aiopandas 0.0.3

Lightweight Pandas monkey-patch for async map, apply, transform, etc.

aiopandas

🚀 Async-Powered Pandas: Lightweight Pandas monkey-patch that adds async support to map, apply, applymap, aggregate, and transform, enabling seamless handling of async functions with controlled parallel execution (max_parallel).

✨ Features

• Drop-in replacement for Pandas functions, now supporting async functions.
• Automatic async execution with controlled concurrency via max_parallel.
• Built-in error handling – choose between raising, ignoring, or logging errors.
• Supports tqdm for real-time progress tracking.

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🚀 Quick Start

import aiopandas as pd  # Monkey-patches Pandas with async methods
import asyncio

# Create a sample DataFrame
df = pd.DataFrame({'x': range(10)})

# Define an async function (simulating API calls, I/O, etc.)
async def f(x):
    await asyncio.sleep(0.1 * x)  # Simulate async processing
    return x * 2  # Example transformation

# Apply the async function to the DataFrame column
df['y'] = await df.x.amap(f, max_parallel=5)  # Default max_parallel=16
print(df)

⚠️ Handling Errors Gracefully

aiopandas includes built-in error handling, allowing you to manage failures without breaking the entire operation.

1. Default behavior (raise) – stops on the first error

async def f(x):
    if x > 50 and x % 3:
        raise Exception('exception example')
    await asyncio.sleep(0.01 * x)
    return x

df = pd.DataFrame({'x': range(100)})

df['y'] = await df.x.amap(f, max_parallel=50)  # Raises an exception

Output (Error traceback):

Exception: exception example

2. Ignore errors (on_error='ignore')

df['y'] = await df.x.amap(f, max_parallel=50, on_error='ignore')  # Easy to ignore exceptions

Now, instead of crashing, rows that trigger exceptions return NaN:

print(df['y'])

0      0.0
1      1.0
2      2.0
...
95     NaN
96    96.0
97     NaN
98     NaN
99    99.0
Name: y, Length: 100, dtype: float64

3. Custom error handling (on_error=print)

You can log or process errors with a custom function (or coroutines):

df['y'] = await df.x.amap(f, max_parallel=50, on_error=print)  # Print errors instead of failing

Output:

exception example
exception example
exception example
...

📊 Progress Tracking with tqdm

To visualize progress, pass tqdm as an argument:

from tqdm import tqdm

df['y'] = await df.x.amap(f, max_parallel=5, tqdm=tqdm)

Example output:

 69%|█████████████████████████████████████████████████████                | 69/100 [00:06<00:03, 9.99it/s]

🎯 Why Use aiopandas?

• Ideal for async API calls (e.g., LLMs, web scraping, database queries).
• Massively speeds up Pandas workflows when dealing with async I/O operations.
• Minimal code changes – just swap .map() for .amap() (or .apply() for aapply(), etc.) and you’re good to go!

📦 Installation

pip install aiopandas

Or, install it manually:

git clone https://github.com/telekinesis-inc/aiopandas.git
cd aiopandas
pip install .

💡 Contributing

Pull requests are welcome! If you find issues or have suggestions, feel free to open an issue. 🚀

🙌 Acknowledgements

The monkey patching in aiopandas was heavily inspired by (basically copy-pasted) and adapted from the tqdm.pandas() method. Special thanks to the tqdm maintainers for their excellent work on integrating progress bars with Pandas.