mapply 0.2.0

Sensible multi-core apply function for Pandas

mapply

mapply provides a sensible multi-core apply function for Pandas.

mapply vs. pandarallel vs. swifter

Where pandarallel relies on in-house multiprocessing and progressbars, and hard-codes 1 chunk per worker (which will cause idle CPUs when one chunk happens to be more expensive than the others), swifter relies on the heavy dask framework for multiprocessing (converting to Dask DataFrames and back). In an attempt to find the golden mean, mapply is highly customizable and remains lightweight, using tqdm for progressbars and leveraging the powerful pathos framework, which shadows Python's built-in multiprocessing module using dill for universal pickling. Chunks of work are assigned to worker processes "just in time" from a shared queue, allowing irregular workloads to finish faster.

mapply vs. Pandas' numba engine

Pandas v1.3 added built-in support for running Numba compatible code in parallel with very low overhead (see Pandas' enhancing performance article). In contrast to mapply, which uses fork or spawn to perform multiprocessing, Numba can only automatically parallelize a subset of operations (see Numba's automatic parallelization article).

Installation

This pure-Python, OS independent package is available on PyPI:

$ pip install mapply

Usage

For documentation, see mapply.readthedocs.io.

import pandas as pd
import mapply

mapply.init(
    n_workers=-1,
    chunk_size=100,
    max_chunks_per_worker=8,
    progressbar=False,
)

df = pd.DataFrame({"A": list(range(100))})

# avoid unnecessary multiprocessing:
# due to chunk_size=100, this will act as regular apply.
# set chunk_size=1 to skip this check and let max_chunks_per_worker decide.
df["squared"] = df.A.mapply(lambda x: x**2)

Development

Run make help for options like installing for development, linting, testing, and building docs.

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