ChernoffFace Python package

Introduction

This Python package implements the function chernoff_face that generates Chernoff diagrams.

The design, implementation strategy, and unit tests closely resemble the Wolfram Repository Function (WFR) ChernoffFace, [AAf1], and the original Mathematica package “ChernoffFaces.m”, [AAp1].

Installation

To install from GitHub use the shell command:

python -m pip install git+https://github.com/antononcube/Python-packages.git#egg=ChernoffFace\&subdirectory=ChernoffFace

To install from PyPI:

python -m pip install ChernoffFace

Usage examples

Setup

from ChernoffFace import *
import numpy
import matplotlib.cm

Random data

# Generate data
numpy.random.seed(32)
data = numpy.random.rand(16, 12)

# Make Chernoff faces
fig = chernoff_face(data=data, 
                    titles=[str(x) for x in list(range(len(data)))], 
                    color_mapper=matplotlib.cm.Pastel1)

# Display
fig.tight_layout()
matplotlib.pyplot.show()

Employee attitude data

Get Employee attitude data

dfData=load_employee_attitude_data_frame()
dfData.head()

##    Rating  Complaints  Privileges  Learning  Raises  Critical  Advancement
## 0      43          51          30        39      61        92           45
## 1      63          64          51        54      63        73           47
## 2      71          70          68        69      76        86           48
## 3      61          63          45        47      54        84           35
## 4      81          78          56        66      71        83           47

Get the numerical data of the data frame into an array:

data = dfData.to_numpy()

Rescale the variables:

data2 = variables_rescale(data)

Make the corresponding Chernoff faces using USA state names as titles:

fig = chernoff_face(data=data2,
                    n_columns=5,
                    long_faces=False,
                    color_mapper=matplotlib.cm.tab20b,
                    figsize=(8, 8), dpi=200)

Display:

fig.tight_layout()
matplotlib.pyplot.show()

USA arrests data

Get USA arrests data:

dfData=load_usa_arrests_data_frame()
dfData.head()

##     StateName  Murder  Assault  UrbanPopulation  Rape
## 0     Alabama    13.2      236               58  21.2
## 1      Alaska    10.0      263               48  44.5
## 2     Arizona     8.1      294               80  31.0
## 3    Arkansas     8.8      190               50  19.5
## 4  California     9.0      276               91  40.6

Get the numerical data of the data frame into an array:

data = dfData.to_numpy()
data = data[:, 1:5]

Rescale the variables:

data2 = variables_rescale(data)

Make the corresponding Chernoff faces using USA state names as titles:

fig = chernoff_face(data=data2,
                    n_columns=5,
                    long_faces=False,
                    color_mapper=matplotlib.cm.jet,
                    titles=dfData.StateName.tolist(),
                    figsize=(12, 12), dpi=200)

Display:

fig.tight_layout()
matplotlib.pyplot.show()

References

Articles

[AA1] Anton Antonov, “Making Chernoff faces for data visualization”, (2016), MathematicaForPrediction at WordPress.

Functions and packages

[AAf1] Anton Antonov, ChernoffFace, (2019), Wolfram Function Repository.

[AAp1] Anton Antonov, Chernoff faces implementation in Mathematica, (2016), MathematicaForPrediction at GitHub.