ChernoffFace Python package

Introduction

This Python package implements the function chernoff_face that generates Chernoff face 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].

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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

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Usage examples

Setup

from ChernoffFace import *
import numpy
import matplotlib.cm

Random data

Here is an example of generating a table (matplotlib figure object) of Chernoff faces over the rows of a two-dimensional array of real numbers:

# 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()

(To be clear, each face represents a row of the given array.)

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

Rescale the variables (columns):

dfData2 = variables_rescale(dfData)
dfData2.head()

##      Rating  Complaints  Privileges  Learning    Raises  Critical  Advancement
## 0  0.066667    0.264151    0.000000  0.121951  0.400000  1.000000     0.425532
## 1  0.511111    0.509434    0.396226  0.487805  0.444444  0.558140     0.468085
## 2  0.688889    0.622642    0.716981  0.853659  0.733333  0.860465     0.489362
## 3  0.466667    0.490566    0.283019  0.317073  0.244444  0.813953     0.212766
## 4  0.911111    0.773585    0.490566  0.780488  0.622222  0.790698     0.468085

Make the corresponding Chernoff faces:

fig = chernoff_face(data=dfData2,
                    n_columns=5,
                    long_face=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

Rescale the variables:

dfData2 = variables_rescale(dfData)
dfData2.head()

##     StateName    Murder   Assault  UrbanPopulation      Rape
## 0     Alabama  0.746988  0.654110         0.440678  0.359173
## 1      Alaska  0.554217  0.746575         0.271186  0.961240
## 2     Arizona  0.439759  0.852740         0.813559  0.612403
## 3    Arkansas  0.481928  0.496575         0.305085  0.315245
## 4  California  0.493976  0.791096         1.000000  0.860465

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

fig = chernoff_face(data=dfData2,
                    n_columns=5,
                    long_face=False,
                    color_mapper=matplotlib.cm.tab20c_r,
                    figsize=(12, 12), dpi=200)

Display:

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

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Additional comments

• In order to have "prettier" and "tighter" looking diagrams (Chernoff faces) by default chernoff_face makes wide faces.
  • The argument long_face controls the shape of the faces.
  • ChernoffFace in [AAf1] makes long faces.
• Additional functions have to be implemented that summarize datasets using Chernoff faces.
  • Such summary diagrams are given in [AA1, AAf1].
• For a given dataset certain color palettes (color mappers) would provide more insightful views.
  • For example:
    • Florida is a clear outlier in the USA arrests plot above
    • Certain clustering of the states is also hinted by faces with similar colors.

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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.