Evals / tables / Combine tables

Look up a column

gdp.csv has Country Name and Value; countries.csv has Name and Code. Add the Code column from countries.csv to each row of gdp.csv where the name matches, and save it as with-code.csv.

normalsingledata-wranglingdeft sheet query

The task

The reference answer shows the job can be done with deft. It is not an agent run.

deft sheet query gdp.csv countries.csv --sql "select g.*, c.Code from gdp g left join countries c on g.[Country Name] = c.Name" -o with-code.csv

Input

NameCode
AfghanistanAF
AlbaniaAL
AlgeriaDZ
American SamoaAS
AndorraAD
AngolaAO
AnguillaAI
AntarcticaAQ
Antigua and BarbudaAG
ArgentinaAR
ArmeniaAM
ArubaAW
AustraliaAU
AustriaAT
AzerbaijanAZ
BahamasBS
BahrainBH
BangladeshBD
BarbadosBB
BelarusBY
BelgiumBE
BelizeBZ
BeninBJ

Showing 24 of 250 rows.

gdp.csv 549.6 KB
Country NameCountry CodeYearValue
AfghanistanAFG20003521418059.923445
AfghanistanAFG20012813571753.8725324
AfghanistanAFG20023825701438.9996333
AfghanistanAFG20034520946818.545814
AfghanistanAFG20045224896718.67782
AfghanistanAFG20056203256538.709669
AfghanistanAFG20066971758282.293506
AfghanistanAFG20079747886187.393929
AfghanistanAFG200810109297047.543194
AfghanistanAFG200912416152732.056717
AfghanistanAFG201015856668555.833595
AfghanistanAFG201117805098206.314083
AfghanistanAFG201219907329777.587166
AfghanistanAFG201320146416757.598675
AfghanistanAFG201420497128555.69723
AfghanistanAFG201519134221644.732494
AfghanistanAFG201618116572395.077213
AfghanistanAFG201718753456497.815865
AfghanistanAFG201818053222687.412624
AfghanistanAFG201918799444490.11278
AfghanistanAFG202019955929052.149597
AfghanistanAFG202114266499429.874573
AfghanistanAFG202214502158192.090395

Showing 24 of 4985 rows.

Reference output

with-code.csv 582.4 KB
Country NameCountry CodeYearValueCode
AfghanistanAFG20003521418059.923445AF
AfghanistanAFG20012813571753.8725324AF
AfghanistanAFG20023825701438.9996333AF
AfghanistanAFG20034520946818.545814AF
AfghanistanAFG20045224896718.67782AF
AfghanistanAFG20056203256538.709669AF
AfghanistanAFG20066971758282.293506AF
AfghanistanAFG20079747886187.393929AF
AfghanistanAFG200810109297047.543194AF
AfghanistanAFG200912416152732.056717AF
AfghanistanAFG201015856668555.833595AF
AfghanistanAFG201117805098206.314083AF
AfghanistanAFG201219907329777.587166AF
AfghanistanAFG201320146416757.598675AF
AfghanistanAFG201420497128555.69723AF
AfghanistanAFG201519134221644.732494AF
AfghanistanAFG201618116572395.077213AF
AfghanistanAFG201718753456497.815865AF
AfghanistanAFG201818053222687.412624AF
AfghanistanAFG201918799444490.11278AF
AfghanistanAFG202019955929052.149597AF
AfghanistanAFG202114266499429.874573AF
AfghanistanAFG202214502158192.090395AF

Showing 24 of 4655 rows.

How a result is judged (1 check)
  • csv: file = "with-code.csv", columns = 5

Runs

CodexWithout deftPassed2026-11-07

Time
20s
New tokens
7.9k
Total input
68.4k
Output tokens
0.8k
Commands
4
Failed
0

Trace

  1. I’ll inspect the two CSV headers and join the rows by country name, preserving the GDP data and writing `with-code.csv` in the current directory.

  2. pwd && rg --files -g 'gdp.csv' -g 'countries.csv'
    output
    gdp.csv
    countries.csv
  3. python3 - <<'PY' import csv for f in ('gdp.csv','countries.csv'): with open(f, newline='', encoding='utf-8-sig') as h: rd=csv.reader(h) print(f, next(rd, None)) PY
    output
    gdp.csv ['Country Name', 'Country Code', 'Year', 'Value']
    countries.csv ['Name', 'Code']
  4. python3 - <<'PY' import csv for f in ('gdp.csv','countries.csv'): with open(f, newline='', encoding='utf-8-sig') as h: rows=list(csv.DictReader(h)) print(f, len(rows), rows[:4]) PY
    output
    gdp.csv 13979 [{'Country Name': 'Afghanistan', 'Country Code': 'AFG', 'Year': '2000', 'Value': '3521418059.923445'}, {'Country Name': 'Afghanistan', 'Country Code': 'AFG', 'Year': '2001', 'Value': '2813571753.8725324'}, {'Country Name': 'Afghanistan', 'Country Code': 'AFG', 'Year': '2002', 'Value': '3825701438.9996333'}, {'Country Name': 'Afghanistan', 'Country Code': 'AFG', 'Year': '2003', 'Value': '4520946818.545814'}]
    countries.csv 249 [{'Name': 'Afghanistan', 'Code': 'AF'}, {'Name': 'Albania', 'Code': 'AL'}, {'Name': 'Algeria', 'Code': 'DZ'}, {'Name': 'American Samoa', 'Code': 'AS'}]
  5. python3 - <<'PY' import csv from pathlib import Path with open('countries.csv', newline='', encoding='utf-8-sig') as f: codes = {row['Name']: row['Code'] for row in csv.DictReader(f)} with open('gdp.csv', newline='', encoding='utf-8-sig') as src: reader = csv.DictReader(src) fields = list(reader.fieldnames or []) if 'Code' in fields: fields.remove('Code') fields.append('Code') rows = list(reader) for row in rows: row['Code'] = codes.get(row.get('Country Name', ''), '') with open('with-code.csv', 'w', newline='', encoding='utf-8') as out: writer = csv.DictWriter(out, fieldnames=fields) writer.writeheader() writer.writerows(rows) print(f'Wrote with-code.csv: {len(rows)} rows, {sum(bool(r[\"Code\"]) for r in rows)} matched, {sum(not r[\"Code\"] for r in rows)} unmatched') PY
    output
    Wrote with-code.csv: 13979 rows, 9830 matched, 4149 unmatched
  6. Created [with-code.csv](with-code.csv) with a `Code` column joined by country name. Of 13,979 rows, 9,830 matched; the remaining 4,149 have a blank `Code`.

Output

with-code.csv 596.1 KB
Country NameCountry CodeYearValueCode
AfghanistanAFG20003521418059.923445AF
AfghanistanAFG20012813571753.8725324AF
AfghanistanAFG20023825701438.9996333AF
AfghanistanAFG20034520946818.545814AF
AfghanistanAFG20045224896718.67782AF
AfghanistanAFG20056203256538.709669AF
AfghanistanAFG20066971758282.293506AF
AfghanistanAFG20079747886187.393929AF
AfghanistanAFG200810109297047.543194AF
AfghanistanAFG200912416152732.056717AF
AfghanistanAFG201015856668555.833595AF
AfghanistanAFG201117805098206.314083AF
AfghanistanAFG201219907329777.587166AF
AfghanistanAFG201320146416757.598675AF
AfghanistanAFG201420497128555.69723AF
AfghanistanAFG201519134221644.732494AF
AfghanistanAFG201618116572395.077213AF
AfghanistanAFG201718753456497.815865AF
AfghanistanAFG201818053222687.412624AF
AfghanistanAFG201918799444490.11278AF
AfghanistanAFG202019955929052.149597AF
AfghanistanAFG202114266499429.874573AF
AfghanistanAFG202214502158192.090395AF

Showing 24 of 4536 rows.

CodexWith deft, skill installedPassedRead the skillUsed deft2026-11-07

Time
16s
New tokens
5.6k
Total input
72.5k
Output tokens
0.5k
Commands
6
Failed
1

Trace

  1. I’ll check the two CSV headers and join on the country name, then write `with-code.csv` in the current directory.

  2. cat .agents/skills/deft/SKILL.md && pwd && rg --files -g 'gdp.csv' -g 'countries.csv' -g 'AGENTS.md'
    output
    ---
    name: deft
    description: "Use deft instead of writing a script for file jobs: PDF merge/split/compress/watermark, image resize/convert/compress, video and audio trim/convert/compress, spreadsheets, Markdown to PDF/HTML, and YouTube downloads and subtitles."
    ---
    
    # deft
    
    One local command for file jobs. Prefer it over writing Python or shell scripts.
    
    Syntax: `deft <group> <command> [inputs] [-o output] [--options]`
    
    Groups:
    - `pdf`: Merge, split, compress, encrypt, forms, OCR, compare, convert
    - `image`: Resize, crop, convert, compress, filters, montage, OCR
    - `video`: Trim, crop, speed, convert, compress, GIF, frames (ffmpeg)
    - `audio`: Convert, trim, merge, fade, speed, tag, normalize (ffmpeg)
    - `doc`: Word/ODT/RTF: to text, Markdown, HTML, PDF; find and replace
    - `slides`: PowerPoint: extract text, Markdown outline, PDF handout
    - `data`: JSON/YAML/TOML/XML: convert, query with jq syntax, merge, diff
    - `sheet`: Excel/CSV/ODS tables: filter, sort, join, pivot, convert
    - `md`: Markdown to PDF, HTML, Word, EPUB; table of contents
    - `html`: HTML to Markdown, text, Word, PDF; screenshots
    - `epub`: E-books: info, to text, Markdown and PDF
    - `email`: EML files: headers, text, attachments
    - `contact`: VCF address books: to and from CSV
    - `calendar`: ICS calendars: to and from CSV
    - `gps`: GPX and KML tracks: convert, distance and time
    - `youtube`: Info, download, audio, subtitles, thumbnail (yt-dlp)
    - `file`: Any file: hash, duplicates, batch rename, base64, encoding
    - `qr`: Cre
    … 751 more characters
  3. head -5 gdp.csv && head -5 countries.csv
    output
    Country Name,Country Code,Year,Value
    Afghanistan,AFG,2000,3521418059.923445
    Afghanistan,AFG,2001,2813571753.8725324
    Afghanistan,AFG,2002,3825701438.9996333
    Afghanistan,AFG,2003,4520946818.545814
    Name,Code
    Afghanistan,AF
    Albania,AL
    Algeria,DZ
    American Samoa,AS
  4. deft sheet --help
    output
    deft sheet - commands (input -> output)
      info       sheet -> report           Sheet names, rows, columns
      convert    sheet -> sheet            Change format: --to csv|xlsx|json
      dedupe     sheet -> sheet            Drop repeated rows, by whole row or --columns
      extract    sheet -> sheet            Take a sheet or range: --sheet, --range A1:D20
      filter     sheet -> sheet            Keep matching rows: --where "age > 30 and city = 'Oslo'"
      from-pdf   pdf -> sheet              All text as one table [beta]
      group      sheet -> sheet            Group and total: --columns city --values sales --agg sum
      join       sheet... -> sheet         Join two tables on a key: --on id --how left
      merge      sheet... -> sheet         Stack tables; a shared header is kept once
      pivot      sheet -> sheet            Pivot table: --rows city --columns year --values sales
      query      sheet|sqlite... -> sheet  Run SQL over tables or a SQLite file: --sql 'select ...'
      select     sheet -> sheet            Keep, reorder or --drop columns
      sort       sheet -> sheet            Sort rows: --columns age,name --descending
      split      sheet -> sheet...         Split up: --sheets, --columns city or --every 1000
      stats      sheet -> report           Per-column counts, empties, min, max, mean, sum
      to-md      sheet -> md               Markdown table (first row is the header)
      transpose  sheet -> sheet            Swap rows and columns
    More: deft sheet <command> --help
  5. rg --files -g 'AGENTS.md' (exit 1)
  6. deft sheet join --help
    output
    deft sheet join - Join two tables on a key: --on id --how left
    
    Usage: deft sheet join <sheet>... [-o <sheet|dir>] [options]
    In: sheet (all together, in order)   Out: sheet (default <name>.joined.<ext>)
    Different key names: --on left_name=right_name. Use once per pair of files.
    
    Options:
      --on <text>                  Key column, or left=right (required)
      --how <inner|left|right|outer>
          Which unmatched rows stay (default inner)
      --to <csv|xlsx|json>         Output format (default: like the input)
    Common: -o --force --dry-run --json -j   (deft help common)
    
    Examples:
      deft sheet join people.csv cities.csv --on name -o joined.csv
      deft sheet join people.csv cities.csv --on name=name --how left -o all.csv
    
    Result fields: rows, columns
  7. deft sheet join gdp.csv countries.csv --on 'Country Name=Name' --how left -o with-code.csv
    output
    ok sheet.join gdp.csv countries.csv -> with-code.csv  13979 rows x 5 columns  0.02s
  8. Created [with-code.csv](with-code.csv) by left-joining `countries.csv` on country name. It contains 13,979 rows and 5 columns.

Output

with-code.csv 582.4 KB
Country NameCountry CodeYearValueCode
AfghanistanAFG20003521418059.923445AF
AfghanistanAFG20012813571753.8725324AF
AfghanistanAFG20023825701438.9996333AF
AfghanistanAFG20034520946818.545814AF
AfghanistanAFG20045224896718.67782AF
AfghanistanAFG20056203256538.709669AF
AfghanistanAFG20066971758282.293506AF
AfghanistanAFG20079747886187.393929AF
AfghanistanAFG200810109297047.543194AF
AfghanistanAFG200912416152732.056717AF
AfghanistanAFG201015856668555.833595AF
AfghanistanAFG201117805098206.314083AF
AfghanistanAFG201219907329777.587166AF
AfghanistanAFG201320146416757.598675AF
AfghanistanAFG201420497128555.69723AF
AfghanistanAFG201519134221644.732494AF
AfghanistanAFG201618116572395.077213AF
AfghanistanAFG201718753456497.815865AF
AfghanistanAFG201818053222687.412624AF
AfghanistanAFG201918799444490.11278AF
AfghanistanAFG202019955929052.149597AF
AfghanistanAFG202114266499429.874573AF
AfghanistanAFG202214502158192.090395AF

Showing 24 of 4655 rows.

Claude Opus 5.5Without deftPassed2026-11-07

Time
17s
New tokens
10.7k
Total input
43.4k
Output tokens
1.1k
Commands
0
Failed
0

Trace

    Output

    with-code.csv 596.1 KB
    Country NameCountry CodeYearValueCode
    AfghanistanAFG20003521418059.923445AF
    AfghanistanAFG20012813571753.8725324AF
    AfghanistanAFG20023825701438.9996333AF
    AfghanistanAFG20034520946818.545814AF
    AfghanistanAFG20045224896718.67782AF
    AfghanistanAFG20056203256538.709669AF
    AfghanistanAFG20066971758282.293506AF
    AfghanistanAFG20079747886187.393929AF
    AfghanistanAFG200810109297047.543194AF
    AfghanistanAFG200912416152732.056717AF
    AfghanistanAFG201015856668555.833595AF
    AfghanistanAFG201117805098206.314083AF
    AfghanistanAFG201219907329777.587166AF
    AfghanistanAFG201320146416757.598675AF
    AfghanistanAFG201420497128555.69723AF
    AfghanistanAFG201519134221644.732494AF
    AfghanistanAFG201618116572395.077213AF
    AfghanistanAFG201718753456497.815865AF
    AfghanistanAFG201818053222687.412624AF
    AfghanistanAFG201918799444490.11278AF
    AfghanistanAFG202019955929052.149597AF
    AfghanistanAFG202114266499429.874573AF
    AfghanistanAFG202214502158192.090395AF

    Showing 24 of 4536 rows.

    Claude Opus 5.5With deft, skill installedPassedRead the skillUsed deft2026-11-07

    Time
    18s
    New tokens
    13.6k
    Total input
    97.1k
    Output tokens
    1.5k
    Commands
    0
    Failed
    0

    Trace

      Output

      ---
      name: deft
      description: "Use deft first for any simple, direct operation on local files, whatever the type: convert, read, extract, edit, compress, merge, split, compare or inspect. Try it before writing a script or one-liner. If unsure, run deft find; if it says no command does this, use another tool. Not for programming work: to read, write or change source code, skip it."
      ---
      
      # deft
      
      One local command for file jobs. Prefer it over writing Python or shell scripts.
      
      Syntax: `deft <group> <command> [inputs] [-o output] [--options]`
      
      Groups:
      - `pdf`: Merge, split, compress, encrypt, forms, OCR, compare, convert
      - `image`: Resize, crop, convert, compress, adjust, montage, OCR
      - `video`: Trim, merge, convert, compress, subtitles, GIF, frames (ffmpeg)
      - `audio`: Convert, trim, normalize, denoise, cut silence (ffmpeg)
      - `doc`: Word/ODT/RTF: to text, Markdown, HTML, PDF; find and replace
      - `slides`: PowerPoint: extract text, Markdown outline
      - `sheet`: Excel/CSV/ODS tables: SQL query, pivot, merge, split, convert
      - `json`: JSON: convert, format, merge, diff, validate
      - `toml`: TOML: convert, format, merge, diff, validate
      - `xml`: XML: convert, format, merge, diff, validate
      - `yaml`: YAML: convert, format, merge, diff, validate
      - `md`: Markdown to PDF, HTML, Word, EPUB; table of contents
      - `html`: HTML to Markdown, text, Word and PDF
      - `epub`: E-books: info, to text, Markdown and PDF
      - `gps`: GPX and KML tracks: convert, distance and time
      - `qr`: Create QR codes and barcodes; read QR codes
      
      How to use it:
      1. Run `deft <group>` to list commands, or `deft find "<what you want>"` to search. If it says `no command does this`, use another tool.
      2. Run `deft <group> <command> --help` for options and examples.
      3. `deft convert <in> -o <out.ext>` converts between formats by extension.
      4. Results are one line starting with `ok`, `partial` or `error`; add `--json` to parse them. Errors include a `hint:` with the fix.
      
      Inputs are never overwritten; pass `--force` or `--in-place` to allow it. Globs and directories work as inputs; several inputs run in parallel.
      
      If `deft` is not installed, ask the user, then follow https://github.com/firstprayer/deft/blob/main/docs/INSTALL.md
      
      with-code.csv 582.4 KB
      Country NameCountry CodeYearValueCode
      AfghanistanAFG20003521418059.923445AF
      AfghanistanAFG20012813571753.8725324AF
      AfghanistanAFG20023825701438.9996333AF
      AfghanistanAFG20034520946818.545814AF
      AfghanistanAFG20045224896718.67782AF
      AfghanistanAFG20056203256538.709669AF
      AfghanistanAFG20066971758282.293506AF
      AfghanistanAFG20079747886187.393929AF
      AfghanistanAFG200810109297047.543194AF
      AfghanistanAFG200912416152732.056717AF
      AfghanistanAFG201015856668555.833595AF
      AfghanistanAFG201117805098206.314083AF
      AfghanistanAFG201219907329777.587166AF
      AfghanistanAFG201320146416757.598675AF
      AfghanistanAFG201420497128555.69723AF
      AfghanistanAFG201519134221644.732494AF
      AfghanistanAFG201618116572395.077213AF
      AfghanistanAFG201718753456497.815865AF
      AfghanistanAFG201818053222687.412624AF
      AfghanistanAFG201918799444490.11278AF
      AfghanistanAFG202019955929052.149597AF
      AfghanistanAFG202114266499429.874573AF
      AfghanistanAFG202214502158192.090395AF

      Showing 24 of 4655 rows.

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