Python anext Method

Last modified April 8, 2025

This comprehensive guide explores Python’s anext method, the special method that enables asynchronous iteration. We’ll cover basic usage, custom async iterators, error handling, and practical examples.

Basic Definitions

The anext method is a coroutine that returns the next item from an asynchronous iterator. It is the async equivalent of next.

Key characteristics: it must be defined as an async function, returns an awaitable, and raises StopAsyncIteration when no more items are available. It’s used with async for loops.

Basic Async Iterator Implementation

Here’s a simple async iterator demonstrating anext usage. It shows the minimal implementation needed for async iteration.

basic_anext.py

class AsyncCounter: def init(self, stop): self.current = 0 self.stop = stop

def __aiter__(self):
    return self

async def __anext__(self):
    if self.current >= self.stop:
        raise StopAsyncIteration
    self.current += 1
    return self.current - 1

async def main(): async for num in AsyncCounter(3): print(num)

import asyncio asyncio.run(main())

This example creates an async counter that yields numbers from 0 to 2. The anext method increments the counter and returns values.

When the counter reaches the stop value, it raises StopAsyncIteration. This signals the end of iteration to the async for loop.

Async Iterator with Delay

This example shows how anext can include awaitable operations, like sleeping, making it useful for I/O-bound tasks.

delayed_anext.py

class DelayedIterator: def init(self, items, delay): self.items = iter(items) self.delay = delay

def __aiter__(self):
    return self

async def __anext__(self):
    try:
        item = next(self.items)
        await asyncio.sleep(self.delay)
        return item
    except StopIteration:
        raise StopAsyncIteration

async def main(): async for char in DelayedIterator(“ABC”, 0.5): print(char)

asyncio.run(main())

This iterator introduces a delay between items. It wraps a regular iterator and adds async behavior. The await asyncio.sleep() demonstrates async operation.

The conversion from StopIteration to StopAsyncIteration is important. It properly signals the end of iteration in async context.

Async Data Fetcher

This practical example shows anext fetching data from multiple URLs asynchronously, demonstrating real-world usage.

async_fetcher.py

import aiohttp

class AsyncDataFetcher: def init(self, urls): self.urls = iter(urls) self.session = None

async def __aenter__(self):
    self.session = aiohttp.ClientSession()
    return self

async def __aexit__(self, *args):
    await self.session.close()

def __aiter__(self):
    return self

async def __anext__(self):
    try:
        url = next(self.urls)
    except StopIteration:
        raise StopAsyncIteration
    
    async with self.session.get(url) as response:
        return await response.text()

async def main(): urls = [ ‘https://example.com’, ‘https://python.org’, ‘https://pypi.org’ ]

async with AsyncDataFetcher(urls) as fetcher:
    async for content in fetcher:
        print(f"Fetched {len(content)} bytes")

asyncio.run(main())

This async iterator fetches web pages one by one. It uses aiohttp for async HTTP requests. The anext method handles each fetch.

The context manager methods (aenter, aexit) manage the HTTP session lifecycle. This ensures proper resource cleanup.

Error Handling in anext

This example demonstrates proper error handling in anext, showing how to manage exceptions during async iteration.

error_handling.py

class SafeAsyncIterator: def init(self, data): self.data = iter(data) self.retries = 3

def __aiter__(self):
    return self

async def __anext__(self):
    for attempt in range(self.retries):
        try:
            return next(self.data)
        except StopIteration:
            raise StopAsyncIteration
        except Exception as e:
            if attempt == self.retries - 1:
                raise
            print(f"Error, retrying... ({e})")
            await asyncio.sleep(1)

async def main(): data = [1, 2, “error”, 3, 4]

try:
    async for item in SafeAsyncIterator(data):
        print(f"Got: {item}")
except Exception as e:
    print(f"Failed after retries: {e}")

asyncio.run(main())

This iterator implements retry logic for exceptions. It attempts each item multiple times before propagating the error. The delay between retries is async.

The example shows how to handle both normal iteration completion (StopAsyncIteration) and error cases. This makes the iterator more robust.

Async Generator Alternative

This example compares anext implementation with async generators, showing a more concise alternative syntax.

async_gen.py

async def async_counter(stop): for i in range(stop): await asyncio.sleep(0.1) yield i

async def main(): # Using async generator async for num in async_counter(3): print(f"Generator: {num}")

# Equivalent __anext__ implementation
class Counter:
    def __init__(self, stop):
        self.current = 0
        self.stop = stop
    
    def __aiter__(self):
        return self
    
    async def __anext__(self):
        if self.current >= self.stop:
            raise StopAsyncIteration
        await asyncio.sleep(0.1)
        self.current += 1
        return self.current - 1

async for num in Counter(3):
    print(f"Class: {num}")

asyncio.run(main())

The async generator provides a cleaner syntax for simple cases. However, class with anext offers more control for complex scenarios.

Both implementations are functionally equivalent. The choice depends on needs for state management, reusability, and complexity.

Best Practices

• Always raise StopAsyncIteration: Signal iteration end properly

• Handle errors gracefully: Clean up resources if iteration fails

• Document iteration behavior: Note if iterator is finite/infinite

• Consider async generators: Often simpler for basic cases

• Manage resources: Use async context managers for cleanup

Source References

• Python anext Documentation

• Python async for Documentation

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

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