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.
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
| Name | Code |
| Afghanistan | AF |
| Albania | AL |
| Algeria | DZ |
| American Samoa | AS |
| Andorra | AD |
| Angola | AO |
| Anguilla | AI |
| Antarctica | AQ |
| Antigua and Barbuda | AG |
| Argentina | AR |
| Armenia | AM |
| Aruba | AW |
| Australia | AU |
| Austria | AT |
| Azerbaijan | AZ |
| Bahamas | BS |
| Bahrain | BH |
| Bangladesh | BD |
| Barbados | BB |
| Belarus | BY |
| Belgium | BE |
| Belize | BZ |
| Benin | BJ |
Showing 24 of 250 rows.
| 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 |
| Afghanistan | AFG | 2004 | 5224896718.67782 |
| Afghanistan | AFG | 2005 | 6203256538.709669 |
| Afghanistan | AFG | 2006 | 6971758282.293506 |
| Afghanistan | AFG | 2007 | 9747886187.393929 |
| Afghanistan | AFG | 2008 | 10109297047.543194 |
| Afghanistan | AFG | 2009 | 12416152732.056717 |
| Afghanistan | AFG | 2010 | 15856668555.833595 |
| Afghanistan | AFG | 2011 | 17805098206.314083 |
| Afghanistan | AFG | 2012 | 19907329777.587166 |
| Afghanistan | AFG | 2013 | 20146416757.598675 |
| Afghanistan | AFG | 2014 | 20497128555.69723 |
| Afghanistan | AFG | 2015 | 19134221644.732494 |
| Afghanistan | AFG | 2016 | 18116572395.077213 |
| Afghanistan | AFG | 2017 | 18753456497.815865 |
| Afghanistan | AFG | 2018 | 18053222687.412624 |
| Afghanistan | AFG | 2019 | 18799444490.11278 |
| Afghanistan | AFG | 2020 | 19955929052.149597 |
| Afghanistan | AFG | 2021 | 14266499429.874573 |
| Afghanistan | AFG | 2022 | 14502158192.090395 |
Showing 24 of 4985 rows.
Reference output
| Country Name | Country Code | Year | Value | Code |
| Afghanistan | AFG | 2000 | 3521418059.923445 | AF |
| Afghanistan | AFG | 2001 | 2813571753.8725324 | AF |
| Afghanistan | AFG | 2002 | 3825701438.9996333 | AF |
| Afghanistan | AFG | 2003 | 4520946818.545814 | AF |
| Afghanistan | AFG | 2004 | 5224896718.67782 | AF |
| Afghanistan | AFG | 2005 | 6203256538.709669 | AF |
| Afghanistan | AFG | 2006 | 6971758282.293506 | AF |
| Afghanistan | AFG | 2007 | 9747886187.393929 | AF |
| Afghanistan | AFG | 2008 | 10109297047.543194 | AF |
| Afghanistan | AFG | 2009 | 12416152732.056717 | AF |
| Afghanistan | AFG | 2010 | 15856668555.833595 | AF |
| Afghanistan | AFG | 2011 | 17805098206.314083 | AF |
| Afghanistan | AFG | 2012 | 19907329777.587166 | AF |
| Afghanistan | AFG | 2013 | 20146416757.598675 | AF |
| Afghanistan | AFG | 2014 | 20497128555.69723 | AF |
| Afghanistan | AFG | 2015 | 19134221644.732494 | AF |
| Afghanistan | AFG | 2016 | 18116572395.077213 | AF |
| Afghanistan | AFG | 2017 | 18753456497.815865 | AF |
| Afghanistan | AFG | 2018 | 18053222687.412624 | AF |
| Afghanistan | AFG | 2019 | 18799444490.11278 | AF |
| Afghanistan | AFG | 2020 | 19955929052.149597 | AF |
| Afghanistan | AFG | 2021 | 14266499429.874573 | AF |
| Afghanistan | AFG | 2022 | 14502158192.090395 | AF |
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
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.
pwd && rg --files -g 'gdp.csv' -g 'countries.csv'output
gdp.csv countries.csv
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)) PYoutput
gdp.csv ['Country Name', 'Country Code', 'Year', 'Value'] countries.csv ['Name', 'Code']
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]) PYoutput
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'}]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') PYoutput
Wrote with-code.csv: 13979 rows, 9830 matched, 4149 unmatched
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
| Country Name | Country Code | Year | Value | Code |
| Afghanistan | AFG | 2000 | 3521418059.923445 | AF |
| Afghanistan | AFG | 2001 | 2813571753.8725324 | AF |
| Afghanistan | AFG | 2002 | 3825701438.9996333 | AF |
| Afghanistan | AFG | 2003 | 4520946818.545814 | AF |
| Afghanistan | AFG | 2004 | 5224896718.67782 | AF |
| Afghanistan | AFG | 2005 | 6203256538.709669 | AF |
| Afghanistan | AFG | 2006 | 6971758282.293506 | AF |
| Afghanistan | AFG | 2007 | 9747886187.393929 | AF |
| Afghanistan | AFG | 2008 | 10109297047.543194 | AF |
| Afghanistan | AFG | 2009 | 12416152732.056717 | AF |
| Afghanistan | AFG | 2010 | 15856668555.833595 | AF |
| Afghanistan | AFG | 2011 | 17805098206.314083 | AF |
| Afghanistan | AFG | 2012 | 19907329777.587166 | AF |
| Afghanistan | AFG | 2013 | 20146416757.598675 | AF |
| Afghanistan | AFG | 2014 | 20497128555.69723 | AF |
| Afghanistan | AFG | 2015 | 19134221644.732494 | AF |
| Afghanistan | AFG | 2016 | 18116572395.077213 | AF |
| Afghanistan | AFG | 2017 | 18753456497.815865 | AF |
| Afghanistan | AFG | 2018 | 18053222687.412624 | AF |
| Afghanistan | AFG | 2019 | 18799444490.11278 | AF |
| Afghanistan | AFG | 2020 | 19955929052.149597 | AF |
| Afghanistan | AFG | 2021 | 14266499429.874573 | AF |
| Afghanistan | AFG | 2022 | 14502158192.090395 | AF |
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
I’ll check the two CSV headers and join on the country name, then write `with-code.csv` in the current directory.
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
head -5 gdp.csv && head -5 countries.csvoutput
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
deft sheet --helpoutput
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
rg --files -g 'AGENTS.md' (exit 1)deft sheet join --helpoutput
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, columnsdeft sheet join gdp.csv countries.csv --on 'Country Name=Name' --how left -o with-code.csvoutput
ok sheet.join gdp.csv countries.csv -> with-code.csv 13979 rows x 5 columns 0.02s
Created [with-code.csv](with-code.csv) by left-joining `countries.csv` on country name. It contains 13,979 rows and 5 columns.
Output
| Country Name | Country Code | Year | Value | Code |
| Afghanistan | AFG | 2000 | 3521418059.923445 | AF |
| Afghanistan | AFG | 2001 | 2813571753.8725324 | AF |
| Afghanistan | AFG | 2002 | 3825701438.9996333 | AF |
| Afghanistan | AFG | 2003 | 4520946818.545814 | AF |
| Afghanistan | AFG | 2004 | 5224896718.67782 | AF |
| Afghanistan | AFG | 2005 | 6203256538.709669 | AF |
| Afghanistan | AFG | 2006 | 6971758282.293506 | AF |
| Afghanistan | AFG | 2007 | 9747886187.393929 | AF |
| Afghanistan | AFG | 2008 | 10109297047.543194 | AF |
| Afghanistan | AFG | 2009 | 12416152732.056717 | AF |
| Afghanistan | AFG | 2010 | 15856668555.833595 | AF |
| Afghanistan | AFG | 2011 | 17805098206.314083 | AF |
| Afghanistan | AFG | 2012 | 19907329777.587166 | AF |
| Afghanistan | AFG | 2013 | 20146416757.598675 | AF |
| Afghanistan | AFG | 2014 | 20497128555.69723 | AF |
| Afghanistan | AFG | 2015 | 19134221644.732494 | AF |
| Afghanistan | AFG | 2016 | 18116572395.077213 | AF |
| Afghanistan | AFG | 2017 | 18753456497.815865 | AF |
| Afghanistan | AFG | 2018 | 18053222687.412624 | AF |
| Afghanistan | AFG | 2019 | 18799444490.11278 | AF |
| Afghanistan | AFG | 2020 | 19955929052.149597 | AF |
| Afghanistan | AFG | 2021 | 14266499429.874573 | AF |
| Afghanistan | AFG | 2022 | 14502158192.090395 | AF |
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
| Country Name | Country Code | Year | Value | Code |
| Afghanistan | AFG | 2000 | 3521418059.923445 | AF |
| Afghanistan | AFG | 2001 | 2813571753.8725324 | AF |
| Afghanistan | AFG | 2002 | 3825701438.9996333 | AF |
| Afghanistan | AFG | 2003 | 4520946818.545814 | AF |
| Afghanistan | AFG | 2004 | 5224896718.67782 | AF |
| Afghanistan | AFG | 2005 | 6203256538.709669 | AF |
| Afghanistan | AFG | 2006 | 6971758282.293506 | AF |
| Afghanistan | AFG | 2007 | 9747886187.393929 | AF |
| Afghanistan | AFG | 2008 | 10109297047.543194 | AF |
| Afghanistan | AFG | 2009 | 12416152732.056717 | AF |
| Afghanistan | AFG | 2010 | 15856668555.833595 | AF |
| Afghanistan | AFG | 2011 | 17805098206.314083 | AF |
| Afghanistan | AFG | 2012 | 19907329777.587166 | AF |
| Afghanistan | AFG | 2013 | 20146416757.598675 | AF |
| Afghanistan | AFG | 2014 | 20497128555.69723 | AF |
| Afghanistan | AFG | 2015 | 19134221644.732494 | AF |
| Afghanistan | AFG | 2016 | 18116572395.077213 | AF |
| Afghanistan | AFG | 2017 | 18753456497.815865 | AF |
| Afghanistan | AFG | 2018 | 18053222687.412624 | AF |
| Afghanistan | AFG | 2019 | 18799444490.11278 | AF |
| Afghanistan | AFG | 2020 | 19955929052.149597 | AF |
| Afghanistan | AFG | 2021 | 14266499429.874573 | AF |
| Afghanistan | AFG | 2022 | 14502158192.090395 | AF |
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
| Country Name | Country Code | Year | Value | Code |
| Afghanistan | AFG | 2000 | 3521418059.923445 | AF |
| Afghanistan | AFG | 2001 | 2813571753.8725324 | AF |
| Afghanistan | AFG | 2002 | 3825701438.9996333 | AF |
| Afghanistan | AFG | 2003 | 4520946818.545814 | AF |
| Afghanistan | AFG | 2004 | 5224896718.67782 | AF |
| Afghanistan | AFG | 2005 | 6203256538.709669 | AF |
| Afghanistan | AFG | 2006 | 6971758282.293506 | AF |
| Afghanistan | AFG | 2007 | 9747886187.393929 | AF |
| Afghanistan | AFG | 2008 | 10109297047.543194 | AF |
| Afghanistan | AFG | 2009 | 12416152732.056717 | AF |
| Afghanistan | AFG | 2010 | 15856668555.833595 | AF |
| Afghanistan | AFG | 2011 | 17805098206.314083 | AF |
| Afghanistan | AFG | 2012 | 19907329777.587166 | AF |
| Afghanistan | AFG | 2013 | 20146416757.598675 | AF |
| Afghanistan | AFG | 2014 | 20497128555.69723 | AF |
| Afghanistan | AFG | 2015 | 19134221644.732494 | AF |
| Afghanistan | AFG | 2016 | 18116572395.077213 | AF |
| Afghanistan | AFG | 2017 | 18753456497.815865 | AF |
| Afghanistan | AFG | 2018 | 18053222687.412624 | AF |
| Afghanistan | AFG | 2019 | 18799444490.11278 | AF |
| Afghanistan | AFG | 2020 | 19955929052.149597 | AF |
| Afghanistan | AFG | 2021 | 14266499429.874573 | AF |
| Afghanistan | AFG | 2022 | 14502158192.090395 | AF |
Showing 24 of 4655 rows.
Claude CodeWithout deftNot run yet
Claude CodeWith deftNot run yet