Quick start#
Requirements#
General requirements#
# Development Python Version: 3.12.0
PyDistances
polars
numpy
pandas
scipy
quantitative requirements#
polars
pandas
numpy
scipy
binary requirements#
polars
pandas
scipy
multiclass requirements#
polars
pandas
scipy
mixed requirements#
polars
pandas
numpy
scipy
Installation#
pip install PyDistances
To see the available versions of the package go to the release history at PyPi: https://pypi.org/project/PyDistances/#history
Example#
from PyDistances.mixed import GGowerDistMatrix
data_url = "https://raw.githubusercontent.com/FabioScielzoOrtiz/PyDistances-demo/refs/heads/main/data/madrid_houses_processed.csv"
data = pd.read_csv(data_url)
quant_cols = ['sq_mt_built', 'n_rooms', 'n_bathrooms', 'n_floors', 'buy_price']
binary_cols = ['is_renewal_needed', 'has_lift', 'is_exterior', 'has_parking']
multiclass_cols = ['energy_certificate', 'house_type']
p1 = len(quant_cols)
p2 = len(binary_cols)
p3 = len(multiclass_cols)
ggower_dist_matrix = GGowerDistMatrix(p1=p1, p2=p2, p3=p3,
d1="robust_mahalanobis", d2="jaccard", d3="hamming",
robust_method="trimmed", alpha=0.07, epsilon=0.05,
n_iters=20, weights=None)
ggower_dist_matrix.compute(X=data)
array([[0. , 2.21871457, 1.93429293, ..., 1.94305438, 3.1223396 ,
2.26768279],
[2.21871457, 0. , 1.22327246, ..., 2.38753004, 2.64304949,
2.00865696],
[1.93429293, 1.22327246, 0. , ..., 2.36077974, 2.50019632,
1.63811682],
...,
[1.94305438, 2.38753004, 2.36077974, ..., 0. , 2.9036275 ,
1.75869492],
[3.1223396 , 2.64304949, 2.50019632, ..., 2.9036275 , 0. ,
3.03987403],
[2.26768279, 2.00865696, 1.63811682, ..., 1.75869492, 3.03987403,
0. ]])