datacell

A fast, unified CLI tool for spreadsheet and columnar data manipulation.

Features

• Multi-format support: CSV, XLSX, XLS, ODS, Parquet, Avro
• Excel-like formulas: SUM, AVERAGE, VLOOKUP, IF, and more
• Data operations: sort, filter, dedupe, transpose, join, groupby
• Convert between formats: One command for any format conversion
• Output options: CSV, JSON, Markdown
• MCP server: For AI assistant integration

Installation

# Homebrew (macOS/Linux)
brew tap yingkitw/datacell
brew install datacell

# From source
cargo build --release
sudo cp target/release/datacell /usr/local/bin/

# Using cargo
cargo install datacell

Quick Start

# Convert formats
datacell convert --input data.csv --output data.xlsx

# Apply formula
datacell formula --input sales.csv --output result.csv --formula "SUM(C2:C100)" --cell "D1"

# Filter and sort
datacell filter --input data.csv --output filtered.csv --where "status = 'active'"
datacell sort --input filtered.csv --output sorted.csv --column date --descending

# Output as JSON
datacell read --input report.xlsx --format json > report.json

Common Commands

Read & Write

# Read any format
datacell read --input data.csv
datacell read --input data.xlsx --sheet "Sheet2"
datacell read --input data.parquet

# Read with format conversion
datacell read --input data.csv --format json
datacell read --input data.csv --format markdown

# Write to any format
datacell write --output output.xlsx --csv input.csv
datacell write --output output.parquet --csv input.csv

Convert Formats

datacell convert --input data.csv --output data.xlsx
datacell convert --input data.xlsx --output data.parquet
datacell convert --input data.ods --output data.csv

Data Operations

# Sort
datacell sort --input data.csv --output sorted.csv --column A

# Filter
datacell filter --input data.csv --output filtered.csv --where "price > 100"

# Remove duplicates
datacell dedupe --input data.csv --output unique.csv

# Transpose
datacell transpose --input data.csv --output transposed.csv

# Find and replace
datacell replace --input data.csv --output result.csv --find "old" --replace "new"

Pandas-Style Operations

# First/last rows
datacell head --input data.csv -n 5
datacell tail --input data.csv -n 5

# Select columns
datacell select --input data.csv --output subset.csv --columns "name,age"

# Summary statistics
datacell describe --input data.csv

# Group and aggregate
datacell groupby --input sales.csv --output summary.csv --by category --agg "sum:amount"

# Join files
datacell join --left orders.csv --right customers.csv --output merged.csv --on customer_id

# Fill/drop missing values
datacell fillna --input data.csv --output filled.csv --value "N/A"
datacell dropna --input data.csv --output clean.csv

Formula Reference

Shell Completions

datacell completions zsh >> ~/.zshrc
datacell completions bash >> ~/.bashrc
datacell completions fish > ~/.config/fish/completions/datacell.fish

Configuration

Generate a config file with default options:

datacell config-init

Example .datacell.toml:

[excel]
header_bold = true
header_bg_color = "4472C4"
auto_filter = true
freeze_header = true

[output]
default_format = "csv"
include_headers = true

Custom XLSX Writer

datacell generates Excel files using a from-scratch XLSX writer — no external Excel writing library needed. The writer produces Office Open XML (OOXML) files directly as ZIP archives containing XML, validated against the ECMA-376 standard.

Why Build From Scratch?

We tried several existing Rust crates for XLSX writing and ran into issues:

Building our own writer using just the zip crate gave us full control over the XML output, a smaller dependency footprint, and the ability to fix compatibility issues directly. The trade-off is that advanced features (charts, conditional formatting) require implementing complex XML markup ourselves.

What It Supports

• Multiple sheets with name validation
• Cell types: String, Number, Formula, Empty
• Column widths (auto-fit and manual)
• Freeze headers (frozen top row)
• Auto-filter for table columns
• Basic styling (bold headers, alignment, borders, fills)
• Proper OOXML structure — opens in Excel, Numbers, LibreOffice, Google Sheets

Usage

# Convert CSV to XLSX
datacell convert --input data.csv --output data.xlsx

# Export with styled headers, freeze panes, and auto-filter
datacell export-styled --input data.csv --output styled.xlsx

# Write formulas to XLSX
datacell formula --input data.csv --output result.xlsx --formula "SUM(C2:C100)" --cell "D1"

// Programmatic usage
use datacell::excel::xlsx_writer::{XlsxWriter, RowData};

let mut writer = XlsxWriter::new();
writer.add_sheet("Sales")?;

let mut header = RowData::new();
header.add_string("Product");
header.add_string("Revenue");
writer.add_row(header);

let mut row = RowData::new();
row.add_string("Widget");
row.add_number(1234.56);
writer.add_row(row);

let file = std::fs::File::create("output.xlsx")?;
writer.save(&mut std::io::BufWriter::new(file))?;

Current Limitations

• Charts — not yet implemented (requires complex XML drawing/chart markup)
• Sparklines — not yet implemented
• Conditional formatting — not yet implemented (color scales, data bars, icon sets)
• Merged cells — not yet implemented
• Rich text within cells — not supported

For charts and visualizations, consider exporting to CSV and using dedicated plotting tools (e.g., Python matplotlib, gnuplot).

MCP Server

Start the MCP server for AI assistant integration:

datacell serve