jenkspy 0.1.6

Compute Natural Breaks (Jenks algorythm)

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

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)

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 (only for building from source):

• C compiler

• Python C headers

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.