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].
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
from ChernoffFace import *
import numpy
import matplotlib.cm
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.)
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()
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()
- In order to have "prettier" and "tighter" looking diagrams (Chernoff faces) by default
chernoff_facemakes wide faces.- The argument
long_facecontrols the shape of the faces. ChernoffFacein [AAf1] makes long faces.
- The argument
- 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.
- For example:
[AA1] Anton Antonov, “Making Chernoff faces for data visualization”, (2016), MathematicaForPrediction at WordPress.
[AAf1] Anton Antonov,
ChernoffFace,
(2019), Wolfram Function
Repository.
[AAp1] Anton Antonov, Chernoff faces implementation in Mathematica, (2016), MathematicaForPrediction at GitHub.