Command

wc -l fh_ratings.csv wb_gdp_data.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc 'wc -l fh_ratings.csv wb_gdp_data.csv'
Chunk ID: 05708a
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 16
Output:
    4306 fh_ratings.csv
     271 wb_gdp_data.csv
    4577 total

Command

sed -n '1,8p' fh_ratings.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "sed -n '1,8p' fh_ratings.csv"
Chunk ID: 0fbc1f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 71
Output:
country,year,political_rights_score,civil_liberties_score,status
Afghanistan,2000,7,7,Not Free
Afghanistan,2001,7,7,Not Free
Afghanistan,2002,6,6,Not Free
Afghanistan,2003,6,6,Not Free
Afghanistan,2004,5,6,Not Free
Afghanistan,2005,5,5,Partly Free
Afghanistan,2006,5,5,Partly Free

Command

sed -n '1,8p' wb_gdp_data.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "sed -n '1,8p' wb_gdp_data.csv"
Chunk ID: 5d97b0
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 397
Output:
Data Source,World Development Indicators,

Last Updated Date,2026-02-24,

Country Name,Country Code,Indicator Name,Indicator Code,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020
Aruba,ABW,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,37524.0346018738,38729.4588237779,38098.9048329158,38086.0123762909,40000.5625183532,38828.2166342999,38566.8026759098,39462.5851547515,39881.5879733887,34986.1484622772,33951.6246182745,34837.9402869986,33972.3401082566,35621.7848546409,35746.8630679768,36660.6310808381,36832.9040242979,38116.8310992499,39278.8297271438,39652.6320700939,30347.6371851417
Africa Eastern and Southern,AFE,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,3281.92231355821,3313.42192907347,3350.14106469179,3357.24486194198,3457.12022216505,3577.58589657798,3718.02241324132,3866.72395287147,3934.08423728651,3869.99812557719,3976.01098918458,4042.84247175787,4061.64163525859,4125.18256263035,4185.23841104229,4206.25295270438,4202.23641584707,4214.39708524398,4220.91250168849,4195.86541780344,3978.93139450095
Afghanistan,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1617.82647494969,1454.11078195559,1774.30874287341,1815.92819979586,1776.91820712187,1908.11478234587,1929.72389709372,2155.35306784413,2191.50435559062,2565.02208584099,2848.58606111495,2757.05258747725,2985.31914452845,3046.57993117128,3017.94254424696,2967.692067153,2958.7853992308,2952.99891598909,2902.39211285546,2927.2451435353,2769.68574507111

Command

awk -F, 'NR>1{years[$2]=1; countries[$1]=1; status[$5]=1} END{printf("rows=%d\nunique_countries=%d\nyear_min=%s\nyear_max=%s\nstatuses=", NR-1, length(countries), min, max)} function upd(y){if(min==""||y<min)min=y; if(max==""||y>max)max=y} NR>1{upd($2)} END{first=1; for (s in status){printf("%s%s", first?"":", ", s); first=0} printf("\n")}' fh_ratings.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "awk -F, 'NR>1{years["'$2]=1; countries[$1]=1; status[$5]=1} END{printf("rows=%d'"\\nunique_countries=%d\\nyear_min=%s\\nyear_max=%s\\nstatuses=\", NR-1, length(countries), min, max)} function upd(y){if(min==\"\"||y<min)min=y; if(max==\"\"||y>max)max=y} NR>1{upd("'$2)} END{first=1; for (s in status){printf("%s%s", first?"":", ", s); first=0} printf("'"\\n\")}' fh_ratings.csv"
Chunk ID: 7e9e15
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 25
Output:
rows=4305
unique_countries=202
year_min= E."
year_max=2020
statuses=Free, Partly Free, Not Free, -

Command

awk -F, 'NR>1{count[$5]++} END{for (s in count) print s ": " count[s]}' fh_ratings.csv | sort

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "awk -F, 'NR>1{count["'$5]++} END{for (s in count) print s ": " count[s]}'"' fh_ratings.csv | sort"
Chunk ID: bb68cf
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 13
Output:
-: 236
Free: 1837
Not Free: 1006
Partly Free: 1226

Command

sed -n '265,271p' wb_gdp_data.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "sed -n '265,271p' wb_gdp_data.csv"
Chunk ID: d9484f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 670
Output:
World,WLD,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,12769.8314917662,12904.861928295,13093.0651208582,13402.9919536458,13914.8111422905,14370.8850287562,14927.5485261449,15510.865766861,15747.7295109752,15452.5012186257,16047.9897391201,16479.4887946509,16784.9491797151,17122.0455272012,17500.1748281222,17868.0908568708,18232.7269157079,18714.9389431259,19189.0202232249,19552.0835148296,18811.4935530315
Samoa,WSM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,4638.59446502597,4941.1152254089,5183.45456244226,5425.44383557984,5568.34734622238,5914.889706043,6017.47704264273,6013.27628827659,6182.76327854773,6104.12751491364,6427.31156227699,6616.7710962495,6311.96070235073,6262.32372017912,6247.90480904724,6432.35979168526,6887.30523545286,6919.01572858501,6805.0451717529,7033.52113384547,6745.29241350438
Kosovo,XKX,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,7098.40301909042,7481.45889832785,7877.10199850556,8359.93621479336,8431.66232694657,8828.27683251638,9150.74997293883,9824.99105955319,10434.8888259268,10856.2902074239,11188.2244627966,11774.1178510908,11137.5028560404
"Yemen, Rep.",YEM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
South Africa,ZAF,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,11183.825503143,11387.492366782,11697.5657261687,11923.1211013633,12341.2102655828,12861.5633089114,13444.2332126058,14016.2444716386,14297.6887802632,13910.7017093394,14164.9958363115,14434.2611456048,14563.7624041032,14681.096638957,14643.3384782936,14541.6748613669,14501.2807823402,14573.5980840004,14553.5615668957,14352.6698709871,13250.5666594456
Zambia,ZMB,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,2220.5653712714,2268.96965564597,2299.33289188114,2383.87611741089,2471.72073328685,2564.47560965912,2673.46151928529,2796.3529332184,2908.14362510668,3065.84420108998,3266.50749293828,3335.64339500657,3474.55252844092,3535.22449043144,3585.57748507131,3576.92544834125,3598.17166023389,3612.50597664593,3646.95966481655,3591.56418883108,3391.59541156095
Zimbabwe,ZWE,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,6170.33390832202,6217.41179425199,5610.19146861437,4601.6607468251,4287.59829196083,4004.66443720945,3819.2334285389,3631.53761566256,2954.09932377926,3299.41379594873,3885.39378454343,4358.92582649309,5003.48706186748,5031.68744400391,5081.11240399225,5102.71432347607,5070.40217016051,5234.3838655748,5415.46976371689,4993.84383894752,4527.71988082724

// codex-js-repl: timeout_ms=15000
const fs = await import("node:fs/promises");

function parseCsv(text) {
  const rows = [];
  let row = [];
  let field = "";
  let inQuotes = false;
  for (let i = 0; i < text.length; i++) {
    const ch = text[i];
    if (inQuotes) {
      if (ch === '"') {
        if (text[i + 1] === '"') {
          field += '"';
          i++;
        } else {
          inQuotes = false;
        }
      } else {
        field += ch;
      }
      continue;
    }
    if (ch === '"') {
      inQuotes = true;
    } else if (ch === ",") {
      row.push(field);
      field = "";
    } else if (ch === "\n") {
      row.push(field);
      rows.push(row);
      row = [];
      field = "";
    } else if (ch === "\r") {
      continue;
    } else {
      field += ch;
    }
  }
  if (field.length || row.length) {
    row.push(field);
    rows.push(row);
  }
  return rows;
}

const fhText = await fs.readFile("fh_ratings.csv", "utf8");
const wbText = await fs.readFile("wb_gdp_data.csv", "utf8");

const fhRows = parseCsv(fhText);
const fhHeader = fhRows[0];
const fhData = fhRows.slice(1);

const fhYears = fhData.map((r) => Number(r[1])).filter(Number.isFinite);
const fhCountries = new Set(fhData.map((r) => r[0]));
const fhStatuses = [...new Set(fhData.map((r) => r[4]))].sort();
const fhStatusCounts = Object.fromEntries(
  [
    ...fhData
      .reduce((m, r) => m.set(r[4], (m.get(r[4]) || 0) + 1), new Map())
      .entries(),
  ].sort(),
);
const fhRights = fhData.map((r) => Number(r[2])).filter(Number.isFinite);
const fhLibs = fhData.map((r) => Number(r[3])).filter(Number.isFinite);

const wbRows = parseCsv(wbText.replace(/^\uFEFF/, ""));
const wbHeaderRowIndex = wbRows.findIndex((r) => r[0] === "Country Name");
const wbHeader = wbRows[wbHeaderRowIndex];
const wbData = wbRows
  .slice(wbHeaderRowIndex + 1)
  .filter((r) => r.length > 1 && r[0]);
const wbYears = wbHeader.slice(4);
const wbCountryNames = new Set(wbData.map((r) => r[0]));
const wbIndicatorNames = [...new Set(wbData.map((r) => r[2]))];
const wbIndicatorCodes = [...new Set(wbData.map((r) => r[3]))];
const wbMissingCount = wbData
  .flatMap((r) => r.slice(4))
  .filter((v) => v === ".." || v === "").length;
const wbCellCount = wbData.length * (wbHeader.length - 4);
const sampleCountries = [
  wbData[0]?.[0],
  wbData[Math.floor(wbData.length / 2)]?.[0],
  wbData.at(-1)?.[0],
];

({
  fh: {
    columns: fhHeader,
    rows: fhData.length,
    countries: fhCountries.size,
    yearMin: Math.min(...fhYears),
    yearMax: Math.max(...fhYears),
    statuses: fhStatuses,
    statusCounts: fhStatusCounts,
    politicalRightsRange: [Math.min(...fhRights), Math.max(...fhRights)],
    civilLibertiesRange: [Math.min(...fhLibs), Math.max(...fhLibs)],
  },
  wb: {
    metadataRows: wbHeaderRowIndex,
    columns: wbHeader,
    rows: wbData.length,
    countries: wbCountryNames.size,
    yearMin: wbYears[0],
    yearMax: wbYears.at(-1),
    yearCount: wbYears.length,
    indicatorNames: wbIndicatorNames,
    indicatorCodes: wbIndicatorCodes,
    missingCells: wbMissingCount,
    totalValueCells: wbCellCount,
    sampleCountries,
  },
});

Raw tool result

// codex-js-repl: timeout_ms=15000
const fs = await import("node:fs/promises");

function parseCsv(text) {
  const rows = [];
  let row = [];
  let field = "";
  let inQuotes = false;
  for (let i = 0; i < text.length; i++) {
    const ch = text[i];
    if (inQuotes) {
      if (ch === '"') {
        if (text[i + 1] === '"') {
          field += '"';
          i++;
        } else {
          inQuotes = false;
        }
      } else {
        field += ch;
      }
      continue;
    }
    if (ch === '"') {
      inQuotes = true;
    } else if (ch === ",") {
      row.push(field);
      field = "";
    } else if (ch === "\n") {
      row.push(field);
      rows.push(row);
      row = [];
      field = "";
    } else if (ch !== "\r") {
      field += ch;
    }
  }
  if (field.length || row.length) {
    row.push(field);
    rows.push(row);
  }
  return rows;
}

const fhRows = parseCsv(await fs.readFile("fh_ratings.csv", "utf8"));
const fhData = fhRows.slice(1);
const fhYears = fhData.map((r) => Number(r[1])).filter(Number.isFinite);
const fhCountries = new Set(fhData.map((r) => r[0]));
const fhStatuses = [...new Set(fhData.map((r) => r[4]))].sort();
const fhStatusCounts = Object.fromEntries(
  [
    ...fhData
      .reduce((m, r) => m.set(r[4], (m.get(r[4]) || 0) + 1), new Map())
      .entries(),
  ].sort(),
);

const wbRows = parseCsv(
  (await fs.readFile("wb_gdp_data.csv", "utf8")).replace(/^\uFEFF/, ""),
);
const wbHeaderRowIndex = wbRows.findIndex((r) => r[0] === "Country Name");
const wbHeader = wbRows[wbHeaderRowIndex];
const wbData = wbRows.slice(wbHeaderRowIndex + 1).filter((r) => r[0]);
const wbYears = wbHeader.slice(4);
const wbCountries = new Set(wbData.map((r) => r[0]));
const wbIndicatorNames = [...new Set(wbData.map((r) => r[2]))];
const wbIndicatorCodes = [...new Set(wbData.map((r) => r[3]))];
const wbMissingCount = wbData
  .flatMap((r) => r.slice(4))
  .filter((v) => v === ".." || v === "").length;

console.log(
  JSON.stringify(
    {
      fh: {
        rows: fhData.length,
        columns: fhRows[0],
        countries: fhCountries.size,
        yearMin: Math.min(...fhYears),
        yearMax: Math.max(...fhYears),
        statuses: fhStatuses,
        statusCounts: fhStatusCounts,
      },
      wb: {
        metadataRows: wbHeaderRowIndex,
        rows: wbData.length,
        columns: wbHeader,
        countries: wbCountries.size,
        yearMin: wbYears[0],
        yearMax: wbYears.at(-1),
        yearCount: wbYears.length,
        indicatorNames: wbIndicatorNames,
        indicatorCodes: wbIndicatorCodes,
        missingCells: wbMissingCount,
      },
    },
    null,
    2,
  ),
);

Raw tool result

Identifier 'fs' has already been declared

// codex-js-repl: timeout_ms=15000
function parseCsv2(text) {
  const rows = [];
  let row = [];
  let field = "";
  let inQuotes = false;
  for (let i = 0; i < text.length; i++) {
    const ch = text[i];
    if (inQuotes) {
      if (ch === '"') {
        if (text[i + 1] === '"') {
          field += '"';
          i++;
        } else {
          inQuotes = false;
        }
      } else {
        field += ch;
      }
      continue;
    }
    if (ch === '"') {
      inQuotes = true;
    } else if (ch === ",") {
      row.push(field);
      field = "";
    } else if (ch === "\n") {
      row.push(field);
      rows.push(row);
      row = [];
      field = "";
    } else if (ch !== "\r") {
      field += ch;
    }
  }
  if (field.length || row.length) {
    row.push(field);
    rows.push(row);
  }
  return rows;
}

const fhRows2 = parseCsv2(await fs.readFile("fh_ratings.csv", "utf8"));
const fhData2 = fhRows2.slice(1);
const fhYears2 = fhData2.map((r) => Number(r[1])).filter(Number.isFinite);
const fhCountries2 = new Set(fhData2.map((r) => r[0]));
const fhStatuses2 = [...new Set(fhData2.map((r) => r[4]))].sort();
const fhStatusCounts2 = Object.fromEntries(
  [
    ...fhData2
      .reduce((m, r) => m.set(r[4], (m.get(r[4]) || 0) + 1), new Map())
      .entries(),
  ].sort(),
);

const wbRows2 = parseCsv2(
  (await fs.readFile("wb_gdp_data.csv", "utf8")).replace(/^\uFEFF/, ""),
);
const wbHeaderRowIndex2 = wbRows2.findIndex((r) => r[0] === "Country Name");
const wbHeader2 = wbRows2[wbHeaderRowIndex2];
const wbData2 = wbRows2.slice(wbHeaderRowIndex2 + 1).filter((r) => r[0]);
const wbYears2 = wbHeader2.slice(4);
const wbCountries2 = new Set(wbData2.map((r) => r[0]));
const wbIndicatorNames2 = [...new Set(wbData2.map((r) => r[2]))];
const wbIndicatorCodes2 = [...new Set(wbData2.map((r) => r[3]))];
const wbMissingCount2 = wbData2
  .flatMap((r) => r.slice(4))
  .filter((v) => v === ".." || v === "").length;

console.log(
  JSON.stringify(
    {
      fh: {
        rows: fhData2.length,
        columns: fhRows2[0],
        countries: fhCountries2.size,
        yearMin: Math.min(...fhYears2),
        yearMax: Math.max(...fhYears2),
        statuses: fhStatuses2,
        statusCounts: fhStatusCounts2,
      },
      wb: {
        metadataRows: wbHeaderRowIndex2,
        rows: wbData2.length,
        columns: wbHeader2,
        countries: wbCountries2.size,
        yearMin: wbYears2[0],
        yearMax: wbYears2.at(-1),
        yearCount: wbYears2.length,
        indicatorNames: wbIndicatorNames2,
        indicatorCodes: wbIndicatorCodes2,
        missingCells: wbMissingCount2,
      },
    },
    null,
    2,
  ),
);

Raw tool result

{
  "fh": {
    "rows": 4305,
    "columns": [
      "country",
      "year",
      "political_rights_score",
      "civil_liberties_score",
      "status"
    ],
    "countries": 205,
    "yearMin": 2000,
    "yearMax": 2020,
    "statuses": [
      "-",
      "Free",
      "Not Free",
      "Partly Free"
    ],
    "statusCounts": {
      "-": 236,
      "Free": 1837,
      "Not Free": 1006,
      "Partly Free": 1226
    }
  },
  "wb": {
    "metadataRows": 4,
    "rows": 266,
    "columns": [
      "Country Name",
      "Country Code",
      "Indicator Name",
      "Indicator Code",
      "2000",
      "2001",
      "2002",
      "2003",
      "2004",
      "2005",
      "2006",
      "2007",
      "2008",
      "2009",
      "2010",
      "2011",
      "2012",
      "2013",
      "2014",
      "2015",
      "2016",
      "2017",
      "2018",
      "2019",
      "2020"
    ],
    "countries": 266,
    "yearMin": "2000",
    "yearMax": "2020",
    "yearCount": 21,
    "indicatorNames": [
      "GDP per capita, PPP (constant 2021 international $)"
    ],
    "indicatorCodes": [
      "NY.GDP.PCAP.PP.KD"
    ],
    "missingCells": 452
  }
}

Command

rg -n ',-$' fh_ratings.csv | sed -n '1,8p'

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "rg -n ',-"'$'"' fh_ratings.csv | sed -n '1,8p'"
Chunk ID: 11a96f
Wall time: 0.0003 seconds
Process exited with code 0
Original token count: 60
Output:
968:Czechoslovakia,2000,-,-,-
969:Czechoslovakia,2001,-,-,-
970:Czechoslovakia,2002,-,-,-
971:Czechoslovakia,2003,-,-,-
972:Czechoslovakia,2004,-,-,-
973:Czechoslovakia,2005,-,-,-
974:Czechoslovakia,2006,-,-,-
975:Czechoslovakia,2007,-,-,-

Command

sed -n '1,20p' wb_gdp_data.csv | tail -n +5

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 2000
}

Raw tool result

Command: /bin/zsh -lc "sed -n '1,20p' wb_gdp_data.csv | tail -n +5"
Chunk ID: 45c1e4
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1637
Output:
Country Name,Country Code,Indicator Name,Indicator Code,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020
Aruba,ABW,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,37524.0346018738,38729.4588237779,38098.9048329158,38086.0123762909,40000.5625183532,38828.2166342999,38566.8026759098,39462.5851547515,39881.5879733887,34986.1484622772,33951.6246182745,34837.9402869986,33972.3401082566,35621.7848546409,35746.8630679768,36660.6310808381,36832.9040242979,38116.8310992499,39278.8297271438,39652.6320700939,30347.6371851417
Africa Eastern and Southern,AFE,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,3281.92231355821,3313.42192907347,3350.14106469179,3357.24486194198,3457.12022216505,3577.58589657798,3718.02241324132,3866.72395287147,3934.08423728651,3869.99812557719,3976.01098918458,4042.84247175787,4061.64163525859,4125.18256263035,4185.23841104229,4206.25295270438,4202.23641584707,4214.39708524398,4220.91250168849,4195.86541780344,3978.93139450095
Afghanistan,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1617.82647494969,1454.11078195559,1774.30874287341,1815.92819979586,1776.91820712187,1908.11478234587,1929.72389709372,2155.35306784413,2191.50435559062,2565.02208584099,2848.58606111495,2757.05258747725,2985.31914452845,3046.57993117128,3017.94254424696,2967.692067153,2958.7853992308,2952.99891598909,2902.39211285546,2927.2451435353,2769.68574507111
Africa Western and Central,AFW,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,3910.06236490428,4004.95777781768,4293.82350586776,4417.36900943689,4642.02674806122,4775.60343378638,4892.69193879293,5016.77062650533,5181.8092768247,5355.44022713777,5575.8352710514,5691.70513839694,5816.27571540301,6001.87015789534,6174.70331573076,6185.24923306943,6026.16020343528,5999.74477975389,6016.31221720046,6059.60602380843,5687.84998774233
Angola,AGO,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,6796.40633958013,6848.68325514498,7523.81291488042,7518.88948314505,8081.75707952672,8895.37660461036,9588.82238860962,10437.5899897179,11134.7047183666,10933.2712187989,11077.1301919405,11037.6388778577,11519.9933863364,11626.1877218484,11721.664415634,11392.9059344932,10805.7122533667,10414.2358842987,10000.9443772509,9648.01103767178,8960.33797242389
Albania,ALB,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,6582.01638790869,7232.99054955267,7590.49000137636,8025.28131314895,8483.29399553028,8964.31799702863,9564.02920336839,10262.9667962049,11056.3517677907,11430.6220336089,11829.0532917923,12153.1140578808,12463.5700560026,12873.4832044649,13366.5595256489,13876.81917653,14643.4892877063,15359.4611530571,16170.990493278,16761.1925261726,16457.7872245882
Andorra,AND,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,54809.1441679423,59109.0136806378,61188.6370851474,63656.1958332184,64353.4858113531,65114.8053663021,66390.293726491,65534.1168555171,60691.6051530637,57203.9708883731,58285.0577756981,60470.4662377044,58172.1323929474,57332.1585611366,59929.2444670501,62105.1542389675,64402.8092369468,63239.6577711911,63048.5985572834,63215.899791913,55488.4902991095
Arab World,ARB,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,12917.0342418817,12895.2135224156,12743.0694781131,12871.7031954571,13703.8513255052,14096.6978098463,14584.203656405,14885.4008312885,15342.6243511252,15154.7950466565,15570.139810079,15896.5397455117,16141.5972975632,16084.0540746644,16145.8898464544,16371.0585517114,16606.2469902024,16542.576023411,16723.8193619802,16782.382332277,15739.007645376
United Arab Emirates,ARE,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,95390.7013642316,90750.3469329386,87479.2448897115,89828.555395832,93166.1449767722,92750.9148604289,94872.5391985501,87180.6059048658,80285.1852028705,71488.9909603622,70913.778039508,73190.3973792075,73644.0532143257,74237.2214478496,74145.1093706462,75238.5539913477,75671.0564378596,72529.0571050174,72671.0652349751,72822.969940695,66808.6477560191
Argentina,ARG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,22281.3814299958,21066.4581290433,18571.7020471274,20005.1385113465,21591.449396517,23262.4075792816,24877.2493922296,26850.6645932771,27658.4853446831,25747.1976809995,28056.2570212342,29425.7593876084,28825.9666865361,29227.6601818437,28200.8152571318,28669.2061336796,27802.1057201498,28334.904184742,27367.115094108,26629.5529419501,23877.0931404855
Armenia,ARM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,4589.00825039977,5044.11647826468,5731.35346609213,6569.26023440631,7299.40001858866,8362.90084037119,9525.23146779778,10898.8483795634,11727.4821272949,10143.2350796867,10438.5641904169,10990.177099035,11796.2741177994,12194.0189523843,12667.3757330249,13112.7600902915,13192.9940731731,14243.8749003795,15037.0452420712,16215.3607965094,15052.9359791136
American Samoa,ASM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
Antigua and Barbuda,ATG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,25547.6120273679,24014.7754593511,23958.1638142071,25136.001691807,26303.6179046605,27692.634871178,30827.3416754916,33245.2678606765,32756.3067839753,28405.6900929523,25826.6086383235,25023.9174488179,25592.9066121735,25206.8458285339,25557.5249124716,25734.1516444171,26627.71453894,27199.1952402262,28878.6067675471,29651.8641526705,23930.6714995115
Australia,AUS,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,45099.0752097084,45428.2000837579,46688.3947760633,47581.2285345879,49061.2761503925,49997.0664953941,50693.7248649798,51663.2403953881,52473.3819160773,52407.9115378584,52739.2681556133,53257.0473237603,54403.8836902433,54895.0052588984,55489.068844146,55879.9958817103,56529.951824687,56881.8569859104,57647.8455068105,58048.6188420831,57260.4650530108
Austria,AUT,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,53534.3421055422,54032.1515612712,54565.0769851287,54919.7872512396,55980.2317146013,56890.2794433829,58460.2370782474,60470.9099906715,61157.9873105684,58810.4422020872,59730.5512005635,61272.2584397775,61376.7207131054,60863.0547666965,60845.6597729815,60951.6892510507,61572.7121353063,62535.8983176843,63778.0257051746,64609.374295614,60276.4153459141

Command

python3 - <<'PY'
import csv
from collections import Counter

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
keys=Counter((r['country'], r['year']) for r in rows)
dups=[k for k,v in keys.items() if v>1]
print('fh_rows', len(rows))
print('fh_duplicate_country_year_keys', len(dups))
print('fh_example_dups', dups[:10])
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
from collections import Counter

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
keys=Counter((r['country'], r['year']) for r in rows)
dups=[k for k,v in keys.items() if v>1]
print('fh_rows', len(rows))
print('fh_duplicate_country_year_keys', len(dups))
print('fh_example_dups', dups[:10])
PY"
Chunk ID: 66afda
Wall time: 0.0103 seconds
Process exited with code 0
Original token count: 17
Output:
fh_rows 4305
fh_duplicate_country_year_keys 0
fh_example_dups []

Command

python3 - <<'PY'
import csv
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
data=rows[header_idx+1:]
print('wb_rows', len(data))
print('wb_unique_entities', len({r[0] for r in data if r}))
print('wb_example_aggregates', [r[0] for r in data[:15]])
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
data=rows[header_idx+1:]
print('wb_rows', len(data))
print('wb_unique_entities', len({r[0] for r in data if r}))
print('wb_example_aggregates', [r[0] for r in data[:15]])
PY"
Chunk ID: b591d4
Wall time: 0.0075 seconds
Process exited with code 0
Original token count: 76
Output:
wb_rows 266
wb_unique_entities 266
wb_example_aggregates ['Aruba', 'Africa Eastern and Southern', 'Afghanistan', 'Africa Western and Central', 'Angola', 'Albania', 'Andorra', 'Arab World', 'United Arab Emirates', 'Argentina', 'Armenia', 'American Samoa', 'Antigua and Barbuda', 'Australia', 'Austria']

Command

python3 - <<'PY'
import csv

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh={r['country'] for r in csv.DictReader(f)}
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb={r[0] for r in rows[header_idx+1:] if r}

only_fh=sorted(fh-wb)
only_wb=sorted(wb-fh)
print('fh_not_in_wb_count', len(only_fh))
print('fh_not_in_wb_examples', only_fh[:40])
print('wb_not_in_fh_count', len(only_wb))
print('wb_not_in_fh_examples', only_wb[:60])
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 4000
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv

with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh={r['country'] for r in csv.DictReader(f)}
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb={r[0] for r in rows[header_idx+1:] if r}

only_fh=sorted(fh-wb)
only_wb=sorted(wb-fh)
print('fh_not_in_wb_count', len(only_fh))
print('fh_not_in_wb_examples', only_fh[:40])
print('wb_not_in_fh_count', len(only_wb))
print('wb_not_in_fh_examples', only_wb[:60])
PY"
Chunk ID: 411461
Wall time: 0.0091 seconds
Process exited with code 0
Original token count: 499
Output:
fh_not_in_wb_count 32
fh_not_in_wb_examples ['Bahamas', 'Brunei', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Czech Republic', 'Czechoslovakia', 'Egypt', 'Germany, E.', 'Germany, W.', 'Iran', 'Kyrgyzstan', 'Laos', 'Micronesia', 'North Korea', 'Russia', 'Serbia and Montenegro', 'Slovakia', 'Somalia', 'South Korea', 'Syria', 'Taiwan', 'The Gambia', 'Turkey', 'USSR', 'Venezuela', 'Vietnam', 'Vietnam, N.', 'Vietnam, S.', 'Yemen', 'Yemen, N.', 'Yemen, S.', 'Yugoslavia']
wb_not_in_fh_count 93
wb_not_in_fh_examples ['Africa Eastern and Southern', 'Africa Western and Central', 'American Samoa', 'Arab World', 'Aruba', 'Bahamas, The', 'Bermuda', 'British Virgin Islands', 'Brunei Darussalam', 'Caribbean small states', 'Cayman Islands', 'Central Europe and the Baltics', 'Channel Islands', 'Congo, Dem. Rep.', 'Congo, Rep.', 'Curacao', 'Czechia', 'Early-demographic dividend', 'East Asia & Pacific', 'East Asia & Pacific (IDA & IBRD countries)', 'East Asia & Pacific (excluding high income)', 'Egypt, Arab Rep.', 'Euro area', 'Europe & Central Asia', 'Europe & Central Asia (IDA & IBRD countries)', 'Europe & Central Asia (excluding high income)', 'European Union', 'Faroe Islands', 'Fragile and conflict affected situations', 'French Polynesia', 'Gambia, The', 'Gibraltar', 'Greenland', 'Guam', 'Heavily indebted poor countries (HIPC)', 'High income', 'Hong Kong SAR, China', 'IBRD only', 'IDA & IBRD total', 'IDA blend', 'IDA only', 'IDA total', 'Iran, Islamic Rep.', 'Isle of Man', "Korea, Dem. People's Rep.", 'Korea, Rep.', 'Kyrgyz Republic', 'Lao PDR', 'Late-demographic dividend', 'Latin America & Caribbean', 'Latin America & Caribbean (excluding high income)', 'Latin America & the Caribbean (IDA & IBRD countries)', 'Least developed countries: UN classification', 'Low & middle income', 'Low income', 'Lower middle income', 'Macao SAR, China', 'Micronesia, Fed. Sts.', 'Middle East, North Africa, Afghanistan & Pakistan', 'Middle East, North Africa, Afghanistan & Pakistan (IDA & IBRD)']

Command

python3 - <<'PY'
import csv, difflib
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_fh=[x for x in fh if x not in wb]
for name in only_fh:
    matches=difflib.get_close_matches(name, wb, n=5, cutoff=0.45)
    print(name, '=>', matches)
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 4000
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv, difflib
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_fh=[x for x in fh if x not in wb]
for name in only_fh:
    matches=difflib.get_close_matches(name, wb, n=5, cutoff=0.45)
    print(name, '=>', matches)
PY"
Chunk ID: ffe0d2
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 535
Output:
Bahamas => ['Bahamas, The', 'Panama', 'Bahrain', 'Barbados', 'Samoa']
Brunei => ['Burundi', 'Benin', 'Brunei Darussalam', 'Guinea', 'France']
Congo (Brazzaville) => ['Brazil', 'Congo, Rep.']
Congo (Kinshasa) => ['Mongolia']
Czech Republic => ['Kyrgyz Republic', 'Central African Republic', 'Dominican Republic', 'Slovak Republic', 'Czechia']
Czechoslovakia => ['Czechia', 'Colombia', 'Costa Rica', 'Moldova', 'Croatia']
Egypt => ['Egypt, Arab Rep.']
Germany, E. => ['Germany', 'Yemen, Rep.', 'Korea, Rep.']
Germany, W. => ['Germany', 'Yemen, Rep.', 'Korea, Rep.']
Iran => ['Iraq', 'Ireland', 'Jordan', 'Israel', 'France']
Kyrgyzstan => ['Kazakhstan', 'Uzbekistan', 'Kyrgyz Republic', 'Afghanistan', 'Turkmenistan']
Laos => ['Lebanon', 'Lao PDR', 'Barbados']
Micronesia => ['Micronesia, Fed. Sts.', 'Indonesia', 'Mongolia', 'French Polynesia', 'Monaco']
North Korea => ['North America', 'Eritrea', 'North Macedonia', 'South Africa', 'Euro area']
Russia => ['Tunisia', 'Austria', 'Australia', 'South Asia', 'Russian Federation']
Serbia and Montenegro => ['Montenegro', 'Trinidad and Tobago', 'Bosnia and Herzegovina', 'Sierra Leone', 'Africa Eastern and Southern']
Slovakia => ['Slovenia', 'Slovak Republic', 'Romania', 'Moldova', 'Croatia']
Somalia => ['Romania', 'Jamaica', 'Croatia', 'Bolivia', 'Somalia, Fed. Rep.']
South Korea => ['South Africa', 'South Asia', 'South Sudan', 'North America', 'Euro area']
Syria => ['Serbia', 'Suriname', 'Sri Lanka', 'Nigeria', 'Liberia']
Taiwan => ['Thailand', 'Tajikistan', 'Tanzania', 'Pakistan', 'Spain']
The Gambia => ['Zambia', 'Namibia', 'Gambia, The', 'Thailand', 'Cambodia']
Turkey => ['Turkiye', 'Turkmenistan', 'Hungary']
USSR => []
Venezuela => ['Venezuela, RB', 'Senegal']
Vietnam => ['Viet Nam', 'Suriname', 'Kenya', 'China', 'Argentina']
Vietnam, N. => ['Viet Nam', 'Venezuela, RB', 'Yemen, Rep.', 'Korea, Rep.']
Vietnam, S. => ['Viet Nam', 'Venezuela, RB', 'Yemen, Rep.', 'Korea, Rep.']
Yemen => ['Yemen, Rep.', 'Sweden', 'Germany', 'Armenia', 'Cameroon']
Yemen, N. => ['Yemen, Rep.']
Yemen, S. => ['Yemen, Rep.']
Yugoslavia => ['Bolivia', 'Mongolia', 'Bulgaria', 'Australia', 'Latvia']

Command

python3 - <<'PY'
import csv
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_wb=[x for x in wb if x not in fh]
for name in only_wb[:120]:
    print(name)
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 5000
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    fh=sorted({r['country'] for r in csv.DictReader(f)})
with open('wb_gdp_data.csv', newline='', encoding='utf-8-sig') as f:
    rows=list(csv.reader(f))
header_idx=next(i for i,r in enumerate(rows) if r and r[0]=='Country Name')
wb=sorted({r[0] for r in rows[header_idx+1:] if r})
only_wb=[x for x in wb if x not in fh]
for name in only_wb[:120]:
    print(name)
PY"
Chunk ID: 046afa
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 500
Output:
Africa Eastern and Southern
Africa Western and Central
American Samoa
Arab World
Aruba
Bahamas, The
Bermuda
British Virgin Islands
Brunei Darussalam
Caribbean small states
Cayman Islands
Central Europe and the Baltics
Channel Islands
Congo, Dem. Rep.
Congo, Rep.
Curacao
Czechia
Early-demographic dividend
East Asia & Pacific
East Asia & Pacific (IDA & IBRD countries)
East Asia & Pacific (excluding high income)
Egypt, Arab Rep.
Euro area
Europe & Central Asia
Europe & Central Asia (IDA & IBRD countries)
Europe & Central Asia (excluding high income)
European Union
Faroe Islands
Fragile and conflict affected situations
French Polynesia
Gambia, The
Gibraltar
Greenland
Guam
Heavily indebted poor countries (HIPC)
High income
Hong Kong SAR, China
IBRD only
IDA & IBRD total
IDA blend
IDA only
IDA total
Iran, Islamic Rep.
Isle of Man
Korea, Dem. People's Rep.
Korea, Rep.
Kyrgyz Republic
Lao PDR
Late-demographic dividend
Latin America & Caribbean
Latin America & Caribbean (excluding high income)
Latin America & the Caribbean (IDA & IBRD countries)
Least developed countries: UN classification
Low & middle income
Low income
Lower middle income
Macao SAR, China
Micronesia, Fed. Sts.
Middle East, North Africa, Afghanistan & Pakistan
Middle East, North Africa, Afghanistan & Pakistan (IDA & IBRD)
Middle East, North Africa, Afghanistan & Pakistan (excluding high income)
Middle income
New Caledonia
North America
Northern Mariana Islands
Not classified
OECD members
Other small states
Pacific island small states
Post-demographic dividend
Pre-demographic dividend
Puerto Rico (US)
Russian Federation
Sint Maarten (Dutch part)
Slovak Republic
Small states
Somalia, Fed. Rep.
South Asia
South Asia (IDA & IBRD)
St. Martin (French part)
Sub-Saharan Africa
Sub-Saharan Africa (IDA & IBRD countries)
Sub-Saharan Africa (excluding high income)
Syrian Arab Republic
Turkiye
Turks and Caicos Islands
Upper middle income
Venezuela, RB
Viet Nam
Virgin Islands (U.S.)
West Bank and Gaza
World
Yemen, Rep.

Command

rg -n '^Tai|Taiwan|Korea, Dem|Korea, Rep.|Russian Federation|Turkiye|Syrian Arab Republic|Venezuela, RB|Viet Nam|Yemen, Rep.|Somalia, Fed. Rep.|Congo, Dem. Rep.|Congo, Rep.' wb_gdp_data.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 3000
}

Raw tool result

Command: /bin/zsh -lc "rg -n '"'^Tai|Taiwan|Korea, Dem|Korea, Rep.|Russian Federation|Turkiye|Syrian Arab Republic|Venezuela, RB|Viet Nam|Yemen, Rep.|Somalia, Fed. Rep.|Congo, Dem. Rep.|Congo, Rep.'"' wb_gdp_data.csv"
Chunk ID: 275017
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 1012
Output:
49:"Congo, Dem. Rep.",COD,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,981.422080026496,930.8578260745,929.452807092197,953.044643237975,987.746920877671,1016.6338907666,1036.13329616563,1091.0040733455,1136.9467210438,1108.57506967795,1158.01422439658,1205.73984565432,1267.23827814087,1342.74156112016,1392.75841334673,1433.21579814709,1388.80163211633,1395.71961761712,1431.7285269693,1394.76461472893,1361.02791099008
50:"Congo, Rep.",COG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,7570.58279297342,7567.43916817104,7729.3889014189,7577.45630456352,7577.44597274153,7876.11806658285,8192.57979719023,7375.82454100145,7587.28826163492,8138.45033818822,8583.66179834288,8492.42098392103,9079.68656400891,8798.49382506314,9159.25664414045,8977.70681641417,8001.8255194505,7372.77046737599,7029.33651566257,6939.36913397466,6350.50913409722
132:"Korea, Rep.",KOR,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,26404.1588575745,27440.6564915847,29395.2766036796,30142.4605585814,31579.5283048867,32886.0472450743,34429.6222013137,36257.3401689702,37064.1518484106,37176.1939270969,39574.0500732947,40719.2273771671,41537.6728611117,42709.9751857887,43806.9250985661,44849.106767089,46088.2430785383,47536.7898568851,48834.4471157733,49790.8679884821,49374.0926148025
199:"Korea, Dem. People's Rep.",PRK,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
208:Russian Federation,RUS,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,20104.75,21219.904296875,22319.677734375,24057.927734375,25894.146484375,27656.658203125,30022.671875,32630.369140625,34342.2265625,31654.00390625,33063.5859375,34444.64453125,35740.69921875,36260.06640625,35853.24609375,35037.15234375,35005.44921875,35570.390625,36536.6953125,37319.3203125,36376.3359375
219:"Somalia, Fed. Rep.",SOM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,702.850071117611,723.37973295051,749.913550912115,777.884552733299,803.059780580571,830.807245939909,857.993655901941,887.36935223579,919.132083452828,956.220325392405,995.225555512318,1054.36214979106,1114.80414892809,1197.72166459747,1259.27432882615,1343.92517439606,1384.15517410135,1527.66029071672,1504.97908203481,1491.29493918405,1396.05544674978
233:Syrian Arab Republic,SYR,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,7807.92995548742,7698.3969367866,7811.81601910972,8177.54506854394,8528.65604597154,8828.94590461523,8885.80539630373,8829.30906367425,8903.77225264131,9284.10162126587,9546.56048805399,9648.82426718361,7144.37244952745,5530.97472757035,5301.62535257941,5302.07413721497,5022.1127178437,4977.79210063798,4956.09296464777,4825.40602053761,4633.34951847728
250:Turkiye,TUR,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,14856.914625257,13872.1287284566,14587.894621656,15255.7177960689,16583.8837094062,17902.5966004487,18972.0089563632,19909.083519675,19839.5845462269,18612.1925231905,19897.4464836887,21760.4291923634,22517.7148287875,24117.3121099839,24888.8521742576,25984.8027306177,26489.6436816046,28194.688309381,28780.3036458151,28741.0535082701,28976.7179161611
260:"Venezuela, RB",VEN,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
263:Viet Nam,VNM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,4349.22348751462,4570.26846577385,4809.62151118535,5090.28990869885,5421.09702829121,5776.36304451268,6098.24495436514,6418.54381402878,6659.13561432065,6914.33863589545,7274.73310698016,7652.61895672271,7979.86828926858,8324.19871250047,8751.88280634375,9248.02396069622,9743.1882231455,10290.5487142244,10936.8937859558,11628.6139147451,11851.3965685649
268:"Yemen, Rep.",YEM,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..

Command

python3 - <<'PY'
import csv
from collections import defaultdict
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
by_country=defaultdict(lambda: [9999, -9999])
for r in rows:
    y=int(r['year'])
    by_country[r['country']][0]=min(by_country[r['country']][0], y)
    by_country[r['country']][1]=max(by_country[r['country']][1], y)
for name in ['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']:
    if name in by_country:
        print(name, by_country[name][0], by_country[name][1])
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
from collections import defaultdict
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    rows=list(csv.DictReader(f))
by_country=defaultdict(lambda: [9999, -9999])
for r in rows:
    y=int(r['year'])
    by_country[r['country']][0]=min(by_country[r['country']][0], y)
    by_country[r['country']][1]=max(by_country[r['country']][1], y)
for name in ['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']:
    if name in by_country:
        print(name, by_country[name][0], by_country[name][1])
PY"
Chunk ID: af7c69
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 60
Output:
USSR 2000 2020
Yugoslavia 2000 2020
Czechoslovakia 2000 2020
Germany, E. 2000 2020
Germany, W. 2000 2020
Serbia and Montenegro 2000 2020
Vietnam, N. 2000 2020
Vietnam, S. 2000 2020
Yemen, N. 2000 2020
Yemen, S. 2000 2020
Taiwan 2000 2020

Command

python3 - <<'PY'
import csv
from collections import defaultdict
names=['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']
vals=defaultdict(set)
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add((r['political_rights_score'], r['civil_liberties_score'], r['status']))
for n in names:
    print(n, sorted(vals[n]))
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 3000
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
from collections import defaultdict
names=['USSR','Yugoslavia','Czechoslovakia','Germany, E.','Germany, W.','Serbia and Montenegro','Vietnam, N.','Vietnam, S.','Yemen, N.','Yemen, S.','Taiwan']
vals=defaultdict(set)
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add((r['political_rights_score'], r['civil_liberties_score'], r['status']))
for n in names:
    print(n, sorted(vals[n]))
PY"
Chunk ID: 30b493
Wall time: 0.1207 seconds
Process exited with code 0
Original token count: 121
Output:
USSR [('-', '-', '-')]
Yugoslavia [('-', '-', '-'), ('3', '2', 'Free'), ('3', '3', 'Partly Free'), ('4', '4', 'Partly Free')]
Czechoslovakia [('-', '-', '-')]
Germany, E. [('-', '-', '-')]
Germany, W. [('-', '-', '-')]
Serbia and Montenegro [('-', '-', '-'), ('3', '2', 'Free')]
Vietnam, N. [('-', '-', '-')]
Vietnam, S. [('-', '-', '-')]
Yemen, N. [('-', '-', '-')]
Yemen, S. [('-', '-', '-')]
Taiwan [('1', '1', 'Free'), ('1', '2', 'Free'), ('2', '1', 'Free'), ('2', '2', 'Free')]

Command

python3 - <<'PY'
import csv
names=['Congo (Brazzaville)','Congo (Kinshasa)','North Korea','South Korea','Russia','Turkey','Venezuela','Vietnam','Syria','Somalia','Yemen']
vals={n:set() for n in names}
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add(r['status'])
for n in names:
    print(n, sorted(vals[n]))
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
names=['Congo (Brazzaville)','Congo (Kinshasa)','North Korea','South Korea','Russia','Turkey','Venezuela','Vietnam','Syria','Somalia','Yemen']
vals={n:set() for n in names}
with open('fh_ratings.csv', newline='', encoding='utf-8') as f:
    for r in csv.DictReader(f):
        if r['country'] in names:
            vals[r['country']].add(r['status'])
for n in names:
    print(n, sorted(vals[n]))
PY"
Chunk ID: 519cde
Wall time: 0.1215 seconds
Process exited with code 0
Original token count: 82
Output:
Congo (Brazzaville) ['Not Free', 'Partly Free']
Congo (Kinshasa) ['Not Free']
North Korea ['Not Free']
South Korea ['Free']
Russia ['Not Free', 'Partly Free']
Turkey ['Not Free', 'Partly Free']
Venezuela ['Not Free', 'Partly Free']
Vietnam ['Not Free']
Syria ['Not Free']
Somalia ['Not Free']
Yemen ['Not Free', 'Partly Free']

*** Begin Patch
*** Add File: /Users/christopherlee/Documents/Github/ucsb-workshop/country_name_mapping.csv
+fh_country,wb_country,action,notes
+Bahamas,"Bahamas, The",map,Direct naming variant
+Brunei,Brunei Darussalam,map,Direct naming variant
+"Congo (Brazzaville)","Congo, Rep.",map,Republic of the Congo
+"Congo (Kinshasa)","Congo, Dem. Rep.",map,Democratic Republic of the Congo
+"Czech Republic",Czechia,map,Renamed country label in World Bank data
+Czechoslovakia,,drop,Historical entity with no direct 2000-2020 World Bank country row
+Egypt,"Egypt, Arab Rep.",map,Direct naming variant
+"Germany, E.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+"Germany, W.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+Iran,"Iran, Islamic Rep.",map,Direct naming variant
+Kyrgyzstan,"Kyrgyz Republic",map,Direct naming variant
+Laos,"Lao PDR",map,Direct naming variant
+Micronesia,"Micronesia, Fed. Sts.",map,Direct naming variant
+"North Korea","Korea, Dem. People's Rep.",map,Direct naming variant
+Russia,"Russian Federation",map,Direct naming variant
+"Serbia and Montenegro",,drop,Historical composite entity with no direct single-country World Bank row
+Slovakia,"Slovak Republic",map,Direct naming variant
+Somalia,"Somalia, Fed. Rep.",map,Direct naming variant
+"South Korea","Korea, Rep.",map,Direct naming variant
+Syria,"Syrian Arab Republic",map,Direct naming variant
+Taiwan,,manual,No Taiwan row appears in this World Bank extract
+"The Gambia","Gambia, The",map,Direct naming variant
+Turkey,Turkiye,map,Renamed country label in World Bank data
+USSR,,drop,Historical entity with no direct 2000-2020 World Bank country row
+Venezuela,"Venezuela, RB",map,Direct naming variant
+Vietnam,"Viet Nam",map,Direct naming variant
+"Vietnam, N.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+"Vietnam, S.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+Yemen,"Yemen, Rep.",map,Direct naming variant
+"Yemen, N.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+"Yemen, S.",,drop,Historical entity with no direct 2000-2020 World Bank country row
+Yugoslavia,,drop,Historical entity with no direct 2000-2020 World Bank country row
*** End Patch

Raw tool result

{"output":"Success. Updated the following files:\nA /Users/christopherlee/Documents/Github/ucsb-workshop/country_name_mapping.csv\n","metadata":{"exit_code":0,"duration_seconds":0.1}}

Command

sed -n '1,40p' country_name_mapping.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 2000
}

Raw tool result

Command: /bin/zsh -lc "sed -n '1,40p' country_name_mapping.csv"
Chunk ID: bf343d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 531
Output:
fh_country,wb_country,action,notes
Bahamas,"Bahamas, The",map,Direct naming variant
Brunei,Brunei Darussalam,map,Direct naming variant
"Congo (Brazzaville)","Congo, Rep.",map,Republic of the Congo
"Congo (Kinshasa)","Congo, Dem. Rep.",map,Democratic Republic of the Congo
"Czech Republic",Czechia,map,Renamed country label in World Bank data
Czechoslovakia,,drop,Historical entity with no direct 2000-2020 World Bank country row
Egypt,"Egypt, Arab Rep.",map,Direct naming variant
"Germany, E.",,drop,Historical entity with no direct 2000-2020 World Bank country row
"Germany, W.",,drop,Historical entity with no direct 2000-2020 World Bank country row
Iran,"Iran, Islamic Rep.",map,Direct naming variant
Kyrgyzstan,"Kyrgyz Republic",map,Direct naming variant
Laos,"Lao PDR",map,Direct naming variant
Micronesia,"Micronesia, Fed. Sts.",map,Direct naming variant
"North Korea","Korea, Dem. People's Rep.",map,Direct naming variant
Russia,"Russian Federation",map,Direct naming variant
"Serbia and Montenegro",,drop,Historical composite entity with no direct single-country World Bank row
Slovakia,"Slovak Republic",map,Direct naming variant
Somalia,"Somalia, Fed. Rep.",map,Direct naming variant
"South Korea","Korea, Rep.",map,Direct naming variant
Syria,"Syrian Arab Republic",map,Direct naming variant
Taiwan,,manual,No Taiwan row appears in this World Bank extract
"The Gambia","Gambia, The",map,Direct naming variant
Turkey,Turkiye,map,Renamed country label in World Bank data
USSR,,drop,Historical entity with no direct 2000-2020 World Bank country row
Venezuela,"Venezuela, RB",map,Direct naming variant
Vietnam,"Viet Nam",map,Direct naming variant
"Vietnam, N.",,drop,Historical entity with no direct 2000-2020 World Bank country row
"Vietnam, S.",,drop,Historical entity with no direct 2000-2020 World Bank country row
Yemen,"Yemen, Rep.",map,Direct naming variant
"Yemen, N.",,drop,Historical entity with no direct 2000-2020 World Bank country row
"Yemen, S.",,drop,Historical entity with no direct 2000-2020 World Bank country row
Yugoslavia,,drop,Historical entity with no direct 2000-2020 World Bank country row

Command

python3 - <<'PY'
import csv
from pathlib import Path

root = Path('.')
fh_path = root / 'fh_ratings.csv'
wb_path = root / 'wb_gdp_data.csv'
map_path = root / 'country_name_mapping.csv'
out_path = root / 'fh_wb_merged.csv'
summary_path = root / 'fh_wb_merge_summary.txt'

# Load mapping table
name_map = {}
map_action = {}
with map_path.open(newline='', encoding='utf-8') as f:
    for row in csv.DictReader(f):
        name_map[row['fh_country']] = row['wb_country']
        map_action[row['fh_country']] = row['action']

# Load WB wide extract and reshape to long
with wb_path.open(newline='', encoding='utf-8-sig') as f:
    rows = list(csv.reader(f))
header_idx = next(i for i, r in enumerate(rows) if r and r[0] == 'Country Name')
header = rows[header_idx]
years = header[4:]
wb_long = {}
for r in rows[header_idx + 1:]:
    if not r or not r[0]:
        continue
    country_name, country_code, indicator_name, indicator_code = r[:4]
    for year, value in zip(years, r[4:]):
        wb_long[(country_name, year)] = {
            'country_wb': country_name,
            'country_code_wb': country_code,
            'indicator_name_wb': indicator_name,
            'indicator_code_wb': indicator_code,
            'year': year,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': None if value in ('', '..') else value,
        }

# Merge onto FH rows
fieldnames = [
    'country_fh',
    'country_wb',
    'year',
    'political_rights_score',
    'civil_liberties_score',
    'status',
    'country_code_wb',
    'indicator_name_wb',
    'indicator_code_wb',
    'gdp_per_capita_ppp_constant_2021_intl_dollars',
    'merge_status',
    'merge_note',
]

merged_rows = []
stats = {
    'exact_match': 0,
    'mapped_alias': 0,
    'dropped_historical': 0,
    'manual_unresolved': 0,
    'no_wb_row_after_mapping': 0,
    'matched_with_gdp_value': 0,
    'matched_but_gdp_missing': 0,
}

with fh_path.open(newline='', encoding='utf-8') as f:
    for row in csv.DictReader(f):
        country_fh = row['country']
        year = row['year']
        pr = None if row['political_rights_score'] == '-' else row['political_rights_score']
        cl = None if row['civil_liberties_score'] == '-' else row['civil_liberties_score']
        status = None if row['status'] == '-' else row['status']

        action = map_action.get(country_fh)
        if action == 'map':
            country_wb = name_map[country_fh]
            merge_status = 'mapped_alias'
            merge_note = 'Matched using country_name_mapping.csv'
            stats['mapped_alias'] += 1
        elif action == 'drop':
            country_wb = None
            merge_status = 'dropped_historical'
            merge_note = 'Historical/composite entity intentionally left unmatched'
            stats['dropped_historical'] += 1
        elif action == 'manual':
            country_wb = None
            merge_status = 'manual_unresolved'
            merge_note = 'Requires manual handling; no safe WB row in this extract'
            stats['manual_unresolved'] += 1
        else:
            country_wb = country_fh
            merge_status = 'exact_match'
            merge_note = 'Exact country-name match'
            stats['exact_match'] += 1

        wb = wb_long.get((country_wb, year)) if country_wb else None
        if wb is None and country_wb and merge_status in ('exact_match', 'mapped_alias'):
            stats['no_wb_row_after_mapping'] += 1

        gdp_value = wb['gdp_per_capita_ppp_constant_2021_intl_dollars'] if wb else None
        if wb is not None:
            if gdp_value is None:
                stats['matched_but_gdp_missing'] += 1
            else:
                stats['matched_with_gdp_value'] += 1

        merged_rows.append({
            'country_fh': country_fh,
            'country_wb': country_wb,
            'year': year,
            'political_rights_score': pr,
            'civil_liberties_score': cl,
            'status': status,
            'country_code_wb': wb['country_code_wb'] if wb else None,
            'indicator_name_wb': wb['indicator_name_wb'] if wb else None,
            'indicator_code_wb': wb['indicator_code_wb'] if wb else None,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': gdp_value,
            'merge_status': merge_status,
            'merge_note': merge_note,
        })

with out_path.open('w', newline='', encoding='utf-8') as f:
    writer = csv.DictWriter(f, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerows(merged_rows)

with summary_path.open('w', encoding='utf-8') as f:
    f.write(f"rows={len(merged_rows)}\n")
    for key, value in stats.items():
        f.write(f"{key}={value}\n")

print(out_path)
print(summary_path)
print(stats)
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 3000
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
from pathlib import Path

root = Path('.')
fh_path = root / 'fh_ratings.csv'
wb_path = root / 'wb_gdp_data.csv'
map_path = root / 'country_name_mapping.csv'
out_path = root / 'fh_wb_merged.csv'
summary_path = root / 'fh_wb_merge_summary.txt'

# Load mapping table
name_map = {}
map_action = {}
with map_path.open(newline='', encoding='utf-8') as f:
    for row in csv.DictReader(f):
        name_map[row['fh_country']] = row['wb_country']
        map_action[row['fh_country']] = row['action']

# Load WB wide extract and reshape to long
with wb_path.open(newline='', encoding='utf-8-sig') as f:
    rows = list(csv.reader(f))
header_idx = next(i for i, r in enumerate(rows) if r and r[0] == 'Country Name')
header = rows[header_idx]
years = header[4:]
wb_long = {}
for r in rows[header_idx + 1:]:
    if not r or not r[0]:
        continue
    country_name, country_code, indicator_name, indicator_code = r[:4]
    for year, value in zip(years, r[4:]):
        wb_long[(country_name, year)] = {
            'country_wb': country_name,
            'country_code_wb': country_code,
            'indicator_name_wb': indicator_name,
            'indicator_code_wb': indicator_code,
            'year': year,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': None if value in ('', '..') else value,
        }

# Merge onto FH rows
fieldnames = [
    'country_fh',
    'country_wb',
    'year',
    'political_rights_score',
    'civil_liberties_score',
    'status',
    'country_code_wb',
    'indicator_name_wb',
    'indicator_code_wb',
    'gdp_per_capita_ppp_constant_2021_intl_dollars',
    'merge_status',
    'merge_note',
]

merged_rows = []
stats = {
    'exact_match': 0,
    'mapped_alias': 0,
    'dropped_historical': 0,
    'manual_unresolved': 0,
    'no_wb_row_after_mapping': 0,
    'matched_with_gdp_value': 0,
    'matched_but_gdp_missing': 0,
}

with fh_path.open(newline='', encoding='utf-8') as f:
    for row in csv.DictReader(f):
        country_fh = row['country']
        year = row['year']
        pr = None if row['political_rights_score'] == '-' else row['political_rights_score']
        cl = None if row['civil_liberties_score'] == '-' else row['civil_liberties_score']
        status = None if row['status'] == '-' else row['status']

        action = map_action.get(country_fh)
        if action == 'map':
            country_wb = name_map[country_fh]
            merge_status = 'mapped_alias'
            merge_note = 'Matched using country_name_mapping.csv'
            stats['mapped_alias'] += 1
        elif action == 'drop':
            country_wb = None
            merge_status = 'dropped_historical'
            merge_note = 'Historical/composite entity intentionally left unmatched'
            stats['dropped_historical'] += 1
        elif action == 'manual':
            country_wb = None
            merge_status = 'manual_unresolved'
            merge_note = 'Requires manual handling; no safe WB row in this extract'
            stats['manual_unresolved'] += 1
        else:
            country_wb = country_fh
            merge_status = 'exact_match'
            merge_note = 'Exact country-name match'
            stats['exact_match'] += 1

        wb = wb_long.get((country_wb, year)) if country_wb else None
        if wb is None and country_wb and merge_status in ('exact_match', 'mapped_alias'):
            stats['no_wb_row_after_mapping'] += 1

        gdp_value = wb['gdp_per_capita_ppp_constant_2021_intl_dollars'] if wb else None
        if wb is not None:
            if gdp_value is None:
                stats['matched_but_gdp_missing'] += 1
            else:
                stats['matched_with_gdp_value'] += 1

        merged_rows.append({
            'country_fh': country_fh,
            'country_wb': country_wb,
            'year': year,
            'political_rights_score': pr,
            'civil_liberties_score': cl,
            'status': status,
            'country_code_wb': wb['country_code_wb'] if wb else None,
            'indicator_name_wb': wb['indicator_name_wb'] if wb else None,
            'indicator_code_wb': wb['indicator_code_wb'] if wb else None,
            'gdp_per_capita_ppp_constant_2021_intl_dollars': gdp_value,
            'merge_status': merge_status,
            'merge_note': merge_note,
        })

with out_path.open('w', newline='', encoding='utf-8') as f:
    writer = csv.DictWriter(f, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerows(merged_rows)

with summary_path.open('w', encoding='utf-8') as f:
    f.write(f\"rows={len(merged_rows)}\\n\")
    for key, value in stats.items():
        f.write(f\"{key}={value}\\n\")

print(out_path)
print(summary_path)
print(stats)
PY"
Chunk ID: 4f4e59
Wall time: 0.2099 seconds
Process exited with code 0
Original token count: 58
Output:
fh_wb_merged.csv
fh_wb_merge_summary.txt
{'exact_match': 3633, 'mapped_alias': 441, 'dropped_historical': 210, 'manual_unresolved': 21, 'no_wb_row_after_mapping': 0, 'matched_with_gdp_value': 3885, 'matched_but_gdp_missing': 189}

Command

sed -n '1,8p' fh_wb_merged.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 2000
}

Raw tool result

Command: /bin/zsh -lc "sed -n '1,8p' fh_wb_merged.csv"
Chunk ID: 657c89
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 355
Output:
country_fh,country_wb,year,political_rights_score,civil_liberties_score,status,country_code_wb,indicator_name_wb,indicator_code_wb,gdp_per_capita_ppp_constant_2021_intl_dollars,merge_status,merge_note
Afghanistan,Afghanistan,2000,7,7,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1617.82647494969,exact_match,Exact country-name match
Afghanistan,Afghanistan,2001,7,7,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1454.11078195559,exact_match,Exact country-name match
Afghanistan,Afghanistan,2002,6,6,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1774.30874287341,exact_match,Exact country-name match
Afghanistan,Afghanistan,2003,6,6,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1815.92819979586,exact_match,Exact country-name match
Afghanistan,Afghanistan,2004,5,6,Not Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1776.91820712187,exact_match,Exact country-name match
Afghanistan,Afghanistan,2005,5,5,Partly Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1908.11478234587,exact_match,Exact country-name match
Afghanistan,Afghanistan,2006,5,5,Partly Free,AFG,"GDP per capita, PPP (constant 2021 international $)",NY.GDP.PCAP.PP.KD,1929.72389709372,exact_match,Exact country-name match

Command

python3 - <<'PY'
import csv
wanted=['Bahamas','South Korea','North Korea','Taiwan','USSR','Turkey','Vietnam']
with open('fh_wb_merged.csv', newline='', encoding='utf-8') as f:
    rows=csv.DictReader(f)
    seen=set()
    for r in rows:
        c=r['country_fh']
        if c in wanted and c not in seen:
            print(c, '|', r['country_wb'], '|', r['year'], '|', r['merge_status'], '|', r['gdp_per_capita_ppp_constant_2021_intl_dollars'])
            seen.add(c)
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 2000
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
wanted=['Bahamas','South Korea','North Korea','Taiwan','USSR','Turkey','Vietnam']
with open('fh_wb_merged.csv', newline='', encoding='utf-8') as f:
    rows=csv.DictReader(f)
    seen=set()
    for r in rows:
        c=r['country_fh']
        if c in wanted and c not in seen:
            print(c, '|', r['country_wb'], '|', r['year'], '|', r['merge_status'], '|', r['gdp_per_capita_ppp_constant_2021_intl_dollars'])
            seen.add(c)
PY"
Chunk ID: ec2faa
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 98
Output:
Bahamas | Bahamas, The | 2000 | mapped_alias | 34597.1621145274
North Korea | Korea, Dem. People's Rep. | 2000 | mapped_alias |
South Korea | Korea, Rep. | 2000 | mapped_alias | 26404.1588575745
Taiwan |  | 2000 | manual_unresolved |
Turkey | Turkiye | 2000 | mapped_alias | 14856.914625257
USSR |  | 2000 | dropped_historical |
Vietnam | Viet Nam | 2000 | mapped_alias | 4349.22348751462

Command

cat fh_wb_merge_summary.txt

Invocation metadata

{
  "workdir": "/Users/christopherlee/Documents/Github/ucsb-workshop",
  "max_output_tokens": 1200
}

Raw tool result

Command: /bin/zsh -lc 'cat fh_wb_merge_summary.txt'
Chunk ID: d7466f
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 43
Output:
rows=4305
exact_match=3633
mapped_alias=441
dropped_historical=210
manual_unresolved=21
no_wb_row_after_mapping=0
matched_with_gdp_value=3885
matched_but_gdp_missing=189

{
  "tool": "mcp__playwright__browser_navigate",
  "arguments": {
    "url": "https://www.car.org/marketdata/data/ftbhai"
  }
}

Raw tool result

[{"type":"text","text":"### Ran Playwright code\n```js\nawait page.goto('https://www.car.org/marketdata/data/ftbhai');\n```\n### Page\n- Page URL: https://www.car.org/marketdata/data/ftbhai\n- Page Title: Housing Affordability Index - First-Time Buyer\n- Console: 0 errors, 1 warnings\n### Snapshot\n- [Snapshot](var/folders/wk/723q3_fs02n32w9xm40mlhg40000gn/T/playwright-mcp-output/page-2026-04-03T05-52-15-762Z.yml)\n### Events\n- New console entries: var/folders/wk/723q3_fs02n32w9xm40mlhg40000gn/T/playwright-mcp-output/console-2026-04-03T05-52-14-913Z.log#L1"}]

{
  "tool": "mcp__playwright__browser_resize",
  "arguments": {
    "width": 1440,
    "height": 1200
  }
}

Raw tool result

[{"type":"text","text":"### Ran Playwright code\n```js\nawait page.setViewportSize({ width: 1440, height: 1200 });\n```\n### Page\n- Page URL: about:blank"}]

{
  "tool": "mcp__playwright__browser_snapshot",
  "arguments": {
    "depth": 6
  }
}

Raw tool result

[{"type":"text","text":"### Page\n- Page URL: https://www.car.org/marketdata/data/ftbhai\n- Page Title: Housing Affordability Index - First-Time Buyer\n- Console: 3 errors, 2 warnings\n### Snapshot\n```yaml\n- generic [active] [ref=e1]:\n  - alert [ref=e1703]: Press Option+1 for screen-reader mode, Option+0 to cancel\n  - button \"Use Website In a Screen-Reader Mode\" [ref=e1704]\n  - link \"Accessibility Screen-Reader Guide, Feedback, and Issue Reporting | New window\" [ref=e1705] [cursor=pointer]:\n    - /url: https://accessibe.com/blog/knowledgebase/screen-reader-guide\n  - generic:\n    - generic:\n      - region \"Skip Links\":\n        - generic:\n          - link \"Skip to Content\":\n            - /url: \"#acsbContent\"\n            - text: Skip to Content\n            - generic\n  - dialog [ref=e1706]:\n    - generic [ref=e1707]:\n      - img [ref=e1708]\n      - text: Simplify Text\n    - button [ref=e1710] [cursor=pointer]:\n      - img [ref=e1711]\n  - generic [ref=e2]:\n    - generic [ref=e5]:\n      - generic [ref=e6] [cursor=pointer]: x\n      - heading [level=5]\n      - separator [ref=e7]\n      - text:      \n    - banner [ref=e8]:\n      - link \"C.A.R. Logo\" [ref=e10] [cursor=pointer]:\n        - /url: /\n        - img \"C.A.R. Logo\" [ref=e11]\n      - navigation [ref=e12]:\n        - generic [ref=e13]:\n          - link \"Transaction Center\" [ref=e15] [cursor=pointer]:\n            - /url: \"#\"\n          - link \"Learn & Thrive\" [ref=e17] [cursor=pointer]:\n            - /url: \"#\"\n          - link \"Industry 360°\" [ref=e19] [cursor=pointer]:\n            - /url: \"#\"\n          - link \"Your C.A.R.\" [ref=e21] [cursor=pointer]:\n            - /url: \"#\"\n      - generic [ref=e23]:\n        - link \"https://www.car.org/membervalidation/rst/sso/store\" [ref=e25] [cursor=pointer]:\n          - /url: https://www.car.org/membervalidation/rst/sso/store\n          - img \"C.A.R. Store\" [ref=e27]\n        - button \"Sign In\" [ref=e30] [cursor=pointer]\n        - link \"Search\" [ref=e32] [cursor=pointer]:\n          - /url: javascript:void(0)\n          - generic [ref=e33]: \n          - generic [ref=e34]: Search\n    - text:                                                                                     \n    - banner [ref=e37]:\n      - generic [ref=e38]:\n        - generic [ref=e40]: \n        - generic [ref=e42]: MAIN MENU\n      - generic [ref=e46]:\n        - generic [ref=e48]:\n          - generic [ref=e49] [cursor=pointer]:\n            - generic [ref=e50]\n            - generic [ref=e56]: \n          - generic [ref=e57] [cursor=pointer]:\n            - generic [ref=e58]\n            - generic [ref=e63]: \n          - generic [ref=e64] [cursor=pointer]:\n            - generic [ref=e65]\n            - generic [ref=e70]: \n          - generic [ref=e71] [cursor=pointer]:\n            - generic [ref=e72]\n            - generic [ref=e77]: \n        - generic [ref=e78]:               \n    - generic [ref=e80]:\n      - generic [ref=e81]:\n        - heading \"Housing Affordability Index - First-Time Buyer\" [level=2] [ref=e85]\n        - generic [ref=e86]:\n          - generic [ref=e88]:\n            - generic [ref=e89]\n            - text:  \n          - generic [ref=e90]:\n            - link \"Market Data  \" [ref=e92] [cursor=pointer]:\n              - /url: /en/marketdata\n            - link \"MLS / Professional Standards  \" [ref=e98] [cursor=pointer]:\n              - /url: /en/mlspro\n            - link \"Political Advocacy  \" [ref=e104] [cursor=pointer]:\n              - /url: /en/advocacy\n      - generic [ref=e110]:\n        - generic [ref=e113]:\n          - generic [ref=e116]:\n            - link \"Home\" [ref=e118] [cursor=pointer]:\n              - /url: /en/\n            - generic [ref=e120]: \n            - link \"Market Data\" [ref=e122] [cursor=pointer]:\n              - /url: /en/marketdata\n            - generic [ref=e124]: \n            - link \"Data & Statistics\" [ref=e126] [cursor=pointer]:\n              - /url: /en/marketdata/data\n            - generic [ref=e128]: \n            - link \"HAI First-Time Buyer\" [ref=e130]:\n              - /url: /en/marketdata/data/ftbhai\n          - generic [ref=e131]:\n            - generic [ref=e132]\n            - generic [ref=e144]\n        - generic [ref=e1469]:\n          - iframe [ref=e1717]:\n            - link [ref=f6e2] [cursor=pointer]:\n              - /url: https://googleads.g.doubleclick.net/pcs/click?xai=AKAOjsuvzZcsbLvNawImHKJwGqpKRfENnWQ6QSgAT6ih9LsC8tzLno61VwcxF0tlW8AlvH2NnO5qMj52u4s_8OYCnVzZdCNnlKNeYkPaSYBprjrse9ZNNYgtTw4oQ2KqfSH0KZKiqIASr8iRmCs6JeJ-dLC1HI4dCSsnQWwvTPA32aapBpCM_zY3L-FanbIB-v2i4C4kKyrukEzmjHibq5-0pHhJHryjnpJYTTvFG-11OLA1gK6BCoeob906NFg2_2e2Scbhf-CW-3Dc0LO9FBHBauKmjFPkYeGDd-7F9WVQ1bHTpCS7LTEyMfcK5fXY9Aj_EyTGZLk57PDYVF2HUrNSoaYeaZWTTowA4kbSeEcvYSRYHRxAdIg803sNZiGB6bQoHns-KNRpJSwPdbRd&sai=AMfl-YRcX1BIFXAb3SQbRN6cKLQqt1ZIZoTwFMn3kdbSFKH9AK4aERfjbaNdi3DLDslY0yku2MufGs7ioD4AWLGh07LTHoZLoyJFPuFTdLwuU1dS1snyC0XMT_l2e5LrbYIKnqyYGKoJ-aHrCHKd1LKzhK51-3A3IvJCrQUUQ5qCoKZmQZvEQYW8BMRm6qivHiYVDBgHHga_X9QSKrKEZHwTUGHJq082sKfm1Mx98Bf3aQ-PR53iIuxgTXBseC2IoGiVneugN-kGFas3kWx79I2qiOHsY1EXhNoeHxwIEYYE6I6aAF40GvegvA8Tt78vnqrxO50ZB5nAdg&sig=Cg0ArKJSzBFGzx_7TJQv&fbs_aeid=%5Bgw_fbsaeid%5D&adurl=https://www.car.org/education/licenserenewal\n          - iframe [ref=e1721]:\n            - link [ref=f7e2] [cursor=pointer]:\n              - /url: https://googleads.g.doubleclick.net/pcs/click?xai=AKAOjsv3VYF0wBdOg8BTfVqbM7QKJZ9T_lzqrGb2HW_N1Uh8TyBkIqc4Ipx883sLw_Rw8m41S_sEpssoJxRiPgeVu4Wjde0IdenAWcnJ1M-GJheEMD9wO3kJMfYI_JXf4Juckq_-CqdN0kFYRT187iepqiyFvBBg7lGfDkxl5JyrrS_RYhagXYWrCZDxJP8nC6XwYtMB7J58VSNm3ZgyL7pQBCqYGmfDNmmgKjmKov4Js8dmQVKOLN2-0AH4Q9pmyD1m8cPAsVPYQ7_XR30wHJBSUA550Zjqo3VmbJfw-81eZK3qXOtGpsPYWFjOCAmAccrOyuYGkvvYteeeqx8hhnnqXnPteqNL8fcPc9TIF0dsJ1aQequYkLNgJ4l6KNt9dLEVlxzPS0sIcuFAbvFI&sai=AMfl-YTK2rAOOxCdxYI7A9RauiJiJRfJJFDHWcIQA-nSKpqZILU-nZL8YlW1qFoXGcolhYS4OexRvSNC6ckxL92PpZfim-wCufzaEFZXMgRm9YKn1RK7Xw7ejaSQgVpfBVSrlSIlGtfR3bFD0NTfEyx_jEYv7S_AfsbPFz6xxhfY81TPl31SyCGzZ1QUc0C5sx-VTu9ISZcGaq7gPYw5VPMsxpDZLfhfqT9zVzd4Ke9X__wyswHOE2aEC_mMFICu6C6TUUaS6U5_AFNomZOgNmlM1ifkDjUfRFV7D-t818QAbP-vh02nLLs7Sypmt0fxJUCbkjfjjXPuWw&sig=Cg0ArKJSzNW2n0lCMUEx&fbs_aeid=%5Bgw_fbsaeid%5D&adurl=https://www.car.org/advocacy/politicsnu/LegislativeDay\n          - generic [ref=e1470]:\n            - generic [ref=e1471]: Related Articles\n            - link \"County Market Updates\" [ref=e1473] [cursor=pointer]:\n              - /url: /en/marketing/chartsandgraphs/marketupdate\n            - link \"Market at a Glance\" [ref=e1475] [cursor=pointer]:\n              - /url: /en/marketing/chartsandgraphs/marketglance\n            - link \"New Agent Sales & Marketing Training\" [ref=e1477] [cursor=pointer]:\n              - /url: /en/education/newagents\n        - generic [ref=e1478]:\n          - heading \"Featured Articles\" [level=2] [ref=e1479]\n          - generic [ref=e1480]:\n            - link \"podcast\" [ref=e1482] [cursor=pointer]:\n              - /url: /en/marketdata/podcast\n            - link \"housingdata\" [ref=e1489] [cursor=pointer]:\n              - /url: /en/marketdata/data/housingdata\n            - link \"interactive\" [ref=e1496] [cursor=pointer]:\n              - /url: /en/marketdata/interactive\n    - contentinfo [ref=e1502]:\n      - generic [ref=e1503]:\n        - generic [ref=e1504]:\n          - generic [ref=e1506]:\n            - generic [ref=e1507]\n            - generic [ref=e1522]\n          - generic [ref=e1538]:\n            - generic [ref=e1539]\n            - generic [ref=e1550]\n          - generic [ref=e1566]:\n            - generic [ref=e1567]\n            - generic [ref=e1576]\n            - generic [ref=e1585]\n          - generic [ref=e1597]:\n            - generic [ref=e1598]\n            - generic [ref=e1605]\n            - generic [ref=e1614]\n        - generic [ref=e1624]:\n          - link \"Twitter Link\" [ref=e1626] [cursor=pointer]:\n            - /url: https://twitter.com/carealtors\n            - generic [ref=e1627]\n          - link \"Facebook Link\" [ref=e1631] [cursor=pointer]:\n            - /url: https://www.facebook.com/CAREALTORS/\n            - generic [ref=e1632]\n          - link \"YouTube Link\" [ref=e1636] [cursor=pointer]:\n            - /url: https://www.youtube.com/user/CAREALTORS\n            - generic [ref=e1637]\n          - link \"Pinterest Link\" [ref=e1641] [cursor=pointer]:\n            - /url: https://www.pinterest.com/carealtors/\n            - generic [ref=e1642]\n          - link \"LinkedIn Link\" [ref=e1646] [cursor=pointer]:\n            - /url: https://www.linkedin.com/groups/133476/profile\n            - generic [ref=e1647]\n          - link \"Instagram Link\" [ref=e1651] [cursor=pointer]:\n            - /url: https://www.instagram.com/carealtors/\n            - generic [ref=e1652]\n        - generic [ref=e1655]:\n          - generic [ref=e1656]:\n            - generic [ref=e1657]\n            - generic [ref=e1670]\n          - text: ..\n    - generic:\n      - text: \"* * *  * *\"\n      - generic [ref=e1672] [cursor=pointer]: SUPPORT\n    - generic [ref=e1673]:\n      - generic [ref=e1726]:\n        - search [ref=e1727]:\n          - combobox \"Search\" [ref=e1730]\n        - button \"Search\" [ref=e1731] [cursor=pointer]:\n          - img \"Search\" [ref=e1733]\n      - heading \"Top Searches\" [level=5] [ref=e1679]\n      - generic [ref=e1680]:\n        - link \"zipForm\" [ref=e1682] [cursor=pointer]:\n          - /url: /SearchResults#q=zipForm\n        - link \"purchase agreement\" [ref=e1684] [cursor=pointer]:\n          - /url: /SearchResults#q=purchase%20agreement\n        - link \"lease agreement\" [ref=e1686] [cursor=pointer]:\n          - /url: /SearchResults#q=lease%20agreement\n        - link \"legal hotline\" [ref=e1688] [cursor=pointer]:\n          - /url: /SearchResults#q=legal%20hotline\n        - link \"docusign\" [ref=e1690] [cursor=pointer]:\n          - /url: /SearchResults#q=docusign\n        - link \"rental agreement\" [ref=e1692] [cursor=pointer]:\n          - /url: /SearchResults#q=rental%20agreement\n        - link \"rpa\" [ref=e1694] [cursor=pointer]:\n          - /url: /SearchResults#q=rpa\n        - link \"residential lease\" [ref=e1696] [cursor=pointer]:\n          - /url: /SearchResults#q=residential%20lease\n        - link \"legal Q&As\" [ref=e1698] [cursor=pointer]:\n          - /url: /SearchResults#q=legal%20Q%26As\n        - link \"license renewal\" [ref=e1700] [cursor=pointer]:\n          - /url: /SearchResults#q=license%20renewal\n        - link \"health insurance\" [ref=e1702] [cursor=pointer]:\n          - /url: /SearchResults#q=health%20insurance\n    - text: ;\n```\n### Events\n- New console entries: var/folders/wk/723q3_fs02n32w9xm40mlhg40000gn/T/playwright-mcp-output/console-2026-04-03T05-52-14-913Z.log#L2-L16"}]

{
  "tool": "mcp__playwright__browser_network_requests",
  "arguments": {
    "requestBody": false,
    "requestHeaders": false,
    "static": false
  }
}

Raw tool result

[{"type":"text","text":"### Result\n[GET] https://script.crazyegg.com/pages/data-scripts/0106/8429/site/www.car.org.json?t=1 => [200] \n[POST] https://analytics.google.com/g/collect?v=2&tid=G-01HXPW4QG9&gtm=45je6411v9123437585za200zd9123437585&_p=1775195535777&_gaz=1&gcd=13l3l3l3l1l1&npa=0&dma=0&cid=874715103.1775116038&ul=en-us&sr=1440x1200&uaa=arm&uab=64&uafvl=Chromium%3B146.0.7680.178%7CNot-A.Brand%3B24.0.0.0%7CGoogle%2520Chrome%3B146.0.7680.178&uamb=0&uam=&uap=macOS&uapv=15.3.2&uaw=0&are=1&frm=0&pscdl=noapi&_eu=AAAAAGQ&_s=1&tag_exp=0~115616986~115938466~115938468~116991817~117884344~118328142&sid=1775195535&sct=2&seg=0&dl=https%3A%2F%2Fwww.car.org%2Fmarketdata%2Fdata%2Fftbhai&dt=Housing%20Affordability%20Index%20-%20First-Time%20Buyer&en=page_view&_ss=1&_ee=1&tfd=833 => [204] \n[GET] https://www.car.org/coveo/rest/token?t=1775195535852 => [200] \n[GET] https://ep1.adtrafficquality.google/getconfig/sodar?sv=200&tid=gpt&tv=m202603260101&st=env&sjk=7224960779384853 => [200] \n[GET] https://securepubads.g.doubleclick.net/gampad/ads?pvsid=7224960779384853&correlator=1155988570666038&eid=95368454&output=ldjh&gdfp_req=1&vrg=202603260101&ptt=17&impl=fifs&iu_parts=3424804%2CCAR%2CCar_Desktop_Rectangle_1%2CCar_Desktop_Rectangle_2%2Cin-site&enc_prev_ius=%2F0%2F1%2F2%2C%2F0%2F1%2F3%2C%2F0%2F1%2F4&prev_iu_szs=300x600%2C300x600%2C1920x96&ifi=1&dids=div-gpt-ad-2150970-1~div-gpt-ad-2150970-2~div-gpt-ad-1625695679588-0&adfs=2697120041~1124573034~&sfv=1-0-45&sc=1&cookie=ID%3Da8e0a4f7470ddd27%3AT%3D1775116038%3ART%3D1775116038%3AS%3DALNI_MaxtWrI_M57MHMhBgmYNtZfZCN5CQ&gpic=UID%3D000013b3b89dad59%3AT%3D1775116038%3ART%3D1775116038%3AS%3DALNI_MYEMEMhSewPMYBR9YFHMqh7J_uXVg&abxe=1&dt=1775195535995&lmt=1775195535&adxs=1084%2C1084%2C-9&adys=385%2C400%2C-9&biw=1440&bih=1200&scr_x=0&scr_y=0&btvi=0%7C0%7C-1&ucis=1%7C2%7C3&oid=2&u_his=2&u_h=1200&u_w=1440&u_ah=1200&u_aw=1440&u_cd=30&u_sd=1&u_tz=-420&dmc=8&bc=31&nvt=1&uach=WyJtYWNPUyIsIjE1LjMuMiIsImFybSIsIiIsIjE0Ni4wLjc2ODAuMTc4IixudWxsLDAsbnVsbCwiNjQiLFtbIkNocm9taXVtIiwiMTQ2LjAuNzY4MC4xNzgiXSxbIk5vdC1BLkJyYW5kIiwiMjQuMC4wLjAiXSxbIkdvb2dsZSBDaHJvbWUiLCIxNDYuMC43NjgwLjE3OCJdXSwwXQ..&uas=3&url=https%3A%2F%2Fwww.car.org%2Fmarketdata%2Fdata%2Fftbhai&vis=1&psz=300x0%7C300x0%7C0x-1&msz=300x0%7C300x0%7C0x-1&fws=4%2C4%2C2&ohw=1440%2C1440%2C0&dlt=1775195535270&idt=692&adks=2945710925%2C626494426%2C30278090&frm=20&eo_id_str=ID%3Dda4be42db560c869%3AT%3D1775116038%3ART%3D1775116038%3AS%3DAA-AfjZ3ZwYiSmo5QrZ9U48xLi7T => [200] \n[GET] https://script.crazyegg.com/pages/data-scripts/0106/8429/sampling/www.car.org.json?t=1 => [200] \n[GET] https://securepubads.g.doubleclick.net/pcs/view?xai=AKAOjsveloJRnbQ1esLaOWU7SaUkMWFgPPzSLfw0iMXO5DWEtS0Kffmg9c83go8KG_H6fdvndP1xwjE3OIBVdjPwVTTkVdnXpChJUQHp9X5CwG9Wjr_F-v5MehLNxxEoePs-GP-u2Gq1QubxANRGdqk6SYdMCurubccS9HRytMnvw7tAqgIzcFLB3v3He9S_ijbxg9DTLoYegXB_nZlJrlopzXxtpDbP7WD6IV9zK_vxkab9q6J04lly8SFHROtRWcVmAH6tl6ucQ4fFqe-YWWaQVHRt3Q7_bWxQDPEMwVdXK3gyNz22LDzUxduibJ0fLv6k_lG9WkCzE06-22yneL-BSwZeCeWfshLefbdc7zffwaf22zLwiriOPqdaiD7PQ4b2aYoBXdwvPzrYOxFlJBH-UfPmgwRtkJNBCIeC&sai=AMfl-YSl-ZULOX0gACGur3o-1O0WTu-26Ud-bQHwAGy-TrsGxP0yrjng1l7iH36xSvD11m7Auj0UgixOytf0OMJINWzrtHUxiOFijQEqMwnT6Ek7mYG7ASX5rZd4WklY1fFgSDJnCcyDq4e2YaTsdsrhUMp8a3Hr4odix9TCrETT-Mn0jJpRfIcTW6_PT6u29r_bfkw5gJc1TQB94A5zatjb4arvP3fPuefS5Y5MTCAz6Tp6Rdo1DcPHvoGDPRN4AYpHKKXT07exwQJEl19fGSn83vBUvmcxtUWrOj_Gd1GGic-TH0klD5E5D_U16zAvNHo8zhZLdr9Ufw&sig=Cg0ArKJSzD-NW3HWSWjJEAE&uach_m=%5BUACH%5D&adurl= => [200] \n[GET] https://www.google.com/ads/measurement/l?ebcid=ALh7CaTj5-OCFI5BxaqZY6VeIv_dHw9_DxGaNEyDrMOCg7hZSfh31jFFp4autYXesUQBmec1LbUG-gUz6nrE9syjmV9u4VNOZQ => [FAILED] net::ERR_BLOCKED_BY_ORB\n[GET] https://securepubads.g.doubleclick.net/pcs/view?xai=AKAOjsvF5rxvLj1YJk4f-p5-0iJ1cSzLJsuK96bJ5DP7TZh0C9I-uqNDeVT2PV3O6rwPoqKQ1RlncCtbhisfkEDf9IxCm7NhXDdOv_LEtJ3wjhC4mY2EkgAQZmYvT4OitTLUsk78mijtn2ZcIZmk15ZX6v3Uf_x8Ox1xoms3761lfrfOLN0wKm0w9U1Ta9oRj7cq-5RYfSG90rYYxEB5BiiOgueewZARJ8hO6iy-5Q1GEFFhTL-8XV0uV4uQyEbE0kqxg0Oow1vfo1y6n9xwZUMLinJ8faTlVhb-c7K838J0MgsH7j7PggE4s3C3CrO8aZzhMfod4QivePM0W0iblN1aZYah68T2MFC9-HSIR95YDU3zyRtG_PYEohGJZTdmTy3WSjF7ARQdncizxSolz67kJA07hFybFB2naVOG&sai=AMfl-YTbB6O3sjmEvHQ0hXkNjIhGGnHqzZ39Fx2qK2hQH4dpdu3qHN_agJw6BjnVUQty_G9qKrWz_j4qDXbjMmjhV0UFnIuUz65MWoDT1EAX1yPG9MUmjjGNuwWjKSBw-7IdZWsEyPmM3Tv_HFoWxJQ0Il6qEKZivTrWxQwiVg3eSCNqpXMr339qOrhgQNazlbGb-rykxEmu8maH3wBFdu7IbnRy0GvZHxXY-_nnNLfG4FBL4QKrMwz4u3WDCl0E8kQEvE_-H7wThq36BovbYyeEdzFsvYKeXNh7H2XSG0CqDvgXGQfF0UJKrP-GvxS0bix8coahN_aQgg&sig=Cg0ArKJSzE25g4gxl1I3EAE&uach_m=%5BUACH%5D&adurl= => [200] \n[GET] https://www.google.com/ads/measurement/l?ebcid=ALh7CaS6r-WFkL-VAmF6q2AbzVYuffSTZ_4Jf_Tpq-hvtRR8IRkNYhQ9x2JnCsA4sR75-N3HeORBjljJHo4_2Icl1tbvPt1VmQ => [FAILED] net::ERR_BLOCKED_BY_ORB\n[GET] https://www.google.com/ads/measurement/l?ebcid=ALh7CaTj5-OCFI5BxaqZY6VeIv_dHw9_DxGaNEyDrMOCg7hZSfh31jFFp4autYXesUQBmec1LbUG-gUz6nrE9syjmV9u4VNOZQ => [FAILED] net::ERR_BLOCKED_BY_ORB\n[GET] https://www.google.com/ads/measurement/l?ebcid=ALh7CaS6r-WFkL-VAmF6q2AbzVYuffSTZ_4Jf_Tpq-hvtRR8IRkNYhQ9x2JnCsA4sR75-N3HeORBjljJHo4_2Icl1tbvPt1VmQ => [FAILED] net::ERR_BLOCKED_BY_ORB\n[GET] https://cdn.acsbapp.com/config/car.org/config.json => [200] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=fle-fetch-start2 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=colleague-executed&name=4 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=fle-fetch-start2 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=colleague-executed&name=4 => [204] \n[GET] https://securepubads.g.doubleclick.net/pcs/view?xai=AKAOjsvWDpAIvWEpLHW-UXg6yelc_NwKtAr_nAABAr9j5swo24bVTZ--FwD6dU4CGRc8Hoo83NkaV2XgOIUOQ97Duvp0EaTbbCRMoIVMbr1F8Qabh_pJWtr3voADLJYa9Ax6xZ1jzilzS7CpRK1NcSL2FdbR7bKD_YE5iL5HlgeNv6rlJMWpmUrvZIQpD-84LHPyBWdK2CboORzTcRambUAyiXHnu_yL5d-_lNun9z1Hivlh78BSU8mCaKjNdg-QBg8heYDcVxNui4dx25b83LJyy-wm8QMH3JySVcefVTmRkBfYQv4vZrJURtzG1UCoAFomfWyCiZV4ISH1cP0ngALTYYwQcNzFXy6QVP8TIQm4bW-VGxHkx3SEDGr5650cloVBrOUP0EoURhcTuRQbSbBuUvpPqvtITjO8GXVXcLY&sai=AMfl-YSQ-BVnGiCUZLzjleQ2HbT_pAmXPzxuX5Byrc3zo8plGNG1xS2XrDg5UkCpKjUSPIwRdx4IqnXyBFT7FXv1H2uLQ6gsiiJd1MY-AhjinQGT3cg2WaKd_qu83kT6K-SZks5GluZpwkpdeOQ6g6dy5GqcUyy9is2nlchay2efcs0YiP8185iHhEZ0dJeYHC_R7eOdEWkWsu-RkTh36gmBBkNwGvlfDHTsM3hMTsAo7q131L2KL8mVx8xrlg_WFRyw_YZ8k0XUqPjUbiJGwKaM7wn1YZOwWxzyiRkDSjBqTuwcSXAEP913flbofjny1OF4OgQ-gmgNTw&sig=Cg0ArKJSzDfm9NPm11yWEAE&uach_m=%5BUACH%5D&adurl= => [200] \n[GET] https://securepubads.g.doubleclick.net/pcs/view?xai=AKAOjsvRHs382MHnCkY-0EJKI_17Czkt2Po9-8koYH-L1hI5O3EX4kRnIhXOixGX6pzf4gyfqgoKXc2uw7THsTIx75vD4jUcc-inGr8RHfTOd1N3AdkXE9Zpddi-H1eAq5n7aXvs--vbebNYxxIVHAW_ZKfoprzTkQWJ2qxA0MQXOxtZiV3AmQ73CKZ6A5NZjEBwdG_dLn-gY3hgM9Qytbb36PY0u5WWh_ZaXvzs5j4JTl_UmpDrAvFeDNdtYM59COm0HrSJwzquUkUBGDrpt-Ae1kaTvQ3P9jD7_738CVMsHG1hQ-2J9c9dYiqaj5XmA8TcRPFugSwWXxx1Tg2cstmqYycblSta-7sAYaxSUMggu9nIbL3SoAH43waKRDMwUeJZ8xDC4GnfrSE4AfwXbTS1W_adZFGtF1H1R4DoYZs&sai=AMfl-YTh85i77Yi_aUyr3NL7yvU7k0HK95keCIJEOZ3pgl7krHkfwr2vjyZ6fDvRviCjIcRsO-jx-PP4EvtgdN9cxmXI7huYelNxsH4e-tFLqHyKfoULibWOi83fh9ikZkw4tesvybOA1ZMbCACkzzLob4cYEM2toC_qar2xyl_5bOy-xcsaG8INvNSgfzIONEsFq9F1Cqz4XYoQwVA47M7CPZ1t9yVBzVE5xoMr_h_yUfiESVIbJOxpPTer2YO8vs1Zr8TIUIU6FuL-X5Xp2D-PitOiQYPI5wwP_DcwAqt9xjrV3uKOkWZbm2SPRWIFgd-1zyhTonkklg&sig=Cg0ArKJSzOopjc1lPmLQEAE&uach_m=%5BUACH%5D&uach=WyJtYWNPUyIsIjE1LjMuMiIsImFybSIsIiIsIjE0Ni4wLjc2ODAuMTc4IixudWxsLDAsbnVsbCwiNjQiLFtbIkNocm9taXVtIiwiMTQ2LjAuNzY4MC4xNzgiXSxbIk5vdC1BLkJyYW5kIiwiMjQuMC4wLjAiXSxbIkdvb2dsZSBDaHJvbWUiLCIxNDYuMC43NjgwLjE3OCJdXSwwXQ%3D%3D&adurl= => [200] \n[POST] https://analytics.google.com/g/collect?v=2&tid=G-01HXPW4QG9&gtm=45je6411v9123437585za200zd9123437585&_p=1775195535777&_gaz=1&gcd=13l3l3l3l1l1&npa=0&dma=0&cid=874715103.1775116038&ul=en-us&sr=1440x1200&uaa=arm&uab=64&uafvl=Chromium%3B146.0.7680.178%7CNot-A.Brand%3B24.0.0.0%7CGoogle%2520Chrome%3B146.0.7680.178&uamb=0&uam=&uap=macOS&uapv=15.3.2&uaw=0&are=1&frm=0&pscdl=noapi&_eu=AAAAAGQ&_s=1&tag_exp=0~115616986~115938466~115938468~116991817~117884344~118328142&sid=1775195535&sct=2&seg=0&dl=https%3A%2F%2Fwww.car.org%2Fmarketdata%2Fdata%2Fftbhai&dt=Housing%20Affordability%20Index%20-%20First-Time%20Buyer&en=page_view&_ss=1&_ee=1&tfd=833 => [204] \n[GET] https://securepubads.g.doubleclick.net/pcs/view?xai=AKAOjsveloJRnbQ1esLaOWU7SaUkMWFgPPzSLfw0iMXO5DWEtS0Kffmg9c83go8KG_H6fdvndP1xwjE3OIBVdjPwVTTkVdnXpChJUQHp9X5CwG9Wjr_F-v5MehLNxxEoePs-GP-u2Gq1QubxANRGdqk6SYdMCurubccS9HRytMnvw7tAqgIzcFLB3v3He9S_ijbxg9DTLoYegXB_nZlJrlopzXxtpDbP7WD6IV9zK_vxkab9q6J04lly8SFHROtRWcVmAH6tl6ucQ4fFqe-YWWaQVHRt3Q7_bWxQDPEMwVdXK3gyNz22LDzUxduibJ0fLv6k_lG9WkCzE06-22yneL-BSwZeCeWfshLefbdc7zffwaf22zLwiriOPqdaiD7PQ4b2aYoBXdwvPzrYOxFlJBH-UfPmgwRtkJNBCIeC&sai=AMfl-YSl-ZULOX0gACGur3o-1O0WTu-26Ud-bQHwAGy-TrsGxP0yrjng1l7iH36xSvD11m7Auj0UgixOytf0OMJINWzrtHUxiOFijQEqMwnT6Ek7mYG7ASX5rZd4WklY1fFgSDJnCcyDq4e2YaTsdsrhUMp8a3Hr4odix9TCrETT-Mn0jJpRfIcTW6_PT6u29r_bfkw5gJc1TQB94A5zatjb4arvP3fPuefS5Y5MTCAz6Tp6Rdo1DcPHvoGDPRN4AYpHKKXT07exwQJEl19fGSn83vBUvmcxtUWrOj_Gd1GGic-TH0klD5E5D_U16zAvNHo8zhZLdr9Ufw&sig=Cg0ArKJSzD-NW3HWSWjJEAE&uach_m=%5BUACH%5D&adurl= => [200] \n[GET] https://securepubads.g.doubleclick.net/pcs/view?xai=AKAOjsvF5rxvLj1YJk4f-p5-0iJ1cSzLJsuK96bJ5DP7TZh0C9I-uqNDeVT2PV3O6rwPoqKQ1RlncCtbhisfkEDf9IxCm7NhXDdOv_LEtJ3wjhC4mY2EkgAQZmYvT4OitTLUsk78mijtn2ZcIZmk15ZX6v3Uf_x8Ox1xoms3761lfrfOLN0wKm0w9U1Ta9oRj7cq-5RYfSG90rYYxEB5BiiOgueewZARJ8hO6iy-5Q1GEFFhTL-8XV0uV4uQyEbE0kqxg0Oow1vfo1y6n9xwZUMLinJ8faTlVhb-c7K838J0MgsH7j7PggE4s3C3CrO8aZzhMfod4QivePM0W0iblN1aZYah68T2MFC9-HSIR95YDU3zyRtG_PYEohGJZTdmTy3WSjF7ARQdncizxSolz67kJA07hFybFB2naVOG&sai=AMfl-YTbB6O3sjmEvHQ0hXkNjIhGGnHqzZ39Fx2qK2hQH4dpdu3qHN_agJw6BjnVUQty_G9qKrWz_j4qDXbjMmjhV0UFnIuUz65MWoDT1EAX1yPG9MUmjjGNuwWjKSBw-7IdZWsEyPmM3Tv_HFoWxJQ0Il6qEKZivTrWxQwiVg3eSCNqpXMr339qOrhgQNazlbGb-rykxEmu8maH3wBFdu7IbnRy0GvZHxXY-_nnNLfG4FBL4QKrMwz4u3WDCl0E8kQEvE_-H7wThq36BovbYyeEdzFsvYKeXNh7H2XSG0CqDvgXGQfF0UJKrP-GvxS0bix8coahN_aQgg&sig=Cg0ArKJSzE25g4gxl1I3EAE&uach_m=%5BUACH%5D&adurl= => [200] \n[GET] https://securepubads.g.doubleclick.net/pcs/view?xai=AKAOjsvWDpAIvWEpLHW-UXg6yelc_NwKtAr_nAABAr9j5swo24bVTZ--FwD6dU4CGRc8Hoo83NkaV2XgOIUOQ97Duvp0EaTbbCRMoIVMbr1F8Qabh_pJWtr3voADLJYa9Ax6xZ1jzilzS7CpRK1NcSL2FdbR7bKD_YE5iL5HlgeNv6rlJMWpmUrvZIQpD-84LHPyBWdK2CboORzTcRambUAyiXHnu_yL5d-_lNun9z1Hivlh78BSU8mCaKjNdg-QBg8heYDcVxNui4dx25b83LJyy-wm8QMH3JySVcefVTmRkBfYQv4vZrJURtzG1UCoAFomfWyCiZV4ISH1cP0ngALTYYwQcNzFXy6QVP8TIQm4bW-VGxHkx3SEDGr5650cloVBrOUP0EoURhcTuRQbSbBuUvpPqvtITjO8GXVXcLY&sai=AMfl-YSQ-BVnGiCUZLzjleQ2HbT_pAmXPzxuX5Byrc3zo8plGNG1xS2XrDg5UkCpKjUSPIwRdx4IqnXyBFT7FXv1H2uLQ6gsiiJd1MY-AhjinQGT3cg2WaKd_qu83kT6K-SZks5GluZpwkpdeOQ6g6dy5GqcUyy9is2nlchay2efcs0YiP8185iHhEZ0dJeYHC_R7eOdEWkWsu-RkTh36gmBBkNwGvlfDHTsM3hMTsAo7q131L2KL8mVx8xrlg_WFRyw_YZ8k0XUqPjUbiJGwKaM7wn1YZOwWxzyiRkDSjBqTuwcSXAEP913flbofjny1OF4OgQ-gmgNTw&sig=Cg0ArKJSzDfm9NPm11yWEAE&uach_m=%5BUACH%5D&adurl= => [200] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=fle-fetch-start2 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=colleague-executed&name=4 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=fle-fetch-start2 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=colleague-executed&name=4 => [204] \n[GET] https://securepubads.g.doubleclick.net/pcs/view?xai=AKAOjsvRHs382MHnCkY-0EJKI_17Czkt2Po9-8koYH-L1hI5O3EX4kRnIhXOixGX6pzf4gyfqgoKXc2uw7THsTIx75vD4jUcc-inGr8RHfTOd1N3AdkXE9Zpddi-H1eAq5n7aXvs--vbebNYxxIVHAW_ZKfoprzTkQWJ2qxA0MQXOxtZiV3AmQ73CKZ6A5NZjEBwdG_dLn-gY3hgM9Qytbb36PY0u5WWh_ZaXvzs5j4JTl_UmpDrAvFeDNdtYM59COm0HrSJwzquUkUBGDrpt-Ae1kaTvQ3P9jD7_738CVMsHG1hQ-2J9c9dYiqaj5XmA8TcRPFugSwWXxx1Tg2cstmqYycblSta-7sAYaxSUMggu9nIbL3SoAH43waKRDMwUeJZ8xDC4GnfrSE4AfwXbTS1W_adZFGtF1H1R4DoYZs&sai=AMfl-YTh85i77Yi_aUyr3NL7yvU7k0HK95keCIJEOZ3pgl7krHkfwr2vjyZ6fDvRviCjIcRsO-jx-PP4EvtgdN9cxmXI7huYelNxsH4e-tFLqHyKfoULibWOi83fh9ikZkw4tesvybOA1ZMbCACkzzLob4cYEM2toC_qar2xyl_5bOy-xcsaG8INvNSgfzIONEsFq9F1Cqz4XYoQwVA47M7CPZ1t9yVBzVE5xoMr_h_yUfiESVIbJOxpPTer2YO8vs1Zr8TIUIU6FuL-X5Xp2D-PitOiQYPI5wwP_DcwAqt9xjrV3uKOkWZbm2SPRWIFgd-1zyhTonkklg&sig=Cg0ArKJSzOopjc1lPmLQEAE&uach_m=%5BUACH%5D&uach=WyJtYWNPUyIsIjE1LjMuMiIsImFybSIsIiIsIjE0Ni4wLjc2ODAuMTc4IixudWxsLDAsbnVsbCwiNjQiLFtbIkNocm9taXVtIiwiMTQ2LjAuNzY4MC4xNzgiXSxbIk5vdC1BLkJyYW5kIiwiMjQuMC4wLjAiXSxbIkdvb2dsZSBDaHJvbWUiLCIxNDYuMC43NjgwLjE3OCJdXSwwXQ%3D%3D&adurl= => [200] \n[GET] https://d.adroll.com/segment/UYZYRGJMPNHWFMA5LBBFAH/RRU6GKU2WVEEZHZRK6WLMK?adroll_fpc=455febdaa12cfc54bbf1e94353fdaaa2-1775116040688&pv=12516158591.261667&arrfrr=https%3A%2F%2Fwww.car.org%2Fmarketdata%2Fdata%2Fftbhai&cookie=UYZYRGJMPNHWFMA5LBBFAH%3A1%7CRRU6GKU2WVEEZHZRK6WLMK%3A1&adroll_s_ref=&keyw=&p0=1146&adroll_external_data=&adroll_version=2.0 => [200] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=colleague-executed&name=1 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAlgAWACYANgBGgD => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=colleague-executed&name=1 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAlgAWACYANgBGgD => [204] \n[GET] https://www.googletagservices.com/agrp/prod/model_person_country_code_US_person_region_code_43415f383037.json => [200] \n[GET] https://www.googletagservices.com/agrp/prod/model_person_country_code_US_person_region_code_43415f383037.json => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAESDk9wZXJhdGlvbkVycm9yGpgBc2hhcmVkU3RvcmFnZS53b3JrbGV0LmFkZE1vZHVsZSBpcyBkaXNhYmxlZCBiZWNhdXNlIGVpdGhlciBzaGFyZWRTdG9yYWdlIGlzIGRpc2FibGVkIG9yIGJvdGggc2hhcmVkU3RvcmFnZS5zZWxlY3RVUkwgYW5kIHByaXZhdGVBZ2dyZWdhdGlvbiBhcmUgZGlzYWJsZWQ%3D => [204] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAESDk9wZXJhdGlvbkVycm9yGpgBc2hhcmVkU3RvcmFnZS53b3JrbGV0LmFkZE1vZHVsZSBpcyBkaXNhYmxlZCBiZWNhdXNlIGVpdGhlciBzaGFyZWRTdG9yYWdlIGlzIGRpc2FibGVkIG9yIGJvdGggc2hhcmVkU3RvcmFnZS5zZWxlY3RVUkwgYW5kIHByaXZhdGVBZ2dyZWdhdGlvbiBhcmUgZGlzYWJsZWQ%3D => [204] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[POST] https://www.googleadservices.com/.well-known/private-aggregation/report-shared-storage => [200] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAESDk9wZXJhdGlvbkVycm9yGpgBc2hhcmVkU3RvcmFnZS53b3JrbGV0LmFkZE1vZHVsZSBpcyBkaXNhYmxlZCBiZWNhdXNlIGVpdGhlciBzaGFyZWRTdG9yYWdlIGlzIGRpc2FibGVkIG9yIGJvdGggc2hhcmVkU3RvcmFnZS5zZWxlY3RVUkwgYW5kIHByaXZhdGVBZ2dyZWdhdGlvbiBhcmUgZGlzYWJsZWQ%3D => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAIgATpBbW9kZWxfcGVyc29uX2NvdW50cnlfY29kZV9VU19wZXJzb25fcmVnaW9uX2NvZGVfNDM0MTVmMzgzMDM3Lmpzb25IyAFSGkNLNncxTnItMEpNREZkWGl1QWdkMFAwWGFnWjYImBYQrOapBBjS8z0g5qOnBCgCMAI4AV0zM3M%2FYOegDOoBCAhkGGQgZChfggIICAIQARgBIAE%3D => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAIgATpBbW9kZWxfcGVyc29uX2NvdW50cnlfY29kZV9VU19wZXJzb25fcmVnaW9uX2NvZGVfNDM0MTVmMzgzMDM3Lmpzb25IyAFSGkNLLXcxTnItMEpNREZkWGl1QWdkMFAwWGFnWjYImBYQrOapBBjS8z0g5qOnBCgCMAI4AV0zM3M%2FYOegDOoBCAhkGGQgZChfggIICAIQARgBIAE%3D => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAESDk9wZXJhdGlvbkVycm9yGpgBc2hhcmVkU3RvcmFnZS53b3JrbGV0LmFkZE1vZHVsZSBpcyBkaXNhYmxlZCBiZWNhdXNlIGVpdGhlciBzaGFyZWRTdG9yYWdlIGlzIGRpc2FibGVkIG9yIGJvdGggc2hhcmVkU3RvcmFnZS5zZWxlY3RVUkwgYW5kIHByaXZhdGVBZ2dyZWdhdGlvbiBhcmUgZGlzYWJsZWQ%3D => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAIgATpBbW9kZWxfcGVyc29uX2NvdW50cnlfY29kZV9VU19wZXJzb25fcmVnaW9uX2NvZGVfNDM0MTVmMzgzMDM3Lmpzb25IyAFSGkNLNncxTnItMEpNREZkWGl1QWdkMFAwWGFnWjYImBYQrOapBBjS8z0g5qOnBCgCMAI4AV0zM3M%2FYOegDOoBCAhkGGQgZChfggIICAIQARgBIAE%3D => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAIgATpBbW9kZWxfcGVyc29uX2NvdW50cnlfY29kZV9VU19wZXJzb25fcmVnaW9uX2NvZGVfNDM0MTVmMzgzMDM3Lmpzb25IyAFSGkNLLXcxTnItMEpNREZkWGl1QWdkMFAwWGFnWjYImBYQrOapBBjS8z0g5qOnBCgCMAI4AV0zM3M%2FYOegDOoBCAhkGGQgZChfggIICAIQARgBIAE%3D => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=colleague-executed&name=1 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAlgAWACYANgBGgD => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=colleague-executed&name=1 => [204] \n[GET] https://pagead2.googlesyndication.com/pagead/gen_204?id=av-js&type=reach&proto=CAlgAWACYANgBGgD => [204] \n[GET] https://pagead2.googlesyndication.com/pcs/activeview?xai=AKAOjssmQukzn9VSdfF213SX2CjMA1x-veesomPjT4XjHqMk30t9yFJEMZ-eUV-quvb0lW8RpmzzoATnJwCD8-Q4u1qj6CdT0FE7hudIApn3rWdsAjH7vpGR3dv88tM-nEjd6jPYJShgcMD8Es-1UxqWqgNrovBKDz9VQdD3kfFv1sEAZmk5YKnGr2RKBVWE&sig=Cg0ArKJSzLiQYkIJWQKrEAE&id=lidar2&mcvt=1001&p=385,1084,985,1384&tm=1045.7999999970198&tu=44.5&mtos=1001,1001,1001,1001,1001&tos=1001,0,0,0,0&v=20260323&bin=7&avms=nio&bs=1440,1200&mc=1&vu=1&app=0&itpl=3&adk=2945710925&rs=4&la=0&cr=0&uach=WyJtYWNPUyIsIjE1LjMuMiIsImFybSIsIiIsIjE0Ni4wLjc2ODAuMTc4IixudWxsLDAsbnVsbCwiNjQiLFtbIkNocm9taXVtIiwiMTQ2LjAuNzY4MC4xNzgiXSxbIk5vdC1BLkJyYW5kIiwiMjQuMC4wLjAiXSxbIkdvb2dsZSBDaHJvbWUiLCIxNDYuMC43NjgwLjE3OCJdXSwwXQ%3D%3D&vs=4&r=v&co=7112833600&rst=1775195536109&rpt=412&isd=0&lsd=0&met=mue&wmsd=0&pbe=0&fle=0&vae=0&spb=0&sfl=0&ffslot=0&reach=8&io2=0 => [200] "}]

{
  "tool": "mcp__playwright__browser_evaluate",
  "arguments": {
    "function": "() => ({ tables: Array.from(document.querySelectorAll('table')).map((t, i) => ({ i, id: t.id || null, className: t.className || null, text: t.innerText.slice(0, 500) })), iframes: Array.from(document.querySelectorAll('iframe')).map((f, i) => ({ i, src: f.src || null, title: f.title || null })), scripts: Array.from(document.scripts).map((s, i) => ({ i, src: s.src || null, text: (s.textContent || '').slice(0, 300) })).filter(s => s.src || s.text.trim()) })"
  }
}

Raw tool result

[{"type":"text","text":"### Result\n{\n  \"tables\": [\n    {\n      \"i\": 0,\n      \"id\": null,\n      \"className\": null,\n      \"text\": \"Q42025\\n\\n\\t\\n\\nFirst Time Buyer Housing Affordability Index\\n\\n\\n\\n\\nSTATE/REGION/COUNTY\\n\\n\\t\\n\\nQ42025\\n\\n\\t\\n\\nQ32025\\n\\n\\t\\n\\n \\n\\n\\t\\n\\nQ42024\\n\\n\\t\\n\\n\\t\\n\\nMedian Home Price\\n\\n\\t\\n\\nMonthly Payment Including Taxes & Insurance\\n\\n\\t\\n\\nMinimum Qualifying Income\\n\\n\\n\\n\\n \\n\\n\\t\\n\\n \\n\\n\\t\\n\\n \\n\\n\\t\\n\\n \\n\\n\\t\\n\\n \\n\\n\\t\\n\\n \\n\\n\\t\\n\\n \\n\\n\\t\\n\\n \\n\\n\\t\\n\\n \\n\\n\\n\\n\\nCA SFH (SAAR)\\n\\n\\t\\n\\n33\\n\\n\\t\\n\\n30\\n\\n\\t\\n\\n \\n\\n\\t\\n\\n29\\n\\n\\t\\n\\n \\n\\n\\t\\n\\n$738,910\\n\\n\\t\\n\\n$4,680\\n\\n\\t\\n\\n$140,400\\n\\n\\n\\n\\nCA Condo/Townhomes\\n\\n\\t\\n\\n46\\n\\n\\t\\n\\n44\\n\\n\\t\\n\\n \\n\\n\\t\\n\\n41\\n\\n\\t\\n\\n \\n\\n\\t\\n\\n$552,500\\n\\n\\t\\n\\n$3,500\\n\\n\\t\\n\\n$105,000\\n\\n\\n\\n\\nLos Angeles Metropolitan Area\\n\\n\\t\\n\\n32\\n\\n\\t\"\n    }\n  ],\n  \"iframes\": [\n    {\n      \"i\": 0,\n      \"src\": \"https://www.google.com/recaptcha/api2/anchor?ar=1&k=6LcpVFEUAAAAAGs59dVqMiwWKlQB0vhk6GwdR1XU&co=aHR0cHM6Ly93d3cuY2FyLm9yZzo0NDM.&hl=en&v=kUYUkUlSyqkjTSMaN2w3RaOh&size=normal&anchor-ms=20000&execute-ms=30000&cb=jow4k2midwye\",\n      \"title\": \"reCAPTCHA\"\n    },\n    {\n      \"i\": 1,\n      \"src\": null,\n      \"title\": null\n    },\n    {\n      \"i\": 2,\n      \"src\": null,\n      \"title\": \"3rd party ad content\"\n    },\n    {\n      \"i\": 3,\n      \"src\": null,\n      \"title\": \"3rd party ad content\"\n    },\n    {\n      \"i\": 4,\n      \"src\": null,\n      \"title\": null\n    },\n    {\n      \"i\": 5,\n      \"src\": null,\n      \"title\": null\n    },\n    {\n      \"i\": 6,\n      \"src\": null,\n      \"title\": null\n    },\n    {\n      \"i\": 7,\n      \"src\": \"https://www.google.com/recaptcha/api2/aframe\",\n      \"title\": null\n    },\n    {\n      \"i\": 8,\n      \"src\": \"https://www.google.com/recaptcha/api2/bframe?hl=en&v=kUYUkUlSyqkjTSMaN2w3RaOh&k=6LcpVFEUAAAAAGs59dVqMiwWKlQB0vhk6GwdR1XU&bft=0dAFcWeA6oWGqdq5a6WlyUXYDNkKAD45PpHMHkhIKto1n06gT8BmZhkmnr1a358XE3W16DQlKhl_TgGXttZrtSsl94VsI7Cj9kPg\",\n      \"title\": \"recaptcha challenge expires in two minutes\"\n    },\n    {\n      \"i\": 9,\n      \"src\": null,\n      \"title\": \"CrazyEgg Tracking iframe\"\n    }\n  ],\n  \"scripts\": [\n    {\n      \"i\": 0,\n      \"src\": \"https://connect.facebook.net/en_US/fbevents.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 1,\n      \"src\": \"https://pi.pardot.com/pd.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 2,\n      \"src\": \"https://s.adroll.com/j/UYZYRGJMPNHWFMA5LBBFAH/roundtrip.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 3,\n      \"src\": \"https://script.crazyegg.com/pages/versioned/common-scripts/fa37f361d63404d4cc11b194e70c4ff0.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 4,\n      \"src\": \"https://www.gstatic.com/recaptcha/releases/kUYUkUlSyqkjTSMaN2w3RaOh/recaptcha__en.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 5,\n      \"src\": null,\n      \"text\": \"\\n        //<![CDATA[\\n        function loadCSS(e, t, n) {\\n            \\\"use strict\\\"; var i = window.document.createElement(\\\"link\\\"); var o = t || window.document.getElementsByTagName(\\\"script\\\")[0]; i.rel = \\\"stylesheet\\\"; i.href = e; i.media = \\\"only x\\\"; o.parentNode.insertBefore(i, o); setTimeout(function\"\n    },\n    {\n      \"i\": 6,\n      \"src\": \"https://use.typekit.net/dzu5fps.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 7,\n      \"src\": null,\n      \"text\": \"\\n        try {\\n            Typekit.load({ async: true });\\n        } catch (e) { }\\n    \"\n    },\n    {\n      \"i\": 8,\n      \"src\": \"https://www.car.org/scripts/lib/jquery.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 9,\n      \"src\": \"https://www.car.org/scripts/lib/jquery-ui.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 10,\n      \"src\": \"https://www.car.org/scripts/lib/modernizr-2.8.3.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 11,\n      \"src\": \"https://www.car.org/scripts/lib/datatables.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 12,\n      \"src\": \"https://www.car.org/scripts/lib/owl.carousel.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 13,\n      \"src\": \"https://www.google.com/recaptcha/api.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 14,\n      \"src\": \"https://www.car.org/scripts/accordion.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 15,\n      \"src\": \"https://www.car.org/scripts/accountReq.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 16,\n      \"src\": \"https://www.car.org/scripts/contactus.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 17,\n      \"src\": \"https://www.car.org/scripts/Infographic_page.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 18,\n      \"src\": \"https://www.car.org/scripts/video_page.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 19,\n      \"src\": \"https://www.car.org/scripts/localdata.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 20,\n      \"src\": \"https://www.car.org/scripts/search.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 21,\n      \"src\": \"https://www.car.org/scripts/events.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 22,\n      \"src\": \"https://www.car.org/scripts/magazine_page.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 23,\n      \"src\": \"https://www.car.org/scripts/home.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 24,\n      \"src\": \"https://www.car.org/scripts/jquery.validate.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 25,\n      \"src\": \"https://www.car.org/scripts/jquery.validate.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 26,\n      \"src\": \"https://www.car.org/scripts/jquery.validate.unobtrusive.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 27,\n      \"src\": \"https://www.car.org/scripts/jquery.validate.unobtrusive.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 28,\n      \"src\": \"https://www.car.org/scripts/pwstrength-bootstrap.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 29,\n      \"src\": \"https://www.car.org/scripts/feedback.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 30,\n      \"src\": \"https://www.car.org/scripts/responsive-tables.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 31,\n      \"src\": \"https://www.car.org/scripts/passhelper.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 32,\n      \"src\": \"https://script.crazyegg.com/pages/scripts/0106/8429.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 33,\n      \"src\": \"https://www.googletagmanager.com/gtag/js?id=G-01HXPW4QG9\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 34,\n      \"src\": null,\n      \"text\": \"\\n    window.dataLayer = window.dataLayer || [];\\n    function gtag() { dataLayer.push(arguments); }\\n    gtag('js', new Date());\\n    gtag('config', 'G-01HXPW4QG9');\\n\"\n    },\n    {\n      \"i\": 35,\n      \"src\": \"https://www.googletagservices.com/tag/js/gpt.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 36,\n      \"src\": null,\n      \"text\": \"\\n\\t  var gptadslots = [];\\n\\t  var googletag = googletag || {cmd:[]};\\n        \"\n    },\n    {\n      \"i\": 37,\n      \"src\": null,\n      \"text\": \"\\n\\t  googletag.cmd.push(function() {\\n\\t\\t//Adslot 1 declaration\\n\\t\\tgptadslots.push(googletag.defineSlot('/3424804/CAR/Car_Desktop_Rectangle_1', [[300,600]], 'div-gpt-ad-2150970-1')\\n\\t.addService(googletag.pubads()));\\n\\t\\t//Adslot 2 declaration\\n\\t\\tgptadslots.push(googletag.defineSlot('/3424804/CAR/Car_Deskto\"\n    },\n    {\n      \"i\": 38,\n      \"src\": \"https://securepubads.g.doubleclick.net/tag/js/gpt.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 39,\n      \"src\": \"https://www.car.org/scripts/reimagine-AdRoll.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 40,\n      \"src\": null,\n      \"text\": \"\\n        window.googletag = window.googletag || { cmd: [] };\\n        googletag.cmd.push(function () {\\n            googletag.defineSlot('/3424804/CAR/in-site', [1920, 96], 'div-gpt-ad-1625695679588-0').addService(googletag.pubads());\\n            googletag.pubads().enableSingleRequest();\\n            g\"\n    },\n    {\n      \"i\": 41,\n      \"src\": \"https://securepubads.g.doubleclick.net/pagead/managed/js/gpt/m202603260101/pubads_impl.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 42,\n      \"src\": \"https://static.cloud.coveo.com/searchui/v2.10110/js/FacetsMobileMode.min__4e9032d9a7117b17aab0.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 43,\n      \"src\": \"https://static.cloud.coveo.com/searchui/v2.10110/js/Searchbox.min__4e9032d9a7117b17aab0.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 44,\n      \"src\": \"https://static.cloud.coveo.com/coveoforsitecore/ui/v0.61.2/js/CoveoForSitecoreConfigureSearchHub.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 45,\n      \"src\": \"https://static.cloud.coveo.com/coveoforsitecore/ui/v0.61.2/js/CoveoForSitecoreBindWithUserContext.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 46,\n      \"src\": \"https://static.cloud.coveo.com/coveoforsitecore/ui/v0.61.2/js/CoveoForSitecoreExpressions.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 47,\n      \"src\": \"https://pi.pardot.com/analytics?ver=3&visitor_id=953206759&visitor_id_sign=f402ff7ea2a3d4e314cb966523d7aadf64c42abe4bd9d7d427dcd3441806ef17a65787c88dfdbe743990bba24137488581b396bd&pi_opt_in=&campaign_id=21764&account_id=86202&title=Housing%20Affordability%20Index%20-%20First-Time%20Buyer&url=https%3A%2F%2Fwww.car.org%2Fmarketdata%2Fdata%2Fftbhai&referrer=\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 48,\n      \"src\": \"https://d.adroll.com/consent/check/UYZYRGJMPNHWFMA5LBBFAH?adroll_fpc=455febdaa12cfc54bbf1e94353fdaaa2-1775116040688&pv=12516158591.261667&arrfrr=https%3A%2F%2Fwww.car.org%2Fmarketdata%2Fdata%2Fftbhai&_s=4a7f12ab92773b334fd478a77e6f8c73&_b=2\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 49,\n      \"src\": \"https://subscribe.car.org/analytics?conly=true&visitor_id=953206759&visitor_id_sign=f402ff7ea2a3d4e314cb966523d7aadf64c42abe4bd9d7d427dcd3441806ef17a65787c88dfdbe743990bba24137488581b396bd&pi_opt_in=&campaign_id=21764&account_id=86202&title=Housing%20Affordability%20Index%20-%20First-Time%20Buyer&url=https%3A%2F%2Fwww.car.org%2Fmarketdata%2Fdata%2Fftbhai&referrer=\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 50,\n      \"src\": \"https://s.adroll.com/j/sendrolling.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 51,\n      \"src\": null,\n      \"text\": \"\\n    if (location.pathname.endsWith(\\\"/login\\\")) {\\n        document.getElementById(\\\"signInButton\\\").click();\\n    }\\n\"\n    },\n    {\n      \"i\": 52,\n      \"src\": null,\n      \"text\": \"\\n    if (location.pathname.endsWith(\\\"/login\\\")) {\\n        document.getElementById(\\\"signInButton\\\").click();\\n    }\\n\"\n    },\n    {\n      \"i\": 53,\n      \"src\": null,\n      \"text\": \"\\n            jQuery(document).ready(function() {\\n                (function(jQuery, window, document, undefined) {\\n                    'use strict';\\n\\n                    function customCheckBox(element, options) {\\n                        this._element = jQuery(element);\\n                        this._\"\n    },\n    {\n      \"i\": 54,\n      \"src\": null,\n      \"text\": \"\\n    var isValid = true;\\n    var recaptchachecked = false;\\n    function recaptchaCallback() {\\n        //If we managed to get into this function it means that the user checked the checkbox.\\n        $('#captca-error').html(\\\"\\\");\\n        recaptchachecked = true;\\n    }\\n\\n    function checkCaptcha() {\\n    \"\n    },\n    {\n      \"i\": 55,\n      \"src\": null,\n      \"text\": \"\\n    jQuery(document)\\n        .ready(function () {\\n            var pageLockMsg = '';\\n            if (pageLockMsg != '') {\\n                share_mobile();\\n                $(window)\\n                    .on('resize scroll',\\n                        function () {\\n                            share_mobile(\"\n    },\n    {\n      \"i\": 56,\n      \"src\": null,\n      \"text\": \"\\n        (function () { var s = document.createElement('script'), e = !document.body ? document.querySelector('head') : document.body; s.src = 'https://acsbapp.com/apps/app/dist/js/app.js'; s.async = true; s.onload = function () { acsbJS.init({ statementLink: '', footerHtml: '', hideMobile: false, h\"\n    },\n    {\n      \"i\": 57,\n      \"src\": null,\n      \"text\": \"\\n    document.addEventListener(\\\"CoveoSearchEndpointInitialized\\\", function() {\\n        var searchboxElement = document.getElementById(\\\"_185F67BF-1D73-4AEE-9564-FDE5B0EB24D9\\\");\\n        searchboxElement.addEventListener(\\\"CoveoComponentInitialized\\\", function() {\\n            CoveoForSitecore.initSearchbo\"\n    },\n    {\n      \"i\": 58,\n      \"src\": null,\n      \"text\": \"\\n    document.addEventListener(\\\"CoveoSearchEndpointInitialized\\\", function() {\\n        var componentId = \\\"_185F67BF-1D73-4AEE-9564-FDE5B0EB24D9\\\";\\n        var componentElement = document.getElementById(componentId);\\n\\n        function showError(error) {\\n                console.error(error);\\n        }\\n\\n\"\n    },\n    {\n      \"i\": 59,\n      \"src\": null,\n      \"text\": \"\\n        document.addEventListener(\\\"CoveoSearchEndpointInitialized\\\", function() {\\n            var externalComponentsSection = document.getElementById(\\\"_B7192B03-BB9B-4086-9FCE-D2169C61AF1C\\\");\\n                 CoveoForSitecore.initExternalComponentsSection(externalComponentsSection);\\n        });\\n    \"\n    },\n    {\n      \"i\": 60,\n      \"src\": \"https://www.car.org/scripts/lib/jquery-ui.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 61,\n      \"src\": \"https://static.cloud.coveo.com/searchui/v2.10110/js/CoveoJsSearch.Lazy.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 62,\n      \"src\": \"https://static.cloud.coveo.com/coveoforsitecore/ui/v0.61.2/js/CoveoForSitecore.Lazy.min.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 63,\n      \"src\": \"https://static.cloud.coveo.com/searchui/v2.10110/js/cultures/en.js\",\n      \"text\": \"\"\n    },\n    {\n      \"i\": 64,\n      \"src\": null,\n      \"text\": \"\\n        var endpointConfiguration = {\\n            itemUri: \\\"sitecore://web/{7B87FB2E-179C-48C7-B703-429F691CFD38}?lang=en&amp;ver=34\\\",\\n            siteName: \\\"car\\\",\\n            restEndpointUri: \\\"/coveo/rest\\\"\\n        };\\n        if (typeof (CoveoForSitecore) !== \\\"undefined\\\") {\\n            CoveoForSite\"\n    },\n    {\n      \"i\": 65,\n      \"src\": null,\n      \"text\": \"\\njQuery(document).ready(function() {\\n_car_global.article.init();\\n});\\n\"\n    },\n    {\n      \"i\": 66,\n      \"src\": null,\n      \"text\": \"\\n    piAId = '86202';\\n    piCId = '21764';\\n    piHostname = 'pi.pardot.com';\\n\\n    (function () {\\n        function async_load() {\\n            var s = document.createElement('script'); s.type = 'text/javascript';\\n            s.src = ('https:' == document.location.protocol ? 'https://pi' : 'http://cdn'\"\n    },\n    {\n      \"i\": 67,\n      \"src\": \"https://acsbapp.com/apps/app/dist/js/app.js\",\n      \"text\": \"\"\n    }\n  ]\n}\n### Ran Playwright code\n```js\nawait page.evaluate('() => ({ tables: Array.from(document.querySelectorAll(\\'table\\')).map((t, i) => ({ i, id: t.id || null, className: t.className || null, text: t.innerText.slice(0, 500) })), iframes: Array.from(document.querySelectorAll(\\'iframe\\')).map((f, i) => ({ i, src: f.src || null, title: f.title || null })), scripts: Array.from(document.scripts).map((s, i) => ({ i, src: s.src || null, text: (s.textContent || \\'\\').slice(0, 300) })).filter(s => s.src || s.text.trim()) })');\n```"}]

{
  "tool": "mcp__playwright__browser_console_messages",
  "arguments": {
    "level": "info",
    "all": true
  }
}

Raw tool result

[{"type":"text","text":"### Result\nTotal messages: 5 (Errors: 3, Warnings: 2)\n\n[WARNING] <meta name=\"apple-mobile-web-app-capable\" content=\"yes\"> is deprecated. Please include <meta name=\"mobile-web-app-capable\" content=\"yes\"> @ https://www.car.org/marketdata/data/ftbhai:0\n[WARNING] [GPT] Attempted to load GPT from both standard and limited ads domains.\nhttps://goo.gle/gpt-message#153 @ https://securepubads.g.doubleclick.net/pagead/managed/js/gpt/m202603260101/pubads_impl.js:18\n[ERROR] Attestation check for Shared Storage on https://www.googleadservices.com failed. @ https://www.googleadservices.com/pagead/managed/js/activeview/current/reach_worklet.html:0\n[ERROR] Attestation check for Shared Storage on https://www.googleadservices.com failed. @ https://www.googleadservices.com/pagead/managed/js/activeview/current/reach_worklet.html:0\nTypeError: Cannot read properties of undefined (reading 'top')\n    at Object.initMoveAdAndLinks (https://www.car.org/scripts/home.js:1464:117)\n    at https://www.car.org/scripts/home.js:1402:36\n    at dispatch (https://www.car.org/scripts/lib/datatables.min.js:15:12444)\n    at r.handle (https://www.car.org/scripts/lib/datatables.min.js:15:9173)\n    at c (https://www.car.org/cdn-cgi/scripts/7d0fa10a/cloudflare-static/rocket-loader.min.js:1:9405)\n    at l (https://www.car.org/cdn-cgi/scripts/7d0fa10a/cloudflare-static/rocket-loader.min.js:1:1105)\n    at t.simulateStateAfterDeferScriptsActivation (https://www.car.org/cdn-cgi/scripts/7d0fa10a/cloudflare-static/rocket-loader.min.js:1:8728)\n    at Object.callback (https://www.car.org/cdn-cgi/scripts/7d0fa10a/cloudflare-static/rocket-loader.min.js:1:3075)\n    at t.run (https://www.car.org/cdn-cgi/scripts/7d0fa10a/cloudflare-static/rocket-loader.min.js:1:11340)\n    at P (https://www.car.org/cdn-cgi/scripts/7d0fa10a/cloudflare-static/rocket-loader.min.js:1:3128)"}]

{
  "tool": "mcp__playwright__browser_evaluate",
  "arguments": {
    "function": "() => {\n  const table = document.querySelector('table');\n  if (!table) return { error: 'No table found' };\n\n  const rows = Array.from(table.querySelectorAll('tr')).map((tr, rowIndex) => {\n    const cells = Array.from(tr.children).filter(el => /^(TH|TD)$/.test(el.tagName)).map((cell, cellIndex) => ({\n      rowIndex,\n      cellIndex,\n      tag: cell.tagName,\n      text: cell.innerText.replace(/\\u00a0/g, ' ').replace(/\\s+/g, ' ').trim(),\n      colspan: cell.colSpan || 1,\n      rowspan: cell.rowSpan || 1,\n      className: cell.className || ''\n    }));\n    return cells;\n  }).filter(r => r.length);\n\n  return {\n    rowCount: rows.length,\n    rows: rows.slice(0, 20),\n    allRowLengths: rows.map(r => r.length)\n  };\n}"
  }
}

Raw tool result

[{"type":"text","text":"### Result\n{\n  \"rowCount\": 69,\n  \"rows\": [\n    [\n      {\n        \"rowIndex\": 0,\n        \"cellIndex\": 0,\n        \"tag\": \"TD\",\n        \"text\": \"Q42025\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 0,\n        \"cellIndex\": 1,\n        \"tag\": \"TD\",\n        \"text\": \"First Time Buyer Housing Affordability Index\",\n        \"colspan\": 8,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      }\n    ],\n    [\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 0,\n        \"tag\": \"TD\",\n        \"text\": \"STATE/REGION/COUNTY\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 1,\n        \"tag\": \"TD\",\n        \"text\": \"Q42025\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 2,\n        \"tag\": \"TD\",\n        \"text\": \"Q32025\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 3,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 4,\n        \"tag\": \"TD\",\n        \"text\": \"Q42024\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 5,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 6,\n        \"tag\": \"TD\",\n        \"text\": \"Median Home Price\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 7,\n        \"tag\": \"TD\",\n        \"text\": \"Monthly Payment Including Taxes & Insurance\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 1,\n        \"cellIndex\": 8,\n        \"tag\": \"TD\",\n        \"text\": \"Minimum Qualifying Income\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      }\n    ],\n    [\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 0,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 1,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 2,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 3,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 4,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 5,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 6,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 7,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 2,\n        \"cellIndex\": 8,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      }\n    ],\n    [\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 0,\n        \"tag\": \"TD\",\n        \"text\": \"CA SFH (SAAR)\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 1,\n        \"tag\": \"TD\",\n        \"text\": \"33\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 2,\n        \"tag\": \"TD\",\n        \"text\": \"30\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 3,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 4,\n        \"tag\": \"TD\",\n        \"text\": \"29\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 5,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 6,\n        \"tag\": \"TD\",\n        \"text\": \"$738,910\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 7,\n        \"tag\": \"TD\",\n        \"text\": \"$4,680\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 3,\n        \"cellIndex\": 8,\n        \"tag\": \"TD\",\n        \"text\": \"$140,400\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      }\n    ],\n    [\n      {\n        \"rowIndex\": 4,\n        \"cellIndex\": 0,\n        \"tag\": \"TD\",\n        \"text\": \"CA Condo/Townhomes\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 4,\n        \"cellIndex\": 1,\n        \"tag\": \"TD\",\n        \"text\": \"46\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 4,\n        \"cellIndex\": 2,\n        \"tag\": \"TD\",\n        \"text\": \"44\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 4,\n        \"cellIndex\": 3,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n   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  \"tag\": \"TD\",\n        \"text\": \"Solano\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 18,\n        \"cellIndex\": 1,\n        \"tag\": \"TD\",\n        \"text\": \"51\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 18,\n        \"cellIndex\": 2,\n        \"tag\": \"TD\",\n        \"text\": \"47\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 18,\n        \"cellIndex\": 3,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 18,\n        \"cellIndex\": 4,\n        \"tag\": \"TD\",\n        \"text\": \"48\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 18,\n        \"cellIndex\": 5,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 18,\n        \"cellIndex\": 6,\n        \"tag\": \"TD\",\n        \"text\": \"$493,000\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 18,\n        \"cellIndex\": 7,\n        \"tag\": \"TD\",\n        \"text\": \"$3,130\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 18,\n        \"cellIndex\": 8,\n        \"tag\": \"TD\",\n        \"text\": \"$93,900\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      }\n    ],\n    [\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 0,\n        \"tag\": \"TD\",\n        \"text\": \"Sonoma\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 1,\n        \"tag\": \"TD\",\n        \"text\": \"37\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 2,\n        \"tag\": \"TD\",\n        \"text\": \"36\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 3,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 4,\n        \"tag\": \"TD\",\n        \"text\": \"35\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 5,\n        \"tag\": \"TD\",\n        \"text\": \"\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 6,\n        \"tag\": \"TD\",\n        \"text\": \"$697,850\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 7,\n        \"tag\": \"TD\",\n        \"text\": \"$4,420\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      },\n      {\n        \"rowIndex\": 19,\n        \"cellIndex\": 8,\n        \"tag\": \"TD\",\n        \"text\": \"$132,600\",\n        \"colspan\": 1,\n        \"rowspan\": 1,\n        \"className\": \"\"\n      }\n    ]\n  ],\n  \"allRowLengths\": [\n    2,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9,\n    9\n  ]\n}\n### Ran Playwright code\n```js\nawait page.evaluate('() => {\\n  const table = document.querySelector(\\'table\\');\\n  if (!table) return { error: \\'No table found\\' };\\n\\n  const rows = Array.from(table.querySelectorAll(\\'tr\\')).map((tr, rowIndex) => {\\n    const cells = Array.from(tr.children).filter(el => /^(TH|TD)$/.test(el.tagName)).map((cell, cellIndex) => ({\\n      rowIndex,\\n      cellIndex,\\n      tag: cell.tagName,\\n      text: cell.innerText.replace(/\\\\u00a0/g, \\' \\').replace(/\\\\s+/g, \\' \\').trim(),\\n      colspan: cell.colSpan || 1,\\n      rowspan: cell.rowSpan || 1,\\n      className: cell.className || \\'\\'\\n    }));\\n    return cells;\\n  }).filter(r => r.length);\\n\\n  return {\\n    rowCount: rows.length,\\n    rows: rows.slice(0, 20),\\n    allRowLengths: rows.map(r => r.length)\\n  };\\n}');\n```"}]

Command

curl -L --silent https://www.car.org/marketdata/data/ftbhai | rg -n -C 2 "First Time Buyer Housing Affordability Index|<table|</table>|CA SFH \(SAAR\)|Minimum Qualifying Income"

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 12000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc "curl -L --silent https://www.car.org/marketdata/data/ftbhai | rg -n -C 2 \"First Time Buyer Housing Affordability Index|<table|</table>|CA SFH \\(SAAR\\)|Minimum Qualifying Income\""
Chunk ID: d1d864
Wall time: 0.1926 seconds
Process exited with code 0
Original token count: 516
Output:
4077-<br />
4078-<a href="/en/marketdata/data/ftbhaimethodology" title="FTB-HAI Methodology">First -Time Buyer Housing Affordability Index Methodology</a></p>
4079:<table border="0" cellspacing="0" cellpadding="0" align="left" width="100%" style="width: 100%; margin-right: 6.75pt; margin-left: 6.75pt; class=;">
4080-    <tbody>
4081-        <tr style="height: 21pt;">
--
4084-            </td>
4085-            <td colspan="8" style="background: #1f497d; height: 21pt; width: 464pt; padding: 0in 5.4pt; border-right: 1pt solid black; border-left: none; border-top-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4086:            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">First Time Buyer Housing Affordability Index</span></strong></p>
4087-            </td>
4088-        </tr>
--
4113-            </td>
4114-            <td style="background: #b8cce4; height: 71.25pt; width: 88.25pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left;">
4115:            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">Minimum Qualifying Income</span></strong></p>
4116-            </td>
4117-        </tr>
--
4147-        <tr style="height: 26pt;">
4148-            <td style="height: 26pt; width: 2.5in; padding: 0in 5.4pt; border-top: none; border-right-style: solid; border-bottom-style: solid; border-left-style: solid; text-align: left; white-space: nowrap;">
4149:            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><span style="color: black;">CA SFH (SAAR)</span></p>
4150-            </td>
4151-            <td style="height: 26pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
--
6060-        </tr>
6061-    </tbody>
6062:</table>
6063-<p>&nbsp;</p>
6064-<p>&nbsp;</p>

Command

curl -L --silent https://www.car.org/marketdata/data/ftbhai | rg -n -C 2 "<tr|<td|<th" | head -n 80

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 12000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc 'curl -L --silent https://www.car.org/marketdata/data/ftbhai | rg -n -C 2 "<tr|<td|<th" | head -n 80'
Chunk ID: 2f1ac0
Wall time: 0.1936 seconds
Process exited with code 0
Original token count: 2364
Output:
4079-<table border="0" cellspacing="0" cellpadding="0" align="left" width="100%" style="width: 100%; margin-right: 6.75pt; margin-left: 6.75pt; class=;">
4080-    <tbody>
4081:        <tr style="height: 21pt;">
4082:            <td style="background: #1f497d; height: 21pt; width: 2.5in; padding: 0in 5.4pt; border-bottom: none; border-top-style: solid; border-right-style: solid; border-left-style: solid; text-align: left; white-space: nowrap;">
4083-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">Q42025</span></strong></p>
4084-            </td>
4085:            <td colspan="8" style="background: #1f497d; height: 21pt; width: 464pt; padding: 0in 5.4pt; border-right: 1pt solid black; border-left: none; border-top-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4086-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">First Time Buyer Housing Affordability Index</span></strong></p>
4087-            </td>
4088-        </tr>
4089:        <tr style="height: 71.25pt;">
4090:            <td style="background: #b8cce4; height: 71.25pt; width: 2.5in; padding: 0in 5.4pt; border-style: solid; border-width: 1pt; text-align: left; white-space: nowrap;">
4091-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">STATE/REGION/COUNTY</span></strong></p>
4092-            </td>
4093:            <td style="background: #b8cce4; height: 71.25pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4094-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">Q42025</span></strong></p>
4095-            </td>
4096:            <td style="background: #b8cce4; height: 71.25pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4097-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">Q32025</span></strong></p>
4098-            </td>
4099:            <td style="background: #b8cce4; height: 71.25pt; width: 14.15pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4100-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4101-            </td>
4102:            <td style="background: #b8cce4; height: 71.25pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4103-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">Q42024</span></strong></p>
4104-            </td>
4105:            <td style="background: #b8cce4; height: 71.25pt; width: 13.9pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4106-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;"> </span></strong></p>
4107-            </td>
4108:            <td style="background: #b8cce4; height: 71.25pt; width: 88.6pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left;">
4109-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">Median Home Price</span></strong></p>
4110-            </td>
4111:            <td style="background: #b8cce4; height: 71.25pt; width: 87.05pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left;">
4112-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">Monthly Payment Including Taxes &amp; Insurance</span></strong></p>
4113-            </td>
4114:            <td style="background: #b8cce4; height: 71.25pt; width: 88.25pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left;">
4115-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><strong><span style="color: white;">Minimum Qualifying Income</span></strong></p>
4116-            </td>
4117-        </tr>
4118:        <tr style="height: 16.75pt;">
4119:            <td style="background: #b8cce4; height: 16.75pt; width: 2.5in; padding: 0in 5.4pt; border-top: none; border-right-style: solid; border-bottom-style: solid; border-left-style: solid; text-align: left; white-space: nowrap;">
4120-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4121-            </td>
4122:            <td style="background: #b8cce4; height: 16.75pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4123-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4124-            </td>
4125:            <td style="background: #b8cce4; height: 16.75pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4126-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4127-            </td>
4128:            <td style="background: #b8cce4; height: 16.75pt; width: 14.15pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4129-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4130-            </td>
4131:            <td style="background: #b8cce4; height: 16.75pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4132-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4133-            </td>
4134:            <td style="background: #b8cce4; height: 16.75pt; width: 13.9pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4135-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4136-            </td>
4137:            <td style="background: #b8cce4; height: 16.75pt; width: 88.6pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left;">
4138-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4139-            </td>
4140:            <td style="background: #b8cce4; height: 16.75pt; width: 87.05pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left;">
4141-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4142-            </td>
4143:            <td style="background: #b8cce4; height: 16.75pt; width: 88.25pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left;">
4144-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>
4145-            </td>
4146-        </tr>
4147:        <tr style="height: 26pt;">
4148:            <td style="height: 26pt; width: 2.5in; padding: 0in 5.4pt; border-top: none; border-right-style: solid; border-bottom-style: solid; border-left-style: solid; text-align: left; white-space: nowrap;">
4149-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><span style="color: black;">CA SFH (SAAR)</span></p>
4150-            </td>
4151:            <td style="height: 26pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4152-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><span style="color: black;">33 </span></p>
4153-            </td>
4154:            <td style="height: 26pt; width: 57.35pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4155-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;"><span style="color: black;">30 </span></p>
4156-            </td>
4157:            <td style="height: 26pt; width: 14.15pt; padding: 0in 5.4pt; border-top: none; border-left: none; border-right-style: solid; border-bottom-style: solid; text-align: left; white-space: nowrap;">
4158-            <p style="margin-bottom: 0in; text-align: center; line-height: normal;">&nbsp;</p>

Command

python3 - <<'PY'
import urllib.request
from html.parser import HTMLParser

url = 'https://www.car.org/marketdata/data/ftbhai'
html = urllib.request.urlopen(url).read().decode('utf-8', 'ignore')

start = html.find('<table')
end = html.find('</table>', start)
frag = html[start:end+8]

class TableParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_tr = False
        self.in_cell = False
        self.rows = []
        self.row = []
        self.parts = []
    def handle_starttag(self, tag, attrs):
        if tag == 'tr':
            self.in_tr = True
            self.row = []
        elif tag in ('td', 'th') and self.in_tr:
            self.in_cell = True
            self.parts = []
    def handle_endtag(self, tag):
        if tag in ('td', 'th') and self.in_tr and self.in_cell:
            text = ' '.join(''.join(self.parts).replace('\xa0',' ').split())
            self.row.append(text)
            self.in_cell = False
            self.parts = []
        elif tag == 'tr' and self.in_tr:
            if self.row:
                self.rows.append(self.row)
            self.in_tr = False
    def handle_data(self, data):
        if self.in_cell:
            self.parts.append(data)

p = TableParser()
p.feed(frag)
rows = p.rows
# rows[0] is title bar, rows[1] header, rows[2] blank. Real data after that.
clean = []
for r in rows[3:]:
    if not any(cell.strip() for cell in r):
        continue
    if len(r) < 9:
        continue
    row = [r[0], r[1], r[2], r[4], r[6], r[7], r[8]]
    if not any(row):
        continue
    clean.append(row)

print('ROWS', len(clean))
for row in clean:
    print('\t'.join(row))
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 20000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import urllib.request
from html.parser import HTMLParser

url = 'https://www.car.org/marketdata/data/ftbhai'
html = urllib.request.urlopen(url).read().decode('utf-8', 'ignore')

start = html.find('<table')
end = html.find('</table>', start)
frag = html[start:end+8]

class TableParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_tr = False
        self.in_cell = False
        self.rows = []
        self.row = []
        self.parts = []
    def handle_starttag(self, tag, attrs):
        if tag == 'tr':
            self.in_tr = True
            self.row = []
        elif tag in ('td', 'th') and self.in_tr:
            self.in_cell = True
            self.parts = []
    def handle_endtag(self, tag):
        if tag in ('td', 'th') and self.in_tr and self.in_cell:
            text = ' '.join(''.join(self.parts).replace('\\xa0',' ').split())
            self.row.append(text)
            self.in_cell = False
            self.parts = []
        elif tag == 'tr' and self.in_tr:
            if self.row:
                self.rows.append(self.row)
            self.in_tr = False
    def handle_data(self, data):
        if self.in_cell:
            self.parts.append(data)

p = TableParser()
p.feed(frag)
rows = p.rows
# rows[0] is title bar, rows[1] header, rows[2] blank. Real data after that.
clean = []
for r in rows[3:]:
    if not any(cell.strip() for cell in r):
        continue
    if len(r) < 9:
        continue
    row = [r[0], r[1], r[2], r[4], r[6], r[7], r[8]]
    if not any(row):
        continue
    clean.append(row)

print('ROWS', len(clean))
for row in clean:
    print('\\t'.join(row))
PY"
Chunk ID: 4786bd
Wall time: 0.2313 seconds
Process exited with code 1
Original token count: 343
Output:
Traceback (most recent call last):
  File "<stdin>", line 5, in <module>
  File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/python3.13/urllib/request.py", line 189, in urlopen
    return opener.open(url, data, timeout)
           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/python3.13/urllib/request.py", line 495, in open
    response = meth(req, response)
  File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/python3.13/urllib/request.py", line 604, in http_response
    response = self.parent.error(
        'http', request, response, code, msg, hdrs)
  File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/python3.13/urllib/request.py", line 533, in error
    return self._call_chain(*args)
           ~~~~~~~~~~~~~~~~^^^^^^^
  File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/python3.13/urllib/request.py", line 466, in _call_chain
    result = func(*args)
  File "/opt/homebrew/Cellar/python@3.13/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/python3.13/urllib/request.py", line 613, in http_error_default
    raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 403: Forbidden

Command

curl -L --silent https://www.car.org/marketdata/data/ftbhai | python3 - <<'PY'
import sys
from html.parser import HTMLParser

html = sys.stdin.read()
start = html.find('<table')
end = html.find('</table>', start)
frag = html[start:end+8]

class TableParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_tr = False
        self.in_cell = False
        self.rows = []
        self.row = []
        self.parts = []
    def handle_starttag(self, tag, attrs):
        if tag == 'tr':
            self.in_tr = True
            self.row = []
        elif tag in ('td', 'th') and self.in_tr:
            self.in_cell = True
            self.parts = []
    def handle_endtag(self, tag):
        if tag in ('td', 'th') and self.in_tr and self.in_cell:
            text = ' '.join(''.join(self.parts).replace('\xa0',' ').split())
            self.row.append(text)
            self.in_cell = False
            self.parts = []
        elif tag == 'tr' and self.in_tr:
            if self.row:
                self.rows.append(self.row)
            self.in_tr = False
    def handle_data(self, data):
        if self.in_cell:
            self.parts.append(data)

p = TableParser()
p.feed(frag)
rows = p.rows
clean = []
for r in rows[3:]:
    if len(r) < 9:
        continue
    row = [r[0], r[1], r[2], r[4], r[6], r[7], r[8]]
    if not any(cell.strip() for cell in row):
        continue
    clean.append(row)

print('ROWS', len(clean))
for row in clean:
    print('\t'.join(row))
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 20000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc "curl -L --silent https://www.car.org/marketdata/data/ftbhai | python3 - <<'PY'
import sys
from html.parser import HTMLParser

html = sys.stdin.read()
start = html.find('<table')
end = html.find('</table>', start)
frag = html[start:end+8]

class TableParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_tr = False
        self.in_cell = False
        self.rows = []
        self.row = []
        self.parts = []
    def handle_starttag(self, tag, attrs):
        if tag == 'tr':
            self.in_tr = True
            self.row = []
        elif tag in ('td', 'th') and self.in_tr:
            self.in_cell = True
            self.parts = []
    def handle_endtag(self, tag):
        if tag in ('td', 'th') and self.in_tr and self.in_cell:
            text = ' '.join(''.join(self.parts).replace('\\xa0',' ').split())
            self.row.append(text)
            self.in_cell = False
            self.parts = []
        elif tag == 'tr' and self.in_tr:
            if self.row:
                self.rows.append(self.row)
            self.in_tr = False
    def handle_data(self, data):
        if self.in_cell:
            self.parts.append(data)

p = TableParser()
p.feed(frag)
rows = p.rows
clean = []
for r in rows[3:]:
    if len(r) < 9:
        continue
    row = [r[0], r[1], r[2], r[4], r[6], r[7], r[8]]
    if not any(cell.strip() for cell in row):
        continue
    clean.append(row)

print('ROWS', len(clean))
for row in clean:
    print('\\t'.join(row))
PY"
Chunk ID: e6ed8c
Wall time: 0.2500 seconds
Process exited with code 1
Original token count: 20
Output:
  File "<stdin>", line 3
    <!DOCTYPE html>
    ^
SyntaxError: invalid syntax

Command

tmp=$(mktemp)
curl -L --silent https://www.car.org/marketdata/data/ftbhai > "$tmp"
python3 - "$tmp" <<'PY'
import sys
from html.parser import HTMLParser

path = sys.argv[1]
with open(path, 'r', encoding='utf-8', errors='ignore') as f:
    html = f.read()

start = html.find('<table')
end = html.find('</table>', start)
frag = html[start:end+8]

class TableParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_tr = False
        self.in_cell = False
        self.rows = []
        self.row = []
        self.parts = []
    def handle_starttag(self, tag, attrs):
        if tag == 'tr':
            self.in_tr = True
            self.row = []
        elif tag in ('td', 'th') and self.in_tr:
            self.in_cell = True
            self.parts = []
    def handle_endtag(self, tag):
        if tag in ('td', 'th') and self.in_tr and self.in_cell:
            text = ' '.join(''.join(self.parts).replace('\xa0',' ').split())
            self.row.append(text)
            self.in_cell = False
            self.parts = []
        elif tag == 'tr' and self.in_tr:
            if self.row:
                self.rows.append(self.row)
            self.in_tr = False
    def handle_data(self, data):
        if self.in_cell:
            self.parts.append(data)

p = TableParser()
p.feed(frag)
rows = p.rows
clean = []
for r in rows[3:]:
    if len(r) < 9:
        continue
    row = [r[0], r[1], r[2], r[4], r[6], r[7], r[8]]
    if not any(cell.strip() for cell in row):
        continue
    clean.append(row)

print('ROWS', len(clean))
for row in clean:
    print('\t'.join(row))
PY
rm -f "$tmp"

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 20000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc 'tmp=$(mktemp)
curl -L --silent https://www.car.org/marketdata/data/ftbhai > "$tmp"
python3 - "$tmp" <<'"'PY'
import sys
from html.parser import HTMLParser

path = sys.argv[1]
with open(path, 'r', encoding='utf-8', errors='ignore') as f:
    html = f.read()

start = html.find('<table')
end = html.find('</table>', start)
frag = html[start:end+8]

class TableParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_tr = False
        self.in_cell = False
        self.rows = []
        self.row = []
        self.parts = []
    def handle_starttag(self, tag, attrs):
        if tag == 'tr':
            self.in_tr = True
            self.row = []
        elif tag in ('td', 'th') and self.in_tr:
            self.in_cell = True
            self.parts = []
    def handle_endtag(self, tag):
        if tag in ('td', 'th') and self.in_tr and self.in_cell:
            text = ' '.join(''.join(self.parts).replace('\\xa0',' ').split())
            self.row.append(text)
            self.in_cell = False
            self.parts = []
        elif tag == 'tr' and self.in_tr:
            if self.row:
                self.rows.append(self.row)
            self.in_tr = False
    def handle_data(self, data):
        if self.in_cell:
            self.parts.append(data)

p = TableParser()
p.feed(frag)
rows = p.rows
clean = []
for r in rows[3:]:
    if len(r) < 9:
        continue
    row = [r[0], r[1], r[2], r[4], r[6], r[7], r[8]]
    if not any(cell.strip() for cell in row):
        continue
    clean.append(row)

print('ROWS', len(clean))
for row in clean:
    print('\\t'.join(row))
PY
rm -f \""'$tmp"'
Chunk ID: c12dad
Wall time: 0.3191 seconds
Process exited with code 0
Original token count: 673
Output:
ROWS 65
CA SFH (SAAR)	33	30	29	$738,910	$4,680	$140,400
CA Condo/Townhomes	46	44	41	$552,500	$3,500	$105,000
Los Angeles Metropolitan Area	32	30	29	$705,500	$4,470	$134,100
Inland Empire	45	43	41	$505,750	$3,210	$96,300
S.F. Bay Area	32	30	29	$1,074,320	$6,810	$204,300
US	57	54	54	$352,670	$2,240	$67,200
S.F. Bay Area						
Alameda	32	30	28	$1,041,250	$6,600	$198,000
Contra Costa	45	43	41	$731,000	$4,630	$138,900
Marin	31	29	26	$1,298,380	$8,230	$246,900
Napa	32	31	34	$785,400	$4,980	$149,400
San Francisco	28	29	27	$1,530,000	$9,700	$291,000
San Mateo	27	26	25	$1,759,500	$11,150	$334,500
Santa Clara	30	29	27	$1,632,000	$10,340	$310,200
Solano	51	47	48	$493,000	$3,130	$93,900
Sonoma	37	36	35	$697,850	$4,420	$132,600
Southern California						
Imperial	43	43	46	$369,750	$2,340	$70,200
Los Angeles	26	24	23	$798,740	$5,060	$151,800
Orange	21	19	18	$1,187,030	$7,520	$225,600
Riverside	44	43	40	$538,540	$3,410	$102,300
San Bernardino	51	49	48	$422,450	$2,680	$80,400
San Diego	30	27	26	$844,900	$5,360	$160,800
Ventura	34	32	29	$792,630	$5,020	$150,600
Central Coast						
Monterey	25	21	23	$786,250	$4,980	$149,400
San Luis Obispo	30	27	24	$769,250	$4,880	$146,400
Santa Barbara	19	19	18	$1,062,500	$6,730	$201,900
Santa Cruz	21	21	22	$1,094,380	$6,940	$208,200
Central Valley						
Fresno	51	48	48	$365,500	$2,320	$69,600
Glenn	60	56	53	$291,340	$1,850	$55,500
Kern	51	49	47	$337,870	$2,140	$64,200
Kings	57	55	55	$314,500	$1,990	$59,700
Madera	53	50	46	$374,000	$2,370	$71,100
Merced	50	47	43	$357,000	$2,260	$67,800
Placer	53	50	50	$556,750	$3,530	$105,900
Sacramento	51	48	47	$458,150	$2,900	$87,000
San Benito	48	47	42	$637,500	$4,040	$121,200
San Joaquin	51	49	48	$459,000	$2,910	$87,300
Stanislaus	51	48	50	$395,250	$2,510	$75,300
Tulare	54	52	48	$324,280	$2,060	$61,800
Far North						
Butte	48	45	46	$374,000	$2,370	$71,100
Lassen	74	69	69	$194,650	$1,230	$36,900
Plumas	58	48	53	$339,580	$2,150	$64,500
Shasta	56	54	53	$314,500	$1,990	$59,700
Siskiyou	58	54	54	$257,130	$1,630	$48,900
Tehama	57	54	54	$273,700	$1,730	$51,900
Trinity	61	51	48	$212,500	$1,350	$40,500
Other Counties in California						
Amador	58	55	54	$357,000	$2,260	$67,800
Calaveras	58	53	54	$373,150	$2,370	$71,100
Del Norte	54	52	51	$322,150	$2,040	$61,200
El Dorado	52	48	44	$573,750	$3,640	$109,200
Humboldt	47	42	39	$351,050	$2,230	$66,900
Lake	50	47	55	$282,200	$1,790	$53,700
Mariposa	47	46	39	$374,850	$2,380	$71,400
Mendocino	44	44	39	$418,630	$2,650	$79,500
Mono	22	15	12	$784,550	$4,970	$149,100
Nevada	48	47	47	$467,500	$2,960	$88,800
Sutter	49	46	45	$382,500	$2,420	$72,600
Tuolumne	61	54	55	$323,000	$2,050	$61,500
Yolo	44	42	41	$529,130	$3,350	$100,500
Yuba	49	48	46	$376,340	$2,390	$71,700

Command

python3 - <<'PY'
import csv
import urllib.request
from html.parser import HTMLParser
from pathlib import Path

url = 'https://www.car.org/marketdata/data/ftbhai'
req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
html = urllib.request.urlopen(req).read().decode('utf-8', 'ignore')

start = html.find('<table')
end = html.find('</table>', start)
frag = html[start:end+8]

class TableParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_tr = False
        self.in_cell = False
        self.rows = []
        self.row = []
        self.parts = []
    def handle_starttag(self, tag, attrs):
        if tag == 'tr':
            self.in_tr = True
            self.row = []
        elif tag in ('td', 'th') and self.in_tr:
            self.in_cell = True
            self.parts = []
    def handle_endtag(self, tag):
        if tag in ('td', 'th') and self.in_tr and self.in_cell:
            text = ' '.join(''.join(self.parts).replace('\xa0', ' ').split())
            self.row.append(text)
            self.in_cell = False
            self.parts = []
        elif tag == 'tr' and self.in_tr:
            if self.row:
                self.rows.append(self.row)
            self.in_tr = False
    def handle_data(self, data):
        if self.in_cell:
            self.parts.append(data)

parser = TableParser()
parser.feed(frag)

rows = []
for raw in parser.rows[3:]:
    if len(raw) < 9:
        continue
    row = [raw[0], raw[1], raw[2], raw[4], raw[6], raw[7], raw[8]]
    if not any(cell.strip() for cell in row):
        continue
    rows.append(row)

out_path = Path('/Users/christopherlee/ftbhai.csv')
with out_path.open('w', newline='', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerow([
        'State/Region/County',
        'Q42025',
        'Q32025',
        'Q42024',
        'Median Home Price',
        'Monthly Payment Including Taxes & Insurance',
        'Minimum Qualifying Income',
    ])
    writer.writerows(rows)

print(out_path)
print(len(rows))
PY

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 4000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc "python3 - <<'PY'
import csv
import urllib.request
from html.parser import HTMLParser
from pathlib import Path

url = 'https://www.car.org/marketdata/data/ftbhai'
req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
html = urllib.request.urlopen(req).read().decode('utf-8', 'ignore')

start = html.find('<table')
end = html.find('</table>', start)
frag = html[start:end+8]

class TableParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_tr = False
        self.in_cell = False
        self.rows = []
        self.row = []
        self.parts = []
    def handle_starttag(self, tag, attrs):
        if tag == 'tr':
            self.in_tr = True
            self.row = []
        elif tag in ('td', 'th') and self.in_tr:
            self.in_cell = True
            self.parts = []
    def handle_endtag(self, tag):
        if tag in ('td', 'th') and self.in_tr and self.in_cell:
            text = ' '.join(''.join(self.parts).replace('\\xa0', ' ').split())
            self.row.append(text)
            self.in_cell = False
            self.parts = []
        elif tag == 'tr' and self.in_tr:
            if self.row:
                self.rows.append(self.row)
            self.in_tr = False
    def handle_data(self, data):
        if self.in_cell:
            self.parts.append(data)

parser = TableParser()
parser.feed(frag)

rows = []
for raw in parser.rows[3:]:
    if len(raw) < 9:
        continue
    row = [raw[0], raw[1], raw[2], raw[4], raw[6], raw[7], raw[8]]
    if not any(cell.strip() for cell in row):
        continue
    rows.append(row)

out_path = Path('/Users/christopherlee/ftbhai.csv')
with out_path.open('w', newline='', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerow([
        'State/Region/County',
        'Q42025',
        'Q32025',
        'Q42024',
        'Median Home Price',
        'Monthly Payment Including Taxes & Insurance',
        'Minimum Qualifying Income',
    ])
    writer.writerows(rows)

print(out_path)
print(len(rows))
PY"
Chunk ID: 5dbc34
Wall time: 0.3672 seconds
Process exited with code 0
Original token count: 9
Output:
/Users/christopherlee/ftbhai.csv
65

Command

sed -n '1,12p' /Users/christopherlee/ftbhai.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 4000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc "sed -n '1,12p' /Users/christopherlee/ftbhai.csv"
Chunk ID: 80027d
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 173
Output:
State/Region/County,Q42025,Q32025,Q42024,Median Home Price,Monthly Payment Including Taxes & Insurance,Minimum Qualifying Income
CA SFH (SAAR),33,30,29,"$738,910","$4,680","$140,400"
CA Condo/Townhomes,46,44,41,"$552,500","$3,500","$105,000"
Los Angeles Metropolitan Area,32,30,29,"$705,500","$4,470","$134,100"
Inland Empire,45,43,41,"$505,750","$3,210","$96,300"
S.F. Bay Area,32,30,29,"$1,074,320","$6,810","$204,300"
US,57,54,54,"$352,670","$2,240","$67,200"
S.F. Bay Area,,,,,,
Alameda,32,30,28,"$1,041,250","$6,600","$198,000"
Contra Costa,45,43,41,"$731,000","$4,630","$138,900"
Marin,31,29,26,"$1,298,380","$8,230","$246,900"
Napa,32,31,34,"$785,400","$4,980","$149,400"

Command

pwd

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 1000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc pwd
Chunk ID: 69ada6
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 6
Output:
/Users/christopherlee

Command

ls -1 /Users/christopherlee | rg '^scrape_ftbhai\.py$|^ftbhai\.csv$'

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 1000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc "ls -1 /Users/christopherlee | rg '"'^scrape_ftbhai'"\\.py"'$|''^ftbhai'"\\.csv"'$'"'"
Chunk ID: 0ec413
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 3
Output:
ftbhai.csv

*** Begin Patch
*** Add File: /Users/christopherlee/scrape_ftbhai.py
+#!/usr/bin/env python3
+"""Scrape CAR's First-Time Buyer Housing Affordability Index table to CSV."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import sys
+from html.parser import HTMLParser
+from pathlib import Path
+from typing import Iterable
+from urllib.error import HTTPError, URLError
+from urllib.request import Request, urlopen
+
+
+SOURCE_URL = "https://www.car.org/marketdata/data/ftbhai"
+USER_AGENT = (
+    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
+    "AppleWebKit/537.36 (KHTML, like Gecko) "
+    "Chrome/124.0.0.0 Safari/537.36"
+)
+CSV_HEADERS = [
+    "State/Region/County",
+    "Q42025",
+    "Q32025",
+    "Q42024",
+    "Median Home Price",
+    "Monthly Payment Including Taxes & Insurance",
+    "Minimum Qualifying Income",
+]
+
+
+class TableParser(HTMLParser):
+    """Collect plain-text cell values from the first HTML table fragment."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.in_tr = False
+        self.in_cell = False
+        self.rows: list[list[str]] = []
+        self.current_row: list[str] = []
+        self.current_parts: list[str] = []
+
+    def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None:
+        if tag == "tr":
+            self.in_tr = True
+            self.current_row = []
+        elif self.in_tr and tag in {"td", "th"}:
+            self.in_cell = True
+            self.current_parts = []
+
+    def handle_endtag(self, tag: str) -> None:
+        if self.in_tr and self.in_cell and tag in {"td", "th"}:
+            text = " ".join("".join(self.current_parts).replace("\xa0", " ").split())
+            self.current_row.append(text)
+            self.in_cell = False
+            self.current_parts = []
+        elif self.in_tr and tag == "tr":
+            if self.current_row:
+                self.rows.append(self.current_row)
+            self.in_tr = False
+
+    def handle_data(self, data: str) -> None:
+        if self.in_cell:
+            self.current_parts.append(data)
+
+
+def fetch_html(url: str, timeout: float) -> str:
+    request = Request(url, headers={"User-Agent": USER_AGENT})
+    with urlopen(request, timeout=timeout) as response:
+        return response.read().decode("utf-8", errors="ignore")
+
+
+def extract_first_table(html: str) -> str:
+    table_start = html.find("<table")
+    if table_start == -1:
+        raise ValueError("No <table> tag found in the page HTML.")
+
+    table_end = html.find("</table>", table_start)
+    if table_end == -1:
+        raise ValueError("Found a <table> tag but not a closing </table> tag.")
+
+    return html[table_start : table_end + len("</table>")]
+
+
+def normalize_rows(raw_rows: Iterable[list[str]]) -> list[list[str]]:
+    normalized: list[list[str]] = []
+
+    for raw in raw_rows:
+        if len(raw) < 9:
+            continue
+
+        row = [raw[0], raw[1], raw[2], raw[4], raw[6], raw[7], raw[8]]
+        if not any(cell.strip() for cell in row):
+            continue
+
+        normalized.append(row)
+
+    return normalized
+
+
+def scrape_ftbhai(url: str, timeout: float) -> list[list[str]]:
+    html = fetch_html(url, timeout)
+    table_html = extract_first_table(html)
+
+    parser = TableParser()
+    parser.feed(table_html)
+
+    # The first three rows are the title row, header row, and a blank spacer row.
+    return normalize_rows(parser.rows[3:])
+
+
+def write_csv(path: Path, rows: list[list[str]]) -> None:
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.writer(handle)
+        writer.writerow(CSV_HEADERS)
+        writer.writerows(rows)
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Scrape CAR's First-Time Buyer Housing Affordability Index table and "
+            "write it to a CSV file."
+        )
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        default="ftbhai.csv",
+        help="Output CSV path. Defaults to ./ftbhai.csv",
+    )
+    parser.add_argument(
+        "--url",
+        default=SOURCE_URL,
+        help=f"Source page URL. Defaults to {SOURCE_URL}",
+    )
+    parser.add_argument(
+        "--timeout",
+        type=float,
+        default=20.0,
+        help="HTTP timeout in seconds. Defaults to 20.",
+    )
+    parser.add_argument(
+        "--stdout",
+        action="store_true",
+        help="Also print the CSV to stdout after writing the file.",
+    )
+    return parser
+
+
+def main() -> int:
+    parser = build_parser()
+    args = parser.parse_args()
+
+    try:
+        rows = scrape_ftbhai(args.url, args.timeout)
+    except HTTPError as exc:
+        print(f"HTTP error while fetching {args.url}: {exc.code} {exc.reason}", file=sys.stderr)
+        return 1
+    except URLError as exc:
+        print(f"Network error while fetching {args.url}: {exc.reason}", file=sys.stderr)
+        return 1
+    except ValueError as exc:
+        print(f"Parse error: {exc}", file=sys.stderr)
+        return 1
+
+    output_path = Path(args.output).expanduser().resolve()
+    write_csv(output_path, rows)
+
+    print(f"Wrote {len(rows)} rows to {output_path}")
+
+    if args.stdout:
+        writer = csv.writer(sys.stdout)
+        writer.writerow(CSV_HEADERS)
+        writer.writerows(rows)
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
*** End Patch

Raw tool result

{"output":"Success. Updated the following files:\nA /Users/christopherlee/scrape_ftbhai.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}

Command

chmod +x /Users/christopherlee/scrape_ftbhai.py

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 1000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc 'chmod +x /Users/christopherlee/scrape_ftbhai.py'
Chunk ID: e80024
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 0
Output:

Command

python3 /Users/christopherlee/scrape_ftbhai.py --output /Users/christopherlee/ftbhai_test.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 4000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc 'python3 /Users/christopherlee/scrape_ftbhai.py --output /Users/christopherlee/ftbhai_test.csv'
Chunk ID: 8d5008
Wall time: 0.3658 seconds
Process exited with code 0
Original token count: 14
Output:
Wrote 65 rows to /Users/christopherlee/ftbhai_test.csv

Command

sed -n '1,8p' /Users/christopherlee/ftbhai_test.csv

Invocation metadata

{
  "workdir": "/Users/christopherlee",
  "max_output_tokens": 4000,
  "yield_time_ms": 1000
}

Raw tool result

Command: /bin/zsh -lc "sed -n '1,8p' /Users/christopherlee/ftbhai_test.csv"
Chunk ID: c9c561
Wall time: 0.0000 seconds
Process exited with code 0
Original token count: 123
Output:
State/Region/County,Q42025,Q32025,Q42024,Median Home Price,Monthly Payment Including Taxes & Insurance,Minimum Qualifying Income
CA SFH (SAAR),33,30,29,"$738,910","$4,680","$140,400"
CA Condo/Townhomes,46,44,41,"$552,500","$3,500","$105,000"
Los Angeles Metropolitan Area,32,30,29,"$705,500","$4,470","$134,100"
Inland Empire,45,43,41,"$505,750","$3,210","$96,300"
S.F. Bay Area,32,30,29,"$1,074,320","$6,810","$204,300"
US,57,54,54,"$352,670","$2,240","$67,200"
S.F. Bay Area,,,,,,