---
id: mapply
version: "0.2.0"
license: BSD-3-Clause
license_treatment: permissive
maintenance: active
---

# mapply — Sensible multi-core apply function for Pandas

License: permissive · Maintenance: active · Downloads: 87.4K/mo

## What it is and 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—allowing 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.

The 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.

Use it for:

- 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.

## Worth the install?

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

Provides a lightweight, customizable multi-core apply function for Pandas DataFrames that distributes work across CPU cores using a shared queue for load balancing.

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).

## Install

pip install mapply
uv add mapply
poetry add mapply

## Installing mapply

Before you install: Low friction: pure Python wheel with five runtime dependencies (pathos, multiprocess, psutil, tqdm, pandas). Actively maintained with recent commits and stable production status.

License in practice: BSD-3-Clause is permissive; you may use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.

Quickstart:

pip install mapply

import pandas as pd
import mapply

mapply.init(n_workers=-1, chunk_size=100)
df = pd.DataFrame({"A": list(range(100))})
df["squared"] = df.A.mapply(lambda x: x**2)

Requires Python 3.11 or later.

Verify before relying:

- Whether the package works reliably on Windows (fork vs. spawn multiprocessing behavior on different OS).
- Performance gains relative to Pandas' built-in Numba engine for specific workload types.
- Memory overhead when processing very large DataFrames with many workers.

## Package facts

- License: BSD-3-Clause (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 87.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pandas parallel apply, multicore dataframe processing, distributed pandas operations, parallel map function pandas, load-balanced dataframe apply, multi-worker pandas apply, lightweight pandas parallelization, pandas-acceleration, multiprocessing

[View on SkillFed](https://skillfed.io/packages/mapply) · [View on PyPI](https://pypi.org/project/mapply/)