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Memory-efficient streaming XLSX to CSV converter for any data source.

Project description

xlsx-streamer

GitHub Actions uv Nox Ruff Type checked with mypy

xlsx-streamer is a memory-efficient Python library and CLI tool for streaming large XLSX files to CSV format from any data source. It supports reading from local files, AWS S3, and HTTP/HTTPS URLs without loading entire files into memory.

Features

  • Multi-source support: Read XLSX files from local filesystem, AWS S3, and HTTP/HTTPS URLs
  • Memory-efficient streaming: Process large files without loading them entirely into memory
  • Simple unified API: Single XLSXReader class works with all source types
  • Flexible output: Stream rows as Python lists or convert directly to CSV files
  • Type hints: Fully typed for better IDE support and type checking
  • CLI tool: Command-line interface with source auto-detection

Quick Start

Python API

from xlsx_streamer import XLSXReader

# Auto-detect source type
reader = XLSXReader("s3://bucket/file.xlsx", sheet_name="Sheet1")  # S3
reader = XLSXReader("https://example.com/file.xlsx")                 # HTTP
reader = XLSXReader("/path/to/file.xlsx")                            # Local file

# Stream rows
for row in reader.stream_rows():
    print(row)

# Convert to CSV
reader.to_csv("output.csv")

CLI

# Local file
xlsx-streamer input.xlsx > output.csv

# From S3
xlsx-streamer s3://bucket/path/file.xlsx > output.csv

# From HTTP
xlsx-streamer https://example.com/file.xlsx > output.csv

# Save to file
xlsx-streamer input.xlsx --output output.csv

# Specify sheet
xlsx-streamer input.xlsx --sheet "Sheet2" > output.csv

# Enable verbose logging
xlsx-streamer input.xlsx --verbose

Installation

Install the base package with support for local files:

pip install xlsx-streamer

Add optional support for S3:

pip install xlsx-streamer[s3]

Add optional support for HTTP:

pip install xlsx-streamer[http]

Install all optional dependencies:

pip install xlsx-streamer[all]

Documentation

Acknowledgments

This project builds on the excellent work of:

License

MIT

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