batch_please

A flexible and efficient Python library for processing large datasets in batches, with support for both synchronous and asynchronous operations.

Features

• Process large datasets in customizable batch sizes
• Support for both synchronous and asynchronous processing
• Checkpoint functionality to resume processing from where it left off
• Optional progress bar using tqdm
• Configurable concurrency limit for asynchronous processing
• Logging support

Installation

pip install batch_please  

Quick Start

Synchronous Processing

from batch_please import BatchProcessor

def process_func(item):
    return f"Processed: {item}"

processor = BatchProcessor(
    process_func=process_func,
    batch_size=100,
    use_tqdm=True
)

input_data = range(1000)
processed_items = processor.process_items_in_batches(input_data)

Asynchronous Processing

import asyncio
from batch_please import AsyncBatchProcessor

async def async_process_func(item):
    await asyncio.sleep(0.1)
    return f"Processed: {item}"

async def main():
    processor = AsyncBatchProcessor(
        process_func=async_process_func,
        batch_size=100,
        max_concurrent=10,
        use_tqdm=True
    )

    input_data = range(1000)
    processed_items = await processor.process_items_in_batches(input_data)

asyncio.run(main())

Advanced Usage

Checkpoint Recovery

processor = BatchProcessor(
    process_func=process_func,
    batch_size=100,
    pickle_file="checkpoint.pkl",
    recover_from_checkpoint=True
)

Logging

processor = BatchProcessor(
    process_func=process_func,
    batch_size=100,
    logfile="processing.log"
)

Further Documentation