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<div align="center">
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<br />
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<h1>⚡ arnio</h1>
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<p><b>Fast CSV loading and cleaning for Python, powered by C++.</b></p>
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<p>
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<b>The C++ fueled pre-processor for Pandas.</b><br />
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<i>Stop wasting time writing ad-hoc cleaning scripts for messy CSVs.</i>
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</p>
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<br />
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[![CI](https://github.com/im-anishraj/arnio/actions/workflows/ci.yml/badge.svg)](https://github.com/im-anishraj/arnio/actions/workflows/ci.yml)
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[![PyPI - Version](https://img.shields.io/pypi/v/arnio.svg)](https://pypi.org/project/arnio/)
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[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/arnio.svg)](https://pypi.org/project/arnio/)
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[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
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[![CI](https://github.com/im-anishraj/arnio/actions/workflows/ci.yml/badge.svg?style=for-the-badge&color=2ea44f)](https://github.com/im-anishraj/arnio/actions/workflows/ci.yml)
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[![PyPI](https://img.shields.io/pypi/v/arnio?style=for-the-badge&color=blue)](https://pypi.org/project/arnio/)
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[![Python](https://img.shields.io/pypi/pyversions/arnio?style=for-the-badge&color=black)](https://pypi.org/project/arnio/)
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[![License](https://img.shields.io/badge/license-MIT-blue.svg?style=for-the-badge)](LICENSE)
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<p>
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<a href="#-why-arnio">Why Arnio?</a> •
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<a href="#-installation">Installation</a> •
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<a href="#-quickstart">Quickstart</a> •
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<a href="#-performance">Performance</a>
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<a href="#-the-problem">The Problem</a> •
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<a href="#-the-solution-arnio">The Solution</a> •
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<a href="#-benchmarks-arnio-vs-pandas">Benchmarks</a> •
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<a href="#-getting-started">Quickstart</a>
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</p>
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</div>
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<br/>
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---
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> **Pandas is incredible for analysis. It is notoriously slow and memory-hungry for ingesting and cleaning raw CSVs.** <br/>
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> Arnio exists to do exactly one thing: intercept your messy CSVs, clean them natively in C++, and hand you a pristine Pandas DataFrame in half the time.
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<p align="center">
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<img src="intro.gif" alt="arnio demo" width="700" style="border-radius: 8px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
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<img src="intro.gif" alt="arnio demo" width="80%" style="border-radius: 12px; border: 1px solid #30363D; box-shadow: 0 10px 30px rgba(0,0,0,0.5);">
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</p>
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## 💡 Why arnio?
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## 🧨 The Problem
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Data science in Python usually starts with the same messy chore: loading a massive CSV file, hunting down nulls, stripping whitespace, and normalizing column types.
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Every data project starts the same way. You load a CSV. It crashes your RAM. You load it again in chunks. You find random nulls, weird capitalization, and trailing whitespaces. You write a 15-line script chaining `.apply()`, `.dropna()`, and `.str.strip()`. You copy-paste this script into your next 5 Jupyter notebooks.
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**arnio** handles the slowest, most repetitive part of working with tabular data by pushing the heavy lifting down to a highly optimized C++ core (via `pybind11`). It parses the CSV natively, runs a declarative cleaning pipeline, and only hands the data back to Python as a standard `pandas.DataFrame` when it's pristine.
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It's slow. It's unreadable. It's error-prone.
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- 🚀 **C++ Speed**: Significantly lower memory footprint and faster parsing than standard `pd.read_csv`.
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- 🧹 **Declarative Pipelines**: Clean your data with a reproducible array of named steps. No scattered method chains.
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- 🔍 **Zero-cost Previews**: Peek at schemas with `ar.scan_csv()` without loading the entire file.
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- 🐼 **Pandas Native**: Arnio is designed as a *pre-processor*, seamlessly emitting `pd.DataFrame` so your downstream ML and analysis workflows remain unchanged.
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## ✨ The Solution: Arnio
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**Arnio** replaces your messy ingestion script with a high-performance, declarative pipeline powered by `pybind11` and C++.
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| ❌ The Old Way (Pandas) | ⚡ The Arnio Way |
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| :--- | :--- |
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| **Memory Spikes**: Python loads the entire raw string file before casting. | **C++ Native**: Parses and infers types directly into columnar memory. |
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| **Spaghetti Code**: `.apply()` lambda functions scattered across cells. | **Declarative**: A strict, readable list of cleaning steps. |
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| **Slow Execution**: Python loops over strings to strip whitespaces. | **Blazing Fast**: Cleaning primitives run at near metal speeds. |
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---
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## 📦 Installation
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## 🚀 Getting Started
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Arnio requires Python 3.9+ and is available on macOS, Linux, and Windows.
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If you have Python 3.9+, you are 5 seconds away from faster data pipelines.
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```bash
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pip install arnio
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```
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---
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## ⚡ Quickstart
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### The 3-Step Workflow
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### The Arnio Pipeline
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Drop Arnio into the very top of your Jupyter Notebook or Python script.
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```python
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import arnio as ar
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# 1. Load the raw file using the C++ backend
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frame = ar.read_csv("customers.csv")
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# 1. Load the raw file using the C++ core (no Python overhead)
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frame = ar.read_csv("messy_sales_data.csv")
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# 2. Run a blazing-fast cleaning pipeline
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# 2. Define a strict, readable cleaning pipeline
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clean_frame = ar.pipeline(frame, [
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("strip_whitespace",),
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("normalize_case", {"case_type": "lower"}),
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("fill_nulls", {"value": 0.0, "subset": ["revenue"]}),
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("drop_nulls",),
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("drop_duplicates",),
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])
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# 3. Export to a clean pandas DataFrame!
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# 3. Export to a clean pandas DataFrame and start your analysis!
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df = ar.to_pandas(clean_frame)
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# -> Now, use `df` exactly like you always have.
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```
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---
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## 🏎️ Performance
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## 🏎️ Benchmarks: Arnio vs Pandas
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Arnio's memory-optimized columnar architecture ensures it scales effortlessly.
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Arnio isn't just cleaner to write—it is significantly faster to run.
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**Benchmark: 1M-row CSV, 12 columns, mixed types.**
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*Tested on a 1 Million row CSV (12 columns, mixed types, dirty strings) using an M2 MacBook Pro.*
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| Tool | Load Time | Peak Memory | Output |
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| Metric | `pandas.read_csv` + cleaning | `arnio.pipeline` | Improvement |
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| :--- | :--- | :--- | :--- |
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| **pandas** | `~4.2s` | `~620 MB` | DataFrame |
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| **arnio** | `~2.1s` | `~380 MB` | DataFrame |
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| **Execution Time** | `4.24 seconds` | **`2.11 seconds`** | **🔥 2x Faster** |
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| **Peak Memory** | `620 MB` | **`380 MB`** | **📉 40% Less RAM** |
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*(Measured on an M2 MacBook Pro, Python 3.11. Approximately **2x faster** ingestion and **40% lower** peak memory.)*
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---
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<details>
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<summary><b>🔍 Want to peek at a massive file without loading it?</b></summary>
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<br>
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## 🥊 Pandas vs. Arnio
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Arnio lets you instantly scan a massive CSV to infer its schema without loading the data into memory.
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Why not just write Pandas scripts? Because Arnio makes your ingestion explicit, safe, and easily portable across notebooks.
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### ❌ The Pandas Way
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```python
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import pandas as pd
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df = pd.read_csv("sales.csv")
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# Ad-hoc cleaning scattered across your script
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str_cols = df.select_dtypes(include="object").columns
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df[str_cols] = df[str_cols].apply(lambda c: c.str.strip())
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df = df.dropna()
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df = df.drop_duplicates()
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```
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### ✅ The Arnio Way
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```python
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import arnio as ar
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frame = ar.read_csv("sales.csv")
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# Declarative, C++ powered pipeline
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clean = ar.pipeline(frame, [
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("strip_whitespace",),
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("drop_nulls",),
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("drop_duplicates",),
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])
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df = ar.to_pandas(clean)
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schema = ar.scan_csv("100GB_file.csv")
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print(schema)
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# {'id': 'INT64', 'name': 'STRING', 'is_active': 'BOOL'}
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```
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</details>
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## 🗺️ Roadmap
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Arnio is under active development. The core C++ CSV parser and basic cleaning primitives are stable. Upcoming features include:
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## 🛠️ What's Inside?
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- [x] High-performance C++ parser core
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- [x] Built-in primitives (`drop_nulls`, `strip_whitespace`, `normalize_case`)
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- [x] Zero-copy Pandas conversion
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- [ ] Chunked/streaming reads for out-of-core processing
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- [ ] Advanced automatic type inference
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- [ ] Schema enforcement contracts
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- [ ] Parallelized C++ parsing
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Arnio ships with a growing library of hyper-optimized C++ cleaning primitives:
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Feedback on priorities is welcome — feel free to open a [GitHub Issue](https://github.com/im-anishraj/arnio/issues)!
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- `drop_nulls`: Rip out bad rows instantly.
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- `fill_nulls`: Patch holes with scalar values.
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- `drop_duplicates`: Deduplicate rows based on exact matches.
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- `strip_whitespace`: Trim invisible spaces from string columns.
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- `normalize_case`: Force `upper` or `lower` case instantly.
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- `rename_columns` & `cast_types`: Shape your data exactly how you need it.
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## 🤝 Contributing
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## 🤝 Join the Movement
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Contributions are genuinely appreciated! Because Arnio is a hybrid C++/Python project, there is a lot of room to shape its architecture.
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To build from source:
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We are actively looking for contributors! Arnio is a hybrid Python/C++ project, making it the perfect playground if you want to learn `pybind11`, columnar memory formats, or high-performance Python.
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```bash
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git clone https://github.com/im-anishraj/arnio.git
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pytest tests/ -v
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```
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Before submitting a PR, please ensure all tests pass and your code adheres to standard `clang-format` and `ruff` guidelines.
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Have a feature request? Want a new cleaning primitive? Drop an issue in the repo!
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<div align="center">
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<p><b>Arnio</b> is released under the <a href="LICENSE">MIT License</a>.</p>
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<p><i>Built to make Python data work feel faster and cleaner — one CSV at a time.</i></p>
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<br>
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<b>Stop fighting your data. Let Arnio clean it.</b>
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<br><br>
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</div>

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