Contributing

Quick Start

macOS / Linux

git clone https://github.com/im-anishraj/arnio.git
cd arnio
make install
make test
make lint

Windows

Install Visual Studio Build Tools with the "Desktop development with C++" workload, then:

git clone https://github.com/im-anishraj/arnio.git
cd arnio
pip install -e ".[dev]"
pre-commit install

Adding a Python Pipeline Step

Many new features don't require touching C++. You can write a pure Python step and register it with Arnio, then add focused tests and documentation.

Step 1: Write the function

import arnio as ar

def remove_special_chars(df, columns=None):
    cols = columns or df.select_dtypes("object").columns
    for col in cols:
        df[col] = df[col].str.replace(r"[^a-zA-Z0-9\s]", "", regex=True)
    return df

ar.register_step("remove_special_chars", remove_special_chars)

Step 2: Write tests

def test_remove_special_chars(sample_csv):
    ar.register_step("remove_special_chars", remove_special_chars)
    frame = ar.read_csv(sample_csv)

    result = ar.pipeline(frame, [
        ("remove_special_chars",),
    ])
    df = ar.to_pandas(result)

    assert "name" in df.columns
    # Add your specific assertions here

Step 3: Open a PR

That's it. No build system changes, no C++ compiler, no pybind11.

Data Quality Contributions

The data quality layer is also friendly to Python contributors. High-impact areas include semantic detectors, validation rules, examples, and tests around messy real-world CSVs.

• Profiling: Add safe, explainable signals to profile() such as date-like columns, constant columns, or suspicious cardinality.
• Validation: Extend Field rules only when the behavior is deterministic and easy to test.
• Auto-cleaning: Keep suggestions conservative; anything destructive should require explicit user choice.
• Documentation: Add before/after examples that show real user workflows instead of isolated toy snippets.

C++ Contributions

For developers comfortable with C++, the highest-impact work right now is performance optimization:

• drop_duplicates — Replace naive O(n²) with hash-based deduplication
• strip_whitespace — Implement in-place mutation to avoid copies

These two functions are the primary performance bottleneck. See Benchmarks for details.

Pull Request Process

1. Fork the repo and create your branch from main.
2. Conventional Commits: Ensure your PR title and commits follow the Conventional Commits specification (e.g., feat: add support for..., fix: resolve issue with...). This is required for our automated release system.
3. If you've added code that should be tested, add tests.
4. If you've changed APIs, update the documentation.
5. Ensure the test suite passes: make test or pytest tests/ -v
6. Ensure your code passes linting and formatting: make lint and pre-commit run --all-files
7. Issue the pull request.

Code Style

Arnio uses:

• Python: black (line-length 88) + ruff
• C++: clang-format

pre-commit runs these automatically before each commit if installed.