processit 0.1.0

Lightweight progress utilities for sync/async workloads

processit

A lightweight progress utility for Python — built for both synchronous and asynchronous iteration.

processit provides a simple, dependency-free progress bar for loops that may be either regular iterables or async iterables.

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Installation

pip install processit

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Quick Example

progress

import asyncio
import time

from processit import progress

def numbers():
    for i in range(10):
        time.sleep(0.3)
        yield i

async def main():
    async for _ in progress(numbers(), total=10, desc="Numbers"):
        await asyncio.sleep(0)

asyncio.run(main())

Numbers [#############.................]  50.00% (5/10) 4.92 it/s 02.1s ETA 02.1s
Numbers: 10 it in 04.1s (2.43 it/s)

import asyncio
import time

from processit import progress

def numbers():
    for i in range(10):
        time.sleep(0.3)
        yield i

async def main():
    async with progress(numbers(), total=10, desc='Numbers') as p:
        async for n in p:
            p.write(f'value: {n}')
            await asyncio.sleep(0.5)


asyncio.run(main())

track_as_completed

import asyncio
import random

from processit import track_as_completed

async def work(n: int) -> int:
    await asyncio.sleep(1.5 + random.random())
    return n * 2

async def main():
    tasks = [work(i) for i in range(10)]
    async for fut in track_as_completed(tasks, desc="Parallel work"):
        await fut

asyncio.run(main())

Parallel work [#####################.........]  70.00% (7/10) 68.68 it/s 00.1s ETA 00.0s14
Parallel work: 10 it in 00.1s (98.01 it/s)

import asyncio

from processit import track_as_completed

async def work(i: int) -> int:
    await asyncio.sleep(2)
    return i * 2

async def main():
    tasks = [work(i) for i in range(10)]
    async with track_as_completed(tasks, desc="Parallel work") as p:
        async for task in p:       # itera mientras re-renderiza
            result = await task
            p.write(f"done: {result}")  # en vez de print(result)
            await asyncio.sleep(2)

asyncio.run(main())

More examples

Asynchronous iteration over a data source

import asyncio

from processit import progress

async def fetch_items():
    for i in range(20):
        await asyncio.sleep(0.05)
        yield f"item-{i}"

async def main():
    async for item in progress(fetch_items(), total=20, desc="Fetching"):
        await asyncio.sleep(0.02)

asyncio.run(main())

Processing a list without a defined total

import asyncio

from processit import progress

items = [x ** 2 for x in range(100)]

async def main():
    async for value in progress(items, desc="Squaring"):
        await asyncio.sleep(0.01)

asyncio.run(main())

Using an async with context

import asyncio

from processit import progress

async def numbers():
    for i in range(8):
        await asyncio.sleep(0.1)
        yield i

async def main():
    async with progress(numbers(), total=8, desc="Context mode") as p:
        async for n in p:
            await asyncio.sleep(0.05)

asyncio.run(main())

Synchronous iterator in an asynchronous environment

import asyncio
import time

from processit import progress

def blocking_iter():
    for i in range(5):
        time.sleep(0.4)
        yield i

async def main():
    async for n in progress(blocking_iter(), total=5, desc="Blocking loop"):
        await asyncio.sleep(0)

asyncio.run(main())

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Features

✅ Works with both async and sync iterables ✅ Displays elapsed time, rate, and ETA (when total is known) ✅ Automatically cleans up and prints a final summary ✅ No dependencies — pure Python, fully type-hinted ✅ Easy to use drop-in function: progress(iterable, ...)

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API

progress(iterable, total=None, *, desc='Processing', width=30, refresh_interval=0.1, show_summary=True)

Creates and returns a Progress instance.

Parameters

Name	Type	Description
iterable	`Iterable[T]	AsyncIterable[T]`
total	`int	None`
desc	str	A short description shown before the bar.
width	int	Width of the progress bar (default: 30).
refresh_interval	float	Time in seconds between updates.
show_summary	bool	Whether to show a final summary line (default: True).

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Usage with Regular Iterables

from processit import progress
import time

def numbers():
    for i in range(5):
        time.sleep(0.3)
        yield i

async def main():
    async for n in progress(numbers(), total=5, desc="Sync loop"):
        await asyncio.sleep(0)

import asyncio
asyncio.run(main())