rs-csv-reader 0.1.4

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CSV Reader - Optimization Performance CSV Parser for Python

A Optimization performance CSV parsing library written in Rust with Python bindings. Designed for efficiently processing large CSV files with minimal memory footprint.

Features

• High Performance: Parse CSV files significantly faster than Python's native CSV module
• Memory Efficient: Process files in optimized batches to reduce memory usage
• Large File Support: Efficiently handle multi-gigabyte CSV files
• Customizable Batch Size: Control memory usage and batch processing

Installation

pip install rs-csv-reader
# Or if you're using UV
uv pip install rs-csv-reader

Usage

Basic Usage

from csv_reader import CSVParser

# Create a parser with default settings
parser = CSVParser("large_file.csv", batch_size=5000)

# Read the file in batches
batches = parser.read()

# Process each batch
for batch in batches:
    for row in batch:
        # Each row is a dictionary with column names as keys
        print(row['id'], row['amount'])

# Count rows without loading the entire file
total_rows = parser.count_rows()
print(f"Total rows: {total_rows}")

Reading Specific Chunks

Efficiently read specific portions of a CSV file without loading the entire file:

# Read a specific chunk (starting from row 10000, reading 1000 rows)
chunk = parser.read_chunk(start_row=10000, num_rows=1000)

# Process the chunk
for row in chunk:
    process_row(row)

Get File Information

# Get file metadata
file_info = parser.get_file_info()
print(f"File size: {file_info['size_mb']} MB")
print(f"Headers: {file_info['headers']}")

Performance

Can see on this repository profiling testing, testing with:

• Intel core i7
• 16 gb memory
• 8 core cpu

Benchmarks on a 2-million row CSV file:

Parser	Time (s)	Memory (MB)	Rows/sec
Python CSV	4.23	1583.94	473028.05
Pandas (full)	9.42	1260.59	212226.58
Pandas (chunked)	9.13	1231.05	219060.70
CSV Reader	2.95	3183.24	678927.10

How It Works

This library uses Rust's high-performance CSV parsing capabilities with smart buffering techniques:

• For files under 100MB: Loads the entire file into memory for maximum speed
• For larger files: Uses efficient buffered reading with a 64KB buffer
• Processes data in batches to balance memory usage and performance

Building from Source

Prerequisites

• Rust toolchain (install from rust-lang.org)
• Maturin (pip install maturin)
• Python development headers (python-dev or python3-dev package on Linux)

# Clone the repository
git clone https://github.com/yourusername/csv-reader.git
cd csv-reader

# Build the Rust library
maturin build --release

# Install the built wheel
pip install target/wheels/rs_csv_reader-*.whl

You can also use development mode for a faster workflow during development:

maturin develop --release

Requirements

• Python 3.7+
• No additional dependencies required!

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Acknowledgments

• The Rust CSV crate for providing the underlying parsing engine
• PyO3 for making Rust-Python bindings seamless

Profiling Tools

For doing a testing, this the tools for test the CSV reader https://github.com/yosephbernandus/csv_reader_profiling