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Distances and divergences between distributions implemented in python.

Project description

Travis CI build SonarCloud Quality SonarCloud Maintainability Codacy Maintainability Maintainability Pypi project Pypi total project downloads

Distances and divergences between distributions implemented in python.

How do I install this package?

As usual, just download it using pip:

pip install dictances

Tests Coverage

Since some software handling coverages sometime get slightly different results, here’s three of them:

Coveralls Coverage SonarCloud Coverage Code Climate Coverate

Available metrics

A number of distances and divergences are available:

Distances Methods
Bhattacharyya distance bhattacharyya
Bhattacharyya coefficient bhattacharyya_coefficient
Canberra distance canberra
Chebyshev distance chebyshev
Chi Square distance chi_square
Cosine Distance cosine
Euclidean distance euclidean
Hamming distance hamming
Jensen-Shannon divergence jensen_shannon
Kullback-Leibler divergence kullback_leibler
Mean absolute error mae
Taxicab geometry manhattan, cityblock, total_variation
Minkowski distance minkowsky
Mean squared error mse
Pearson’s distance pearson
Squared deviations from the mean squared_variation

Usage example

from dictances import cosine

cosine(my_first_dictionary, my_second_dictionary)

Handling nested dictionaries

If you need to compute the distance between two nested dictionaries you can use deflate_dict as follows:

from dictances import cosine
from deflate_dict import deflate

my_first_dictionary = {
    "a": 8,
    "b": {
        "c": 3,
        "d": 6

my_second_dictionary = {
    "b": {
        "c": 8,
        "d": 1
    "y": 3,


cosine(deflate(my_first_dictionary), deflate(my_second_dictionary))

Project details

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Source Distribution

dictances-1.5.3.tar.gz (8.6 kB view hashes)

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