XLcompress
Compress Excel spreadsheets for LLM context windows. Written in Rust.
Excel files are one of the most common data formats, but feeding them to LLMs wastes tokens on empty cells, repetitive formatting, and verbose address-value pairs. xlcompress applies the SpreadsheetLLM compression pipeline to reduce spreadsheet token usage by 80-95% while preserving the structural information LLMs need.
Compression results
| Sheet type | Original tokens | Compressed tokens | Reduction |
|---|---|---|---|
| Financial model (200 rows) | 5,240 | 799 | 84.7% |
| Lookup table (100 rows) | 1,922 | 186 | 90.3% |
| Sparse sheet (few values) | 106 | 106 | 0% (auto-fallback) |
Sparse sheets with scattered values automatically fall back to raw encoding when compression would increase size.
Install
pip install xlcompress
Pre-built wheels for Linux (x86_64, aarch64), macOS (x86_64, ARM), and Windows (x86_64). No Python dependencies. No Rust toolchain required.
Quickstart
import xlcompress
# One-liner: compress and get a prompt-ready string
prompt = xlcompress.compress_to_string("financials.xlsx")
# Per-sheet results with token counts
results = xlcompress.compress("financials.xlsx")
for sheet in results:
print(f"{sheet.name}: {sheet.original_tokens} -> {sheet.compressed_tokens} tokens")
Usage
Excel files
import xlcompress
# All sheets
results = xlcompress.compress("report.xlsx")
# Specific sheets
results = xlcompress.compress("data.xlsb", sheets=["Q1", "Q2"])
# From bytes (S3, HTTP responses, file uploads)
results = xlcompress.compress(file_bytes, format="xlsx")
# List sheets without compressing
names = xlcompress.list_sheets("workbook.xlsx")
CSV
# From a CSV file
result = xlcompress.compress_csv("data.csv")
# From a CSV string
csv_text = "Name,Age,City\nAlice,30,NYC\nBob,25,LA"
result = xlcompress.compress_csv(csv_text)
# Custom delimiter
result = xlcompress.compress_csv("data.tsv", delimiter="\t")
Text and grids
# Tab-delimited text (e.g. pasted from a spreadsheet)
pasted = "Region\tQ1\tQ2\nNorth\t1000\t2000\nSouth\t800\t1200"
result = xlcompress.compress_text(pasted)
# Raw 2D grid
grid = [["Name", "Score"], ["Alice", "95"], ["Bob", "87"]]
result = xlcompress.compress_grid(grid)
Output modes
# Aggregated (default) — best compression, type-labeled ranges
xlcompress.compress("file.xlsx", mode="aggregated")
# -> "(IntNum|A1:B10),(DateData|C1:C5),..."
# Vanilla — preserves all cell values, no compression
xlcompress.compress("file.xlsx", mode="vanilla")
# -> "|A1, Revenue|\n|B1, 1000|\n..."
# Inverted index — value-to-address mapping
xlcompress.compress("file.xlsx", mode="inverted")
# -> {"Revenue": ["A1"], "1000": ["B1", "C3"], ...}
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
source |
str, PathLike, bytes |
required | File path or raw file bytes |
format |
str | None |
None |
"xlsx" or "xlsb". Required for bytes. Auto-detected for paths. |
sheets |
list[str] | None |
None |
Filter to specific sheets. None = all. |
mode |
str |
"aggregated" |
"aggregated", "vanilla", or "inverted" |
structural |
bool |
True |
Run table boundary detection |
structural_k |
int |
4 |
Rows/cols to keep around detected boundaries |
Return type
class SheetResult:
name: str # Sheet name
original: str # Raw |Address, Value| representation
compressed: str # Compressed output
original_tokens: int # Token count before compression
compressed_tokens: int # Token count after compression
How it works
The compression pipeline runs three stages:
1. Structural compression — Detects table boundaries using heuristic analysis of cell patterns, then keeps only rows and columns near detected tables. Large empty regions are removed entirely.
2. Data-format aggregation — Groups contiguous cells of the same type (integers, dates, text, currencies, etc.) into labeled ranges. A column of 100 integers becomes (IntNum|A1:A100) instead of 100 separate entries.
3. Smart output selection — Renders the result in the chosen mode. For sparse sheets where aggregation would inflate the output, automatically falls back to vanilla encoding.
Why Rust?
The pipeline involves BFS flood-fill over cell grids, regex-based type detection, and boundary analysis — all CPU-bound work that benefits from compiled performance. The Python interface is a thin PyO3 wrapper over the Rust implementation, with no Python dependencies.
Pipeline
Based on the SheetCompressor pipeline from:
Hao, Y., et al. "SpreadsheetLLM: Encoding Spreadsheets for Large Language Models." arXiv:2407.09025, 2024.
Architecture
| Crate | Role |
|---|---|
xlcompress |
Python bindings (PyO3), Excel parsing (calamine), pipeline orchestration |
compress_aggregation |
Data-format aggregation via BFS flood-fill |
compress_structure |
Structural compression with boundary-based filtering |
heuristic_detection |
Table boundary detection (TableSense hybrid algorithm) |
compress_excel |
Standalone CLI for JSONL input |
xlsb_to_xlsx |
.xlsb to .xlsx converter |
wasm_api |
WebAssembly bindings for browser use |
Supported inputs
| Input | Function | Notes |
|---|---|---|
.xlsx |
compress() |
Excel 2007+ XML format |
.xlsb |
compress() |
Excel binary format |
| Bytes | compress() |
Raw file bytes with format="xlsx" or format="xlsb" |
| CSV | compress_csv() |
File path, string, or file-like object |
| Text | compress_text() |
Tab-delimited or custom delimiter |
| 2D grid | compress_grid() |
list[list[str]] directly |
Browser UI
A drag-and-drop browser interface is included in docs/. Serve it locally or deploy to GitHub Pages.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for xlcompress 0.3.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| xlcompress-0.3.5.tar.gz | 61.9 kB | With provenance | Details |
Built distributions (wheels)
Total release size: 36.6 MB
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| Tags | Source |
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