blackbear 1.0.0

Standard Library Data and Math Tools

Blackbear: Standard Library Data and Math Tools

.. image:: https://github.com/cgdeboer/blackbear/actions/workflows/python-app.yml/badge.svg :target: https://app.travis-ci.com/cgdeboer/blackbear

.. image:: https://img.shields.io/pypi/v/blackbear.svg :target: https://pypi.org/project/blackbear/

Blackbear is an organic (standard library) library for key-based data manipulation and math using only built-in python dicts and sets (without numpy, pandas, polars).

Justification: there are a lot of great python-based data tools that make working with relational data much easier. pandas_ is such a tool, and is well suited to working with large(ish) datasets. numpy_ and polars_ are also great tools, though not as helpful if you rely on relational/labeled data. Unfortunately, pandas_ can be excruciatingly slow when used repeatedly on smaller data sets (see benchmarks). This can be the case in simulation tools, that have "unvectorizable" step functions. blackbear can be used to replace some of the functionality used in pandas.Series, basic math on aligned index (keys()).

.. image:: https://raw.githubusercontent.com/cgdeboer/blackbear/master/docs/blackbear.jpeg :width: 400

Example Code:

.. code-block:: python

>>> import blackbear as bb
>>> data = {'foo': 60.0,
            'bar': 16.0,
            'baz': 24.0}
>>> bb.add_scalar(data, 10)
{'foo': 70.0,
 'bar': 26.0,
 'baz': 34.0}
>>> blue = {'foo': 60.0,
            'bar': 16.0,
            'baz': 24.0}
>>> green = {'foo': 40.0,
             'bar': 4.0,
             'baz': 6.0}
>>> bb.add(blue, green)
{'foo': 100.0,
 'bar': 20.0,
 'baz': 40.0}

Performance

"No numpy_, no pandas_, not even polars_, I bet this is really, really slow. Right ?"

For certain use cases, it can be faster than any of those. Here is a guide:

• Use blackbear for frequent (millions) operations on small collections (< 20 items) where matching on an index (i.e dict keys) is needed.
• Do not use blackbear for operations on larger collections (> 50000).

See benchmark details and data below.

.. _numpy: https://numpy.org/ .. _pandas: https://pandas.pydata.org/ .. _polars: https://www.pola.rs/

Feature Support

You are responsible for passing in the correct types to blackbear functions, we didn't want the additional overhead of type checking.

Blackbear officially supports Python 3.8+.

Installation

To install Blackbear, use pipenv <http://pipenv.org/>_ (or pip, of course):

.. code-block:: bash

$ pipenv install blackbear

Documentation

Documentation beyond this readme will be available soon.

How to Contribute

#. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug. #. Fork the repository_ on GitHub to start making your changes to the master branch (or branch off of it). #. Write a test which shows that the bug was fixed or that the feature works as expected. #. Send a pull request. Make sure to add yourself to AUTHORS_.

.. _the repository: https://github.com/cgdeboer/blackbear .. _AUTHORS: https://github.com/cgdeboer/blackbear/blob/master/AUTHORS.rst

Benchmarks

Performed on an Intel x64 chipped Mac (i7) with real blas and lapack installed.

100000 X 5 Element-wise ops on collection of 10

.. code-block::

Pandas
user 	0m35.212s
Polars
user	0m3.398s
Numpy
user	0m1.437s
Blackbear
user	0m0.601s

1000000 X 5 Element-wise ops on collection of 10

.. code-block::

Pandas
user	5m26.803s
Polars
user	0m24.115s
Numpy
user	0m6.734s
Blackbear
user	0m5.574s

1000 X 5 Element-wise ops on collection of 10000

.. code-block::

Pandas
user	0m1.406s
Polars
user	0m1.055s
Numpy
user	0m0.737s
Blackbear
user	0m2.703s

1000 X 5 Element-wise ops on collection of 100000

.. code-block::

Pandas
user	0m1.725s
Polars
user	0m1.230s
Numpy
user	0m1.035s
Blackbear
user	0m39.090s

500000 X 5 Element-wise ops on collection of 5

.. code-block::

Pandas
user	2m46.098s
Polars
user	0m12.899s
Numpy
user	0m3.674s
Blackbear
user	0m2.025s