---
id: p-tqdm
version: "1.4.2"
license: MIT
license_treatment: permissive
maintenance: dormant
---

# p-tqdm — Parallel processing with progress bars

License: permissive · Maintenance: dormant · Downloads: 172.2K/mo

## What it is and what it does

p_tqdm is a thin wrapper combining pathos.multiprocessing and tqdm to make parallel processing more convenient. It provides functions like p_map, p_imap, p_umap, and p_uimap that apply a function across multiple iterables in parallel, while displaying a live progress bar with completion estimates. Unlike Python's built-in multiprocessing, pathos supports lambda functions, nested functions, and class methods, which p_tqdm exposes with minimal syntax overhead.

The package is designed for CPU-bound workloads where you want to parallelize a map operation without wrestling with multiprocessing pool setup. It supports controlling CPU count (as an integer or proportion), custom tqdm flavors, and both ordered and unordered result collection. Sequential variants (t_map, t_imap) are also provided for cases where you want the same interface without parallelism.

Use it for:

- Batch-process large datasets with a function while monitoring progress in real time
- Apply an expensive computation across multiple inputs using all available CPU cores
- Replace a slow for-loop with parallel execution while keeping code readable and adding progress visibility
- Parallelize functions with multiple arguments or complex signatures that standard multiprocessing struggles with
- Collect results in order (p_map) or accept any order (p_umap) depending on whether result sequence matters

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

p_tqdm wraps pathos and tqdm to apply a function in parallel across multiple inputs while displaying a live progress bar with time estimates.

Yes, if you need parallel map with a progress bar and can tolerate the high install friction (three dependencies) and dormant maintenance. The package is stable and has no known vulnerabilities, but expect no active bug fixes or new features. Best suited for one-off scripts or internal tools where the convenience of p_map outweighs the dependency cost; for production systems, consider whether direct pathos + tqdm usage or a more actively maintained alternative is preferable.

## Install

pip install p-tqdm
uv add p-tqdm
poetry add p-tqdm

## Installing p-tqdm

Before you install: High install friction due to three runtime dependencies (tqdm, pathos, six). Maintenance is dormant—last release was 2024-08-08 but no commits since then; the repository remains active with 493 stars but receives infrequent updates.

License in practice: MIT license is permissive and imposes no restrictions on use, modification, or distribution in proprietary or open-source projects.

Quickstart:

pip install p_tqdm

from p_tqdm import p_map

def add(a, b):
    return a + b

result = p_map(add, ['1', '2', '3'], ['a', 'b', 'c'])
print(result)  # ['1a', '2b', '3c']

Verify before relying:

- Whether pathos dependency works reliably on all platforms (Windows, macOS, Linux) where p_tqdm is deployed
- Performance overhead of the wrapper compared to direct pathos.multiprocessing calls
- Compatibility with modern async/await patterns or asyncio-based concurrency

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: high
- Maintenance: dormant
- Downloads: 172.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

parallel map with progress bar, multiprocessing progress tracking, parallel processing python, tqdm parallel execution, batch processing with progress, parallel function application, multicore map function, parallel-processing, progress-tracking, multiprocessing

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