mapply

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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

readthedocs

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

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BSD 3-Clause License

Copyright (c) 2024, ddelange, ddelange@delange.dev

All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

SPDX-License-Identifier: BSD-3-Clause

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  • Released:
Newer release available (0.2.0)

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Credits

Author: ddelange

GitHub Statistics

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License expression

BSD-3-Clause
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Requires

Python >=3.9

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