{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"p_tqdm wraps pathos and tqdm to apply a function in parallel across multiple inputs while displaying a live progress bar with time estimates.","skillfed_tags":["parallel-processing","progress-tracking","multiprocessing"],"use_cases":["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"],"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.\n\nThe 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.","worth_installing":"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."},"id":"p-tqdm","links":{"html":"https://skillfed.io/packages/p-tqdm","md":"https://skillfed.io/packages/p-tqdm.md","pypi":"https://pypi.org/project/p-tqdm/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-08-08","license_spdx":null,"license_treatment":"permissive","name":"p-tqdm","python_support":"unspecified","summary":"Parallel processing with progress bars"},"popularity":{"monthly_downloads":172237,"position":10344,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.4.2"}