{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/21"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/11"}],"enrichment":{"capability":"Provides a lightweight, customizable multi-core apply function for Pandas DataFrames that distributes work across CPU cores using a shared queue for load balancing.","skillfed_tags":["pandas-acceleration","multiprocessing"],"use_cases":["Apply expensive transformations (e.g., NLP, image processing) to millions of rows in a DataFrame without rewriting code.","Speed up feature engineering pipelines where different rows have variable computation cost.","Process large CSV or database query results in parallel without converting to Dask or Spark.","Parallelize custom Python functions on Pandas Series when Numba is not applicable.","Benchmark and tune multiprocessing behavior for your specific hardware and workload."],"what_it_does":"mapply wraps Pandas' apply method to run operations in parallel across multiple CPU cores. Instead of processing a DataFrame sequentially, it splits the work into chunks, assigns them to worker processes from a shared queue, and collects results\u2014allowing irregular workloads to finish faster when some chunks are more expensive than others. It uses pathos for multiprocessing (which handles complex Python objects better than the standard library) and tqdm for progress tracking.\n\nThe package sits between lightweight (but rigid) solutions like pandarallel and heavier frameworks like Dask. You configure it once with init(), specifying worker count, chunk size, and queue depth, then call .mapply() on Series or DataFrame columns as you would .apply(). It avoids unnecessary multiprocessing overhead for small datasets and lets you tune parallelism for your specific workload.","worth_installing":"Yes, if you apply custom Python functions to large Pandas DataFrames and want a lightweight, tunable alternative to Dask. The low dependency count, active maintenance, and permissive license make it a safe choice. Requires Python 3.11+; verify it works on your OS (fork vs. spawn behavior) and that your workload benefits from parallelization (overhead is not free for small datasets)."},"id":"mapply","links":{"html":"https://skillfed.io/packages/mapply","md":"https://skillfed.io/packages/mapply.md","pypi":"https://pypi.org/project/mapply/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-23","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"mapply","python_support":"supports_current","summary":"Sensible multi-core apply function for Pandas"},"popularity":{"monthly_downloads":87449,"position":13798,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.0"}