How to Build Progress Monitoring Using Advanced tqdm for Async, Parallel, Pandas, Logging, and High-Performance Workflows

nums = list(range(80))
thread_results = thread_map(cpuish, nums, max_workers=8, desc=”thread_map”)
print(“thread_map done:”, len(thread_results))

proc_results = process_map(cpuish, nums[:20], max_workers=2, chunksize=2, desc=”process_map”)
print(“process_map done:”, len(proc_results))
print()

print(“6) logging_redirect_tqdm (logs won’t break bars)”)
logger = logging.getLogger(“demo”)
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter(“%(levelname)s: %(message)s”))
logger.handlers = [handler]

with logging_redirect_tqdm():
for k in tqdm(range(60), desc=”Work with logs”):
time.sleep(0.01)
if k in (5, 25, 45):
logger.info(f”checkpoint k={k}”)
print()

print(“7) asyncio progress (as_completed) — Colab/Jupyter-safe”)
async def io_task(i: int):
await asyncio.sleep(random.uniform(0.02, 0.12))
return i, random.random()

async def run_async():
tasks = [asyncio.create_task(io_task(i)) for i in range(80)]
results = []
for fut in tqdm(asyncio.as_completed(tasks), total=len(tasks), desc=”async tasks”):
results.append(await fut)
return results

results = await run_async()
print(“async done:”, len(results), “results”)