| name | sheetsmith |
| description | Pandas-powered CSV & Excel management for quick previews, summaries, filtering, transforming, and format conversions. Use this skill whenever you need to inspect spreadsheet files, compute column-level summaries, apply queries or expressions, or export cleansed data to a new CSV/TSV/XLSX output without rewriting pandas every time. |
Sheetsmith
Overview
Sheetsmith is a lightweight pandas wrapper that keeps the focus on working with CSV/Excel files: previewing, describing, filtering, transforming, and converting them in one place. The CLI lives at skills/sheetsmith/scripts/sheetsmith.py, and it automatically loads any CSV/TSV/Excel file, reports structural metadata, runs pandas expressions, and writes the results back safely.
Quick start
- Place the spreadsheet (CSV, TSV, or XLS/XLSX) inside the workspace or reference it via a full path.
- Run
python3 skills/sheetsmith/scripts/sheetsmith.py <command> <path> with the command described below.
- When you modify data, either provide
--output new-file to save a copy or pass --inplace to overwrite the source file.
- Check
references/usage.md for extra sample commands and tips.
Commands
summary
Prints row/column counts, dtype breakdowns, columns with missing data, and head/tail previews. Use --rows to control how many rows are shown after the summary and --tail to preview the tail instead of the head.
describe
Runs pandas.DataFrame.describe(include='all') (customizable with --include) so you instantly see numeric statistics, cardinality, and frequency information. Supply --percentiles to add additional percentile lines.
preview
Shows a quick tabulated peek at the first (--rows) or last (--tail) rows so you can sanity-check column order or formatting before taking actions.
filter
Enter a pandas query string via --query (e.g., state == 'CA' and population > 1e6). The command can either print the filtered rows or, when you also pass --output, write the filtered table to a new CSV/TSV/XLSX file. Add --sample to inspect a random subset instead of the entire result.
transform
Compose new columns, rename or drop existing ones, and immediately inspect the resulting table. Provide one or more --expr expressions such as total = quantity * price. Use --rename old:new and --drop column to reshape the table, and persist changes via --output or --inplace. The preview version (without writing) reuses the same --rows/--tail flags as the other commands.
convert
Convert between supported formats (CSV/TSV/Excel). Always specify --output with the desired extension, and the helper will detect the proper writer (Excel uses openpyxl, CSV preserves the comma separator by default, TSV uses tabs). This is the simplest way to normalize data before running other commands.
Workflow rules
- Always keep a copy of the raw file or write to a new path; the script will only overwrite the original when you explicitly demand
--inplace.
- Use the same CLI for both exploration (
summary, preview, describe) and editing (filter, transform). The --output flag works for filter/transform so you can easily branch results.
- Behind the scenes, the script relies on pandas +
tabulate for Markdown previews and supports Excel/CSV/TSV, so ensure those dependencies are present (pandas, openpyxl, xlrd, tabulate are installed via apt on this system).
- Use
references/usage.md for extended examples (multi-step cleaning, dataset comparison, expression tips) when the basic command descriptions above are not enough.
References
- Usage guidelines:
references/usage.md (contains ready-to-copy commands, expression patterns, and dataset cleanup recipes).
Resources
