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Fast Jenks breaks for Python

Compute "natural breaks" (Fisher-Jenks algorithm) on list / tuple / array / numpy.ndarray of integers/floats.

Intented compatibility: CPython 3.4+

Wheels are provided via PyPI for windows users - Also available on conda-forge channel for Anaconda users

Version Anaconda-Server Badge Build Status travis Build status appveyor PyPI download month

Usage :

This package consists of a single function (named jenks_breaks) which takes as input a list / tuple / array.array / numpy.ndarray of integers or floats. It returns a list of values that correspond to the limits of the classes (starting with the minimum value of the series - the lower bound of the first class - and ending with its maximum value - the upper bound of the last class).

>>> import jenkspy
>>> import random
>>> list_of_values = [random.random()*5000 for _ in range(12000)]

>>> breaks = jenkspy.jenks_breaks(list_of_values, nb_class=6)

>>> breaks
    (0.1259707312994962, 1270.571003315598, 2527.460251085392, 3763.0374498649376, 4999.87456576267)

>>> import json
>>> with open('tests/test.json', 'r') as f:
...     data = json.loads(f.read())
...
>>> jenkspy.jenks_breaks(data, nb_class=5) # Asking for 5 classes
(0.0028109620325267315, 2.0935479691252112, 4.205495140049607, 6.178148351609707, 8.09175917180255, 9.997982932254672)
# ^                      ^                    ^                 ^                  ^                 ^
# Lower bound            Upper bound          Upper bound       Upper bound        Upper bound       Upper bound
# 1st class              1st class            2nd class         3rd class          4th class         5th class
# (Minimum value)                                                                                    (Maximum value)

This package also support a JenksNaturalBreaks (require NumPy) class as interface (inspired by scikit-learn classes). The .fit and .group behavior is slightly different from jenks_breaks, by accepting value outside the range of the minimum and maximum value of breaks_, retaining the input size. It means that fit and group will use only the inner_bound_. All value below the min bound will be included in the first group and all value higher than the max bound will be included in the last group.

>>> from jenkspy import JenksNaturalBreaks

>>> x = [0,1,2,3,4,5,6,7,8,9,10,11]

>>> jnb = JenksNaturalBreaks()

>>> try:
...     print(jnb.labels_)
...     print(jnb.groups_)
...     print(jnb.inner_breaks_)
>>> except:
...     pass

>>> jnb.fit(x)
>>> try:
...     print(jnb.labels_)
...     print(jnb.groups_)
...     print(jnb.inner_breaks_)
>>> except:
...     pass
[0 0 0 1 1 1 2 2 2 3 3 3]
[array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8]), array([ 9, 10, 11])]
[2.0, 5.0, 8.0]

>>> print(jnb.predict(15))
3

>>> print(jnb.predict([2.5, 3.5, 6.5]))
[1 1 2]

>>> print(jnb.group([2.5, 3.5, 6.5]))
[array([], dtype=float64), array([2.5, 3.5]), array([6.5]), array([], dtype=float64)]

Installation

  • From pypi
pip install jenkspy
  • From source
git clone http://github.com/mthh/jenkspy
cd jenkspy/
python setup.py install
  • For anaconda users
conda install -c conda-forge jenkspy

Requirements :

  • NumPy*
  • C compiler+
  • Python C headers+

* only for using JenksNaturalBreaks interface

+ only for building from source

Motivation :

  • Making a painless installing C extension so it could be used more easily as a dependency in an other package (and so learning how to build wheels using appveyor).
  • Getting the break values! (and fast!). No fancy functionnality provided, but contributions/forks/etc are welcome.
  • Other python implementations are currently existing but not as fast nor available on PyPi.