Metadata-Version: 2.1
Name: mapply
Version: 0.1.13
Summary: Sensible multi-core apply function for Pandas
Home-page: https://github.com/ddelange/mapply
Author: ddelange
Author-email: ddelange@delange.dev
License: MIT
Project-URL: Documentation, https://mapply.readthedocs.io
Keywords: pandas parallel apply multicore multiprocessing multiprocess dill
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pathos (>=0.2.0)
Requires-Dist: psutil
Requires-Dist: tqdm (>=4.27)

# mapply

[![build](https://img.shields.io/github/workflow/status/ddelange/mapply/CI/master?logo=github&cacheSeconds=86400)](https://github.com/ddelange/mapply/actions?query=branch%3Amaster)
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[![pypi Version](https://img.shields.io/pypi/v/mapply.svg?logo=pypi&logoColor=white)](https://pypi.org/project/mapply/)
[![python](https://img.shields.io/pypi/pyversions/mapply.svg?logo=python&logoColor=white)](https://pypi.org/project/mapply/)
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[`mapply`](https://github.com/ddelange/mapply) provides a sensible multi-core apply function for Pandas.

### mapply vs. pandarallel vs. swifter

Where [`pandarallel`](https://github.com/nalepae/pandarallel) only requires [`dill`](https://github.com/uqfoundation/dill) (and therefore has to rely on in-house multiprocessing and progressbars), [`swifter`](https://github.com/jmcarpenter2/swifter) relies on the heavy [`dask`](https://github.com/dask/dask) framework, converting to Dask DataFrames and back. In an attempt to find the golden mean, `mapply` is highly customizable and remains lightweight, leveraging the powerful [`pathos`](https://github.com/uqfoundation/pathos) framework, which shadows Python's built-in multiprocessing module using `dill` for universal pickling.


## Installation

This pure-Python, OS independent package is available on [PyPI](https://pypi.org/project/mapply):

```sh
$ pip install mapply
```


## Usage

[![readthedocs](https://readthedocs.org/projects/mapply/badge/?version=latest)](https://mapply.readthedocs.io)

For documentation, see [mapply.readthedocs.io](https://mapply.readthedocs.io/en/stable/_code_reference/mapply.html).

```py
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

[![gitmoji](https://img.shields.io/badge/gitmoji-%20%F0%9F%98%9C%20%F0%9F%98%8D-ffdd67)](https://github.com/carloscuesta/gitmoji-cli)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)

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