 88.8
         },
         {
          "ancestry_code": 495,
          "ancestry_name": "Arab",
          "black_count": 4517,
          "black_percent": 1.9,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 1576,
          "other_asian_percent": 0.7,
          "other_count": 6378,
          "other_percent": 2.7,
          "three_count": 173,
          "three_percent": 0.1,
          "total": 238145,
          "two_count": 26619,
          "two_percent": 11.2,
          "white_count": 198882,
          "white_percent": 83.5
         },
         {
          "ancestry_code": 434,
          "ancestry_name": "Turkish",
          "black_count": 2337,
          "black_percent": 1.3,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 964,
          "other_asian_percent": 0.6,
          "other_count": 779,
          "other_percent": 0.4,
          "three_count": 0,
          "three_percent": 0,
          "total": 174800,
          "two_count": 4691,
          "two_percent": 2.7,
          "white_count": 166029,
          "white_percent": 95
         },
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          "ancestry_name": "Somalian",
          "black_count": 165981,
          "black_percent": 96.8,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 577,
          "other_asian_percent": 0.3,
          "other_count": 90,
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         {
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          "ancestry_name": "Middle Eastern",
          "black_count": 988,
          "black_percent": 0.7,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
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          "white_percent": 91.4
         },
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          "ancestry_name": "Iraqi",
          "black_count": 68,
          "black_percent": 0.1,
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          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 2713,
          "other_asian_percent": 2,
          "other_count": 334,
          "other_percent": 0.2,
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          "three_percent": 0,
          "total": 133824,
          "two_count": 8552,
          "two_percent": 6.4,
          "white_count": 122157,
          "white_percent": 91.3
         },
         {
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          "ancestry_name": "Syrian",
          "black_count": 82,
          "black_percent": 0.1,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 108,
          "native_percent": 0.1,
          "other_asian_count": 141,
          "other_asian_percent": 0.1,
          "other_count": 209,
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          "two_count": 4325,
          "two_percent": 3.4,
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          "white_percent": 96.2
         },
         {
          "ancestry_code": 600,
          "ancestry_name": "Afghan",
          "black_count": 299,
          "black_percent": 0.3,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 2986,
          "other_asian_percent": 2.6,
          "other_count": 152,
          "other_percent": 0.1,
          "three_count": 92,
          "three_percent": 0.1,
          "total": 113162,
          "two_count": 54880,
          "two_percent": 48.5,
          "white_count": 54753,
          "white_percent": 48.4
         },
         {
          "ancestry_code": 419,
          "ancestry_name": "Israeli",
          "black_count": 3740,
          "black_percent": 3.4,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 190,
          "native_percent": 0.2,
          "other_asian_count": 0,
          "other_asian_percent": 0,
          "other_count": 126,
          "other_percent": 0.1,
          "three_count": 0,
          "three_percent": 0,
          "total": 108577,
          "two_count": 4389,
          "two_percent": 4,
          "white_count": 100132,
          "white_percent": 92.2
         },
         {
          "ancestry_code": 465,
          "ancestry_name": "Palestinian",
          "black_count": 252,
          "black_percent": 0.2,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 559,
          "other_asian_percent": 0.5,
          "other_count": 1248,
          "other_percent": 1.2,
          "three_count": 542,
          "three_percent": 0.5,
          "total": 103195,
          "two_count": 11331,
          "two_percent": 11,
          "white_count": 89263,
          "white_percent": 86.5
         },
         {
          "ancestry_code": 406,
          "ancestry_name": "Moroccan",
          "black_count": 9404,
          "black_percent": 9.3,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 2251,
          "native_percent": 2.2,
          "other_asian_count": 632,
          "other_asian_percent": 0.6,
          "other_count": 12,
          "other_percent": 0,
          "three_count": 1613,
          "three_percent": 1.6,
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          "two_count": 5180,
          "two_percent": 5.1,
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          "white_percent": 81.1
         },
         {
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          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 12,
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          "other_asian_count": 0,
          "other_asian_percent": 0,
          "other_count": 0,
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          "two_count": 6082,
          "two_percent": 7.3,
          "white_count": 76866,
          "white_percent": 91.8
         },
         {
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          "two_percent": 4.9,
          "white_count": 61471,
          "white_percent": 93.9
         },
         {
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          "japanese_count": 0,
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          "other_asian_percent": 0,
          "other_count": 3989,
          "other_percent": 6.4,
          "three_count": 66,
          "three_percent": 0.1,
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          "two_count": 988,
          "two_percent": 1.6,
          "white_count": 1235,
          "white_percent": 2
         },
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          "ancestry_name": "Yemeni",
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          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
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          "other_asian_count": 1071,
          "other_asian_percent": 1.9,
          "other_count": 0,
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          "three_count": 359,
          "three_percent": 0.6,
          "total": 55540,
          "two_count": 7626,
          "two_percent": 13.7,
          "white_count": 45956,
          "white_percent": 82.7
         },
         {
          "ancestry_code": 427,
          "ancestry_name": "Saudi Arabian",
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          "black_percent": 0.3,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 1271,
          "other_asian_percent": 3.5,
          "other_count": 0,
          "other_percent": 0,
          "three_count": 111,
          "three_percent": 0.3,
          "total": 36264,
          "two_count": 8587,
          "two_percent": 23.7,
          "white_count": 26190,
          "white_percent": 72.2
         },
         {
          "ancestry_code": 496,
          "ancestry_name": "Other Arab",
          "black_count": 606,
          "black_percent": 2.7,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 371,
          "other_asian_percent": 1.7,
          "other_count": 336,
          "other_percent": 1.5,
          "three_count": 182,
          "three_percent": 0.8,
          "total": 22045,
          "two_count": 1791,
          "two_percent": 8.1,
          "white_count": 18759,
          "white_percent": 85.1
         },
         {
          "ancestry_code": 400,
          "ancestry_name": "Algerian",
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          "chinese_count": 0,
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          "japanese_count": 0,
          "japanese_percent": 0,
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          "other_count": 0,
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          "three_count": 0,
          "three_percent": 0,
          "total": 17770,
          "two_count": 276,
          "two_percent": 1.6,
          "white_count": 17020,
          "white_percent": 95.8
         },
         {
          "ancestry_code": 120,
          "ancestry_name": "Georgian",
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          "black_percent": 0,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 0,
          "other_asian_percent": 0,
          "other_count": 0,
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          "three_count": 0,
          "three_percent": 0,
          "total": 17722,
          "two_count": 0,
          "two_percent": 0,
          "white_count": 17722,
          "white_percent": 100
         },
         {
          "ancestry_code": 442,
          "ancestry_name": "Kurdish",
          "black_count": 0,
          "black_percent": 0,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
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          "other_asian_percent": 0,
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          "other_percent": 2.2,
          "three_count": 127,
          "three_percent": 0.7,
          "total": 17662,
          "two_count": 3263,
          "two_percent": 18.5,
          "white_count": 13880,
          "white_percent": 78.6
         },
         {
          "ancestry_code": 411,
          "ancestry_name": "North African",
          "black_count": 1999,
          "black_percent": 12.7,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
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          "three_count": 43,
          "three_percent": 0.3,
          "total": 15742,
          "two_count": 534,
          "two_percent": 3.4,
          "white_count": 13051,
          "white_percent": 82.9
         },
         {
          "ancestry_code": 404,
          "ancestry_name": "Libyan",
          "black_count": 966,
          "black_percent": 10.6,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 0,
          "other_asian_percent": 0,
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          "three_percent": 0,
          "total": 9100,
          "two_count": 311,
          "two_percent": 3.4,
          "white_count": 7823,
          "white_percent": 86
         },
         {
          "ancestry_code": 423,
          "ancestry_name": "Kuwaiti",
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          "black_percent": 0,
          "chinese_count": 0,
          "chinese_percent": 0,
          "japanese_count": 0,
          "japanese_percent": 0,
          "native_count": 0,
          "native_percent": 0,
          "other_asian_count": 356,
          "other_asian_percent": 4.3,
          "other_count": 0,
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          "three_count": 0,
          "three_percent": 0,
          "total": 8309,
          "two_count": 0,
          "two_percent": 0,
          "white_count": 7953,
          "white_percent": 95.7
         }
        ]
       },
       "encoding": {
        "x": {
         "field": "white_percent",
         "title": "Percent",
         "type": "quantitative"
        },
        "y": {
         "field": "ancestry_name",
         "sort": {
          "field": "white_percent",
          "op": "sum",
          "order": "descending"
         },
         "title": "Ancestry",
         "type": "nominal"
        }
       },
       "mark": "bar",
       "title": "White percentage by ancestry",
       "width": 630
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(ipums_swana_data.sort_values(\"total\", ascending=False)).mark_bar().encode(\n",
    "    x=alt.X(\n",
    "        \"white_percent:Q\",\n",
    "        title=\"Percent\"\n",
    "    ),\n",
    "    y=alt.Y(\n",
    "        \"ancestry_name:N\",\n",
    "        title=\"Ancestry\",\n",
    "        sort=alt.EncodingSortField(field=\"white_percent\", op=\"sum\", order=\"descending\")\n",
    "    )\n",
    ").properties(\n",
    "    width=630,\n",
    "    title=\"White percentage by ancestry\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the overall denominator we selected as our answer to the first question in this notebook includes the more expansive SWANA group, and in the interest of being conservative in our analysis, we decided its percentage figure in our story, despite the fact that a more restrictive definition like MENA would boost the figure. We reported that, \"more than 80% in this group have called themselves white.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many people in this group live in Los Angeles County?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We ran a similar cut from the IPUMS microdata, this time limited the results to Los Angeles County."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipums_la_ancestry_data = pd.read_csv(\"input/ipums-LA.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we will filter to our target ancestries. We will skip straight to the broader SWANA definition that answered the questions above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "ipums_la_swana_data = ipums_la_ancestry_data[ipums_la_ancestry_data.ancestry_code.isin(ipums_swana_codes.ancestry_code)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's sum the total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "356,633\n"
     ]
    }
   ],
   "source": [
    "print(\"{:,d}\".format(ipums_la_swana_data.total.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As recommended by staff at IPUMS, we decided to compare counties by consulting another data set: The NHGIS geospatial database. It provides ancestry totals by county."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhgis_ancestry_data = pd.read_csv(\"input/nhgis0003_ds234_20175_2017_county_clean.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like the ACS and IPUMS data, the NHGIS dataset has its own unique set of codes for describing ancestry answers. Again, we decoded them so we could limit the data to the group we're studying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhgis_swana_codes = {\n",
    "  \"AH8TE002\": \"Afghan\",\n",
    "  \"AH8TE007\": \"Arab Egyptian\",\n",
    "  \"AH8TE008\": \"Arab Iraqi\",\n",
    "  \"AH8TE009\": \"Arab Jordanian\",\n",
    "  \"AH8TE010\": \"Arab Lebanese\",\n",
    "  \"AH8TE011\": \"Arab Moroccan\",\n",
    "  \"AH8TE012\": \"Arab Palestinian\",\n",
    "  \"AH8TE013\": \"Arab Syrian\",\n",
    "  \"AH8TE014\": \"Arab Arab\",\n",
    "  \"AH8TE015\": \"Arab Other Arab\",\n",
    "  \"AH8TE016\": \"Armenian\",\n",
    "  \"AH8TE017\": \"Assyrian/Chaldean/Syriac\",\n",
    "  \"AH8TE082\": \"Subsaharan African Somali\",\n",
    "  \"AH8TE050\": \"Israeli\",\n",
    "  \"AH8TE048\": \"Iranian\",\n",
    "  \"AH8TE091\": \"Turkish\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use those to filter and trim the data to only what we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhgis_metadata = [\n",
    "    'AH8TE001',\n",
    "    'GISJOIN',\n",
    "    'YEAR',\n",
    "    'STATE',\n",
    "    'STATEA',\n",
    "    'COUNTY',\n",
    "    'COUNTYA',\n",
    "    'NAME_E',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhgis_swana_data = nhgis_ancestry_data[nhgis_metadata + list(nhgis_swana_codes.keys())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The unique identifier of each county, the FIPS code, needs a little cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhgis_swana_data['GEOID'] = nhgis_swana_data['GISJOIN'].str.slice(1,3) + nhgis_swana_data['GISJOIN'].str.slice(4,7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will calculate the SWANA total for each county using the ancestry codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhgis_swana_data['swana'] = nhgis_swana_data[list(nhgis_swana_codes.keys())].sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And decode the column names so we can read them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhgis_swana_data.rename(\n",
    "    columns=nhgis_swana_codes,\n",
    ").rename(\n",
    "    columns={\"AH8TE001\": \"Total\"},\n",
    "    inplace=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure we're in the ballpark of our early estimates, let's proof the nationwide SWANA total from this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3,616,149\n"
     ]
    }
   ],
   "source": [
    "print(\"{:,d}\".format(nhgis_swana_data.swana.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>GEOID</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>swana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>06037</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles County</td>\n",
       "      <td>389905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>26163</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Wayne County</td>\n",
       "      <td>102350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>17031</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>101300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>06059</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange County</td>\n",
       "      <td>92354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>06073</td>\n",
       "      <td>California</td>\n",
       "      <td>San Diego County</td>\n",
       "      <td>84065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851</th>\n",
       "      <td>36047</td>\n",
       "      <td>New York</td>\n",
       "      <td>Kings County</td>\n",
       "      <td>63800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>26125</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Oakland County</td>\n",
       "      <td>56407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2848</th>\n",
       "      <td>51059</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>Fairfax County</td>\n",
       "      <td>54953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>26099</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Macomb County</td>\n",
       "      <td>54799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2623</th>\n",
       "      <td>48201</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Harris County</td>\n",
       "      <td>53402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      GEOID       STATE              COUNTY   swana\n",
       "204   06037  California  Los Angeles County  389905\n",
       "1312  26163    Michigan        Wayne County  102350\n",
       "610   17031    Illinois         Cook County  101300\n",
       "215   06059  California       Orange County   92354\n",
       "222   06073  California    San Diego County   84065\n",
       "1851  36047    New York        Kings County   63800\n",
       "1293  26125    Michigan      Oakland County   56407\n",
       "2848  51059    Virginia      Fairfax County   54953\n",
       "1280  26099    Michigan       Macomb County   54799\n",
       "2623  48201       Texas       Harris County   53402"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhgis_swana_data[\n",
    "    ['GEOID', 'STATE', 'COUNTY', 'swana']\n",
    "].sort_values(\"swana\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the NGHIS total for Los Angeles County is slightly higher than the IPUMS story, we decided to describe the population as \"more than 350,000 people \\[who\\] can trace their roots to a region that stretches from Mauritania to the mountains of Afghanistan.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since New York City is divided into different counties, let's add them up to see if where the combined area would rank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc = [\n",
    "    'New York County',\n",
    "    'Bronx County',\n",
    "    'Kings County',\n",
    "    'Queens County',\n",
    "    'Richmond County'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163165"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhgis_swana_data[\n",
    "    (nhgis_swana_data.STATE == 'New York') &\n",
    "    (nhgis_swana_data.COUNTY.isin(nyc))\n",
    "].sort_values(\"swana\", ascending=False).swana.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having established that, we decided it was fair to say, \"No county is home to more of these communities than Los Angeles.\""
   ]
  }
 ],
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