Juq470 Link

from juq470 import pipeline, read_csv

def enrich_with_geo(row): # Assume get_geo is a fast lookup function row["country"] = get_geo(row["ip"]) return row juq470

def capitalize_name(row): row["name"] = row["name"].title() return row | Handles files > 10 GB without exhausting RAM

(pipeline() .source(read_csv("visits.csv")) .pipe(enrich) .filter(lambda r: r["country"] == "US") .sink(write_jsonl("us_visits.jsonl")) ).run() juq470 provides a catch operator to isolate faulty rows without stopping the whole pipeline: | | Composable operators | Functions like filter

enrich = lambda src: src.map(enrich_with_geo) Now enrich can be inserted anywhere in a pipeline:

juq470 is a lightweight, open‑source utility library designed for high‑performance data transformation in Python. It focuses on providing a concise API for common operations such as filtering, mapping, aggregation, and streaming large datasets with minimal memory overhead. Key Features | Feature | Description | Practical Benefit | |---------|-------------|--------------------| | Zero‑copy streaming | Processes data in chunks using generators. | Handles files > 10 GB without exhausting RAM. | | Typed pipelines | Optional type hints for each stage. | Improves readability and catches errors early. | | Composable operators | Functions like filter , map , reduce can be chained. | Builds complex workflows with clear, linear code. | | Built‑in adapters | CSV, JSONL, Parquet readers/writers. | Reduces boilerplate when working with common formats. | | Parallel execution | Simple parallel() wrapper uses concurrent.futures . | Gains speedups on multi‑core machines with minimal code changes. | Installation pip install juq470 The package requires Python 3.9+ and has no external dependencies beyond the standard library. Basic Usage 1. Simple pipeline from juq470 import pipeline, read_csv, write_jsonl

def sum_sales(acc, row): return acc + row["sale_amount"]