im-anishraj

im-anishraj / arnio

Public

C++-accelerated data quality toolkit for Python: clean CSVs, profile messy datasets, validate schemas, and work smoothly with pandas.

arnio.vercel.app cpp csv csv-parser data-cleaning data-engineering
25
38
100% credibility
Found May 14, 2026 at 26 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Arnio is a fast data cleaning tool that processes messy CSV files by removing whitespace, handling nulls and duplicates, inferring types, and delivering clean tables for analysis.

How It Works

1
📖 Discover Arnio

You hear about a handy tool that quickly cleans up messy spreadsheet files full of extra spaces, missing values, and repeats.

2
💻 Set it up

You add this helper to your computer setup in just seconds with a simple instruction.

3
📁 Load your file

You pick your messy data file, like a sales list or customer sheet, and let it read it right away.

4
🧹 Clean with ease

You list simple fixes like trimming spaces, filling gaps, or removing duplicates, and it handles everything smoothly.

5
🔍 Check quality

You get a quick report on data health and smart suggestions to make it even better.

🎉 Perfect data ready

Now you have a clean, reliable table to explore trends and make decisions without any frustration.

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Star Growth

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AI-Generated Review

What is arnio?

Arnio is a Python toolkit with C++ acceleration for cleaning CSVs, profiling messy datasets, and validating schemas before handing off to pandas. Load a raw file with `ar.read_csv`, declare a pipeline of steps like stripping whitespace or dropping duplicates, and get a pristine DataFrame via zero-copy conversion. It skips pandas' slow initial string parsing, solving the pain of multiple full-data passes on dirty data.

Why is it gaining traction?

It stands out with declarative pipelines that run natively in C++ for speed parity with pandas on memory, plus built-in profiling (`ar.profile`) and schema checks (`ar.validate`). Developers dig the quick schema scans on huge files without loading them, and easy custom Python steps that bridge to C++ later. Benchmarks show it handles 1M-row CSVs cleanly, beating imperative chains.

Who should use this?

Data engineers building ETL pipelines on messy customer CSVs, ML practitioners preprocessing large datasets for pandas models, and analysts tired of boilerplate null-filling and deduping. Ideal for anyone hitting RAM spikes or slow `.str.strip()` loops on production data imports.

Verdict

Try it for CSV-heavy workflows—solid docs, benchmarks, and PyPI wheels make evaluation easy despite 25 stars and 1.0% credibility score. Still early (v1.1), but MIT-licensed with CI and contributor-friendly extensions; optimize hot paths as it matures.

(198 words)

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